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

Abstract

The invention relates to the technical field of visual distance measurement, in particular to a power transmission line visual monitoring distance measurement method and system based on three-dimensional laser point cloud. Laser is emitted to the power transmission line through the unmanned aerial vehicle, regional point cloud data are obtained, and power line point cloud data and reference point cloud data of the power transmission line are obtained; analyzing a point cloud data measurement precision index, carrying out subsequent processing when the point cloud data measurement precision index is not lower than a set threshold value, and otherwise, obtaining data again; obtaining a first power line point cloud group and a second power line point cloud group, and extracting a standard power line point cloud; and fitting the standard power line point cloud and the reference object point cloud data to obtain a power line and reference object model, and carrying out distance measurement based on the model. According to the method, the point cloud data measurement precision index is introduced to screen the data, and the point cloud data of the power line is accurately extracted in combination with multiple algorithms, so that the modeling precision and the distance measurement accuracy of the power transmission line are improved.

Description

Technical Field

[0001] The present invention relates to the field of visualized distance measurement technology, and in particular to a method and system for visualized monitoring and distance measurement of power transmission lines based on three-dimensional laser point cloud. Background Art

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

[0003] The State Grid Corporation of China has conducted research on the application of distance measurement technology for transmission lines. For example, it uses the Global Navigation Satellite System (GNSS) and laser rangefinders to perform high-precision distance measurement on transmission lines and measure parameters such as the length and height of the lines. For example, it uses drones equipped with distance measurement equipment to conduct comprehensive inspections and measurements of transmission lines to quickly obtain 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 danger points in a transmission corridor based on laser point cloud and GIM three-dimensional model, including: obtaining laser point cloud LAS data of the transmission corridor and GIM three-dimensional model data of the transmission line on the same line as the transmission corridor; objectifying the laser point cloud LAS data of the transmission corridor into monomers according to the entity object type of the transmission corridor to obtain point cloud monomer LAS data of different entity objects; lightweight processing is performed on the point cloud monomer LAS data and the GIM three-dimensional model data of the transmission line to obtain lightweight point cloud monomer data and transmission line three-dimensional model data; determining potential danger points based on the coordinate fitting relationship between the point cloud monomer data and the three-dimensional model data of the transmission line; constructing the operation and maintenance ledger data of the three-dimensional model of the transmission line according to the determined potential danger points and the constructed association relationship, and loading it to the constructed transmission digital twin platform for visualization.

[0005] For example, the invention patent with publication number: CN114119917A discloses a visual hidden danger ranging method based on a high-precision three-dimensional model of a transmission line, which includes: obtaining monitoring data of the target area, scanning the target area to collect point cloud data; preprocessing the point cloud data, including eliminating redundant point cloud data, eliminating ground point cloud data, and density checking; determining the location of the hidden danger target and measuring the 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 to be solved by the present invention is that in the prior art, the UAV is 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, which will cause insufficient accuracy in the subsequent modeling of the transmission line, thereby affecting the accuracy of ranging and causing ranging errors.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, an embodiment of the present invention provides a method for visual monitoring and ranging of a power transmission line based on a three-dimensional laser point cloud, which includes: emitting a laser to the transmission line through a drone, acquiring regional point cloud data, and obtaining power line point cloud data and reference point cloud data of the transmission line;

[0010] Analyze the measurement accuracy index of the point cloud data, and when the measurement accuracy index of the point cloud data is not lower than a set threshold, perform subsequent processing, otherwise reacquire the point cloud data of the region;

[0011] Acquire a first power line point cloud group and a second power line point cloud group, and extract a standard power line point cloud therefrom;

[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 solution of the visual monitoring and ranging method of power transmission lines based on three-dimensional laser point cloud of the present invention, the specific steps of classifying regional point cloud data include:

[0014] Read the regional point cloud data collected by the drone and store it in a three-dimensional point cloud data format;

[0015] Filtering out noise and outlier processing on the point cloud data of the area;

[0016] The ground point cloud data and low-altitude point cloud data are filtered out to obtain the power line point cloud data and reference point cloud data of the transmission line.

[0017] As a preferred solution of the visual monitoring and ranging method for power transmission lines based on three-dimensional laser point cloud of the present invention, the steps of obtaining the first power line point cloud group and the second power line point cloud group include:

[0018] Based on a statistical filtering algorithm, multiple rough extractions are performed on the preprocessed power line point cloud data of the transmission line to obtain multiple first rough extracted power line point clouds;

[0019] Refining the plurality of first roughly extracted power line point clouds respectively by using a cloth simulation filtering algorithm to obtain the first power line point cloud group;

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

[0021] The plurality of second roughly extracted power line point clouds are respectively refined by using a fast Euclidean clustering algorithm to obtain the second power line point cloud group.

[0022] As a preferred solution of the three-dimensional laser point cloud-based transmission line visual monitoring and ranging method of the present invention, the step of extracting the standard power line point cloud from the first power line point cloud group and the second power line point cloud group includes:

[0023] Performing pairwise similarity comparison on a plurality of first power line point clouds in the first power line point cloud group to obtain a group N of power line point clouds with the highest similarity;

[0024] Performing pairwise similarity comparison on a plurality of second power line point clouds in the second power line point cloud group to obtain a group of power line point cloud groups M with the highest similarity;

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

[0026] As a preferred solution of the visual monitoring and ranging method of power transmission lines based on three-dimensional laser point cloud of the present invention, the step of fitting the standard power line point cloud with the reference point cloud data includes:

[0027] Based on the geometric shape analysis method, the point cloud data that meets the characteristics of the reference object is marked as the reference object point cloud;

[0028] Selecting a reference point cloud from the obtained standard power line point cloud and reference object point cloud by geometric features;

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

[0030] The fitted standard power line point cloud and reference point cloud data are smoothed by Gaussian smoothing;

[0031] The smoothed data is modeled using a three-dimensional modeling tool to obtain a model of the power lines and reference objects.

[0032] As a preferred solution of the visual monitoring and ranging method of power transmission lines based on three-dimensional laser point cloud of the present invention, the step of measuring the distance 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] For the distance from a point to a plane, the shortest distance formula from a point to a plane is used for calculation;

[0035] For the distance from a point to a surface, the surface shortest distance calculation method is used for calculation;

[0036] The ranging error index is analyzed in real time, and the ranging error index is used to measure the error degree of the measurement result.

[0037] As a preferred solution of the visual monitoring and ranging method of power transmission lines based on three-dimensional laser point cloud of the present invention, the step of real-time analysis of ranging error index includes:

[0038] Connect the sensor through the SCADA system to obtain the stability, linearity and temperature of the distance meter during distance measurement;

[0039] Standardizing the stability, linearity and temperature of the rangefinder during range measurement;

[0040] The weight factor of each parameter on the ranging error index is obtained through the objective weighting method;

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

[0042] In a second aspect, an embodiment of the present invention provides a three-dimensional laser point cloud-based transmission line visualization monitoring and ranging system, which includes:

[0043] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds as described in the first aspect of the present invention are implemented.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds as described in the first aspect of the present invention are implemented.

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

[0046] By combining statistical filtering algorithm, cloth simulation filtering algorithm, PCA principal component analysis method and fast Euclidean clustering algorithm, the power line point cloud data is accurately extracted, thereby reducing the interference of noise point cloud and improving the extraction accuracy of power line point cloud, thereby achieving high precision and stability in power line modeling and ranging, and effectively solving the problems of misextraction and noise interference in the power line point cloud extraction process in the existing technology.

[0047] By real-time analysis of the ranging error index, combined with real-time monitoring of parameters such as rangefinder stability, linearity and temperature, the error control strategy is dynamically adjusted to achieve real-time error correction and calibration during the ranging process, thereby ensuring the accuracy and stability of the monitoring data, and effectively solving the error problems caused by the failure to correct the ranging error in a timely manner and environmental changes in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 The flowchart of the transmission line visual monitoring and ranging method based on three-dimensional laser point cloud;

[0050] Figure 2 Computer equipment diagram for the visual monitoring and ranging method of transmission lines based on three-dimensional laser point cloud. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] Example 1

[0055] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a transmission line visual monitoring and ranging method based on three-dimensional laser point cloud, comprising:

[0056] S100: Use the drone to emit laser to the power transmission line to obtain regional point cloud data, and obtain the power line point cloud data of the power transmission line and the reference point cloud data;

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

[0058] Read the regional point cloud data collected by the drone and store it in a three-dimensional point cloud data format;

[0059] Filter out noise and outliers from regional point cloud data;

[0060] The ground point cloud data and low-altitude point cloud data are filtered out to obtain the power line point cloud data and reference point cloud data of the transmission line.

[0061] S200: Analyze the measurement accuracy index of the point cloud data. When the measurement accuracy index of the point cloud data is not lower than the set threshold, perform subsequent processing, otherwise re-acquire the regional point cloud data;

[0062] S300: Acquire a first power line point cloud group and a second power line point cloud group, and extract a standard power line point cloud therefrom;

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

[0064] Based on a statistical filtering algorithm, multiple rough extractions are performed on the preprocessed power line point cloud data of the transmission line to obtain multiple first rough extracted power line point clouds;

[0065] Refining the plurality of first roughly extracted power line point clouds respectively by using a cloth simulation filtering algorithm to obtain a first power line point cloud group;

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

[0067] The plurality of second roughly extracted power line point clouds are respectively refined by using a fast Euclidean clustering algorithm to obtain a second power line point cloud group.

[0068] S302: The step of extracting a standard power line point cloud from the first power line point cloud group and the second power line point cloud group includes:

[0069] Performing pairwise similarity comparison on a plurality of first power line point clouds in the first power line point cloud group to obtain a group N of power line point clouds with the highest similarity;

[0070] Performing pairwise similarity comparison on a plurality of second power line point clouds in the second power line point cloud group to obtain a group of power line point cloud groups M with the highest similarity;

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

[0072] S400: Fitting 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 performing distance measurement based on the model.

[0073] S401: The step of fitting the standard power line point cloud with the reference object point cloud data includes:

[0074] Based on the geometric shape analysis method, the point cloud data that meets the characteristics of the reference object is marked as the reference object point cloud;

[0075] Selecting a reference point cloud from the obtained standard power line point cloud and reference object point cloud by geometric features;

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

[0077] The fitted standard power line point cloud and reference point cloud data are smoothed by Gaussian smoothing;

[0078] The smoothed data is modeled using a three-dimensional modeling tool to obtain a model of the power lines and reference objects.

[0079] S402: The step of measuring distance based on the model of the power line and the reference object includes:

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

[0081] For the distance from a point to a plane, the shortest distance formula from a point to a plane is used for calculation;

[0082] For the distance from a point to a surface, the surface shortest distance calculation method is used for calculation;

[0083] Real-time analysis of the ranging error index, which is used to measure the error degree of the measurement results.

[0084] S403: The step of analyzing the ranging error index in real time includes:

[0085] Connect the sensor through the SCADA system to obtain the stability, linearity and temperature of the distance meter during distance measurement;

[0086] Standardize the rangefinder's stability, linearity, and temperature during range measurement;

[0087] The weight factor of each parameter on the ranging error index is obtained through the objective weighting method;

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

[0089] Furthermore, this embodiment also provides a transmission line visual monitoring and ranging system based on three-dimensional laser point cloud, comprising:

[0090] The data acquisition module uses the drone to emit laser to the transmission line to obtain regional point cloud data, and obtains the power line point cloud data of the transmission line and the reference point cloud data;

[0091] The data analysis module analyzes the measurement accuracy index of the point cloud data. When the measurement accuracy index of the point cloud data is not lower than the set threshold, subsequent processing is performed, otherwise the regional point cloud data is re-acquired;

[0092] A point cloud extraction module is used to obtain a first power line point cloud group and a second power line point cloud group, and extract a standard power line point cloud therefrom;

[0093] The distance measurement 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 distance measurement based on the model.

[0094] In summary, by introducing the point cloud data measurement accuracy index, the point cloud data can be accurately evaluated and screened, thereby ensuring that only data that meets the high-precision requirements can be used for subsequent analysis, thereby achieving high-precision acquisition of power line monitoring data, avoiding the error propagation problem caused by low-precision data, and effectively solving the problem of measurement error accumulation caused by insufficient data accuracy in the existing technology.

[0095] By combining statistical filtering algorithm, cloth simulation filtering algorithm, PCA principal component analysis method and fast Euclidean clustering algorithm, the power line point cloud data is accurately extracted, thereby reducing the interference of noise point cloud and improving the extraction accuracy of power line point cloud, thereby achieving high precision and stability in power line modeling and ranging, and effectively solving the problems of misextraction and noise interference in the power line point cloud extraction process in the existing technology.

[0096] By real-time analysis of the ranging error index, combined with real-time monitoring of parameters such as rangefinder stability, linearity and temperature, the error control strategy is dynamically adjusted to achieve real-time error correction and calibration during the ranging process, thereby ensuring the accuracy and stability of the monitoring data, and effectively solving the error problems caused by the failure to correct the ranging error in a timely manner and environmental changes in the existing technology.

[0097] Example 2

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

[0099] The laser is emitted to the transmission line by the unmanned aerial vehicle to obtain regional point cloud data, and the obtained regional point cloud data is classified to obtain the power line point cloud data and the reference point cloud data of the transmission line; the point cloud data measurement accuracy index is analyzed, and the point cloud data measurement accuracy index is used to reflect the accuracy of the obtained power line point cloud data. If the point cloud data measurement accuracy index is not lower than the set threshold, the power line point cloud data and the reference point cloud data of the obtained transmission line are preprocessed; if the point cloud data measurement accuracy index is lower than the set threshold, the regional point cloud data is re-acquired; a first power line point cloud group and a second power line point cloud group are obtained, the first power line point cloud group includes a plurality of first power line point clouds, and the second power line point cloud group includes a plurality of second power line point clouds; standard power line point clouds are extracted from the first power line point cloud group and the second power line point cloud group; the standard power line point cloud and the reference point cloud data are fitted to obtain the model of the power line and the reference object, and the distance is measured based on the modeling of the power line and the reference object. In the distance measurement process, the distance measurement error index is analyzed in real time, and the distance measurement error index is used to measure the error degree of the measurement result.

[0100] In this embodiment, point cloud data is three-dimensional spatial data obtained by laser scanning or other 3D measurement methods, including a large number of points, each of which records the coordinate information of an object in space, and is used to represent the three-dimensional structure of an object or scene. Power line point cloud data refers to point data specific to power lines extracted from point cloud data, which is 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 in measurement, as a comparison or reference object for the power line model.

[0101] Furthermore, the acquired regional point cloud data are classified, and the specific process of obtaining the power line point cloud data of the transmission line and the reference point cloud data is as follows: reading the regional point cloud data collected by the drone, storing it in a three-dimensional point cloud data format, filtering out noise and outliers from the regional point cloud data, filtering out ground point cloud data and low-altitude point cloud data, and obtaining the power line point cloud data of the transmission line and the reference point cloud data.

[0102] In this embodiment, the three-dimensional 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 space, which is convenient for describing and processing the three-dimensional shape of the object. The regional point cloud data collected by the drone is first read and stored in the three-dimensional point cloud data format, which can ensure the integrity of the data and facilitate subsequent processing. Noise and outliers refer to abnormal data that need to be filtered out in the point cloud data due to measurement errors or other interferences, and noise and outliers usually do not represent the real object characteristics. By filtering out these interfering data, the point cloud can be purified, the data representativeness can be maintained, and the error can be reduced, thereby improving the data quality. Ground point cloud data refers to point data close to the ground in the three-dimensional point cloud, which usually does not contain power line information, so the ground point cloud data is removed to reduce the amount of data and improve the analysis accuracy. Point cloud connectivity analysis refers to an analysis method based on the spatial connection relationship between points, which is used to identify and distinguish different objects or structures in the point cloud. The geometric shape analysis method refers to the shape characteristics used to identify objects in order to distinguish different types of objects in the point cloud, such as power lines and reference objects.

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

[0104] In this embodiment, the target monitoring area is measured by geographic information system (GIS) software (such as ArcGIS, QGIS) or high-precision topographic maps. After the UAV flies, the flight record data and aerial images are used to import the path coverage area into the GIS software for area calculation. At the same time, the flight trajectory of the UAV can be recorded in real time by UAV control software (such as DJI GS Pro, Pix4D, etc.) and GIS software, and the UAV flight path coverage area is automatically generated according to the flight data. The sensor resolution is usually determined by the equipment specifications, but can be calibrated by actual measurement. Under field conditions, a calibration plate can be selected as the test target, the sensor is aimed at the calibration plate for shooting or scanning, and the number of acquisition points per unit area is measured to confirm the actual resolution. It can also be obtained through the sensor specification table, calibration plate and rangefinder. There are precise scales on the calibration plate to test whether the sensor resolution meets the design specifications. The laser emission frequency is generally indicated in the equipment parameters, but it can be verified by an oscilloscope or pulse counter. Connect the laser beam emitted by the sensor to a photodetector, transmit the output signal to an oscilloscope or pulse counter, and display or count the number of pulses per second in real time to derive the actual emission frequency.

[0105] Furthermore, the point cloud data measurement accuracy index formula 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 FB is the sensor resolution, α2 is the weight factor of FB for DYJD, FS is the laser emission frequency, and α3 is the weight factor of FS for DYJD.

[0108] In this embodiment, when FG=40, MB=100, α1=0.4, FB=10, α2=0.3, FS=20, α3=0.3, it is found that DYJD=2.755.

[0109] By calculating the measurement accuracy index, the overall quality of the point cloud data can be accurately evaluated to ensure that the data meets the accuracy requirements of the project. It is used to feedback the actual effect of acquisition parameters (such as drone flight coverage, sensor resolution, laser emission frequency, etc.), thereby helping to optimize the configuration of data acquisition, reduce unnecessary repeated acquisition, improve efficiency, save time, manpower and equipment resources, especially in large-area or complex terrain monitoring.

[0110] Further, the steps of obtaining the first power line point cloud group and the second power line point cloud group are as follows: based on the statistical filtering algorithm, the power line point clouds are roughly extracted multiple times from the preprocessed power line point cloud data of the transmission line to obtain multiple first roughly extracted power line point clouds; the cloth simulation filtering algorithm is used to finely extract the multiple first roughly 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 power line point clouds extracted by the statistical filtering algorithm and the cloth simulation filtering algorithm. The total number of the first power line point cloud obtained; based on the PCA principal component analysis method, the power line point clouds are roughly extracted multiple times from the preprocessed power line point cloud data of the transmission line to obtain multiple second roughly extracted power line point clouds; the multiple second roughly extracted power line point clouds are respectively finely extracted by 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, 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, and is used to preliminarily screen out point cloud data that meets specific statistical characteristics. The cloth simulation filtering algorithm is a filtering algorithm based on physical simulation, which regards the point cloud data as the suspended state of cloth under gravity, and is used to extract features similar to linear structures, such as power lines. The PCA principal component analysis method is a dimensionality reduction method that maps the data to the principal components by analyzing the main directionality of the point cloud data to highlight the main structural features of the data, which is convenient for clustering and classification. The fast Euclidean clustering algorithm clusters and groups point cloud data based on Euclidean distance, and is used to cluster data points with similar shapes together to help extract the target structure. From preliminary extraction to fine classification, two groups of power line point cloud data are obtained. The combination of different extraction algorithms not only reduces the noise and error of the data, but also ensures that the extracted point cloud data has a clear structure and high precision. This step-by-step processing method improves the quality and reliability of point cloud data, and provides a high-quality data foundation for subsequent power line structure modeling, distance measurement and other applications.

[0112] Furthermore, extracting the standard power line point cloud from the first power line point cloud group and the second power line point cloud group includes the following steps: performing a pairwise similarity comparison on the first power line point cloud 1, the first power line point cloud 2, ..., and the first power line point cloud n to obtain a group of power line point cloud groups N with the highest similarity; performing a pairwise similarity comparison on the second power line point cloud 1, the second power line point cloud 2, ..., and the second power line point cloud m to obtain a group of power line point cloud groups M with the highest similarity; and extracting the standard power line point cloud from the first power line point cloud group N corresponding to the first power line point cloud group N. The similarity is compared with the similarity corresponding to the power line point cloud group M: if the similarity corresponding to the power line point cloud group N is greater than the similarity corresponding to the power line point cloud group M, the power line point cloud group N is used as the standard power line point cloud; if the similarity corresponding to the power line point cloud group N is less than the similarity corresponding to the power line point cloud group M, the power line point cloud group M is used as the standard power line point cloud; if the similarity corresponding to the power line point cloud group N is equal to the similarity corresponding to the power line point cloud group M, 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, through this comparison method, the consistency of structure and morphology between point clouds can be determined, so as to screen out the point cloud group N and point cloud group M with the highest similarity. This process effectively extracts point clouds that are more consistent in spatial distribution and structure, and provides the most accurate and best quality benchmark data for the final selection of standard power line point clouds. The standard power line point cloud with the highest similarity is screened out, ensuring the accuracy and consistency of the selected data and improving the reliability of subsequent analysis. As a benchmark point cloud, the standard power line point cloud provides high-quality reference data for distance measurement, structural analysis, etc. At the same time, through the comparison and screening of the two groups of point clouds, the stability and representativeness of the data are greatly improved, which helps to reduce errors and optimize the accuracy of the overall analysis results.

[0114] Furthermore, fitting the standard power line point cloud with the reference object point cloud data to obtain the model of the power line and the reference object includes the following steps: based on the obtained power line point cloud data of the transmission line and the reference object point cloud data, the point cloud data that meets the characteristics of the reference object is marked as the reference object point cloud through a geometric shape analysis method to obtain a point cloud that meets the characteristics of the reference object; the reference point cloud is selected from the obtained standard power line point cloud and the reference object point cloud through 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 through the ICP algorithm to obtain the fitting of the standard power line point cloud and the reference object point cloud data; the fitted standard power line point cloud and the reference object point cloud data are smoothed through Gaussian smoothing, and the smoothed standard power line point cloud and the reference object point cloud data are modeled through a three-dimensional modeling tool to obtain a model of the power line and the reference object.

[0115] In this embodiment, the ICP algorithm (iterative closest point algorithm) is used for point cloud data alignment. By iteratively adjusting the position, the two sets of point clouds are gradually matched together to achieve high-precision spatial alignment. Gaussian smoothing technology is a data smoothing processing method that reduces noise and irregularities in the point cloud through Gaussian functions to make the data surface smoother. By generating a high-precision three-dimensional model, the relative spatial structure of the power line and the reference object is accurately reflected. The entire process improves the accuracy of point cloud alignment and the smoothness of the model, ensuring that the model is geometrically accurate and provides reliable support for subsequent monitoring, maintenance and risk assessment of power lines.

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

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

[0118]

[0119] Where 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 is the plane equation, and (x1, y1, z1) is the coordinate of the point. The shortest distance calculation method from a point to a surface can be used to find the vertical projection between the point and the surface through differential geometry methods. Whether it is a simple distance from a point to a plane or a complex distance calculation 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 the surrounding environment, but also provides high-precision measurement results for the maintenance and safety monitoring of power lines, helping to identify potential dangerous factors, such as the close distance between power lines and reference objects, which helps to make risk assessments and decisions in advance.

[0120] Furthermore, real-time analysis of the ranging error index includes the following acquisition steps: the SCADA system is connected to the sensor, the stability of the rangefinder, the linearity of the rangefinder, and the temperature during ranging are acquired based on the sensor, and standardized; the weight factors of the standardized rangefinder stability, the linearity of the rangefinder, and the temperature during ranging for the ranging error index are acquired through an objective weighting method, and the ranging error index is calculated through a ranging error index formula.

[0121] In the present embodiment, the SCADA (Supervisory Control and Data Acquisition System) system is connected to a stability sensor, a linearity sensor, and a temperature sensor to collect the stability, linearity, and temperature data of the rangefinder in real time. The temperature during ranging can also be measured by a temperature sensor (e.g., a thermocouple, an RTD temperature sensor, or a digital thermometer). By dynamically evaluating the accuracy and reliability of the measurement, it is ensured that the ranging data always meets the expected accuracy standard, and the accumulation of measurement errors caused by equipment or environmental factors is avoided, thereby improving the accuracy and reliability of the measurement results. At the same time, through the real-time monitoring and analysis of the SCADA system, potential measurement deviations can be quickly responded to, ensuring the efficient operation of the equipment and reducing the risks and errors in the measurement process.

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

[0123]

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

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

[0126] Example 3

[0127] This embodiment also provides a computer device, which is applicable to a method for visual monitoring and ranging of transmission lines based on three-dimensional laser point clouds, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a forced oscillation detection and positioning method for distribution networks proposed in the above embodiment.

[0128] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0129] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0130] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

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

[0132] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0133] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A visual monitoring and ranging method for power transmission lines based on three-dimensional laser point cloud, characterized in that: This includes: using a drone to emit lasers to the power transmission line, obtaining regional point cloud data, and obtaining power line point cloud data and reference point cloud data of the power transmission line; Analyze the measurement accuracy index of the point cloud data, and when the measurement accuracy index of the point cloud data is not lower than a set threshold, perform subsequent processing, otherwise reacquire the point cloud data of the region; Acquire a first power line point cloud group and a second power line point cloud group, and extract a standard power line point cloud therefrom; 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 power transmission lines based on three-dimensional laser point cloud according to claim 1, characterized in that: The specific steps of classifying the regional point cloud data include: Read the regional point cloud data collected by the drone and store it in a three-dimensional point cloud data format; Filtering out noise and outlier processing on the point cloud data of the area; The ground point cloud data and low-altitude point cloud data are filtered out to obtain the power line point cloud data and reference point cloud data of the transmission line.

3. The method for visual monitoring and ranging of power transmission lines based on three-dimensional laser point cloud according to claim 2, characterized in that: The steps of acquiring the first power line point cloud group and the second power line point cloud group include: Based on a statistical filtering algorithm, multiple rough extractions are performed on the preprocessed power line point cloud data of the transmission line to obtain multiple first rough extracted power line point clouds; Refining the plurality of first roughly extracted power line point clouds respectively by using a cloth simulation filtering algorithm to obtain the first power line point cloud group; Based on the PCA principal component analysis method, multiple rough extractions are performed from the preprocessed power line point cloud data of the transmission line to obtain multiple second rough extraction power line point clouds; The plurality of second roughly extracted power line point clouds are respectively refined by using a fast Euclidean clustering algorithm to obtain the second power line point cloud group.

4. The method for visual monitoring and ranging of power transmission lines based on three-dimensional laser point cloud according to claim 3, characterized in that: The step of extracting a standard power line point cloud from the first power line point cloud group and the second power line point cloud group comprises: Performing pairwise similarity comparison on a plurality of first power line point clouds in the first power line point cloud group to obtain a group N of power line point clouds with the highest similarity; Performing pairwise similarity comparison on a plurality of second power line point clouds in the second power line point cloud group to obtain a group of power line point cloud groups M with the highest similarity; The similarity corresponding to the power line point cloud group N is compared with the similarity corresponding to the power line point cloud group M, and the group with higher similarity is selected as the standard power line point cloud.

5. The method for visual monitoring and ranging of power transmission lines based on three-dimensional laser point cloud according to claim 4, characterized in that: The step of fitting the standard power line point cloud with the reference object point cloud data comprises: Based on the geometric shape analysis method, the point cloud data that meets the characteristics of the reference object is marked as the reference object point cloud; Selecting a reference point cloud from the obtained standard power line point cloud and reference object point cloud by geometric features; Based on the selected reference point cloud as the alignment target, the standard power line point cloud and the reference point cloud data are spatially aligned using the ICP algorithm; The fitted standard power line point cloud and reference point cloud data are smoothed by Gaussian smoothing; The smoothed data is modeled using a three-dimensional modeling tool to obtain a model of the power lines and reference objects.

6. The method for visual monitoring and ranging of power transmission lines based on three-dimensional laser point cloud according to claim 5, characterized in that: The step of measuring distance based on the model of power lines and reference objects comprises: Calculate the shortest distance from the power line to the reference object; For the distance from a point to a plane, the shortest distance formula from a point to a plane is used for calculation; For the distance from a point to a surface, the surface shortest distance calculation method is used for calculation; The ranging error index is analyzed in real time, and the ranging error index is used to measure the error degree of the measurement result.

7. The method for visual monitoring and ranging of power transmission lines based on three-dimensional laser point cloud according to claim 6, characterized in that: The step of real-time analysis of the ranging error index comprises: Connect the sensor through the SCADA system to obtain the stability, linearity and temperature of the distance meter during distance measurement; Standardizing the stability, linearity and temperature of the rangefinder during range measurement; The weight factor of each parameter on the ranging error index is obtained through the objective weighting method; The ranging error index is calculated based on the normalized parameters and their weight factors.

8. A transmission line visual monitoring and ranging system based on three-dimensional laser point cloud, based on the transmission line visual monitoring and ranging method based on three-dimensional laser point cloud according to any one of claims 1 to 7, characterized in that: It also includes a data acquisition module, which uses a drone to emit laser to the power transmission line to obtain regional point cloud data, and obtains power line point cloud data and reference point cloud data of the power transmission line; A data analysis module, which analyzes the measurement accuracy index of the point cloud data, and performs subsequent processing when the measurement accuracy index of the point cloud data is not lower than a set threshold, otherwise reacquires the point cloud data of the region; A point cloud extraction module is used to obtain a first power line point cloud group and a second power line point cloud group, and extract a standard power line point cloud therefrom; The distance measurement 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 distance measurement based on the model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for visual monitoring and ranging of power transmission lines based on three-dimensional laser point cloud as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for visual monitoring and ranging of power transmission lines based on three-dimensional laser point cloud according to any one of claims 1 to 7 are implemented.

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