CN115018983A - Phase-shifting transformer site selection method, device, electronic equipment and storage medium - Google Patents
Phase-shifting transformer site selection method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for selecting addresses of a phase-shifting transformer, electronic equipment and a storage medium. The method comprises the following steps: acquiring first point cloud data, wherein the first point cloud data is obtained by scanning a candidate installation area of the phase-shifting transformer by a three-dimensional laser scanner; acquiring second point cloud data, wherein the second point cloud data is generated according to image data obtained by oblique photography of the candidate installation area by the unmanned aerial vehicle; constructing a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data; and determining the installation position of the phase-shifting transformer based on the three-dimensional model. According to the technical scheme, three-dimensional laser scanning and oblique photography are combined, and a three-dimensional model is constructed by utilizing two kinds of point cloud data in a cooperative mode, so that an accurate foundation is provided for spatial analysis of installation and site selection of the phase-shifting transformer, and reliability of site selection is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of installation and site selection of power equipment, in particular to a method and a device for site selection of a phase-shifting transformer, electronic equipment and a storage medium.
Background
With the rapid development of power systems, the scale of power grids is gradually increased, the mutual influence and electrical connection among the power grids are more complex, and the problems of safety and stability of the power systems, blocked power transmission channel tide and the like are also faced while benefits are brought to the society. The actual operation of the power system is influenced by the aspects of the grid structure, the line parameters, the operation mode and the like, and the phenomenon of unreasonable line tide distribution can exist, so that the line transmission capacity is limited or the system safety is endangered. As an effective device capable of controlling tide, the phase-shifting transformer has the advantages of low investment cost, no change of grid structure, good regulation performance and the like. The phase-shifting transformer is additionally arranged at a proper position, so that the utilization rate of the grid frame and the system stability can be effectively improved, the dynamic performance of a power grid line is remarkably improved, the operation efficiency of a power system is improved, and the phase-shifting transformer has important significance on the stability of power grid interconnection.
At present, the site selection of the phase-shifting transformer is carried out based on methods such as load flow calculation, sensitivity analysis, blocking management and the like, the methods do not consider whether enough space exists in a transformer substation for installing the phase-shifting transformer, and the problem is a prerequisite condition for installing the phase-shifting transformer. If the accurate basis cannot be provided for the spatial analysis of the installation site of the phase-shifting transformer, the installation reliability of the phase-shifting transformer can be directly influenced.
Disclosure of Invention
The invention provides a method, a device, electronic equipment, a storage medium and a system for selecting a site of a phase-shifting transformer, which are used for improving the reliability of the installation and site selection of the phase-shifting transformer.
In a first aspect, an embodiment of the present invention provides a method for selecting an address of a phase-shifting transformer, including:
acquiring first point cloud data, wherein the first point cloud data is obtained by scanning a candidate installation area of a phase-shifting transformer by a three-dimensional laser scanner;
acquiring second point cloud data, wherein the second point cloud data is generated according to image data obtained by oblique photography of the candidate installation area by the unmanned aerial vehicle;
constructing a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data;
and determining the installation position of the phase-shifting transformer based on the three-dimensional model.
In a second aspect, an embodiment of the present invention provides an address selection apparatus for a phase-shifting transformer, including:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first point cloud data, and the first point cloud data is obtained by scanning a candidate installation area of the phase-shifting transformer by a three-dimensional laser scanner;
the second acquisition module is used for acquiring second point cloud data, and the second point cloud data is generated according to image data obtained by oblique photography of the candidate installation area by the unmanned aerial vehicle;
the modeling module is used for constructing a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data;
and the addressing module is used for determining the installation position of the phase-shifting transformer based on the three-dimensional model.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of addressing a phase shifting transformer according to the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for addressing a phase-shifting transformer according to the first aspect.
The embodiment of the invention provides a method and a device for selecting addresses of a phase-shifting transformer, electronic equipment and a storage medium. The method comprises the following steps: acquiring first point cloud data, wherein the first point cloud data is obtained by scanning a candidate installation area of a phase-shifting transformer by a three-dimensional laser scanner; acquiring second point cloud data, wherein the second point cloud data is generated according to image data obtained by oblique photography of the candidate installation area by the unmanned aerial vehicle; constructing a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data; and determining the installation position of the phase-shifting transformer based on the three-dimensional model. According to the technical scheme, three-dimensional laser scanning and oblique photography are combined, and a three-dimensional model is constructed by utilizing two kinds of point cloud data in a cooperative mode, so that an accurate foundation is provided for spatial analysis of installation and site selection of the phase-shifting transformer, and reliability of site selection is improved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a flowchart of a method for addressing a phase-shifting transformer according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for addressing a phase-shifting transformer according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of a phase-shifting transformer site selection process according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an address selecting apparatus for a phase-shifting transformer according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. In addition, the embodiments and features of the embodiments in the present invention may be combined with each other without conflict. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but could have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
It should be noted that the terms "first", "second", and the like in the embodiments of the present invention are only used for distinguishing different apparatuses, modules, units, or other objects, and are not used for limiting the order or interdependence relationship of the functions performed by these apparatuses, modules, units, or other objects.
Example one
Fig. 1 is a flowchart of a method for addressing a phase-shifting transformer according to an embodiment of the present invention, where the embodiment is applicable to addressing when a phase-shifting transformer is installed or installed. Specifically, the site selection method of the phase-shifting transformer can be executed by a site selection device of the phase-shifting transformer, and the site selection device of the phase-shifting transformer can be realized in a software and/or hardware mode and is integrated in electronic equipment. Further, electronic devices include, but are not limited to: desktop computers, notebook computers, industrial integration servers, system background servers, cloud servers and the like.
As shown in fig. 1, the method specifically includes the following steps:
and S110, obtaining first point cloud data, wherein the first point cloud data is obtained by scanning the candidate installation area of the phase-shifting transformer through a three-dimensional laser scanner.
Specifically, the three-dimensional laser scanner can rapidly acquire three-dimensional point cloud data of the surface of the measured object in a large area and at a high resolution by a high-speed laser scanning measurement method, so that spatial point location information of the measured object is effectively acquired, and a basis is provided for establishing a three-dimensional model of the measured object. In this embodiment, the object to be measured is a candidate installation area, and the three-dimensional point cloud data obtained by scanning is the first point cloud data. The candidate installation area mainly refers to an area where a phase-shifting transformer can be installed or installed, for example, a substation and a measurement area around the substation within a certain range. As a flexible power flow control device, the phase-shifting transformer can effectively realize the adjustment of the power grid power flow, and the candidate installation area can be selected according to the power flow adjustment effect, the adjustment margin and the like of the installation of the phase-shifting transformer.
Further, acquiring the first point cloud data includes: arranging control points and targets in the candidate installation areas; controlling a three-dimensional laser scanner arranged at the control points to scan the candidate installation area to obtain point cloud data corresponding to each control point; and splicing the point cloud data corresponding to each control point into the same coordinate system according to the target to obtain first point cloud data.
In the embodiment, control points and targets are reasonably arranged in the transformer substation and the peripheral measurement area thereof. The control point is used for controlling the precision of oblique photography and three-dimensional laser scanning point cloud; the target is convenient for integration and subsequent registration of two kinds of point cloud data, and the point cloud data are unified under the same coordinate system. The positions of the control points need to avoid high and large buildings and electric facilities, each control point is provided with a three-dimensional laser scanner, so that a plurality of three-dimensional point clouds are obtained through scanning, and the target can be used as a connecting point and a reference point in coordinate conversion when the plurality of three-dimensional point clouds are spliced. In the laying process, the laying density should be increased in a dense facility area, and the targets cannot be arranged on the same height and horizontal line. Optionally, the data overlapping rate between the control points is not lower than a set ratio (e.g. 30%). Furthermore, the laying of control points and targets at sheltered buildings, vegetation or electrical installations should also be avoided.
Furthermore, denoising, simplifying and registering can be carried out on the first point cloud data obtained by scanning. Noise points generated by reflection of different objects can be processed in point cloud processing software by using a filtering method, obvious noise points caused by the shielding objects can be removed in batches in a man-machine interaction mode, and only point cloud data capable of reflecting the surface characteristics of equipment are reserved. And splicing the three-dimensional point cloud by adopting a target registration-based mode, and splicing the point cloud data corresponding to each control point into the same coordinate system to obtain first point cloud data.
And S120, acquiring second point cloud data, wherein the second point cloud data is generated according to image data obtained by oblique photography of the candidate installation area by the unmanned aerial vehicle.
Specifically, oblique photography refers to that multiple sensors are mounted on the same flight platform, images of a measured object are synchronously acquired from different visual angles (such as a vertical visual angle and four oblique visual angles), image data are matched accordingly, a large number of dense point clouds are screened from two or multiple overlapped images, an oblique photography image point cloud, namely second point cloud data, is obtained, and meanwhile high-resolution textures of candidate installation areas can also be obtained. On the basis, a high-precision three-dimensional model can be generated according to high-resolution texture information by combining the technologies of positioning, fusion, modeling and the like.
Further, acquiring second point cloud data, comprising: arranging image control points in the candidate installation area; controlling the unmanned aerial vehicle to carry out oblique photography on the candidate installation area from different angles according to the planned route to obtain image data; carrying out aerial triangulation (aerial triangulation encryption) on the image data according to the image control point to obtain image control data; merging the image data and the image control data into a coordinate system of the distributed image control points, and calculating orientation elements of the image data; and generating second point cloud data according to the combined data and the orientation elements.
In the embodiment, the image control points are distributed in the candidate installation area, and the distribution positions of the image control points are required to obtain clear images, so that the image control points are obviously distinguished from other ground feature facilities. According to the electric power facilities in the candidate installation area, the surrounding pre-expanded open space area and the like, the air routes of the unmanned aerial vehicle can be planned, and the number of the air routes is generally more than three. If electric power towers are arranged in and near the candidate installation area, the route can be properly increased, and the direction of the route is consistent with the transverse and longitudinal directions of the main building and the electric power facilities as much as possible. And acquiring image data of the candidate installation area, wherein the course overlapping degree of the unmanned aerial vehicle can be 70% or more, and the lateral overlapping degree is 60% or more, so that the overlapping degree of the images acquired by the inclined lens and the vertical lens is not lower than 10%.
Further, image data shot by the unmanned aerial vehicle is preprocessed, for example, noise is removed, and fuzzy images can be removed manually; then, carrying out aerial triangulation encryption processing on the image data to obtain image control data, wherein the encrypted points are all clear points on adjacent images; merging the image data and the image control data into a coordinate system of the distributed control points, and calculating orientation elements of the image data; and generating second point cloud data according to the combined data and the orientation elements.
S130, building a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data.
Specifically, the three-dimensional point cloud model is obtained by taking one of the first point cloud data and the second point cloud data as a basis and the distributed targets as a reference, wherein the first point cloud data and the second point cloud data both adopt coordinate systems of control points, and the two point cloud data can be registered and fused. In addition, texture mapping and rendering can be performed on the white membrane of the three-dimensional point cloud model, and the three-dimensional model is built.
And S140, determining the installation position of the phase-shifting transformer based on the three-dimensional model.
Specifically, whether the reserved space in the candidate installation area is enough to accommodate the phase-shifting transformer or not can be judged according to the three-dimensional model, so that the phase-shifting transformer is installed or additionally installed in a reasonable position.
According to the site selection method for the phase-shifting transformer, provided by the embodiment of the invention, three-dimensional laser scanning and oblique photography are combined, the limitation of a single data source is broken, a three-dimensional model is constructed by utilizing two kinds of point cloud data in a cooperation manner, modeling of a transformer substation and the surrounding area of the transformer substation is accurate, and a corresponding reserved space is accurately judged, so that the problem that actual installation conditions are not completely considered in related site selection technologies is solved, an accurate foundation is provided for spatial analysis of installation and site selection of the phase-shifting transformer, and the installation and site selection of the phase-shifting transformer has testability, a good three-dimensional visualization effect, engineering practicability and reliability.
Example two
Fig. 2 is a flowchart of a phase-shifting transformer addressing method according to a second embodiment of the present invention, where this embodiment is optimized based on the foregoing embodiment, and specifically describes the phase-shifting transformer addressing method. It should be noted that technical details that are not described in detail in the present embodiment may be referred to any of the above embodiments.
Specifically, as shown in fig. 2, the method specifically includes the following steps:
s210, selecting a candidate installation area of the phase-shifting transformer according to the relative sensitivity of the adjustment amplitude of the phase-shifting transformer and the relative power flow change quantity of the line.
In the embodiment, a transformer substation with a good power flow regulation effect and a large regulation margin can be selected as a candidate installation area. The power flow adjusting effect is measured by two indexes, namely adjusting amplitude relative sensitivity of the phase-shifting transformer and relative power flow changing quantity of a line, of a changing index of the phase-shifting transformer along with the installation position. Specifically, the method comprises the following steps:
wherein L is i Is a phase-shifting transformer regulationRelative sensitivity of amplitude; Δ P is the amount of change in line power flow; Δ δ is the phase angle of the phase shifting transformer.
Wherein, g i Is the relative power flow change of the power flow increasing line; alpha and beta are respectively the line tidal flow before and after the phase-shifting transformer is installed.
Based on the above two indexes, L can be selected i The larger, g i The smaller nodes at the two ends of the line serve as candidate installation positions of the phase-shifting transformer.
And S220, acquiring first point cloud data.
And S230, determining the curved surface fluctuation degree of the sampling point according to the normal vector change degree of the point in the neighborhood of the sampling point in the first point cloud data.
In this embodiment, redundant data in the first point cloud data is deleted by using a normal vector-based reduction algorithm. Specifically, the curve fluctuation degree of the sampling point is judged according to the normal vector change degree of the point in the k neighborhood where the sampling point is located in the first point cloud data, and then the key point is extracted.
Optionally, determining the curved surface fluctuation degree of the sampling point according to the normal vector variation degree of the point in the neighborhood of the sampling point in the first point cloud data, including:
s2310, for any sampling point in the first point cloud data, fitting k points in the neighborhood where the sampling point is located to obtain a local plane, and calculating a normal vector of each point relative to the local plane.
S2320, constructing a covariance matrix according to the centroids of the k points.
S2330, performing eigenvalue decomposition on the covariance matrix to obtain a minimum eigenvalue of the covariance matrix, wherein an eigenvector corresponding to the minimum eigenvalue is a normal vector of the sampling point.
S2340, calculating an arithmetic average value of k normal vector included angles to obtain the curved surface fluctuation degree of the sampling point, wherein each normal vector included angle is an included angle between the normal vector of the sampling point and a normal vector of one of the k points relative to the local plane.
Specifically, for any sampling point f in the first point cloud data, a local plane P is fitted to k points in the nearest neighbor domain of the sampling point f:
wherein n is a normal vector of the local plane P at the point P; d is the distance of p from the origin of coordinates.
Calculate the centroid of the k nearest neighbors at point f:
the covariance matrix C is constructed accordingly:
and (3) carrying out eigenvalue decomposition on the covariance matrix:
m ═ {1,2,3}, where C is i Is a covariance matrix; lambda [ alpha ] i (m) Is C i A characteristic value of (d); v i (m) Is composed of
The corresponding feature vector.
Calculating the included angle theta between the normal vector of the point f and the normal vector of the k neighborhood point i Is arithmetic mean of
And on the basis, deleting redundant data in the first point cloud data according to the curved surface fluctuation degree of the sampling points. Specifically, the sampling point with a large curved surface undulation degree shows that the sampling point and the neighborhood thereof have large transformation amplitude and good characteristics and are reserved as effective data; and the sampling point with smaller curved surface fluctuation degree and the neighborhood transformation range of the sampling point are smaller, the sampling point is flatter and has poorer characteristics, and the sampling point and the neighborhood transformation range can be regarded as redundant data to be deleted, so that all points can be traversed to finish the simplification of point cloud.
Optionally, deleting redundant data in the first point cloud data according to the degree of curvature of the sampling point, including: if the curved surface fluctuation degree of the sampling point is smaller than a set threshold value, deleting the data of the sampling point; and if the curved surface fluctuation degree of the sampling point is greater than or equal to the set threshold, retaining the data of the sampling point.
Illustratively, a suitable threshold value ε is set to 5 when
Keeping the data of the sampling point f; when in use
The data at sample point f may be deleted.
On the basis, the point cloud data are spliced and initially registered based on a target registration mode, corresponding points of targets in the point cloud data are overlapped, and therefore all point cloud data are spliced to the same coordinate system, and first point cloud data are obtained.
And S250, acquiring second point cloud data.
And S260, determining an optimal rotation matrix and an optimal translation vector between the source point cloud data and the target point cloud data by taking one of the first point cloud data and the second point cloud data as the source point cloud data and taking the other one as the target point cloud data.
In this embodiment, an iterative algorithm may be used to find an optimal rotation matrix and an optimal translation vector, so as to provide a basis for registration of the first point cloud data and the second point cloud data.
Optionally, determining an optimal rotation matrix and an optimal translation vector between the source point cloud data and the target point cloud data includes:
s2610, for each point in the source point cloud data, determining a corresponding closest point in the target point cloud data;
s2620, determining a rotation matrix and a translation vector between the source point cloud data and the target point cloud data based on a least square method according to each point in the source point cloud data and the corresponding closest point;
s2630, converting source point cloud data according to the rotation matrix and the translation vector, and calculating an iterative error between the converted source point cloud data and the target point cloud;
s2640, repeatedly executing S2620-S2630 until the iteration error is smaller than a set threshold, and stopping iteration to obtain the optimal rotation matrix and the optimal translation vector.
For example, let r be the rotation matrix, t be the translation vector, and Q be the first point cloud data Q ═ Q 1 ,q 2, …,q j And the second point cloud data P ═ P 1 ,p 2 …,p i }. Taking the first point cloud as a source point cloud, and for each point q in the first point cloud i Matching the corresponding closest point in the second point cloud (target point cloud):
and solving a rotation matrix and a translation vector by using a least square method:
transforming the source point cloud using the obtained rotation matrix and translation vector:
iterative calculation, determining an iterative error E<3cm, the iteration is terminated, wherein,
and S270, registering the source point cloud data and the target point cloud data according to the optimal rotation matrix and the optimal translation vector. Specifically, the second Point cloud data is used as a basis, the first Point cloud data is used as an auxiliary, registration fusion is carried out on the two Point cloud data, an Iterative Closest Point (ICP) algorithm and a manual registration combination method can be used, one Point cloud coordinate system is used as a global coordinate system, the two groups of Point cloud overlapped parts are completely overlapped after rotation and translation are carried out on the other Point cloud, a rotation matrix and a translation vector between the source Point cloud and the target Point cloud are found, the positions of the two Point clouds are close through Iterative calculation, an optimal rotation matrix and an optimal translation vector between the source Point cloud and the target Point cloud are obtained, and precise registration of the two Point cloud data is completed.
And S280, constructing a three-dimensional model of the candidate installation area according to the image data and the point cloud data after registration.
Optionally, constructing a three-dimensional model of the candidate installation area according to the image data and the point cloud data after registration includes:
s2810, constructing an Irregular Triangulated Network (TIN) model according to the point cloud data after registration;
s2820, calculating the corresponding relation between a triangular patch in the TIN model and image data by utilizing modeling software according to Position and Orientation System (POS) information carried by the image data;
and S2830, mapping texture information contained in the image data to corresponding triangular patches according to the corresponding relation to obtain a three-dimensional model of the candidate installation area.
Specifically, firstly, a finer TIN model is constructed by using the fused point cloud data to generate a three-dimensional model with a white membrane; the image data obtained by oblique photography carries the (POS) information of the image, and contains the accurate spatial coordinate Position of each image, so that the geometric corresponding relation of each triangular patch between the image data and the constructed TIN model can be determined, then the corresponding texture is automatically calculated by utilizing modeling software (such as ContextCapture), and the texture is mapped to the white film of the corresponding triangular patch according to the corresponding relation. In addition, if the hue, saturation, and the like of the image data deviate from the actual values, the images may be individually processed by Photoshop or the like to generate an optimal texture image, and texture structure information may be attached by a texture mapping technique to complete the construction of the three-dimensional model.
And S290, comparing the reserved space in the three-dimensional model with the actual size of the phase-shifting transformer to determine the installation position of the phase-shifting transformer.
Specifically, according to the established three-dimensional model, the space size of the reserved area in the three-dimensional model is compared with the actual size of the phase-shifting transformer, and the phase-shifting transformer can be additionally installed in a reasonable area which meets the following conditions:
1) the construction operation is easy;
2) the electric power facility obstruction influence is small;
3) the requirements of landform and geology of the extension around the transformer substation are met;
4) has enough area and space area.
Fig. 3 is a schematic diagram of an address selection process of a phase-shifting transformer according to a second embodiment of the present invention. As shown in fig. 3, the field investigation and data collection are performed on the transformer substation and the measurement area in a certain range around the transformer substation, so that the air route of the unmanned aerial vehicle can be reasonably planned, and control points and targets can be distributed; then, three-dimensional laser scanning is carried out to obtain first point cloud data, and oblique photography is carried out by an unmanned aerial vehicle to obtain second point cloud data, wherein denoising, splicing and the like are required to be carried out on the three-dimensional point cloud data obtained by scanning, and preprocessing, air-to-air encryption and the like are required to be carried out on the image data obtained by shooting; fusing and registering the first point cloud data and the second point cloud data, and then establishing a three-dimensional model; and analyzing the reserved space in the three-dimensional model, and comparing the analyzed reserved space with the actual size of the phase-shifting transformer to determine the installation position of the phase-shifting transformer.
The second embodiment of the invention provides a method for addressing a phase-shifting transformer, which is optimized on the basis of the second embodiment of the invention, and candidate installation areas are selected according to the relative sensitivity of the adjustment amplitude of the phase-shifting transformer and the relative power flow change quantity of a line, so that the power flow adjustment effect and the adjustment margin of the phase-shifting transformer can be ensured; redundant data in the first point cloud data are deleted by adopting a normal vector-based simplification algorithm, the characteristics of effective data are reserved, the data processing amount can be reduced, and the site selection efficiency is improved; an iterative algorithm is adopted to find an optimal rotation matrix and an optimal translation vector, and a reliable basis is provided for the precise registration of the first point cloud data and the second point cloud data; the first point cloud data and the second point cloud data are fused and registered, and the POS information of the image data is utilized to perform texture mapping to complete the construction of the three-dimensional model, so that the appearance of the three-dimensional model is closer to a real scene, the appearance details and the spatial information of a modeling entity are more accurately reflected, the method has the advantages of measurability, better three-dimensional visualization effect and engineering practicability, and the accuracy and the practicability of site selection of the phase-shifting transformer are improved.
EXAMPLE III
Fig. 4 is a schematic structural diagram of an address selecting device for a phase-shifting transformer according to a third embodiment of the present invention. As shown in fig. 4, the addressing apparatus for a phase-shifting transformer provided in this embodiment includes:
the first obtaining module 310 is configured to obtain first point cloud data, where the first point cloud data is obtained by scanning a candidate installation area of a phase-shifting transformer by using a three-dimensional laser scanner; (ii) a
A second obtaining module 320, configured to obtain second point cloud data, where the second point cloud data is generated according to image data obtained by performing oblique photography on the candidate installation area by the unmanned aerial vehicle;
the modeling module 330 is configured to construct a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data;
and the addressing module 340 is used for determining the installation position of the phase-shifting transformer based on the three-dimensional model.
According to the site selection device for the phase-shifting transformer, provided by the third embodiment of the invention, three-dimensional laser scanning and oblique photography are combined, and a three-dimensional model is cooperatively constructed by utilizing two point cloud data, so that an accurate basis is provided for spatial analysis of site selection of the phase-shifting transformer, and the reliability of site selection is improved.
On the basis of the above embodiment, the apparatus further includes:
the selecting module is used for selecting the candidate installation area of the phase-shifting transformer according to the relative adjustment amplitude sensitivity of the phase-shifting transformer and the relative power flow variation of a line before first point cloud data obtained by scanning the candidate installation area of the phase-shifting transformer by the three-dimensional laser scanner is obtained.
On the basis of the above embodiment, the apparatus further comprises:
the determining module is used for determining the surface fluctuation degree of a sampling point according to the normal vector change degree of a point in the neighborhood of the sampling point in first point cloud data after the first point cloud data obtained by scanning the candidate installation area of the phase-shifting transformer by the three-dimensional laser scanner is obtained;
and the deleting module is used for deleting the redundant data in the first point cloud data according to the curved surface fluctuation degree of the sampling point.
On the basis of the foregoing embodiment, the determining module is specifically configured to:
fitting k points in the neighborhood of any sampling point in the first point cloud data to obtain a local plane, and calculating normal vectors of the points relative to the local plane;
constructing a covariance matrix according to the centroids of the k points;
performing eigenvalue decomposition on the covariance matrix to obtain a minimum eigenvalue of the covariance matrix, wherein an eigenvector corresponding to the minimum eigenvalue is a normal vector of the sampling point;
and calculating the arithmetic mean of the included angles of the k normal vectors to obtain the curve surface fluctuation degree of the sampling point, wherein each included angle of the normal vectors is the included angle between the normal vector of the sampling point and the normal vector of one of the k points relative to the local plane.
On the basis of the above embodiment, the deletion module is specifically configured to:
if the curved surface fluctuation degree of the sampling point is smaller than a set threshold value, deleting the data of the sampling point;
and if the curved surface fluctuation degree of the sampling point is greater than or equal to the set threshold, retaining the data of the sampling point.
On the basis of the above embodiment, the modeling module 330 includes:
a determining unit, configured to determine an optimal rotation matrix and an optimal translation vector between the source point cloud data and the target point cloud data by using one of the first point cloud data and the second point cloud data as source point cloud data and using the other one as target point cloud data;
a registration unit, configured to register the source point cloud data and the target point cloud data according to the optimal rotation matrix and the optimal translation vector;
and the construction unit is used for constructing a three-dimensional model of the candidate installation area according to the image data and the point cloud data after registration.
On the basis of the foregoing embodiment, the determining unit is specifically configured to:
determining a corresponding closest point in the target point cloud data for each point in the source point cloud data;
determining a rotation matrix and a translation vector between the source point cloud data and the target point cloud data based on a least square method according to each point in the source point cloud data and the corresponding closest point;
converting the target point cloud according to the rotation matrix and the translation vector, and calculating an iterative error between the converted target point cloud and the source point cloud;
and repeating the steps of determining the rotation matrix and the translation vector, converting the target point cloud and calculating the iteration error until the iteration error is smaller than a set threshold, and stopping iteration to obtain the optimal rotation matrix and the optimal translation vector.
On the basis of the above embodiment, the construction unit is specifically configured to:
constructing a TIN model according to the point cloud data after registration;
calculating the corresponding relation between a triangular patch in the TIN model and the image data by utilizing modeling software according to POS information carried by the image data;
and mapping texture information contained in the image data to corresponding triangular patches according to the corresponding relation to obtain a three-dimensional model of the candidate installation area.
On the basis of the foregoing embodiment, the addressing module 340 is specifically configured to:
and comparing the reserved space in the three-dimensional model with the actual size of the phase-shifting transformer to determine the installation position of the phase-shifting transformer.
The addressing device for the phase-shifting transformer provided by the third embodiment of the invention can be used for executing the addressing method for the phase-shifting transformer provided by any embodiment, and has corresponding functions and beneficial effects.
Example four
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device 10 may also represent various forms of mobile devices, such as personal digital assistants, user equipment, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication, wireless networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. Processor 11 performs the various methods and processes described above, such as the phase shifting transformer addressing method.
In some embodiments, the phase shifting transformer addressing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the phase shifting transformer addressing method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here may be implemented on an electronic device 10, the electronic device 10 having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the electronic device 10. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method for addressing a phase-shifting transformer, comprising:
acquiring first point cloud data, wherein the first point cloud data is obtained by scanning a candidate installation area of a phase-shifting transformer by a three-dimensional laser scanner;
acquiring second point cloud data, wherein the second point cloud data is generated according to image data obtained by oblique photography of the candidate installation area by the unmanned aerial vehicle;
constructing a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data;
and determining the installation position of the phase-shifting transformer based on the three-dimensional model.
2. The method of claim 1, prior to obtaining the first point cloud data, further comprising:
and selecting a candidate installation area of the phase-shifting transformer according to the relative sensitivity of the adjustment amplitude of the phase-shifting transformer and the relative power flow change quantity of the line.
3. The method of claim 1, after obtaining the first point cloud data, further comprising:
determining the curved surface fluctuation degree of the sampling points according to the normal vector change degree of the points in the neighborhood of the sampling points in the first point cloud data;
and deleting redundant data in the first point cloud data according to the curved surface fluctuation degree of the sampling points.
4. The method according to claim 3, wherein determining the degree of curvature of the sampling point according to the degree of change of the normal vector of the point in the neighborhood of the sampling point in the first point cloud data comprises:
fitting k points in the neighborhood of any sampling point in the first point cloud data to obtain a local plane, and calculating normal vectors of the points relative to the local plane, wherein k is a positive integer;
constructing a covariance matrix according to the centroids of the k points;
performing eigenvalue decomposition on the covariance matrix to obtain the minimum eigenvalue of the covariance matrix, wherein the eigenvector corresponding to the minimum eigenvalue is the normal vector of the sampling point;
and calculating the arithmetic mean of the included angles of the k normal vectors to obtain the curve fluctuation degree of the sampling point, wherein each normal vector included angle is the included angle between the normal vector of the sampling point and the normal vector of one of the k points relative to the local plane.
5. The method of claim 3, wherein deleting redundant data in the first point cloud data according to the degree of curvature of the sampling point comprises:
if the curved surface fluctuation degree of the sampling point is smaller than a set threshold value, deleting the data of the sampling point;
and if the curved surface fluctuation degree of the sampling point is greater than or equal to the set threshold, retaining the data of the sampling point.
6. The method of claim 1, wherein constructing a three-dimensional model of the candidate installation area from the first point cloud data and the second point cloud data comprises:
determining an optimal rotation matrix and an optimal translation vector between the source point cloud data and the target point cloud data by taking one of the first point cloud data and the second point cloud data as source point cloud data and the other one as target point cloud data;
registering the source point cloud data and the target point cloud data according to the optimal rotation matrix and the optimal translation vector;
and constructing a three-dimensional model of the candidate installation area according to the image data and the point cloud data after registration.
7. The method of claim 6, wherein determining an optimal rotation matrix and an optimal translation vector between the source point cloud data and the target point cloud data comprises:
determining a corresponding closest point in the target point cloud data for each point in the source point cloud data;
determining a rotation matrix and a translation vector between the source point cloud data and the target point cloud data based on a least square method according to each point in the source point cloud data and the corresponding closest point;
converting the source point cloud data according to the rotation matrix and the translation vector, and calculating an iterative error between the converted source point cloud data and the target point cloud;
and repeating the steps of determining the rotation matrix and the translation vector, converting the target point cloud and calculating the iteration error until the iteration error is smaller than a set threshold, and stopping iteration to obtain the optimal rotation matrix and the optimal translation vector.
8. The method of claim 6, wherein constructing a three-dimensional model of the candidate installation area from the image data and the registered point cloud data comprises:
constructing an irregular triangulation network TIN model according to the registered point cloud data;
calculating the corresponding relation between a triangular patch in the TIN model and the image data by utilizing modeling software according to POS information of a positioning and orientation system carried by the image data;
and mapping texture information contained in the image data to corresponding triangular patches according to the corresponding relation to obtain a three-dimensional model of the candidate installation area.
9. The method of claim 1, wherein determining the mounting location of the phase shifting transformer based on the three-dimensional model comprises:
and comparing the reserved space in the three-dimensional model with the actual size of the phase-shifting transformer to determine the installation position of the phase-shifting transformer.
10. A phase-shifting transformer site selection device, comprising:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first point cloud data, and the first point cloud data is obtained by scanning a candidate installation area of the phase-shifting transformer by a three-dimensional laser scanner;
the second acquisition module is used for acquiring second point cloud data, and the second point cloud data is generated according to image data obtained by oblique photography of the candidate installation area by the unmanned aerial vehicle;
the modeling module is used for constructing a three-dimensional model of the candidate installation area according to the first point cloud data and the second point cloud data;
and the addressing module is used for determining the installation position of the phase-shifting transformer based on the three-dimensional model.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of addressing a phase shifting transformer of any of claims 1-9.
12. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of addressing a phase shifting transformer according to any one of claims 1-9.
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| CN117523111B (en) * | 2024-01-04 | 2024-03-22 | 山东省国土测绘院 | Method and system for generating three-dimensional scenic spot cloud model |
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