A method and apparatus for identifying split conductors based on cross-sectional density and nonlinear fitting
By acquiring three-dimensional data of transmission lines and using nonlinear fitting methods, split conductors are identified, solving the problem of low identification accuracy in existing technologies and realizing accurate modeling and split identification of multi-strand conductors.
Patent Information
- Application Number
- CN202610430522.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-02
- Publication Date
- 2026-06-30
Smart Images
Figure CN122313458A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power transmission line technology, and in particular to a method and apparatus for identifying split conductors based on cross-sectional density and nonlinear fitting. Background Technology
[0002] In modern power transmission systems, split conductors are widely used in high-voltage or ultra-high-voltage lines due to their significant advantages in corona resistance, reduced resistance, and increased transmission capacity. With the development of non-contact measurement methods such as UAV lidar and photogrammetry, large amounts of 3D point cloud data of conductors can be acquired, providing a data foundation for conductor condition identification and geometric modeling. However, due to environmental factors (such as wind and vibration), measurement errors, and the complexity of conductor morphology, point cloud data suffers from problems such as splitting ambiguity, linear curvature, and cross-sectional overlap. Traditional methods based on direct fitting or regular templates are difficult to use for accurate modeling, especially in identifying the split structure of conductors and their dynamic morphological changes, where a bottleneck exists.
[0003] Current traverse modeling methods include direct geometric fitting and 2D cross-section clustering and geometric reconstruction. Direct geometric fitting involves fitting the traverse to its boundary points using curves such as circular arcs, B-splines, or cubic polynomials. This method struggles with the complex structures of multi-strand traverses and cannot automatically identify splits; its ability to fit traverse geometric features (such as local undulations and curvatures) is also limited. 2D cross-section clustering and geometric reconstruction extracts a 2D projection image from a cross-section of the traverse, uses density clustering (such as DBSCAN and K-Means) to determine the number of splits, and then reconstructs a circular or straight line. This method relies on a single cross-section, lacks spatial continuity, lacks a continuous segment fitting strategy, and cannot generate curves that conform to physical consistency. Therefore, current traverse modeling methods struggle to produce accurate traverse modeling results, leading to difficulties in identifying split traverses. Summary of the Invention
[0004] This invention provides a method and apparatus for identifying split conductors based on cross-sectional density and nonlinear fitting, which can solve the problem of low accuracy in the identification of split conductors in the prior art.
[0005] To address the aforementioned technical problems, this invention provides a method for identifying split conductors based on cross-sectional density and nonlinear fitting, comprising: acquiring three-dimensional data of the transmission line to obtain a set of spatial points of the conductors of the transmission line; Analyze the principal axis of the traverse spatial point set, and adjust the position of each spatial point in the traverse spatial point set according to the principal axis so that the principal axis is parallel to the horizontal axis of the spatial coordinate system, forming an aligned point set; Using the horizontal axis of the spatial coordinate system as a reference, the aligned point set is sliced to form several cross-sectional point sets; Cross-sectional density estimation is performed on each set of cross-sectional points to obtain a set of cross-sectional density values corresponding to each set of cross-sectional points; wherein, each spatial point in the set of cross-sectional points corresponds to a cross-sectional density value; Determine the local maxima of each set of cross-sectional density values, and form the conductor center sequence of each set of cross-sectional points based on the local maxima of each set of cross-sectional density values; Based on Euclidean distance and density similarity, the conductor center sequence of each cross-section point set is analyzed to construct a continuous point sequence corresponding to several conductors; The chain-line model is used to perform nonlinear curve fitting on the continuous point sequence of each conductor to obtain the modeling results of the transmission line. Based on the modeling results, the split conductors of the transmission line are identified.
[0006] As a preferred embodiment, the principal axis of the analysis of the traverse spatial point set includes: Calculate the centroid of the traverse spatial point set based on the coordinates of each spatial point in the traverse spatial point set; Calculate the covariance matrix of the set of spatial points of the traverse line based on the coordinates of the centroid and the coordinates of each spatial point. The covariance matrix is decomposed into eigenvalues to obtain eigenvalues corresponding to several spatial directions; The axis corresponding to the spatial direction with the largest eigenvalue is determined as the principal axis of the spatial point set of the traverse.
[0007] As a preferred embodiment, the aligned point set is sliced using the horizontal axis of the spatial coordinate system as a reference to form several cross-sectional point sets, including: Using each coordinate point in the spatial coordinate system as the slicing reference point, and according to the preset slicing deviation, the aligned point set is sliced to form several cross-sectional point sets. The set of cross-sectional points is as follows: In the formula, Let j be the set of points on the j-th cross section; For the set of cross-section points The ordinate of the i-th spatial point in the image; For the set of cross-section points The vertical coordinate of the i-th spatial point in the image; For the set of cross-section points The x-axis coordinate of the i-th spatial point in the image; Let j be the slicing reference point for the j-th cross-section point set; This is the preset slice deviation.
[0008] As a preferred embodiment, the step of estimating the cross-sectional density of each set of cross-sectional points to obtain a set of cross-sectional density values corresponding to each set of cross-sectional points includes: When performing density estimation on the first spatial point in the cross-section point set, all spatial points in the cross-section point set other than the first spatial point are determined as sample points. Using kernel functions, the contribution weight of each sample point to the first spatial point is calculated to form several kernel contribution weights of the first spatial point. Based on several kernel contribution weights of the first spatial point, calculate the cross-sectional density value of the first spatial point; Based on the cross-sectional density values of each spatial point in the cross-sectional point set, a set of cross-sectional density values for the cross-sectional point set is formed; The kernel contribution weight of the first spatial point is calculated using the following formula: The cross-sectional density value of the first spatial point is calculated using the following formula: In the formula, For the set of cross-section points The cross-sectional density value at the first spatial point; For the set of cross-section points The number of spatial points; Preset bandwidth; For kernel functions; Let be the ordinate and vertical coordinate of the first spatial point; For the set of cross-section points The ordinate and vertical coordinate of the i-th spatial point.
[0009] As a preferred embodiment, determining the local maxima of each of the cross-sectional density value sets, and forming the conductor center sequence of each of the cross-sectional point sets based on the local maxima of each of the cross-sectional density value sets, includes: Several local maxima are determined in each of the aforementioned cross-sectional density value sets; Based on the aforementioned local maxima, a number of conductor center points are determined; The center points of the conductors located in the same cross-section point set are sorted to form a conductor center sequence.
[0010] As a preferred embodiment, the analysis of the conductor center sequence of each cross-section point set based on Euclidean distance and density similarity, and the construction of a continuous point sequence corresponding to several conductors, includes: Determine the set of cross-sections with the most conductor center points from all cross-section point sets, and use it as the reference cross-section point set. Based on cross-section point sets other than the reference cross-section point set, a spatial correlation analysis is performed on all conductor center points in the reference cross-section point set to determine several associated conductor center points corresponding to each conductor center point in the reference cross-section point set; wherein, each cross-section point set other than the reference cross-section point set includes one associated conductor center point. For each conductor center point in the reference section point set, the conductor center point and its corresponding several conductor center points are arranged to obtain a continuous point sequence of the conductor.
[0011] As a preferred embodiment, the step of performing spatial correlation analysis on all traverse center points in the reference cross-section point set to determine several associated traverse center points corresponding to each traverse center point in the reference cross-section point set includes: For the first traverse center point in the reference cross-section point set, calculate the spatial correlation value between the first traverse center point and all traverse center points in the cross-section point sets other than the reference cross-section point set; wherein, the spatial correlation value is composed of Euclidean distance and density similarity. In the cross-section point set other than the reference cross-section point set, the center point of the conductor with the highest spatial correlation value in the same cross-section point set is selected and determined as the associated center point of the first conductor center point.
[0012] As a preferred approach, the spatial correlation value is calculated using the following formula: In the formula, This represents the degree of spatial correlation. Euclidean distance; Density similarity; For the set of cross-section points The center point of the k-th traverse line; For the set of cross-section points The center point of the l-th traverse line; for At the cross-section point set The cross-sectional density value; for At the cross-section point set The cross-sectional density value; The coefficient for the preset distance term; The preset similarity coefficient.
[0013] As a preferred embodiment, identifying the split conductors of the transmission line based on the modeling results includes: The conductor trajectory of the transmission line is determined based on the modeling results; A conductor with a branch is identified in the conductor trajectory and is thus identified as a split conductor of the transmission line.
[0014] Accordingly, the present invention provides a split conductor identification device based on cross-sectional density and nonlinear fitting, comprising: a data acquisition module, a point set alignment module, a point set slicing module, a density estimation module, a conductor center extraction module, a conductor continuous point extraction module, a curve fitting module, and a split conductor identification module; The data acquisition module is used to acquire three-dimensional data of the transmission line and obtain the spatial point set of the conductor of the transmission line; The point set alignment module is used to analyze the principal axis of the traverse spatial point set and adjust the position of each spatial point in the traverse spatial point set according to the principal axis, so that the principal axis is parallel to the horizontal axis of the spatial coordinate system, forming an aligned point set; The point set slicing module is used to slice the aligned point set with the horizontal axis of the spatial coordinate system as a reference to form several cross-sectional point sets. The density estimation module is used to perform cross-sectional density estimation on each of the cross-sectional point sets to obtain a set of cross-sectional density values corresponding to each of the cross-sectional point sets; wherein, each spatial point in the cross-sectional point set corresponds to a cross-sectional density value; The conductor center extraction module is used to determine the local maxima of each of the cross-section density value sets, and to form the conductor center sequence of each cross-section point set based on the local maxima of each of the cross-section density value sets; The continuous point extraction module for conductors is used to analyze the conductor center sequence of each cross-section point set based on Euclidean distance and density similarity, and to construct a continuous point sequence corresponding to several conductors. The curve fitting module is used to perform nonlinear curve fitting on the continuous point sequence of each conductor using a chain model, so as to obtain the modeling result of the transmission line. The split conductor identification module is used to identify the split conductors of the transmission line based on the modeling results.
[0015] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention provides a method for identifying split conductors based on cross-sectional density and nonlinear fitting. The method involves: acquiring a spatial point set of conductors for a transmission line; adjusting the spatial point set through principal axis analysis to form an aligned point set; slicing the aligned point set to form multiple cross-sectional point sets; estimating the cross-sectional density of each cross-sectional point set to obtain a set of cross-sectional density values; forming a conductor center sequence for each cross-sectional point set based on the local maxima of each cross-sectional density value set; analyzing the conductor center sequences of each cross-sectional point set to construct a continuous point sequence of several conductors; performing nonlinear curve fitting on the continuous point sequences of each conductor to obtain the modeling result of the transmission line; and identifying split conductors of the transmission line based on the modeling result. This invention slices the spatial point set of the conductors in a transmission line to obtain multiple cross-sectional point sets. By analyzing each cross-sectional point set, it obtains the conductor center point sequence. Then, by performing correlation analysis on the conductor center points, it obtains the continuous point sequence of each conductor. Finally, based on the continuous point sequence, it performs nonlinear fitting to obtain the conductor modeling result. Therefore, this invention can handle the complex structure of multi-strand conductors by performing spatial analysis on the transmission line; and by analyzing multiple cross-sectional point sets of the transmission line, it can fully preserve the geometric features of the conductors, making the modeling result consistent with physical characteristics. Furthermore, based on the accurate conductor modeling result, it can identify the conductor splitting situation of the transmission line, effectively improving the accuracy of split conductor identification. Attached Figure Description
[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0017] Figure 1 A schematic flowchart of an embodiment of the split conductor identification method based on cross-sectional density and nonlinear fitting provided by the present invention; Figure 2 This is a flowchart illustrating another embodiment of the split conductor identification method based on cross-sectional density and nonlinear fitting provided by the present invention. Figure 3 This is a schematic diagram of an embodiment of the split conductor identification device based on cross-sectional density and nonlinear fitting provided by the present invention. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.
[0020] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.
[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0022] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.
[0023] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).
[0024] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.
[0025] See Figure 1 To address the problem of low accuracy in identifying split conductors in existing technologies, an embodiment of the present invention provides a method for identifying split conductors based on cross-sectional density and nonlinear fitting. This method includes steps 101 to 108, each step of which is detailed below: Step 101: Perform three-dimensional data acquisition on the transmission line to obtain the spatial point set of the conductor of the transmission line.
[0026] In this embodiment of the invention, acquiring three-dimensional data of the transmission line yields its corresponding point cloud data. Methods for acquiring three-dimensional data include 3D scanning, photogrammetry, and lidar detection. Among these, 3D scanning is the most commonly used technique for obtaining object point clouds, accurately reconstructing the morphology and dimensions of the conductor. Specifically, by using instruments such as laser scanners, 3D photogrammetry equipment, or industrial CT scanners, a comprehensive scan of the transmission line can be performed, recording the three-dimensional coordinates (X, Y, Z) of a large number of discrete points on the surface and inside the conductor. The set of these discrete points constitutes the conductor's spatial point set.
[0027] Step 102: Analyze the principal axis of the traverse spatial point set, and adjust the position of each spatial point in the traverse spatial point set according to the principal axis so that the principal axis is parallel to the horizontal axis of the spatial coordinate system, forming an aligned point set.
[0028] As a preferred embodiment, analyzing the principal axis of the traverse spatial point set includes: Calculate the centroid of the traverse spatial point set based on the coordinates of each spatial point in the traverse spatial point set; Calculate the covariance matrix of the set of spatial points of the traverse line based on the coordinates of the centroid and the coordinates of each spatial point. The covariance matrix is decomposed into eigenvalues to obtain eigenvalues corresponding to several spatial directions; The axis corresponding to the spatial direction with the largest eigenvalue is determined as the principal axis of the spatial point set of the traverse.
[0029] In this embodiment of the invention, the spatial point set of the conductors of a transmission line can be defined as: ,in, For the set of points in the traverse space; Let i be the i-th spatial point in the set of spatial points of the traverse. For spatial points The three-dimensional coordinates; N is the number of spatial points in the traverse spatial point set; Let be the dimension. The centroid of the point set in the traverse space is calculated using the following formula: In the formula, The coordinates are the centroid coordinates.
[0030] After obtaining the centroid coordinates, the covariance matrix of the traverse space point set is calculated using the following formula: In the formula, Let be the covariance matrix.
[0031] Then, the covariance matrix above is decomposed into eigenvalues: In the formula, The eigenvector matrix; It is an eigenvalue diagonal matrix; These are the diagonal elements of the eigenvalue diagonal matrix, i.e., the eigenvalues. Each eigenvalue corresponds to a spatial direction, and the spatial direction with the largest eigenvalue is determined as the principal axis. The collected conductor spatial point set is analyzed in a spatial coordinate system, which is constructed by the horizontal, vertical, and axial axes, and its initial direction can be preset. After determining the principal axis of the conductor spatial point set, the conductor spatial point set is rotated to align the principal axis of the conductor spatial point set with the horizontal axis (X-axis) of the spatial coordinate system, making the conductors of the transmission line parallel to the X-axis, forming a unified coordinate system, thus obtaining the aligned point set.
[0032] Step 103: Using the horizontal axis of the spatial coordinate system as a reference, slice the aligned point set to form several cross-sectional point sets.
[0033] As a preferred embodiment, the aligned point set is sliced using the horizontal axis of the spatial coordinate system as a reference to form several cross-sectional point sets, including: Using each coordinate point in the spatial coordinate system as the slicing reference point, and according to the preset slicing deviation, the aligned point set is sliced to form several cross-sectional point sets. The set of cross-sectional points is as follows: In the formula, Let j be the set of points on the j-th cross section; For the set of cross-section points The ordinate of the i-th spatial point in the image; For the set of cross-section points The vertical coordinate of the i-th spatial point in the image; For the set of cross-section points The x-axis coordinate of the i-th spatial point in the image; Let j be the slicing reference point for the j-th cross-section point set; This is the preset slice deviation.
[0034] In this embodiment of the invention, for the rotated and aligned point set, the X coordinate is fixed, and the... As a reference point for slicing, combined with the preset slicing deviation By performing slicing, multiple cross-sectional point sets are obtained, which can be represented as: That is, the set of points on the j-th cross section includes the X coordinates in All spatial points within this small area.
[0035] Step 104: Perform cross-sectional density estimation on each set of cross-sectional points to obtain a set of cross-sectional density values corresponding to each set of cross-sectional points; wherein, each spatial point in the set of cross-sectional points corresponds to a cross-sectional density value.
[0036] In this embodiment of the invention, the cross-sectional density is estimated on multiple cross-sectional point sets obtained by slicing, and the cross-sectional density values of each spatial point in the cross-sectional point set are obtained, thereby forming a cross-sectional density value set of the cross-sectional point set.
[0037] As a preferred embodiment, cross-sectional density estimation is performed on each set of cross-sectional points to obtain a set of cross-sectional density values corresponding to each set of cross-sectional points, including: When performing density estimation on the first spatial point in the cross-section point set, all spatial points in the cross-section point set other than the first spatial point are determined as sample points. Using kernel functions, the contribution weight of each sample point to the first spatial point is calculated to form several kernel contribution weights of the first spatial point. Based on several kernel contribution weights of the first spatial point, calculate the cross-sectional density value of the first spatial point; Based on the cross-sectional density values of each spatial point in the cross-sectional point set, a set of cross-sectional density values for the cross-sectional point set is formed; The kernel contribution weight of the first spatial point is calculated using the following formula: The cross-sectional density value of the first spatial point is calculated using the following formula: In the formula, For the set of cross-section points The cross-sectional density value at the first spatial point; For the set of cross-section points The number of spatial points; Preset bandwidth; For kernel functions; Let be the ordinate and vertical coordinate of the first spatial point; For the set of cross-section points The ordinate and vertical coordinate of the i-th spatial point.
[0038] In this embodiment of the invention, to calculate the cross-sectional density value of each spatial point in the cross-sectional point set, the spatial point for which the cross-sectional density value is to be calculated is first determined as the first spatial point. All other spatial points in the same cross-sectional point set as the first spatial point are determined as sample points of the first spatial point. The cross-sectional density value of the first spatial point is calculated by analyzing the sample points. Specifically, a kernel function is used to calculate the contribution weight of each sample point to the first spatial point. The kernel function is defined as follows: ,Will This involves standardizing the coordinate offsets between the first spatial point and the sample points. The preset bandwidth can be determined based on prior knowledge. It is the absolute distance between the first spatial point and the sample point in the Y direction. This is the absolute distance between the first spatial point and the sample points in the Z direction. Using the kernel function, the contribution weight of each sample point to the first spatial point can be calculated, thus yielding the kernel contribution weight of the first spatial point. For example, It is the i-th sample point For the first spatial point The contribution weights of the first spatial point are then calculated. The cross-sectional density function is then used to sum the contribution weights of all kernels at the first spatial point, yielding the cross-sectional density value of that point. Finally, the cross-sectional density values of all spatial points on the cross-sectional point set are combined to form the cross-sectional density value set of the cross-sectional point set.
[0039] Step 105: Determine the local maxima of each set of cross-sectional density values, and form the conductor center sequence of each set of cross-sectional points based on the local maxima of each set of cross-sectional density values.
[0040] As a preferred embodiment, determining the local maxima of each set of cross-sectional density values and forming a conductor center sequence of each set of cross-sectional point values based on the local maxima of each set of cross-sectional density values includes: Several local maxima are determined in each of the aforementioned cross-sectional density value sets; Based on the aforementioned local maxima, a number of conductor center points are determined; The center points of the conductors located in the same cross-section point set are sorted to form a conductor center sequence.
[0041] In this embodiment of the invention, by analyzing the cross-sectional density value set, which contains cross-sectional density values corresponding to multiple spatial points, and filtering out local maxima from these cross-sectional density values, the center point of the conductor can be identified. Here, the center point of the conductor refers to a spatial point belonging to the conductor; non-center points in the cross-sectional point set are spatial points that do not belong to the conductor, and only the center point can be considered when modeling the conductor subsequently. There are two methods to determine the center point of the conductor: one is to determine the point corresponding to the local maximum as the center point of the conductor; the other is to perform curve fitting on the coordinates near the coordinates of the local maximum, and the spatial point corresponding to the peak position of the curve is taken as the center point of the conductor. The number of local maxima in the cross-sectional point set is equal to the number of conductors corresponding to that cross-sectional point set. For example, if there are two local maxima in the cross-sectional point set, it means that there are two conductors corresponding to that cross-sectional point set, where each of the two local maxima corresponds to one conductor.
[0042] In this embodiment of the invention, after selecting multiple conductor center points on the cross-section point set, these conductor center points are sorted according to certain rules to form a conductor center sequence. The sorting rules can be: Y-coordinate from smallest to largest, Z-coordinate from smallest to largest, or spatial order, etc. Among them, the conductor center sequence... Represents the set of cross-section points The sequence of conductor centers, For the set of cross-section points The kth center point of the conductor.
[0043] Step 106: Based on Euclidean distance and density similarity, analyze the conductor center sequence of each cross-section point set to construct a continuous point sequence corresponding to several conductors.
[0044] In this embodiment of the invention, after forming the conductor center sequence of each cross-section point set, each conductor center point is matched with conductor center points on other cross-section point sets to obtain multiple continuous points corresponding to each conductor, thereby constructing a continuous point sequence. Determining the correlation between two conductor center points in different cross-section point sets requires correlation analysis. The Euclidean distance and density similarity between two conductor center points can reflect this correlation. Therefore, based on Euclidean distance and density similarity, continuous points on the same conductor can be analyzed.
[0045] As a preferred embodiment, based on Euclidean distance and density similarity, the conductor center sequence of each cross-section point set is analyzed to construct a continuous point sequence corresponding to several conductors, including: Determine the set of cross-sections with the most conductor center points from all cross-section point sets, and use it as the reference cross-section point set. Based on cross-section point sets other than the reference cross-section point set, a spatial correlation analysis is performed on all conductor center points in the reference cross-section point set to determine several associated conductor center points corresponding to each conductor center point in the reference cross-section point set; wherein, each cross-section point set other than the reference cross-section point set includes one associated conductor center point. For each conductor center point in the reference section point set, the conductor center point and its corresponding several conductor center points are arranged to obtain a continuous point sequence of the conductor.
[0046] In this embodiment of the invention, since transmission lines may have conductor splits, the number of conductor center points in each cross-section point set will differ. To comprehensively obtain the conductor information of the transmission line, the cross-section point set with the largest number of conductor center points is determined as the reference cross-section point set. Correlation analysis is performed on all conductor center points in the reference cross-section point set, and a correlated conductor center point is obtained in each cross-section point set other than the reference cross-section point set. The conductor center point in the reference cross-section point set and its corresponding correlated conductor center points from the other cross-section point sets constitute a continuous point sequence for one of the conductors. A continuous point sequence can be defined as... ,in, For the k-th conductor There are several consecutive points. When forming a sequence of consecutive points, the center points of the traverse lines in the reference cross-section point set and their corresponding multiple associated traverse center points can be ordered according to the order of the cross-section point set. For example, the cross-section point set... In the diagram, the center point of the conductor belonging to the k-th conductor is... Its order in the sequence of continuous points of the k-th conductor is j.
[0047] As a preferred embodiment, a spatial correlation analysis is performed on all traverse center points in the reference cross-section point set to determine several associated traverse center points corresponding to each traverse center point in the reference cross-section point set, including: For the first traverse center point in the reference cross-section point set, calculate the spatial correlation value between the first traverse center point and all traverse center points in the cross-section point sets other than the reference cross-section point set; wherein, the spatial correlation value is composed of Euclidean distance and density similarity. In the cross-section point set other than the reference cross-section point set, the center point of the conductor with the highest spatial correlation value in the same cross-section point set is selected and determined as the associated center point of the first conductor center point.
[0048] In this embodiment of the invention, all traverse center points in the reference cross-section point set are determined as the first traverse center point. For each first traverse center point, a related traverse center point is selected from the cross-section point sets other than the reference cross-section point set through correlation analysis. The method for selecting related traverse center points is to calculate the spatial correlation degree value between the first traverse center point and other traverse center points, and determine the traverse center point with the largest spatial correlation degree value in the same cross-section point set as the related traverse center point of the first traverse center point. The spatial correlation degree value is obtained by calculating Euclidean distance and density similarity.
[0049] As a preferred embodiment, the spatial correlation value is calculated using the following formula: In the formula, This represents the degree of spatial correlation. Euclidean distance; Density similarity; For the set of cross-section points The center point of the k-th traverse line; For the set of cross-section points The center point of the l-th traverse line; for At the cross-section point set The cross-sectional density value; for At the cross-section point set The cross-sectional density value; The coefficient for the preset distance term; The preset similarity coefficient.
[0050] As an example of an embodiment of the present invention, assume a set of cross-sectional points. It is the reference cross-section point set, when in the cross-section point set Filtering cross-section point sets The center point of the k-th conductor When the corresponding associated traverse center point is reached, for the cross-section point set... All conductor center points Calculate its relationship with The Euclidean distance and density similarity between them are used to calculate its relationship with... The spatial correlation value between them will be the cross-sectional point set. Zhongyu The center point of the traverse with the highest spatial correlation value is determined as The center point of the associated conductor.
[0051] Step 107: Using the chain line model, perform nonlinear curve fitting on the continuous point sequence of each conductor to obtain the modeling result of the transmission line.
[0052] In this embodiment of the invention, after obtaining the continuous point sequence corresponding to each conductor, nonlinear fitting is performed on each conductor to obtain the modeling result of the transmission line. Specifically, a chain-wire model can be used. Nonlinear fitting is performed to estimate the model parameters of the chainline model by minimizing the energy functional U, where the energy functional U is: The energy functional U includes data fitting terms. Regularization term and endpoint constraint terms Three parts. Among them, These are the model's predicted values. For the actual traverse points, the data fitting term represents the error between the model's predicted values and the actual traverse points. The regularization term is used to constrain the curvature smoothness of the model. It's the weight, the second derivative of the curve. Used to reflect curvature. Endpoint constraint terms are the terms that force the model to predict the beginning and end points. and end Close to the actual two ends of the conductor and , It's weight. The larger the endpoint, the stronger the constraint. Furthermore, to obtain model stability and parameter reliability estimates, a Bayesian model can be introduced to achieve robust fission tracking and uncertainty analysis, resulting in more accurate modeling results.
[0053] Step 108: Based on the modeling results, identify the split conductors of the transmission line.
[0054] As a preferred embodiment, based on the modeling results, identifying the split conductors of the transmission line includes: The conductor trajectory of the transmission line is determined based on the modeling results; A conductor with a branch is identified in the conductor trajectory and is thus identified as a split conductor of the transmission line.
[0055] In this embodiment of the invention, after obtaining the modeling results of the transmission line, the conductor trajectory of the transmission line can be reconstructed. Since the number of local maxima of the cross-section point set is equivalent to the number of conductors in that cross-section, when the conductor trajectory identifies a single conductor in a certain cross-section, and the single conductor branches in subsequent cross-sections, the conductor is determined to be a split conductor.
[0056] As an example of an embodiment of the present invention, see Figure 2This is a flowchart illustrating another embodiment of the split conductor identification method based on cross-sectional density and nonlinear fitting provided by the present invention. First, the original spatial point set of the transmission line conductors is obtained; then, the principal axis of the conductor spatial point set is determined and aligned with the principal axis of the spatial coordinate system to obtain the aligned point set; next, the aligned point set is sliced to obtain multiple cross-sectional point sets; by analyzing the cross-sectional point sets, the conductor center point sequence of each cross-sectional point set is obtained, and then the continuous point sequence of each conductor is obtained through correlation analysis; finally, nonlinear fitting is performed based on the continuous point sequence of the conductors to obtain the modeling result of the conductors; the conductor splitting situation of the transmission line can be identified based on the modeling result of the conductors.
[0057] Implementing the above embodiments has the following effects: This invention provides a method for identifying split conductors based on cross-sectional density and nonlinear fitting. The method involves: acquiring a spatial point set of conductors for a transmission line; adjusting the spatial point set through principal axis analysis to form an aligned point set; slicing the aligned point set to form multiple cross-sectional point sets; estimating the cross-sectional density of each cross-sectional point set to obtain a set of cross-sectional density values; forming a conductor center sequence for each cross-sectional point set based on the local maxima of each cross-sectional density value set; analyzing the conductor center sequences of each cross-sectional point set to construct a continuous point sequence of several conductors; performing nonlinear curve fitting on the continuous point sequences of each conductor to obtain the modeling result of the transmission line; and identifying split conductors of the transmission line based on the modeling result. This invention slices the spatial point set of the conductors in a transmission line to obtain multiple cross-sectional point sets. By analyzing each cross-sectional point set, it obtains the conductor center point sequence. Then, by performing correlation analysis on the conductor center points, it obtains the continuous point sequence of each conductor. Finally, based on the continuous point sequence, it performs nonlinear fitting to obtain the conductor modeling result. Therefore, by analyzing multiple cross-sectional point sets of the transmission line, this invention can comprehensively preserve the geometric features of the conductors, making the modeling result consistent with physical characteristics. Furthermore, based on the accurate conductor modeling result, it can identify the conductor splitting situation of the transmission line, effectively improving the accuracy of split conductor identification.
[0058] like Figure 3 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a split conductor identification device based on cross-sectional density and nonlinear fitting, comprising: a data acquisition module, a point set alignment module, a point set slicing module, a density estimation module, a conductor center extraction module, a conductor continuous point extraction module, a curve fitting module, and a split conductor identification module; The data acquisition module is used to acquire three-dimensional data of the transmission line and obtain the spatial point set of the conductor of the transmission line; The point set alignment module is used to analyze the principal axis of the traverse spatial point set and adjust the position of each spatial point in the traverse spatial point set according to the principal axis, so that the principal axis is parallel to the horizontal axis of the spatial coordinate system, forming an aligned point set; The point set slicing module is used to slice the aligned point set with the horizontal axis of the spatial coordinate system as a reference to form several cross-sectional point sets. The density estimation module is used to perform cross-sectional density estimation on each of the cross-sectional point sets to obtain a set of cross-sectional density values corresponding to each of the cross-sectional point sets; wherein, each spatial point in the cross-sectional point set corresponds to a cross-sectional density value; The conductor center extraction module is used to determine the local maxima of each of the cross-section density value sets, and to form the conductor center sequence of each cross-section point set based on the local maxima of each of the cross-section density value sets; The continuous point extraction module for conductors is used to analyze the conductor center sequence of each cross-section point set based on Euclidean distance and density similarity, and to construct a continuous point sequence corresponding to several conductors. The curve fitting module is used to perform nonlinear curve fitting on the continuous point sequence of each conductor using a chain model, so as to obtain the modeling result of the transmission line. The split conductor identification module is used to identify the split conductors of the transmission line based on the modeling results.
[0059] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the split conductor identification method based on cross-sectional density and nonlinear fitting provided by any of the above-described method embodiments of the present invention.
[0060] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0061] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.
Claims
1. A method for identifying split conductors based on cross-sectional density and nonlinear fitting, characterized in that, include: Three-dimensional data acquisition is performed on the transmission line to obtain the spatial point set of the conductor of the transmission line; Analyze the principal axis of the traverse spatial point set, and adjust the position of each spatial point in the traverse spatial point set according to the principal axis so that the principal axis is parallel to the horizontal axis of the spatial coordinate system, forming an aligned point set; Using the horizontal axis of the spatial coordinate system as a reference, the aligned point set is sliced to form several cross-sectional point sets; Cross-sectional density estimation is performed on each set of cross-sectional points to obtain a set of cross-sectional density values corresponding to each set of cross-sectional points; wherein, each spatial point in the set of cross-sectional points corresponds to a cross-sectional density value; Determine the local maxima of each set of cross-sectional density values, and form the conductor center sequence of each set of cross-sectional point values based on the local maxima of each set of cross-sectional density values; Based on Euclidean distance and density similarity, the conductor center sequence of each cross-section point set is analyzed to construct a continuous point sequence corresponding to several conductors; The chain-line model is used to perform nonlinear curve fitting on the continuous point sequence of each conductor to obtain the modeling result of the transmission line. Based on the modeling results, the split conductors of the transmission line are identified.
2. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 1, characterized in that, The principal axis of the analysis of the spatial point set of the traverse includes: Calculate the centroid of the traverse spatial point set based on the coordinates of each spatial point in the traverse spatial point set; Calculate the covariance matrix of the set of spatial points of the traverse line based on the coordinates of the centroid and the coordinates of each spatial point. The covariance matrix is decomposed into eigenvalues to obtain eigenvalues corresponding to several spatial directions; The axis corresponding to the spatial direction with the largest eigenvalue is determined as the principal axis of the spatial point set of the traverse.
3. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 1, characterized in that, The process of slicing the aligned point set using the horizontal axis of the spatial coordinate system as a reference to form several cross-sectional point sets includes: Using each coordinate point in the spatial coordinate system as the slicing reference point, and according to the preset slicing deviation, the aligned point set is sliced to form several cross-sectional point sets. The set of cross-sectional points is as follows: In the formula, Let j be the set of points on the j-th cross section; For the set of cross-section points The ordinate of the i-th spatial point in the image; For the set of cross-section points The vertical coordinate of the i-th spatial point in the image; For the set of cross-section points The x-axis coordinate of the i-th spatial point in the image; Let j be the slicing reference point for the j-th cross-section point set; This is the preset slice deviation.
4. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 1, characterized in that, The step of estimating the cross-sectional density of each set of cross-sectional points to obtain the set of cross-sectional density values corresponding to each set of cross-sectional points includes: When performing density estimation on the first spatial point in the cross-section point set, all spatial points in the cross-section point set other than the first spatial point are determined as sample points. Using kernel functions, the contribution weight of each sample point to the first spatial point is calculated to form several kernel contribution weights of the first spatial point. Based on several kernel contribution weights of the first spatial point, calculate the cross-sectional density value of the first spatial point; Based on the cross-sectional density values of each spatial point in the cross-sectional point set, a set of cross-sectional density values for the cross-sectional point set is formed; The kernel contribution weight of the first spatial point is calculated using the following formula: The cross-sectional density value of the first spatial point is calculated using the following formula: In the formula, For the set of cross-section points The cross-sectional density value at the first spatial point; For the set of cross-section points The number of spatial points; Preset bandwidth; For kernel functions; Let be the ordinate and vertical coordinate of the first spatial point; For the set of cross-section points The ordinate and vertical coordinate of the i-th spatial point.
5. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 1, characterized in that, The step of determining the local maxima of each of the cross-sectional density value sets and forming the conductor center sequence of each of the cross-sectional point sets based on the local maxima of each of the cross-sectional density value sets includes: Several local maxima are determined in each of the aforementioned cross-sectional density value sets; Based on the aforementioned local maxima, a number of conductor center points are determined; The center points of the conductors located in the same cross-section point set are sorted to form a conductor center sequence.
6. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 5, characterized in that, The method involves analyzing the conductor center sequence of each cross-section point set based on Euclidean distance and density similarity to construct a continuous point sequence corresponding to several conductors, including: Determine the set of cross-sections with the most conductor center points from all cross-section point sets, and use it as the reference cross-section point set. Based on cross-section point sets other than the reference cross-section point set, a spatial correlation analysis is performed on all conductor center points in the reference cross-section point set to determine several associated conductor center points corresponding to each conductor center point in the reference cross-section point set; wherein, each cross-section point set other than the reference cross-section point set includes one associated conductor center point. For each conductor center point in the reference section point set, the conductor center point and its corresponding several conductor center points are arranged to obtain a continuous point sequence of the conductor.
7. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 6, characterized in that, The spatial correlation analysis of all traverse center points in the reference cross-section point set is performed to determine several associated traverse center points corresponding to each traverse center point in the reference cross-section point set, including: For the first traverse center point in the reference cross-section point set, calculate the spatial correlation value between the first traverse center point and all traverse center points in the cross-section point sets other than the reference cross-section point set; wherein, the spatial correlation value is composed of Euclidean distance and density similarity. In the cross-section point set other than the reference cross-section point set, the center point of the conductor with the highest spatial correlation value in the same cross-section point set is selected and determined as the associated center point of the first conductor center point.
8. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 7, characterized in that, The spatial correlation value is calculated using the following formula: In the formula, This represents the degree of spatial correlation. Euclidean distance; Density similarity; For the set of cross-section points The center point of the k-th traverse line; For the set of cross-section points The center point of the l-th traverse line; for At the cross-section point set The cross-sectional density value; for At the cross-section point set The cross-sectional density value; The coefficient for the preset distance term; The preset similarity coefficient.
9. The method for identifying split conductors based on cross-sectional density and nonlinear fitting according to claim 8, characterized in that, The step of identifying the split conductors of the transmission line based on the modeling results includes: The conductor trajectory of the transmission line is determined based on the modeling results; A conductor with a branch is identified in the conductor trajectory and is thus identified as a split conductor of the transmission line.
10. A split conductor identification device based on cross-sectional density and nonlinear fitting, characterized in that, include: The module includes a data acquisition module, a point set alignment module, a point set slicing module, a density estimation module, a conductor center extraction module, a conductor continuous point extraction module, a curve fitting module, and a split conductor identification module. The data acquisition module is used to acquire three-dimensional data of the transmission line and obtain the spatial point set of the conductor of the transmission line; The point set alignment module is used to analyze the principal axis of the traverse spatial point set and adjust the position of each spatial point in the traverse spatial point set according to the principal axis, so that the principal axis is parallel to the horizontal axis of the spatial coordinate system, forming an aligned point set; The point set slicing module is used to slice the aligned point set with the horizontal axis of the spatial coordinate system as a reference to form several cross-sectional point sets. The density estimation module is used to perform cross-sectional density estimation on each of the cross-sectional point sets to obtain a set of cross-sectional density values corresponding to each of the cross-sectional point sets; wherein, each spatial point in the cross-sectional point set corresponds to a cross-sectional density value; The conductor center extraction module is used to determine the local maxima of each of the cross-section density value sets, and to form the conductor center sequence of each cross-section point set based on the local maxima of each of the cross-section density value sets; The continuous point extraction module for conductors is used to analyze the conductor center sequence of each cross-section point set based on Euclidean distance and density similarity, and to construct a continuous point sequence corresponding to several conductors. The curve fitting module is used to perform nonlinear curve fitting on the continuous point sequence of each conductor using a chain model, so as to obtain the modeling result of the transmission line. The split conductor identification module is used to identify the split conductors of the transmission line based on the modeling results.