CN115797545A - Modeling method and system for split conductors in power transmission channel scene - Google Patents

Abstract

The invention discloses a modeling method and a system for a split conductor in a power transmission channel scene, wherein the modeling method comprises the following steps: segmenting the split conductor along the direction of the conductor based on the point cloud data of the power line to obtain segmented data; performing segment-by-segment clustering analysis based on the data after the point segmentation to separate point clouds from different split conductors; merging point clouds belonging to the same power line section by section; and fitting a second-order curve to each split conductor to obtain a classified conductor model so as to complete modeling of the split conductors. The method can realize the rapid and accurate modeling of the single-stranded split conductor.

Description

Modeling method and system for split conductors in power transmission channel scene

Technical Field

The invention relates to the technical field of power transmission, in particular to a modeling method and a modeling system for a split conductor in a power transmission channel scene.

Background

In the erection of high-voltage, ultrahigh-voltage and extra-high-voltage transmission lines, in order to inhibit corona discharge and line reactance, a split line mode is generally adopted for conducting wire erection. Each phase of the split conductors is composed of a plurality of split conductors with smaller diameters, the distance between the split conductors is generally 20cm to 45cm, the split conductors are generally distributed in a polygonal shape, for example, four split conductors are distributed on four vertexes of a rectangle, and meanwhile, the split conductors are usually fixed by adopting spacing rods at intervals along the direction of the power transmission line. Therefore, compared with common non-split transmission lines, the space structure of the split conductor is more complex, and the difficulty of fine modeling is higher.

At present, researches on power grid application by utilizing an airborne Li DAR technology at home and abroad mainly focus on power transmission line corridor point cloud classification, power line point cloud extraction, non-split conductor modeling and the like, and the rare researches relate to split conductor modeling. In the similar research, for example, dadamang and the like, point cloud is projected to the direction of the power transmission line, an optimal plane coordinate system for fitting the power lines is established, and least square curve fitting is carried out on each power line, but fitting and analysis are not carried out on multi-split wires; the McLaugh l i n separates and connects the wire point clouds through iterative calculation based on the power line trend prediction model, but the model prediction precision is not tested under the condition of multi-split wires. Jw and the like provide a power line segment growth algorithm, find out the optimally-fitted power line by using a hypothesis test method, and separate the split conductors to a certain extent, but the calculation process is complex, the influence of point cloud loss on the growth of the model is large, and the number of the split conductors needs to be preset. Cheng and the like extract power line point clouds of urban areas from vehicle-mounted laser point clouds and perform modeling, the algorithm considers the condition that the space distance between power lines is close, but the point cloud data distribution characteristics are different due to different observation platforms, for example, the vehicle-mounted power line point clouds are obtained by scanning from the side or the lower part, and the power line point clouds of a helicopter platform are obtained from the upper part of the power lines, so that the data loss and the point cloud density distribution conditions are different, and moreover, because the distance between split conductors is smaller, the span is longer, the method can not be suitable for modeling of the split conductors.

In conjunction with the above studies and analysis, the main difficulties of the current fine modeling of split conductors are: (1) The span of the split conductors is large, the space distance between adjacent conductors is small (often less than 0.5 m), and the common methods such as least square curve fitting and the like are difficult to distinguish different classification conductors and fit an indefinite number of split conductors; (2) Due to the noise of a laser radar system and the influence of observation environment (such as wire windage yaw during data acquisition), the noise level of the split wire point cloud is higher than that of a common wire, and more noise points exist in the space between the wires, so that the mutual separation and fitting of the wires are directly influenced; (3) Since the flying height of a general airborne point cloud is high (> 200 m) when the point cloud is acquired, even obvious split line characteristics cannot be observed from the recorded point cloud, and in addition, situations such as data loss exist, which also limits the research of split conductor fine modeling.

Therefore, based on the problems and the current situation of engineering application, a split conductor fine modeling method is needed for high-density helicopter platform power transmission line point cloud data.

Disclosure of Invention

The invention provides a modeling method and a system for a split conductor in a power transmission channel scene, which aim to solve the problem of how to realize fine modeling of classified conductors.

In order to solve the above problem, according to an aspect of the present invention, there is provided a method of modeling a split conductor in a power transmission channel scenario, the method including:

segmenting the split conductor along the direction of the conductor based on the point cloud data of the power line to obtain segmented data;

performing segment-by-segment clustering analysis based on the data after the point segmentation to separate point clouds from different split conductors;

merging point clouds belonging to the same power line section by section;

and fitting a second-order curve to each split conductor to obtain a classified conductor model so as to complete modeling of the split conductors.

Preferably, the segmenting the split conductor along the direction of the conductor based on the power line point cloud data, and acquiring segmented data includes:

projecting the wire point cloud onto a two-dimensional horizontal plane, and regarding the point cloud on a projection surface as linear distribution;

performing straight line fitting by using all point clouds on a two-dimensional plane, taking a straight line obtained by fitting as the horizontal trend of a wire point cloud set, and simultaneously taking a point on a trend line corresponding to the minimum value of a horizontal coordinate or a vertical coordinate in the point clouds as an initial endpoint of a wire;

and carrying out sectional management on the original three-dimensional wire point cloud along the direction of the trend line from the starting endpoint based on a preset distance threshold value to obtain point cloud sectional data.

Preferably, the performing a segment-by-segment cluster analysis based on the segmented data to separate point clouds from different split wires includes:

for each section, projecting the coordinates of the corresponding point cloud data onto a plane capable of classifying the split conductor point clouds, taking the plane vertical to the main direction of the section point clouds as a projection plane, and performing clustering analysis by adopting a k-means clustering algorithm so as to enable the point clouds on the projection plane to be adaptively distributed into corresponding categories, and obtaining the point cloud clusters in each section by utilizing the corresponding relation between the points in the projection plane and the original coordinates, wherein each cluster represents a laser point cloud set from different split conductors.

Preferably, wherein the method further comprises:

when the point cloud data of the first section is subjected to cluster analysis, setting a split number, calculating three-dimensional distances among all categories after the cluster analysis, if the three-dimensional distances are smaller than a preset distance threshold, subtracting 1 from the split number until all the three-dimensional distances are larger than the preset distance threshold, and determining the split number at the moment as the final split number of the whole lead; starting from the second section, a cluster analysis is performed according to the final split number.

Preferably, the segment-by-segment merging point clouds belonging to the same power line includes:

a. calculating all possible merging modes between adjacent section clusters by adopting a full-permutation method according to the number of the split conductors, wherein if the number of the split conductors is n, all possible connection combinations are shared

Seed growing;

b. in each combination, accumulating the spatial distance between the merged clusters;

c. comparing the space distances of all the combinations, and considering the connection mode corresponding to the minimum value as a correct combination;

d. repeating steps a-d along the power line trend until all clusters are assigned to a split conductor.

According to another aspect of the invention, a modeling method and a system for split conductors in a power transmission channel scene are provided, wherein the system comprises the following steps:

the segmentation module is used for segmenting the split conductor along the direction of the conductor based on the power line point cloud data and acquiring segmented data;

the cluster analysis module is used for carrying out section-by-section cluster analysis on the data after the point segmentation so as to separate point clouds from different split conductors;

the merging module is used for merging the point clouds belonging to the same power line section by section;

and the fitting module is used for fitting a second-order curve to each split conductor to obtain a classified conductor model so as to complete modeling of the split conductors.

Preferably, the segmenting module segments the split conductor along the direction of the conductor based on the power line point cloud data, and acquires segmented data, including:

projecting the wire point cloud onto a two-dimensional horizontal plane, and regarding the point cloud on a projection surface as linear distribution;

performing straight line fitting by using all point clouds on a two-dimensional plane, taking a straight line obtained by fitting as the horizontal trend of a wire point cloud set, and simultaneously taking a point on a trend line corresponding to the minimum value of a horizontal coordinate or a vertical coordinate in the point clouds as an initial endpoint of a wire;

and carrying out sectional management on the original three-dimensional wire point cloud along the direction of the trend line from the starting endpoint based on a preset distance threshold value to obtain point cloud sectional data.

Preferably, the cluster analysis module performs segment-by-segment cluster analysis based on the segmented data to separate point clouds from different split wires, including:

for each section, projecting the coordinates of the corresponding point cloud data onto a plane capable of classifying the split conductor point clouds, taking the plane vertical to the main direction of the section point clouds as a projection plane, and performing clustering analysis by adopting a k-means clustering algorithm so as to enable the point clouds on the projection plane to be adaptively distributed into corresponding categories, and obtaining the point cloud clusters in each section by utilizing the corresponding relation between the points in the projection plane and the original coordinates, wherein each cluster represents a laser point cloud set from different split conductors.

Preferably, the cluster analysis module further comprises:

when the point cloud data of the first section is subjected to cluster analysis, setting a split number, calculating three-dimensional distances among all categories after the cluster analysis, if the three-dimensional distances are smaller than a preset distance threshold, subtracting 1 from the split number until all the three-dimensional distances are larger than the preset distance threshold, and determining the split number at the moment as the final split number of the whole lead; starting from the second section, cluster analysis is performed according to the final split number.

Preferably, the merging module merges point clouds belonging to the same power line segment by segment, and includes:

a. calculating all possible merging modes between adjacent section clusters by adopting a full-arrangement system according to the number of the split conductors, wherein if the number of the split conductors is n, all possible connection combinations are shared

Seed growing;

b. in each combination, accumulating the spatial distance between the merged clusters;

c. comparing the space distances of all the combinations, and considering the connection mode corresponding to the minimum value as a correct combination;

d. repeating steps a-d along the power line until all clusters are assigned to a split conductor.

The invention provides a modeling method and a system for a split conductor in a power transmission channel scene, which comprise the following steps: segmenting the split conductor along the direction of the conductor based on the power line point cloud data to obtain segmented data; performing segment-by-segment clustering analysis based on the data after the point segmentation to separate point clouds from different split conductors; merging point clouds belonging to the same power line section by section; and fitting a second-order curve to each split conductor to obtain a classified conductor model so as to complete modeling of the split conductors. The method can realize the rapid and accurate modeling of the single-stranded split conductor.

Drawings

Exemplary embodiments of the invention may be more completely understood in consideration of the following drawings:

fig. 1 is a flow chart of a method 100 of modeling a split conductor in a power transmission channel scenario according to an embodiment of the invention;

FIG. 2 is a schematic projection view of a quad-split conductor segment according to an embodiment of the invention;

FIG. 3 is a schematic diagram of a clustering connection of two split conductors according to an embodiment of the present invention;

FIG. 4 is a diagram illustrating modeling results after manual deletion of a portion of a wire point according to an embodiment of the invention;

FIG. 5 is a diagram illustrating modeling results after thinning out the sparse route points according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of a modeling system 600 for a split conductor in a power transmission channel scenario according to an embodiment of the invention.

Detailed Description

The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.

Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.

Fig. 1 is a flow chart of a method 100 for modeling a split conductor in a power transmission channel scenario according to an embodiment of the invention. As shown in fig. 1, in the modeling method for a split conductor in a power transmission channel scene provided by the embodiment of the present invention, the split conductor is segmented along the direction of the conductor based on power line point cloud data, and segmented data is obtained; performing segment-by-segment clustering analysis on the data segmented by the points to separate point clouds from different split conductors; merging point clouds belonging to the same power line section by section; and fitting a second-order curve to each split conductor to obtain a classified conductor model so as to complete modeling of the split conductors. The method can realize the rapid and accurate modeling of the single-stranded split conductor. The modeling method 100 for the split conductor in the scene of the power transmission channel provided by the embodiment of the invention starts from step 101, and carries out segmentation on the split conductor along the direction of the conductor based on the point cloud data of the power line in step 101 to obtain segmented data.

Preferably, the segmenting the split conductor along the direction of the conductor based on the power line point cloud data, and acquiring segmented data includes:

projecting the wire point cloud onto a two-dimensional horizontal plane, and regarding the point cloud on a projection surface as linear distribution;

performing straight line fitting by using all point clouds on a two-dimensional plane, taking a straight line obtained by fitting as the horizontal direction of a wire point cloud set, and simultaneously taking a point on a direction line corresponding to the minimum value of a horizontal coordinate or a vertical coordinate in the point clouds as an initial endpoint of a wire;

and carrying out sectional management on the original three-dimensional wire point cloud along the direction of the trend line from the starting endpoint based on a preset distance threshold value to obtain point cloud sectional data.

In the invention, the wire point cloud is firstly projected on a two-dimensional horizontal plane, and then the horizontal direction of points on the projection plane needs to be determined. Because the span of the wire is long and is generally greater than 100 meters, and the distance between the split wires does not exceed 0.5 meter, the point cloud on the projection surface can be regarded as linear distribution. The algorithm utilizes all point clouds on a two-dimensional plane to carry out straight line fitting, and a straight line obtained by fitting is regarded as the horizontal trend of a wire point cloud set. Meanwhile, a point on a trend line corresponding to the minimum value of the abscissa (or the ordinate) in the point cloud is used as the starting endpoint of the lead. Then, the original three-dimensional wire point cloud is subjected to segmented management at certain intervals along the direction of the trend line from the starting end point.

At step 102, segment-by-segment cluster analysis is performed based on the point segmented data to separate point clouds from different split wires.

Preferably, the performing a segment-by-segment cluster analysis based on the segmented data to separate point clouds from different split wires includes:

for each section, projecting the coordinates of the corresponding point cloud data onto a plane capable of classifying the split conductor point clouds, taking the plane vertical to the main direction of the section point clouds as a projection plane, and performing clustering analysis by adopting a k-means clustering algorithm so as to enable the point clouds on the projection plane to be adaptively distributed into corresponding categories, and obtaining the point cloud clusters in each section by utilizing the corresponding relation between the points in the projection plane and the original coordinates, wherein each cluster represents a laser point cloud set from different split conductors.

Preferably, wherein the method further comprises:

when the point cloud data of the first section are subjected to cluster analysis, setting a splitting number, calculating three-dimensional distances among all categories after the cluster analysis, if the three-dimensional distances are smaller than a preset distance threshold, subtracting 1 from the splitting number until all the three-dimensional distances are larger than the preset distance threshold, and determining the splitting number at the moment as the final splitting number of the whole wire; starting from the second section, a cluster analysis is performed according to the final split number.

In the invention, the distribution of the power lines between the two towers is in a catenary shape, usually the highest at the position of a wire hanging point, and the fall of the lowest point of the arc line and the hanging point can reach more than ten meters, and in extreme cases can reach tens of meters. However, the span of the high-voltage transmission line is often large, so the local bending characteristics of the wire are not obvious, and if the segmentation interval is small, the split line point cloud of each segment can be regarded as a straight line segment. Accordingly, the present report proposes a division line point cloud classification method based on the segmentation data.

Firstly, the coordinates of the point clouds in the interval are projected on a plane capable of classifying the split lead point clouds, and the algorithm takes the plane vertical to the main direction of the point clouds in the section as a projection plane.

On the two-dimensional projection plane, the point clouds of the split conductors are in gathering distribution. Taking fig. 2 as an example, 4 aggregation centers are shown after the four-split conductor is projected, but due to the existence of noise points and the bending of local power lines, the edge points of the four clusters are very close to each other, in order to distribute the point cloud into the four clusters, the report adopts a k-means clustering algorithm, and simultaneously, in order to avoid the influence of seed selection on the result and increase the stability of the algorithm, the clustering process is optimized by the algorithm. Each class obtained by cluster analysis represents a certain split conductor of the section.

Meanwhile, in order to increase the self-adaptive capacity of the algorithm to the splitting number, when the clustering analysis is carried out on the data of the first section, a larger splitting number is firstly set, for example, 6, then the three-dimensional distance between each category after the clustering analysis is calculated, if the clustering is smaller than the threshold value, the splitting number is reduced by 1 until all the distances are larger than the threshold value, and the splitting number at this time is the splitting number of the whole lead. Starting from the second section, the cluster analysis is performed using the determined number of splits. The distance between the split wires is generally not less than 20cm according to the erection specifications of the split conductors, so that the threshold value can be set to 20cm.

Through the steps, the point clouds on the projection plane are adaptively distributed into corresponding categories, and the point cloud clusters in each section can be obtained by utilizing the corresponding relation between the points in the projection plane and the original coordinates, wherein each cluster represents a laser point cloud set from different split conductors.

In step 103, point clouds belonging to the same power line are merged segment by segment.

Preferably, the segment-by-segment merging point clouds belonging to the same power line includes:

a. calculating all possible merging modes between adjacent section clusters by adopting a full arrangement method according to the number of the split conductors, wherein if the number of the split conductors is n, all possible connection combinations are shared

Seed growing;

b. in each combination, accumulating the spatial distance between the merged clusters;

c. comparing the space distances of all the combinations, and considering the connection mode corresponding to the minimum value as a correct combination;

d. repeating steps a-d along the power line until all clusters are assigned to a split conductor.

In the invention, after the point cloud of each section of wire is subjected to clustering analysis, the clusters belonging to the same wire among the sections need to be merged. The merging strategy adopted in this study was as follows:

a. calculating all possible merging ways between adjacent section clusters by using a full arrangement method according to the number of the split conductors, wherein if the number of the split conductors is n, all the possible merging ways are calculatedIn the connection combination of

Seed;

b. in each combination, accumulating the spatial distance between the merged clusters;

c. comparing the space distances of all the combinations, and considering the connection mode corresponding to the minimum value as a correct combination;

d. repeating steps (a) - (d) along the power line until all clusters are assigned to a split conductor.

Taking the binary split conductor of FIG. 3 as an example, the solid line and the dotted line respectively represent the potential between 4 clusters on the adjacent 2 projection planes

And (5) merging and combining. In the figure, the spatial distance of the combination in the dotted line manner is smaller than that of the solid line, so that the adjacent clusters are merged according to the dotted line.

In step 104, a second order curve is fitted to each split conductor to obtain a classification conductor model, so as to complete modeling of the split conductors.

In the present invention, at the end, a least squares curve fit is performed on each split conductor and a split conductor model is obtained.

In actual engineering, due to mutual occlusion, instrument system errors and the like, the situation of point cloud shortage occurs occasionally, and the density of the point cloud may be lower than the reported test data. Therefore, part of the point cloud is deleted manually, the conditions are simulated by methods such as density rarefaction and the like, the processed point cloud is analyzed by adopting the same test parameters, and finally the obtained result is shown in the following graph, wherein the obtained result represents the superposition of the vector line after modeling and the input point cloud.

The part of the wire point cloud split line in fig. 4 has the defects with different lengths of 2m to 10m, but the point cloud classification and the section connection have no errors. The local enlarged area in the figure is that four split-conductor point clouds are simultaneously lost, the gap length is about 6m, but the final modeling result is still not influenced.

The wire point clouds in fig. 5 were manually thinned, the total number of point clouds was reduced from 8838 points to 5248 points, and the average distance between points was increased to about 25cm, however, the split-wire separation and fitting curves shown in the figure indicate that the algorithm still achieves satisfactory results.

Meanwhile, the fact that due to factors such as mutual shielding among leads and the like, the point cloud density and the missing situation among the split lines are different, but a correct result can still be obtained, and the report algorithm is reflected to have strong stability and adaptability.

The fitting error is mainly caused by that more noise points exist between the split conductors, the environmental factors also influence the data quality, and the laser point cloud contains a certain positioning error.

In order to quantitatively evaluate the accuracy of the split conductor modeling, the report counts the plane error and the elevation error of the split conductor modeling. The plane error is the average value of the horizontal distances from all the laser points to the fitting lead, and the elevation error corresponds to the average value of the elevation distances from all the laser points to the fitting lead. The statistical results are shown in table 1 below.

TABLE 1 statistical table of modeling errors of split conductors

As can be seen from the table above, the error of the coarse modeling is the largest, only the intermediate lines of the four split conductors can be obtained approximately, and both the horizontal error and the elevation error exceed 20cm, which has great influence on the safety analysis of the conductors and the simulation of sag and tension under different working conditions. The fine modeling method provided by the report can reduce the fitting error in the horizontal direction and the vertical direction to be within 7cm, and still keep good fitting accuracy (the maximum fitting error is not more than 8 cm) under the conditions of point cloud part loss and rarefaction, so that the result meets the application and analysis requirements of various power line models at present.

The research provides a method for finely modeling a split conductor by using helicopter laser point cloud data of a power transmission line, and mainly comprises the processes of power line point cloud segmentation, space projection, cluster analysis, curve fitting and the like. The method mainly introduces a wire point cloud segmentation, a split wire cluster analysis method and a segmented wire merging strategy. The quadripartion conductor point cloud obtained by the helicopter platform is used for testing and analyzing the precision of the algorithm, and the precision of the result surface fine modeling can meet the requirements of current application. In consideration of the situation that data quality is low in actual production, original data are manually removed, the situations of point cloud loss and low density are simulated, the same test is carried out, and good test results further show that the report method has strong robustness and adaptability. The algorithm can provide method reference for the application of the airborne laser radar technology in power line patrol.

The influence of the length of the wire point cloud section on the result of the clustering analysis is large in the algorithm, for example, for wires with short span and large sag, the length of the section is too large, so that point clouds of different splitting lines can be superposed on a projection plane. In addition, the noise level of a split conductor is higher than that of a non-split conductor, which also directly affects the final model fitting accuracy. Therefore, an adaptive wire point cloud segmentation scheme and a noise point elimination algorithm are important for future research.

Fig. 6 is a schematic structural diagram of a modeling system 600 for a split conductor in a power transmission channel scenario according to an embodiment of the invention. As shown in fig. 6, a modeling system 600 for a split conductor in a power transmission channel scenario according to an embodiment of the present invention includes: a segmentation module 601, a cluster analysis module 602, a merging module 603, and a fitting module 606.

Preferably, the segmenting module 601 is configured to segment the split conductor along the direction of the conductor based on the power line point cloud data, and acquire segmented data.

Preferably, the cluster analysis module 602 is configured to perform segment-by-segment cluster analysis based on the segmented data of the points to separate point clouds from different split wires.

Preferably, the merging module 603 is configured to merge point clouds belonging to the same power line segment by segment.

Preferably, the fitting module 604 is configured to perform a second-order curve fitting on each split conductor to obtain a classified conductor model, so as to complete modeling of the split conductor.

Preferably, the segmenting module segments the division conductor along the direction of the conductor based on the power line point cloud data, and acquires segmented data, including:

projecting the wire point cloud onto a two-dimensional horizontal plane, and regarding the point cloud on a projection surface as linear distribution;

performing straight line fitting by using all point clouds on a two-dimensional plane, taking a straight line obtained by fitting as the horizontal trend of a wire point cloud set, and simultaneously taking a point on a trend line corresponding to the minimum value of a horizontal coordinate or a vertical coordinate in the point clouds as an initial endpoint of a wire;

and carrying out sectional management on the original three-dimensional wire point cloud along the direction of the trend line from the starting endpoint based on a preset distance threshold value to obtain point cloud sectional data.

Preferably, the cluster analysis module performs segment-by-segment cluster analysis based on the segmented data to separate point clouds from different split wires, including:

for each section, projecting the coordinates of the corresponding point cloud data onto a plane capable of classifying the split conductor point clouds, taking the plane vertical to the main direction of the section point clouds as a projection plane, and performing clustering analysis by adopting a k-means clustering algorithm so as to enable the point clouds on the projection plane to be adaptively distributed into corresponding categories, and obtaining the point cloud clusters in each section by utilizing the corresponding relation between the points in the projection plane and the original coordinates, wherein each cluster represents a laser point cloud set from different split conductors.

Preferably, the cluster analysis module further comprises:

when the point cloud data of the first section are subjected to cluster analysis, setting a splitting number, calculating three-dimensional distances among all categories after the cluster analysis, if the three-dimensional distances are smaller than a preset distance threshold, subtracting 1 from the splitting number until all the three-dimensional distances are larger than the preset distance threshold, and determining the splitting number at the moment as the final splitting number of the whole wire; starting from the second section, cluster analysis is performed according to the final split number.

Preferably, the merging module merges point clouds belonging to the same power line segment by segment, and includes:

a. calculating all possible merging modes between adjacent section clusters by adopting a full-arrangement system according to the number of the split conductors, wherein if the number of the split conductors is n, all possible connection combinations are shared

Seed;

b. in each combination, accumulating the spatial distance between the merged clusters;

c. comparing the space distances of all the combinations, and considering the connection mode corresponding to the minimum value as a correct combination;

d. repeating steps a-d along the power line trend until all clusters are assigned to a split conductor.

The modeling system 600 for a split conductor in a power transmission channel scenario according to an embodiment of the present invention corresponds to the modeling method 100 for a split conductor in a power transmission channel scenario according to another embodiment of the present invention, and details thereof are not repeated here.

The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.

Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ means, component, etc ]" are to be interpreted openly as referring to at least one instance of said means, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A modeling method for a split conductor in a power transmission channel scene, the method comprising:

segmenting the split conductor along the direction of the conductor based on the power line point cloud data to obtain segmented data;

performing segment-by-segment clustering analysis on the data segmented by the points to separate point clouds from different split conductors;

merging point clouds belonging to the same power line section by section;

and fitting a second-order curve to each split conductor to obtain a classified conductor model so as to complete modeling of the split conductors.

2. The method of claim 1, wherein the segmenting the split conductor along the direction of the conductor based on the power line point cloud data, and obtaining segmented data comprises:

projecting the wire point cloud onto a two-dimensional horizontal plane, and regarding the point cloud on a projection surface as linear distribution;

performing straight line fitting by using all point clouds on a two-dimensional plane, taking a straight line obtained by fitting as the horizontal trend of a wire point cloud set, and simultaneously taking a point on a trend line corresponding to the minimum value of a horizontal coordinate or a vertical coordinate in the point clouds as an initial endpoint of a wire;

and carrying out sectional management on the original three-dimensional wire point cloud along the direction of the trend line from the starting endpoint based on a preset distance threshold value to obtain point cloud sectional data.

3. The method of claim 2, wherein performing a piece-by-piece cluster analysis based on the segmented data to separate point clouds from different split wires comprises:

for each section, projecting the coordinates of the corresponding point cloud data onto a plane capable of classifying the split conductor point clouds, taking the plane vertical to the main direction of the section point clouds as a projection plane, and performing clustering analysis by adopting a k-means clustering algorithm so as to enable the point clouds on the projection plane to be adaptively distributed into corresponding categories, and obtaining the point cloud clusters in each section by utilizing the corresponding relation between the points in the projection plane and the original coordinates, wherein each cluster represents a laser point cloud set from different split conductors.

4. The method of claim 3, further comprising:

when the point cloud data of the first section is subjected to cluster analysis, setting a split number, calculating three-dimensional distances among all categories after the cluster analysis, if the three-dimensional distances are smaller than a preset distance threshold, subtracting 1 from the split number until all the three-dimensional distances are larger than the preset distance threshold, and determining the split number at the moment as the final split number of the whole lead; starting from the second section, a cluster analysis is performed according to the final split number.

5. The method of claim 1, wherein merging point clouds belonging to a same power line segment by segment comprises:

a. calculating all possible merging modes between adjacent section clusters by adopting a full arrangement method according to the number of the split conductors, wherein if the number of the split conductors is n, all possible connection combinations are shared

Seed growing;

b. in each combination, accumulating the spatial distance between the merged clusters;

c. comparing the space distances of all the combinations, and considering the connection mode corresponding to the minimum value as a correct combination;

d. repeating steps a-d along the power line trend until all clusters are assigned to a split conductor.

6. A modeling method system for a split conductor in a power transmission channel scene is characterized by comprising the following steps:

the segmentation module is used for segmenting the split conductor along the direction of the conductor based on the power line point cloud data and acquiring segmented data;

the cluster analysis module is used for carrying out section-by-section cluster analysis on the data after the point segmentation so as to separate point clouds from different split conductors;

the merging module is used for merging the point clouds belonging to the same power line section by section;

and the fitting module is used for fitting a second-order curve to each split conductor to obtain a classified conductor model so as to complete modeling of the split conductors.

7. The system of claim 6, wherein the segmentation module segments the split conductor along the direction of the conductor based on the power line point cloud data, and obtains segmented data, comprising:

projecting the wire point cloud onto a two-dimensional horizontal plane, and regarding the point cloud on a projection surface as linear distribution;

performing straight line fitting by using all point clouds on a two-dimensional plane, taking a straight line obtained by fitting as the horizontal trend of a wire point cloud set, and simultaneously taking a point on a trend line corresponding to the minimum value of a horizontal coordinate or a vertical coordinate in the point clouds as an initial endpoint of a wire;

and carrying out sectional management on the original three-dimensional wire point cloud along the direction of the trend line from the starting endpoint based on a preset distance threshold value to obtain point cloud sectional data.

8. The system of claim 6, wherein the cluster analysis module performs a segment-by-segment cluster analysis based on the segmented data to separate point clouds from different split wires, comprising:

for each section, projecting the coordinates of the corresponding point cloud data onto a plane capable of classifying the split conductor point clouds, taking the plane vertical to the main direction of the section point clouds as a projection plane, and performing clustering analysis by adopting a k-means clustering algorithm so as to enable the point clouds on the projection plane to be adaptively distributed into corresponding categories, and obtaining the point cloud clusters in each section by utilizing the corresponding relation between the points in the projection plane and the original coordinates, wherein each cluster represents a laser point cloud set from different split conductors.

9. The system of claim 8, wherein the cluster analysis module further comprises:

when the point cloud data of the first section are subjected to cluster analysis, setting a splitting number, calculating three-dimensional distances among all categories after the cluster analysis, if the three-dimensional distances are smaller than a preset distance threshold, subtracting 1 from the splitting number until all the three-dimensional distances are larger than the preset distance threshold, and determining the splitting number at the moment as the final splitting number of the whole wire; starting from the second section, a cluster analysis is performed according to the final split number.

10. The system of claim 6, wherein the merging module merges the point clouds belonging to the same power line segment by segment, comprising:

a. calculating all possible merging modes between adjacent section clusters by adopting a full-permutation system according to the number of the split conductors, wherein if the number of the split conductors is n, all possible connection combinations are shared

Seed growing;

b. in each combination, accumulating the spatial distance between the merged clusters;

c. comparing the space distances of all the combinations, and considering the connection mode corresponding to the minimum value as a correct combination;

d. repeating steps a-d along the power line trend until all clusters are assigned to a split conductor.

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