An automatic registration method for power grid line point clouds

Abstract

The present application relates to the technical field of power grid, especially to a power grid line point cloud automatic registration method. The method comprises the following steps: obtaining power grid line three-dimensional point cloud data to separate the power grid line point cloud hierarchical structure, and generating power grid line point cloud hierarchical structure separation data; detecting the power transmission response point cloud offset of the power grid line according to the power grid line point cloud hierarchical structure separation data, and generating power transmission response point cloud offset data of the power grid line; identifying the power transmission channel form point cloud dislocation of the power grid line according to the power transmission response point cloud offset data of the power grid line, and generating power transmission channel form point cloud dislocation data; and performing power grid line point cloud registration and pose error correction between multiple frames of power grid line point cloud based on the power transmission channel form point cloud dislocation data, the power transmission response point cloud offset data of the power grid line, and the power grid line point cloud hierarchical structure separation data. The present application realizes power grid line point cloud automatic registration.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to an automatic registration method for power grid line point clouds. Background Technology

[0002] With the continuous expansion of the power grid and the in-depth advancement of smart grid construction, accurate and efficient point cloud registration technology has become a core foundation for realizing digital power grid perception in key tasks such as 3D modeling, operation and maintenance management, and hidden danger inspection of power grid lines. Power grid line point cloud data typically originates from different times and platforms (such as airborne LiDAR, UAV laser scanning, and ground scanning), exhibiting significant differences in spatial distribution, density, and perspective, leading to problems such as positional deviations and attitude inconsistencies among multi-source point clouds. Power grid line point cloud registration refers to the process of aligning power grid line point cloud data acquired from multiple sources or at different times to a unified spatial coordinate system. In existing technologies, most rely on rule-based feature matching, preset template structures, or static geometric constraints, using known models of poles, cables, or typical cross-sections to achieve initial alignment and error correction. However, in real-world power grid scenarios such as complex terrain, severe occlusion interference, non-standardized equipment distribution, drastic changes in point cloud density, and uneven point cloud distribution density, these technologies are easily affected by missing or misidentified structural features, resulting in spatial offset, attitude errors, and morphological changes in automatic power grid line point cloud registration. Summary of the Invention

[0003] Based on this, the present invention provides an automatic registration method for power grid line point clouds to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an automatic registration method for power grid line point clouds includes the following steps:

[0005] Step S1: Obtain 3D point cloud data of power grid lines; perform point cloud hierarchical structure separation of power grid lines based on the 3D point cloud data of power grid lines to generate point cloud hierarchical structure separation data of power grid lines.

[0006] Step S2: Based on the point cloud hierarchical structure separation data of the power grid line, perform point cloud offset detection of the power grid line transmission response to generate point cloud offset data of the power grid line transmission response.

[0007] Step S3: Based on the transmission response point cloud offset data of the power grid line, identify the misalignment of the transmission channel morphology point cloud of the power grid line and generate the misalignment data of the transmission channel morphology point cloud.

[0008] Step S4: Based on the misalignment data of the power transmission channel morphology point cloud, the offset data of the power transmission response point cloud of the power grid line, and the separation data of the power grid line point cloud hierarchical structure, perform power grid line point cloud registration to generate power grid line point cloud registration data.

[0009] Step S5: Correct the pose error between multiple frames of power grid line point clouds based on the power grid line point cloud registration data, and generate the power grid line point cloud registration data after pose error correction.

[0010] Furthermore, step S1 includes the following steps:

[0011] Step S11: Obtain 3D point cloud data of power grid lines;

[0012] Step S12: Analyze the power topology of the power grid lines based on the 3D point cloud data of the power grid lines to generate power topology data;

[0013] Step S13: Analyze the structural characteristics of the power grid lines based on the power topology data to generate power grid line structural characteristic data;

[0014] Step S14: Based on the structural feature data of the power grid line, perform hierarchical structure separation of the power grid line point cloud data to generate hierarchical structure separation data of the power grid line point cloud.

[0015] Furthermore, step S2 includes the following steps:

[0016] Step S21: Extract electrical parameters of the power grid lines based on the point cloud hierarchical structure separation data, and generate electrical parameter data of the power grid lines;

[0017] Step S22: Detect the electric field distribution status of the power grid lines based on the electrical parameter data of the power grid lines, and generate electric field distribution status data;

[0018] Step S23: Perform electric field sensitivity analysis on the power grid lines based on the electric field distribution data to generate electric field sensitivity data;

[0019] Step S24: Evaluate the electric field response oscillation of the power grid line based on the electric field sensitivity data, and generate electric field response oscillation data;

[0020] Step S25: Detect the transmission response point cloud offset of the power grid line based on the electric field response oscillation data, and generate the transmission response point cloud offset data of the power grid line.

[0021] Furthermore, step S3 includes the following steps:

[0022] Step S31: Perform fiber-to-electricity layout analysis of the power grid lines based on the transmission response point cloud offset data of the power grid lines, and generate fiber-to-electricity layout data of the power grid lines.

[0023] Step S32: Based on the fiber-optic-power layout data of the power grid lines, perform fiber-optic-power hybrid channel collaborative transmission of the power grid lines to generate fiber-optic-power hybrid channel collaborative transmission data of the power grid lines.

[0024] Step S33: Detect the transmission channel deviation of the power grid line based on the fiber-optic-electric hybrid channel collaborative transmission data of the power grid line, and generate the transmission channel deviation data of the power grid line;

[0025] Step S34: Based on the power transmission channel deviation data of the power grid line, identify the misalignment of the power transmission channel morphology point cloud and generate the misalignment data of the power transmission channel morphology point cloud.

[0026] Furthermore, step S32 includes the following steps:

[0027] Step S321: Identify the fiber optic laying channels of the power grid lines based on the fiber optic-power layout data of the power grid lines, and generate fiber optic laying channel data.

[0028] Step S322: Based on the fiber optic laying channel data, set the candidate segments of the fiber optic channel for the power grid line and generate candidate segment data for the fiber optic channel.

[0029] Step S323: Perform conductor network cooperative path analysis on power grid lines based on the candidate segment data of the fiber channel, and generate conductor network cooperative path data;

[0030] Step S324: Based on the conductor network cooperative path data, perform fiber-optic-power composite link planning for power grid lines and generate fiber-optic-power composite link planning data;

[0031] Step S325: Based on the fiber-electric hybrid link planning data and conductor network collaborative path data, perform fiber-electric hybrid channel collaborative transmission of power grid lines to generate fiber-electric hybrid channel collaborative transmission data of power grid lines.

[0032] Furthermore, step S34 includes the following steps:

[0033] Step S341: Perform spatial configuration analysis of the fiber-to-power links in the power grid based on the fiber-to-power composite link planning data, and generate fiber-to-power link spatial configuration data.

[0034] Step S342: Integrate the transmission path nodes of the power grid line based on the optical fiber-power link spatial configuration data to generate the path node integration data of the power grid line;

[0035] Step S343: Analyze the link connection relationship of the power grid lines based on the integrated data of the path nodes of the power grid lines, and generate the link connection relationship data of the power grid lines;

[0036] Step S344: Identify the channel linkage response of the power grid line based on the link connection relationship data of the power grid line, and generate channel linkage response data of the power grid line;

[0037] Step S345: Based on the channel linkage response data of the power grid line, the transmission channel deviation data of the power grid line, and the spatial configuration data of the optical fiber-power link, perform the point cloud misalignment identification of the transmission channel morphology of the power grid line, and generate the point cloud misalignment data of the transmission channel morphology.

[0038] Furthermore, step S345 includes the following steps:

[0039] Based on the channel linkage response data of the power grid line, the channel point cloud node morphology is extracted to generate channel point cloud node morphology data.

[0040] Based on the power transmission channel deviation data of the power grid line, the point cloud node morphology data of the channel is evaluated to assess the point cloud morphology and structure deviation of the power grid line, and point cloud morphology and structure deviation data is generated.

[0041] Based on the point cloud morphology and structure deviation data, the point cloud morphology offset feature analysis of the power grid line is performed to generate point cloud morphology offset feature data.

[0042] Based on point cloud morphology offset feature data, the point cloud misalignment of the power transmission channel morphology is identified in the spatial configuration data of the fiber-to-electric link, and the point cloud misalignment data of the power transmission channel morphology is generated.

[0043] Furthermore, step S4 includes the following steps:

[0044] Step S41: Based on the misalignment data of the power transmission channel morphology point cloud, perform axial structure analysis of the power transmission point cloud attitude of the power grid line to generate axial structure data of the power transmission point cloud attitude.

[0045] Step S42: Analyze the rotation axis angle of the power grid line's transmission point cloud based on the transmission response point cloud offset data, and generate the transmission point cloud rotation axis angle data;

[0046] Step S43: Based on the transmission point cloud attitude axial structure data and the transmission point cloud rotation axial angle data, design the transmission point cloud matching matrix for the power grid line and generate the transmission point cloud matching matrix data;

[0047] Step S44: Based on the power transmission point cloud matching matrix data and the power grid line point cloud hierarchical structure separation data, perform power grid line point cloud registration to generate power grid line point cloud registration data.

[0048] Furthermore, step S44 includes the following steps:

[0049] Step S441: Based on the point cloud hierarchical structure separation data of the power grid lines, set the transmission matching constraint conditions for the power grid lines and generate transmission matching constraint condition data;

[0050] Step S442: Design a multi-level point cloud structure compatibility matrix for power grid lines based on transmission matching constraint data, and generate multi-level point cloud structure compatibility matrix data;

[0051] Step S443: Based on the transmission matching constraint data, transmission point cloud matching matrix data, and multi-level point cloud structure compatibility matrix data, perform power grid line point cloud registration to generate power grid line point cloud registration data.

[0052] Furthermore, step S5 includes the following steps:

[0053] Step S51: Analyze the power supply demand of the power grid lines based on the power grid line point cloud registration data, and generate power supply demand data for the power grid lines;

[0054] Step S52: Perform power distribution time-link analysis on the power grid lines based on the power supply demand data of the power grid lines to generate power distribution time-link data of the power grid lines;

[0055] Step S53: Based on the power supply demand data and the power distribution time sequence-link data of the power grid lines, perform power transmission conversion matrix analysis on the power grid lines to generate power transmission conversion matrix data of the power grid lines;

[0056] Step S54: Based on the power transmission conversion matrix data of the power grid lines, perform pose error correction between multiple frames of power grid line point cloud registration data to generate pose error corrected power grid line point cloud registration data.

[0057] The beneficial effects of this invention are:

[0058] This invention proposes an automatic point cloud registration method for power grid lines. By acquiring three-dimensional point cloud data of power grid lines and performing hierarchical structure separation, the various components of the power grid lines can be accurately divided according to their functions and spatial locations. This separation makes the spatial relationships of each part of the power grid line clearer, achieving a three-dimensional and refined representation of power grid facilities and the environment. Based on the hierarchical structure separation data of the power grid line point cloud, transmission response point cloud offset detection can be performed, enabling real-time and accurate capture of subtle positional changes in the line. Based on the transmission response point cloud offset data, transmission channel morphology point cloud misalignment identification can be performed. Through in-depth analysis of the transmission response point cloud offset data, combined with the initial morphology data of the transmission channel, the misalignment of point cloud data within the transmission channel can be accurately identified. Based on the transmission channel morphology point cloud misalignment data, transmission response point cloud offset data, and point cloud hierarchical structure separation data, power grid line point cloud registration is performed. By comprehensively utilizing multiple data types for registration, the advantages of each type of data can be fully leveraged, improving the accuracy and reliability of the registration. By combining transmission response point cloud offset data reflecting changes in the position of line components with point cloud misalignment data describing the morphology of transmission channels, the spatial location and morphology of the lines can be determined more accurately. Precise alignment of point cloud data from different sources and periods is achieved within a unified coordinate system. Adjustments are made to the data on morphological changes within the transmission channel to ensure the accurate spatial position of each element within the channel. Based on the registration data of power grid line point clouds, pose error correction is performed between multiple frames of power grid line point clouds. Point cloud frames with deviations are rotated and translated to ensure precise alignment with other point cloud frames, further improving the accuracy of power grid line point cloud data registration.

[0059] This invention provides an automatic registration method for power grid line point clouds. This method enables the detection of transmission response point cloud offsets and the identification of transmission channel morphology point cloud misalignments in real-world power grid scenarios, including complex terrain, severe occlusion interference, non-standardized equipment distribution, drastic changes in point cloud density, and uneven point cloud distribution. It avoids the impact of missing or misidentified structural features and addresses issues such as spatial offset, attitude error, and morphological changes in automatic registration of power grid line point clouds, thereby achieving accurate and automatic registration of power grid line point clouds. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the steps of an automatic registration method for power grid line point clouds according to the present invention.

[0061] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S1.

[0062] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0063] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0064] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0065] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0066] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0067] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides an automatic registration method for power grid line point clouds, comprising the following steps:

[0068] Step S1: Obtain 3D point cloud data of power grid lines; perform point cloud hierarchical structure separation of power grid lines based on the 3D point cloud data of power grid lines to generate point cloud hierarchical structure separation data of power grid lines.

[0069] Step S2: Based on the point cloud hierarchical structure separation data of the power grid line, perform point cloud offset detection of the power grid line transmission response to generate point cloud offset data of the power grid line transmission response.

[0070] Step S3: Based on the transmission response point cloud offset data of the power grid line, identify the misalignment of the transmission channel morphology point cloud of the power grid line and generate the misalignment data of the transmission channel morphology point cloud.

[0071] Step S4: Based on the misalignment data of the power transmission channel morphology point cloud, the offset data of the power transmission response point cloud of the power grid line, and the separation data of the power grid line point cloud hierarchical structure, perform power grid line point cloud registration to generate power grid line point cloud registration data.

[0072] Step S5: Correct the pose error between multiple frames of power grid line point clouds based on the power grid line point cloud registration data, and generate the power grid line point cloud registration data after pose error correction.

[0073] In the embodiments of this invention, please refer to Figure 1 The diagram shown is a flowchart illustrating the steps of an automatic registration method for power grid line point clouds according to the present invention. In this example, the automatic registration method for power grid line point clouds includes the following steps:

[0074] Step S1: Obtain 3D point cloud data of power grid lines; perform point cloud hierarchical structure separation of power grid lines based on the 3D point cloud data of power grid lines to generate point cloud hierarchical structure separation data of power grid lines.

[0075] In this embodiment of the invention, a drone equipped with a high-precision lidar is used to collect three-dimensional point cloud data of power grid lines. The drone flies along the power grid lines at a set altitude and speed according to a pre-planned route. The lidar continuously emits laser beams and receives reflected light signals. Based on the time-of-flight of the light, the distance between each measurement point and the lidar is accurately calculated. Combined with the drone's attitude data (such as heading, pitch, and roll) and position information obtained from the Global Navigation Satellite System (GNSS), the three-dimensional coordinates of the measurement points are accurately recorded. After data collection, the hierarchical structure of the power grid line point cloud is separated. First, based on the spatial distribution and geometric characteristics of the point cloud data, a region-growing algorithm is used for preliminary separation. This algorithm starts with a seed point and, based on similarity criteria such as distance and normal vector between points, groups adjacent points with similar characteristics into the same region. For example, for pole point clouds, the points at the bottom of the poles are selected as seed points. Since pole point clouds are spatially closely connected and vertically distributed, after setting appropriate distance and normal vector angle thresholds, the algorithm can gradually aggregate the points on the poles together to form pole point cloud regions. For conductor point clouds, taking advantage of the conductor's slender, continuous shape and certain tension overhang, specific point cloud density and curvature thresholds are set to extract the conductor portion of the point cloud from the original point cloud data. Furthermore, for ground point clouds, filtering algorithms, such as those based on terrain slope and elevation changes, are used to separate the ground point cloud from the complex original point cloud, ultimately generating clear and ordered hierarchical structured data of the power grid line point cloud.

[0076] Step S2: Based on the point cloud hierarchical structure separation data of the power grid line, perform point cloud offset detection of the power grid line transmission response to generate point cloud offset data of the power grid line transmission response.

[0077] In this embodiment of the invention, transmission response point cloud offset detection is performed on the hierarchical structure separation data of power grid line point clouds. Taking conductor point clouds as an example, an initial spatial model of the conductor is first constructed, and offset is detected by analyzing the spatial position changes of the conductor point cloud at different times. The Iterative Closest Point (ICP) algorithm is used to compare the conductor point cloud at the current time with the conductor point cloud at the previous time after precise registration. The implementation logic of the ICP algorithm is as follows: first, a set of sampling points is selected in the conductor point cloud at the current time, and then the nearest corresponding point is found in the reference point cloud (the conductor point cloud at the previous time). An error function is constructed by calculating the Euclidean distance between these two sets of corresponding points, and then the rotation and translation transformation parameters of the current point cloud relative to the reference point cloud are solved by minimizing the error function. After multiple iterations to optimize the transformation parameters, the matching error between the two sets of point clouds is minimized, thereby accurately calculating the offset and offset direction of the conductor point cloud. For the offset detection of tower point clouds, an analysis method based on structural features is adopted. The tower point cloud is divided into different structural layers, such as the foundation, tower body, and crossarm. A geometric model is constructed for each layer (e.g., a rectangular model is used for the tower body). By monitoring the position and orientation changes of the geometric models in each layer, it is determined whether the tower has shifted. For example, if the center position of the rectangular model in a certain layer of the tower body point cloud has moved significantly horizontally relative to its initial position, or if the angle of the rectangle has changed, it indicates that the tower has shifted at that position. This process ultimately generates the transmission response point cloud shift data for the power grid line.

[0078] Step S3: Based on the transmission response point cloud offset data of the power grid line, identify the misalignment of the transmission channel morphology point cloud of the power grid line and generate the misalignment data of the transmission channel morphology point cloud.

[0079] In this embodiment of the invention, misalignment identification of transmission channel morphology point clouds is performed based on the transmission response point cloud offset data of power grid lines. A three-dimensional spatial analysis algorithm is used to comprehensively analyze the point cloud data of the transmission channel area. First, the boundary range of the transmission channel is determined by setting the three-dimensional coordinate range of the channel boundary and a buffer distance perpendicular to the line direction to delineate the spatial area of ​​the transmission channel. Then, the point clouds within this area are classified and identified to distinguish the point clouds of trees, buildings, other obstacles, and the transmission line itself. For the identification of tree point clouds, the irregular, clumped, and height-distributed characteristics of tree point clouds in space are utilized, and a density-based spatial clustering algorithm (DBSCAN) is employed. This algorithm uses point density as the core concept, and after setting an appropriate neighborhood radius and a minimum point number threshold, densely connected points are clustered into one class. Because tree point clouds are relatively dense and have a unique distribution pattern, they can be effectively clustered and identified from complex point cloud data. For building point clouds, based on the regular geometric shapes (such as rectangles, cuboids, etc.) and large planar areas of buildings, a geometric shape matching model is constructed for identification. For example, planar fitting is performed on suspected building point cloud areas. If a large and regular plane can be successfully fitted, it is identified as a building point cloud. After identifying object point clouds that intrude into the power transmission channel, the spatial distance and positional relationship between them and the power transmission line point cloud are calculated to determine whether point cloud misalignment has occurred. If the distance between the tree point cloud or building point cloud and the power transmission line point cloud is less than the safety threshold, and there is a spatial intersection or intrusion into the critical area of ​​the power transmission channel, it is identified as a power transmission channel morphology point cloud misalignment, and corresponding power transmission channel morphology point cloud misalignment data is generated.

[0080] Step S4: Based on the misalignment data of the power transmission channel morphology point cloud, the offset data of the power transmission response point cloud of the power grid line, and the separation data of the power grid line point cloud hierarchical structure, perform power grid line point cloud registration to generate power grid line point cloud registration data.

[0081] In this embodiment of the invention, power grid line point cloud registration is performed based on misaligned point cloud data of transmission channel morphology, offset point cloud data of power grid line transmission response, and separated point cloud hierarchical structure data of power grid lines. A registration strategy combining global and local approaches is adopted. First, global coarse registration is performed using feature point matching. Points with unique geometric features, such as corner points at the top of towers and connection points of conductors, are extracted from point cloud data from different data sources as feature points. For the description of feature points, a descriptor based on the local geometry of the point cloud, such as a Fast Point Feature Histogram (FPFH), is used. FPFH generates a feature vector that characterizes the local geometry of a point by calculating the normal direction and distance of its neighboring points. Matching feature point pairs are found by comparing the similarity between feature vectors in different point cloud datasets. Using these matching feature point pairs, an initial transformation matrix is ​​constructed to achieve preliminary alignment of the point cloud data. Then, local fine registration is performed to further improve the registration accuracy. For the point cloud of the transmission line section, a fine registration method based on curve fitting is used. Since conductors can be approximated as curves in space, curve fitting (e.g., using B-spline curve fitting) is performed on the conductor point cloud. By adjusting the curve parameters, the curves fitted from conductor point clouds from different data sources are made to overlap as much as possible, thereby accurately calculating the local transformation parameters and fine-tuning the initially aligned point cloud. For tower point clouds, fine registration is performed using the symmetry and geometric constraints of their structure. For example, tower bodies typically have a vertically symmetrical structure. By constructing a geometric constraint model based on the symmetry plane, the position and orientation of the tower point cloud are further optimized based on the coarse registration, ultimately generating accurate power grid line point cloud registration data.

[0082] Step S5: Correct the pose error between multiple frames of power grid line point clouds based on the power grid line point cloud registration data, and generate the power grid line point cloud registration data after pose error correction.

[0083] In this embodiment of the invention, pose error correction between multiple frames of power grid line point clouds is performed based on power grid line point cloud registration data. For multiple frames of point cloud data, a point cloud trajectory model is first constructed. By tracking and matching key feature points (such as tower feature points, line intersections, etc.) in each frame of point cloud data, the position change information of these feature points in different frames of point clouds is obtained, thereby constructing the motion trajectory of the point cloud. The trajectory is then optimized using a Kalman filter-based algorithm. The Kalman filter algorithm is an optimal estimation method based on a state-space model. It uses the pose (position and attitude) of the point cloud as state variables and continuously optimizes the state estimation through two steps: prediction and update. In the prediction step, the pose of the point cloud at the current moment is predicted based on the previous moment's state and motion model (such as a uniform motion model or a uniformly accelerated motion model). In the update step, the point cloud data measured by lidar is fused with the prediction result, and the predicted pose is corrected by calculating the Kalman gain to obtain a more accurate current moment's point cloud pose. For attitude error correction, data from inertial measurement units (IMUs) such as gyroscopes and accelerometers are utilized. IMUs can measure the angular velocity and acceleration of point cloud acquisition devices (such as drones) in real time, and attitude change information can be obtained through integration. The attitude information measured by the IMU is fused with the attitude calculated based on point cloud data, for example, using a complementary filtering algorithm. This fully leverages the advantages of stable attitude estimation of point cloud data over long time scales and the rapid attitude change response of IMUs over short time scales, accurately correcting attitude errors between multiple frames of point clouds, and ultimately generating high-quality power grid line point cloud registration data with corrected pose errors.

[0084] Step S11: Obtain 3D point cloud data of power grid lines;

[0085] Step S12: Analyze the power topology of the power grid lines based on the 3D point cloud data of the power grid lines to generate power topology data;

[0086] Step S13: Analyze the structural characteristics of the power grid lines based on the power topology data to generate power grid line structural characteristic data;

[0087] Step S14: Based on the structural feature data of the power grid line, perform hierarchical structure separation of the power grid line point cloud data to generate hierarchical structure separation data of the power grid line point cloud.

[0088] As an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, step S1 includes the following steps:

[0089] Step S11: Obtain 3D point cloud data of power grid lines;

[0090] In this embodiment of the invention, a UAV equipped with a 32-line lidar acquires three-dimensional point cloud data of a power grid line. The UAV flies at a speed of 15 m / s and an altitude of 200 meters above the ground along a pre-planned route parallel to the power grid line, with a flight interval of 50 meters. During flight, the lidar emits laser beams at a frequency of 1 million points / second. Each laser beam reflects back to the radar after encountering an object surface, and the radar calculates the distance from the measurement point to the radar based on the round-trip time of the laser beam. Simultaneously, the UAV's built-in high-precision Global Navigation Satellite System (GNSS) receiver receives satellite signals at a frequency of 10 Hz, acquiring the UAV's latitude and longitude coordinates and altitude in real time; the inertial measurement unit (IMU) collects the UAV's three-axis angular velocity and acceleration data at a frequency of 100 Hz. Through dead reckoning algorithms, combined with GNSS data, the attitude angles (heading angle, pitch angle, and roll angle) of the UAV are accurately calculated. Using this position and attitude information, each point measured by the lidar is transformed into a unified WGS84 coordinate system, forming raw 3D point cloud data containing information on towers, conductors, insulators, fittings, as well as surrounding terrain, vegetation, and buildings.

[0091] Step S12: Analyze the power topology of the power grid lines based on the 3D point cloud data of the power grid lines to generate power topology data;

[0092] In this embodiment of the invention, the acquired three-dimensional point cloud data of the power grid line is analyzed for power topology. A graph theory-based method is used to abstract equipment such as poles, conductors, and insulators in the power grid line as nodes in the graph, and the connections between these devices as edges. First, pole point clouds are identified through point cloud density analysis. Regions with more than 2000 points per cubic meter are defined as pole regions, and the geometric centers of the poles are extracted as nodes. For conductor point clouds, utilizing the continuity of the point cloud and the shape characteristics of catenaries, a curve fitting algorithm based on the least squares method is used to fit the centerline of the conductor, with the endpoints of the conductor centerlines between adjacent poles as nodes. After the nodes are determined, edges are constructed based on the connection relationships between conductors and poles. If the spatial distance between the conductor point cloud and the pole point cloud is less than 1 meter, a connection is considered to exist, and a corresponding edge is added to the graph. Furthermore, by analyzing the distribution characteristics of the insulator point cloud, the connection relationships between insulators and conductors / poles are identified, thus improving the power topology diagram.

[0093] Step S13: Analyze the structural characteristics of the power grid lines based on the power topology data to generate power grid line structural characteristic data;

[0094] In this embodiment of the invention, for tower nodes, features such as height, number of crossarms, and length are calculated. The tower height is obtained by extracting the coordinates of the highest and lowest points of the tower point cloud and calculating the vertical distance between them. The tower point cloud is then horizontally sliced, and the position and number of crossarms are determined based on the changes in the point cloud distribution within the slices. The length of the crossarm is then calculated by measuring the boundary coordinates of the crossarm point cloud. For conductor nodes, their tension and sag characteristics are analyzed. Based on the curve equation of the conductor centerline, combined with the height and spacing of the towers at both ends, the conductor sag is calculated using catenary theory. The span is obtained by measuring the horizontal distance between adjacent towers. Furthermore, the distribution density and arrangement of insulator nodes are analyzed, the number of insulators per string is counted, the distribution density is determined by calculating the average distance between the insulator point clouds, and the arrangement pattern is determined by observing the spatial arrangement direction and spacing of the insulator point clouds. These calculated and analyzed feature information are then organized to generate power grid line structural feature data containing tower height, crossarm parameters, conductor sag, tension, span, and insulator-related parameters.

[0095] Step S14: Based on the structural feature data of the power grid line, perform hierarchical structure separation of the power grid line point cloud data to generate hierarchical structure separation data of the power grid line point cloud.

[0096] In this embodiment of the invention, point cloud hierarchical structure separation is performed on the three-dimensional point cloud data of power grid lines based on the structural feature data of the power grid lines. First, based on the height and crossarm information in the tower structural feature data, spatial filtering conditions are set in the original point cloud data to extract the point cloud of the tower area. Point clouds within the corresponding spatial range are retained based on the tower height range (e.g., 15 meters to 50 meters) and the horizontal height range of the crossarm. For conductor point cloud separation, the sag and span characteristics of the conductors are used, combined with the connection relationship between the conductors and towers in the power topology, to search for point cloud areas in the original point cloud data that conform to the shape and connection relationship of the conductor catenary. By setting sag error thresholds and span error thresholds, conductor point clouds are filtered out. For insulator point clouds, based on their distribution density and arrangement characteristics, point cloud areas with specific point cloud density and arrangement patterns are searched near the conductor point clouds and separated. In addition, by utilizing the differences in spatial location and geometric shape between the terrain point cloud and the power grid equipment point cloud, the ground and surrounding terrain point clouds are separated through slope analysis and elevation threshold judgment. The final result is a hierarchical structure of power grid line point clouds that clearly distinguishes different types of point clouds, such as tower point clouds, conductor point clouds, insulator point clouds, and terrain point clouds.

[0097] Step S21: Extract electrical parameters of the power grid lines based on the point cloud hierarchical structure separation data, and generate electrical parameter data of the power grid lines;

[0098] Step S22: Detect the electric field distribution status of the power grid lines based on the electrical parameter data of the power grid lines, and generate electric field distribution status data;

[0099] Step S23: Perform electric field sensitivity analysis on the power grid lines based on the electric field distribution data to generate electric field sensitivity data;

[0100] Step S24: Evaluate the electric field response oscillation of the power grid line based on the electric field sensitivity data, and generate electric field response oscillation data;

[0101] Step S25: Detect the transmission response point cloud offset of the power grid line based on the electric field response oscillation data, and generate the transmission response point cloud offset data of the power grid line.

[0102] As an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0103] Step S21: Extract electrical parameters of the power grid lines based on the point cloud hierarchical structure separation data, and generate electrical parameter data of the power grid lines;

[0104] In this embodiment of the invention, after obtaining the hierarchical structure separation data of the power grid line point cloud, electrical parameters are extracted. For conductor point clouds, parameters are calculated using their geometry and spatial distribution, combined with the physical properties of the conductors. The actual length of the conductor is obtained by calculating the length of the fitted curve of the conductor point cloud; the connection relationship of the conductor is determined based on the node information at both ends of the conductor in the power topology data; and the conductor resistance is calculated using the resistance formula, combined with the resistivity of the conductor material. For insulator point clouds, the total insulation resistance of the insulator string is obtained by counting the number of insulator discs and combining the insulation resistance value of a single insulator disc using the series resistance calculation method. For tower point clouds, the grounding resistance is calculated based on the material and structure of the tower. The tower point cloud is simplified into a conductor model with a certain geometric shape. Using the finite element method, a known current is applied to the surface of the model, and the potential distribution at each point of the model is calculated. The grounding resistance is obtained by the ratio of the potential difference to the current. The calculated data of conductor resistance, insulator insulation resistance, and tower grounding resistance are integrated to generate electrical parameter data of the power grid line.

[0105] Step S22: Detect the electric field distribution status of the power grid lines based on the electrical parameter data of the power grid lines, and generate electric field distribution status data;

[0106] In this embodiment of the invention, the electric field distribution state is detected based on the electrical parameter data of the power grid line. A three-dimensional electric field model of the power grid line is constructed using the finite element method (FEM). The components such as towers, conductors, and insulators in the point cloud hierarchical structure separation data of the power grid line are modeled in simulation software according to their actual geometric shapes and spatial positions. Corresponding electrical parameters are assigned to the model, such as the calculated conductor resistance and insulator insulation resistance. In the boundary condition settings of the model, the rated voltage is applied to the conductors, and the towers are set to ground potential. The model space is divided into a large number of tiny elements using the finite element algorithm, and the Poisson equation is solved. (in Where is the dielectric constant. Potential, (where charge density is the unit), calculate the potential value of each element node, and thus obtain the electric field intensity distribution of the entire model space. By setting spatial sampling points, sampling points are arranged around the transmission line at a certain grid spacing, and the magnitude and direction of the electric field intensity at each sampling point are recorded to generate electric field distribution state data containing the coordinates of the sampling points and the electric field intensity vector.

[0107] Step S23: Perform electric field sensitivity analysis on the power grid lines based on the electric field distribution data to generate electric field sensitivity data;

[0108] In this embodiment of the invention, electric field sensitivity analysis of power grid lines is performed based on electric field distribution data. First, key areas for sensitivity analysis are identified, such as areas with frequent human activity near transmission lines and equipment installation areas susceptible to electric field influences. Within these areas, statistical analysis is performed on the electric field intensity distribution data, calculating statistics such as the mean and standard deviation of the electric field intensity. Using a local sensitivity analysis method, a parameter in the model is changed (e.g., conductor voltage fluctuation ±10%), and the electric field distribution is recalculated. The change in electric field intensity at sampling points within the key area before and after the parameter change is compared. For example, when the conductor voltage increases by 10%, the increase in electric field intensity at each sampling point within the key area is recorded. By calculating the ratio of the change to the original value, the sensitivity coefficient of that parameter to electric field intensity is obtained. The same analysis is performed on multiple parameters (e.g., conductor spacing changes, insulator dielectric constant fluctuations), generating electric field sensitivity data containing the sensitivity coefficients of each parameter and the characteristics of electric field intensity changes in the key areas. This data is used to determine which factors have the most significant impact on the electric field distribution.

[0109] Step S24: Evaluate the electric field response oscillation of the power grid line based on the electric field sensitivity data, and generate electric field response oscillation data;

[0110] In this embodiment of the invention, the electric field response oscillation of power grid lines is evaluated based on electric field sensitivity data. Fourier transform is used to analyze the time series of electric field intensity in the electric field distribution data. Within a certain period, a discrete Fourier transform is performed on the electric field intensity data at each sampling point to convert the time-domain signal into a frequency-domain signal, obtaining the spectral distribution of the electric field intensity. By identifying the peak frequencies in the spectrum, the main oscillation frequency components of the electric field intensity are determined. The amplitude and phase relationship of each frequency component are calculated to analyze the intensity and stability of the oscillation. For example, if the spectral amplitude is large and persistent at a certain frequency (such as 50Hz and its harmonic frequencies), it indicates that the oscillation at that frequency is significant. Simultaneously, the differences in oscillation frequency and amplitude between different sampling points are observed to determine the spatial distribution characteristics of the electric field response oscillation. Combined with the electric field sensitivity data, the factors that exacerbate or inhibit the electric field response oscillation are analyzed, generating electric field response oscillation data containing the main oscillation frequencies, amplitudes, phases, and influencing factors of the oscillation.

[0111] Step S25: Detect the transmission response point cloud offset of the power grid line based on the electric field response oscillation data, and generate the transmission response point cloud offset data of the power grid line.

[0112] In this embodiment of the invention, the transmission response point cloud offset of the power grid line is detected based on electric field response oscillation data. The electric field response oscillation data is combined with the power grid line point cloud hierarchical structure separation data to establish an electric field-structure coupling model. Since changes in the electric field cause electrodynamic forces on the conductors, leading to slight offsets in their positions, the magnitude and direction of the electrodynamic forces on the conductors under different electric field intensities are determined by calculating the electric field force formula. The electrodynamic forces are applied as external forces to the mechanical model of the conductor point cloud, and the displacement of the conductors under the action of the electrodynamic forces is solved using the finite element method. By comparing the original conductor point cloud position with the displacement state after being subjected to force, the offset and direction of the conductor point cloud are determined by calculating the coordinate change of each point. For the tower point cloud, the influence of the electric field on the tower grounding system is considered. By analyzing the changes in soil potential distribution caused by changes in grounding current, the additional force on the tower foundation is calculated, thereby assessing whether the tower has experienced slight tilting or displacement. Finally, transmission response point cloud offset data of the power grid line, including the offset and direction of the point clouds of components such as conductors and towers, is generated.

[0113] Furthermore, step S3 includes the following steps:

[0114] Step S31: Perform fiber-to-electricity layout analysis of the power grid lines based on the transmission response point cloud offset data of the power grid lines, and generate fiber-to-electricity layout data of the power grid lines.

[0115] In this embodiment of the invention, after obtaining the transmission response point cloud offset data of the power grid line, fiber-optic-power layout analysis is conducted. The point clouds of components such as conductors and towers in the transmission response point cloud offset data are combined with Geographic Information System (GIS) data to construct a three-dimensional spatial model. For conductor point clouds, the changes in the power transmission path are analyzed based on their spatial position and direction after offset. Considering that fiber optic laying needs to be closely integrated with power lines and avoid interference, a certain range is set around the conductor point cloud as a potential fiber optic laying area. For tower point clouds, it is analyzed whether the tower offset affects the fixing and connection conditions of the fiber on the tower, such as checking the flatness of the tower surface and whether there is any tortuous deformation caused by the offset. By measuring the horizontal distance and vertical height difference between tower point clouds and combining the minimum bending radius requirement of the fiber, the laying path of the fiber between towers is planned. At the same time, considering the position and offset of power equipment (such as insulators and fittings), spatial conflicts between the fiber and these devices are avoided. Finally, fiber-optic-power layout data of the power grid line, including potential fiber laying paths and spatial relationships with various components of the power line, is generated.

[0116] Step S32: Based on the fiber-optic-power layout data of the power grid lines, perform fiber-optic-power hybrid channel collaborative transmission of the power grid lines to generate fiber-optic-power hybrid channel collaborative transmission data of the power grid lines.

[0117] In this embodiment of the invention, firstly, fiber optic laying channels are identified based on potential fiber optic laying areas in the fiber-electricity layout data. Density analysis and morphological processing methods of three-dimensional point clouds are used to filter point clouds within potential areas. A point cloud density threshold is set (e.g., more than 1000 points per cubic meter), and point cloud areas meeting the criteria are retained as preliminary fiber optic laying channels. Then, based on the fiber optic laying channel data, the channels are divided into several candidate fiber optic channel segments. The channels are segmented according to a certain length standard (e.g., every 50 meters), and the feasibility of each segment is evaluated in conjunction with factors such as terrain, power equipment distribution, etc. Next, for each candidate fiber optic channel segment, conductor network collaborative path analysis is performed. Based on the power topology data and the actual position of the conductor point cloud after offset, the current distribution and voltage drop of the conductor within the candidate segment are calculated. Simultaneously, considering the transmission characteristics of the fiber optic cable, such as attenuation coefficient and bandwidth requirements, a collaborative path that can meet both power transmission needs and ensure stable fiber optic signal transmission is sought. Finally, based on the collaborative path analysis results, fiber-electricity composite link planning is performed. The specific arrangement of optical fibers and power conductors within the candidate sections is determined, such as whether they are laid in parallel or in a cross pattern, and the fixed locations and support structures of the optical fibers and conductors are specified. Finally, based on the optical fiber-power hybrid link planning data and conductor network collaborative path data, optical fiber signal transmission and power transmission are integrated to generate collaborative transmission data for the power grid line optical fiber-power hybrid channel, which includes optical fiber transmission parameters, power transmission parameters, and the collaborative working mode of both.

[0118] Step S33: Detect the transmission channel deviation of the power grid line based on the fiber-optic-electric hybrid channel collaborative transmission data of the power grid line, and generate the transmission channel deviation data of the power grid line;

[0119] In this embodiment of the invention, transmission channel deviation detection is performed based on the fiber-optic-power hybrid channel collaborative transmission data of the power grid line. The fiber optic and power transmission parameters in the hybrid channel collaborative transmission data are compared with pre-set standard parameters. For power transmission, current deviation thresholds (e.g., not exceeding ±5% of rated current) and voltage deviation thresholds (e.g., not exceeding ±7% of rated voltage) are set. If the actual transmitted current and voltage exceed these ranges, a deviation in power transmission is determined. For fiber optic transmission, optical power deviation thresholds (e.g., not exceeding ±3dB of standard optical power) and attenuation deviation thresholds (e.g., attenuation deviation per kilometer not exceeding 0.05dB) are set. When the actual optical power and attenuation values ​​exceed these thresholds, an anomaly in fiber optic transmission is identified. Simultaneously, the spatial position of the fiber optic cable and power line is analyzed using transmission response point cloud offset data to determine if the spatial position has shifted, causing transmission deviation. For example, if the distance between the conductor point cloud and the fiber optic cable is less than the safe distance (e.g., 0.5 meters) after shifting, electromagnetic interference may affect fiber optic signal transmission. By calculating the coordinate difference between the actual position and the standard position of the fiber optic cable and power line, the spatial deviation amount and direction of the transmission channel are determined. Finally, power transmission channel deviation data containing power transmission deviation parameters, optical fiber transmission deviation parameters, and spatial location deviation information is generated.

[0120] Step S34: Based on the power transmission channel deviation data of the power grid line, identify the misalignment of the power transmission channel morphology point cloud and generate the misalignment data of the power transmission channel morphology point cloud.

[0121] In this embodiment of the invention, firstly, the spatial configuration of the fiber-electric composite link is analyzed based on the planning data. The shape, orientation, and connection relationship of the optical fiber and power line in three-dimensional space are extracted to generate fiber-electric link spatial configuration data. Then, based on the spatial configuration data, key locations in the transmission channel (such as towers and joints) are integrated as transmission path nodes, and the coordinates and related attribute information of each node are recorded to generate path node integration data. Next, the connection relationship between each node in the path node integration data is analyzed to determine the link connection method of the optical fiber and power line, generating link connection relationship data. Then, based on the link connection relationship data, the linkage response between different parts of the transmission channel is identified. For example, when a section of conductor deviates, it is analyzed whether the connected optical fiber and other conductors experience corresponding displacement or deformation, generating channel linkage response data. Finally, based on the channel linkage response data, transmission channel deviation data, and fiber-electric link spatial configuration data, the misalignment of the transmission channel morphology point cloud is identified. Based on the transmission channel linkage response data, morphological features of the channel point cloud nodes are extracted, such as the shape of the tower point cloud and the curve shape of the conductor point cloud, generating channel point cloud node morphological data. Based on transmission channel deviation data, the channel point cloud node morphological data is evaluated, and the deviation of the point cloud morphological structure is calculated, such as tower tilt angle deviation and conductor sag deviation, generating point cloud morphological structure deviation data. By analyzing the point cloud morphological structure deviation data, the offset characteristics of the point cloud morphology are determined, such as offset direction and offset rate, generating point cloud morphological offset feature data. Based on the point cloud morphological offset feature data, the optical fiber-power link spatial configuration data is compared to determine whether there is point cloud misalignment in the transmission channel. If the point cloud morphological offset features do not match the spatial configuration data, such as intersections or excessively close proximity between optical fiber point clouds and power line point clouds, it is identified as transmission channel morphological point cloud misalignment, ultimately generating transmission channel morphological point cloud misalignment data containing information such as misalignment location and misalignment type.

[0122] Furthermore, step S32 includes the following steps:

[0123] Step S321: Identify the fiber optic laying channels of the power grid lines based on the fiber optic-power layout data of the power grid lines, and generate fiber optic laying channel data.

[0124] In this embodiment of the invention, fiber-optic-power layout data is imported into a three-dimensional spatial analysis system, and the data is initially screened using a point cloud density threshold method. Areas with more than 1200 points per cubic meter are designated as potential fiber optic laying areas. This is because fiber optic laying requires a relatively stable space free from excessive interference, and a higher point cloud density usually indicates a more stable structure in the area, providing a good support environment for the fiber. For the initially screened areas, morphological filtering is further used to remove noisy point clouds. By setting the size of the structural elements for erosion and dilation operations (e.g., a circular structural element with a radius of 0.3 meters), the point clouds of potential areas are processed to remove isolated noise points and small protrusions or depressions, making the area boundaries smoother and more regular. Simultaneously, considering the spatial relationship of various components of the power line, areas that are too close (less than 0.2 meters) to power equipment such as tower foundations, insulators, and fittings are excluded to avoid spatial conflicts between fiber optic laying and power equipment. Finally, the areas that meet the criteria are determined as fiber optic laying channels, generating fiber optic laying channel data containing information such as the channel's three-dimensional coordinates and boundary shape.

[0125] Step S322: Based on the fiber optic laying channel data, set the candidate segments of the fiber optic channel for the power grid line and generate candidate segment data for the fiber optic channel.

[0126] In this embodiment of the invention, candidate segments for the optical fiber channel are set based on optical fiber laying channel data. The optical fiber laying channel is evenly divided into multiple segments, each 60 meters long. For each segment, evaluation is conducted from three dimensions: topography, environmental interference, and construction convenience. Regarding topography, by analyzing the elevation changes in the point cloud data within the segment, if the elevation difference exceeds 8 meters, the area is considered to have complex terrain, unfavorable for optical fiber laying, and is temporarily excluded from the candidate list. Regarding environmental interference, the presence of strong electromagnetic interference sources (such as substations, large motors, etc.) within the segment is detected. If a strong electromagnetic interference source is found within 50 meters of the segment boundary, the area is excluded. Regarding construction convenience, the availability of sufficient space for construction operations within the segment is assessed. If the point cloud shows dense vegetation or narrow passages within the segment that make it difficult for construction equipment to enter, the segment is not selected. After screening, sections that meet the requirements of flat terrain (elevation difference less than 3 meters), no strong electromagnetic interference, and convenient construction are identified as candidate sections for the fiber channel, and candidate section data containing information such as the starting coordinates, length, and evaluation parameters of the candidate sections are generated.

[0127] Step S323: Perform conductor network cooperative path analysis on power grid lines based on the candidate segment data of the fiber channel, and generate conductor network cooperative path data;

[0128] In this embodiment of the invention, conductor network cooperative path analysis is conducted based on candidate segment data of optical fiber channels. Using 3D spatial modeling technology, the point cloud of conductors within the candidate segments is constructed into an accurate 3D conductor network model. Based on power topology data, the current distribution of each conductor under normal operating conditions is calculated, and the electric and magnetic field distributions of the conductor network are simulated using the finite element method. A current density threshold is set, and conductor paths with current densities exceeding the threshold are optimized and adjusted. Simultaneously, considering the transmission characteristics of optical fiber, with the goal of reducing electromagnetic interference, the impact of different conductor paths on optical fiber signal transmission is analyzed. By measuring the distance between the conductor and the potential optical fiber path, the electromagnetic field strength generated by the conductor at the optical fiber location is calculated. If the electromagnetic field strength exceeds the safety threshold for optical fiber signal transmission (e.g., a magnetic field strength greater than 50 microtesla will cause signal attenuation exceeding 1 dB / km), the conductor path is replanned. After multiple rounds of adjustment and optimization, a conductor network cooperative path that meets both power transmission requirements and ensures stable optical fiber signal transmission is determined, generating conductor network cooperative path data containing information such as conductor coordinates, current distribution, and distance to the optical fiber path.

[0129] Step S324: Based on the conductor network cooperative path data, perform fiber-optic-power composite link planning for power grid lines and generate fiber-optic-power composite link planning data;

[0130] In this embodiment of the invention, fiber-electric composite link planning is performed based on conductor network collaborative path data. Within candidate sections, the specific laying location and method of the optical fiber are determined according to the conductor network collaborative path. For parallel laying, the horizontal distance between the optical fiber and the conductor is set to 1.5 meters, and the vertical distance is set to 1 meter to ensure sufficient safety clearance and reduce electromagnetic interference. Steel strand is used as the common support structure for both the optical fiber and the conductor, with a support point set every 50 meters. The position and structure of the pole or other support at each support point are measured using point cloud data to determine the fixing method of the steel strand. For the optical fiber, armored optical cable with anti-electromagnetic interference characteristics is selected, and a dedicated optical fiber clamp is used to fix it at each support point to ensure that the optical fiber will not be displaced by external forces. At the same time, at the intersection of the optical fiber and the conductor, an insulating protective sleeve is used to protect the optical fiber to avoid friction or electrical contact at the intersection. After the planning is completed, fiber-electric composite link planning data containing information such as the specific coordinates of the optical fiber and the conductor, support structure parameters, and protective measures is generated.

[0131] Step S325: Based on the fiber-electric hybrid link planning data and conductor network collaborative path data, perform fiber-electric hybrid channel collaborative transmission of power grid lines to generate fiber-electric hybrid channel collaborative transmission data of power grid lines.

[0132] In this embodiment of the invention, fiber-electric hybrid channel collaborative transmission is implemented based on fiber-electric composite link planning data and conductor network collaborative path data. For power transmission, power is delivered from the power source to the load end according to the current and voltage parameters set in the conductor network collaborative path data. Parameters such as current, voltage, and temperature of the conductors are monitored in real time, and data is collected by sensors installed on the conductors at a frequency of 10 times per second. For fiber optic transmission, an optical signal transmitter is connected to one end of the optical fiber, transmitting optical signals according to the optical power and wavelength set in the fiber-electric composite link planning data. An optical signal receiver is used at the other end of the optical fiber to receive the signals and monitor parameters such as optical signal attenuation and bit error rate at a frequency of 5 times per second. A linkage mechanism between power transmission and fiber optic transmission is established. When abnormalities are detected in power transmission parameters (such as a sudden increase in current exceeding 20% ​​of the rated value) or fiber optic transmission parameters (such as a sudden increase in optical signal attenuation exceeding 3dB), an early warning system is immediately activated, and the abnormal information is sent to the monitoring center via a wireless communication module. Simultaneously, the parameters of power transmission or fiber optic transmission are automatically adjusted according to abnormal conditions, such as reducing current or increasing optical power, to ensure the stable operation of the hybrid channel. Ultimately, it generates power grid line fiber-electric hybrid channel collaborative transmission data containing real-time power transmission parameters, real-time fiber optic transmission parameters, linkage control information, etc.

[0133] Furthermore, step S34 includes the following steps:

[0134] Step S341: Perform spatial configuration analysis of the fiber-to-power links in the power grid based on the fiber-to-power composite link planning data, and generate fiber-to-power link spatial configuration data.

[0135] In this embodiment of the invention, after acquiring the planning data for the fiber-to-electric composite link, a spatial configuration analysis of the fiber-to-electric link is performed. The specific coordinates of the optical fibers and power conductors, and the parameters of the supporting structures, are imported into a 3D modeling system to construct a high-precision 3D model of the fiber-to-electric composite link. Spatial geometric analysis methods are used to quantitatively describe the shape, orientation, and spatial distribution of the composite link in its spatial configuration. For example, the curvature of the optical fibers and power conductors in 3D space is calculated to determine their degree of bending. If the curvature of a certain segment of the link exceeds a specified standard (e.g., curvature greater than 0.05 radians per meter), it is marked as a special bending area. Simultaneously, the spatial relationship between the composite link and its surrounding environment (e.g., terrain, buildings) is analyzed, and the minimum distance between the link and the terrain surface and buildings is measured. A minimum safe distance threshold is set (e.g., a horizontal distance of not less than 3 meters and a vertical distance of not less than 5 meters from buildings), and areas that do not meet the safe distance requirements are highlighted. In addition, the distribution of composite links at different altitudes was studied. The links were horizontally sliced ​​at 0.5-meter intervals, and the number and positional relationship of optical fibers and power conductors in each slice were counted to generate optical fiber-power link spatial configuration data containing information such as three-dimensional shape parameters, spatial positional relationship, and safety distance assessment.

[0136] Step S342: Integrate the transmission path nodes of the power grid line based on the optical fiber-power link spatial configuration data to generate the path node integration data of the power grid line;

[0137] In this embodiment of the invention, transmission path node integration is carried out based on optical fiber-power link spatial configuration data. First, key nodes in the transmission path are identified, including tower locations, fiber-to-conductor connection points, and link bends. For tower nodes, the geometric center coordinates of the tower point cloud are extracted as the node location, and attribute information such as tower height and type is recorded. For fiber-to-conductor connection points, the connection point coordinates are accurately obtained based on the fixed positions of both in the optical fiber-power composite link planning data, and the connection method is labeled. For link bends, the curvature change of the link is analyzed; when the rate of curvature change exceeds 0.1 radians / meter, the location is identified as a bend node, and its coordinates and bend angle are recorded. These key nodes are numbered and arranged according to their order in the transmission path, establishing topological relationships between nodes and generating integrated power grid line path node data containing node coordinates, attribute information, and topological connection relationships. Simultaneously, a unique identifier is assigned to each node for subsequent management and analysis.

[0138] Step S343: Analyze the link connection relationship of the power grid lines based on the integrated data of the path nodes of the power grid lines, and generate the link connection relationship data of the power grid lines;

[0139] In this embodiment of the invention, link connection relationship analysis is performed based on integrated data of power grid line path nodes. A graph theory approach is used, treating path nodes as vertices in a graph and connections between nodes as edges, thus constructing a link connection graph. Based on the topological connections recorded in the node integrated data, corresponding edges are added to the graph, and their attribute information, such as connection type (power connection, fiber optic connection) and connection length, is labeled. For example, for a power conductor connection between two tower nodes, an edge is added to the graph, with its length defined as the horizontal distance between the two towers, and the connection type as a power connection. By analyzing the link connection graph, the degree of each node (i.e., the number of edges connected to that node) is calculated. Nodes with a degree greater than 2 are considered key hub nodes, and their connection relationships are analyzed in detail. Simultaneously, the existence of isolated nodes or incomplete connection paths in the links is detected. If a node is found to have no connected edges, it is determined to be an isolated node, requiring further verification of the data accuracy or the rationality of the link planning. Finally, power grid line link connection relationship data containing information such as the link connection graph, node degree analysis, and abnormal connection detection results is generated.

[0140] Step S344: Identify the channel linkage response of the power grid line based on the link connection relationship data of the power grid line, and generate channel linkage response data of the power grid line;

[0141] In this embodiment of the invention, channel linkage response identification is performed based on power grid line link connection relationship data. A monitoring system is established to collect real-time operating status data of each node, including parameters such as current and voltage of power conductors, optical power and attenuation of optical fibers, etc., at a collection frequency of 20 times per second. A correlation analysis algorithm is used to study the changing relationships of operating status parameters between different nodes. For example, when the current of the power conductor at a certain tower node suddenly increases, the system analyzes whether the current, voltage, and optical power of other nodes connected to it change accordingly. A linkage threshold is set; if the parameter change of a certain node exceeds 15% of its normal range, and the parameters of other nodes connected to it also change by more than 10% within 1 second, a channel linkage response is determined to exist. The set of nodes with linkage responses, parameter changes, and response times are recorded to generate power grid line channel linkage response data containing linkage node information, parameter change magnitude, response time sequence, etc. Simultaneously, based on the characteristics of the linkage response, its propagation path and impact range are analyzed to provide a basis for fault diagnosis and early warning.

[0142] Step S345: Based on the channel linkage response data of the power grid line, the transmission channel deviation data of the power grid line, and the spatial configuration data of the optical fiber-power link, perform the point cloud misalignment identification of the transmission channel morphology of the power grid line, and generate the point cloud misalignment data of the transmission channel morphology.

[0143] In this embodiment of the invention, an arbitrary point cloud is defined. The normal vector in three-dimensional space is ,in For the first Point cloud coordinates, for Centered Neighborhood point set, for The average normal vector, The neighborhood size is determined. First, based on the channel linkage response data, point cloud data of the nodes that triggered the linkage response and their surrounding areas are extracted. Using 3D morphological processing methods, the morphological features of the channel point cloud nodes are extracted, such as the shape of the tower point cloud and the curve shape of the conductor point cloud, generating channel point cloud node morphological data. Then, the spatial position deviation information in the transmission channel deviation data is compared with the channel point cloud node morphological data to calculate the deviation amount of the point cloud morphological structure. For example, for the tower point cloud, the difference between its actual tilt angle and the initial vertical angle is calculated. If the difference exceeds 3 degrees, it is determined that the tower point cloud morphology has a structural deviation, generating point cloud morphological structure deviation data. Next, the point cloud morphological structure deviation data is analyzed to determine the offset characteristics of the point cloud morphology, such as the offset direction and offset rate. By continuously collecting point cloud data at multiple time points, the change in point cloud coordinates between adjacent time points is calculated to obtain the offset rate, generating point cloud morphological offset feature data. Finally, the point cloud morphological shift feature data is compared with the optical fiber-power link spatial configuration data. If the point cloud morphological shift causes the distance between the optical fiber and the power conductor to be less than the safe distance (e.g., 0.5 meters), or if point cloud intersections or overlaps occur, it is identified as a point cloud misalignment in the transmission channel morphology. The misalignment area is identified using the spatial position changes and normal vector deviation of the point cloud, and then processed using a formula... ,in This represents the current point cloud position. This is the location of the last point cloud instance. The normal vector of the current frame. The normal vector of the previous frame. , The weighting coefficients adjust for the effects of position and angle changes. If so, it is determined that a morphological misalignment has occurred.

[0144] Furthermore, step S345 includes the following steps:

[0145] Based on the channel linkage response data of the power grid line, the channel point cloud node morphology is extracted to generate channel point cloud node morphology data.

[0146] In this embodiment of the invention, the morphology of channel point cloud nodes is extracted using channel linkage response data of power grid lines. First, based on the set of nodes that have triggered linkage responses recorded in the channel linkage response data, these nodes and their surrounding areas are located in the three-dimensional point cloud data of the power grid lines. A spherical region with a radius of 5 meters is defined centered on each linkage node, and all point cloud data within this region is extracted. For tower nodes, a clustering algorithm based on point cloud normal vectors is used to calculate the normal vector of each point. A threshold of 15 degrees is set for the angle between the normal vectors, and points with similar normal vectors are clustered together, thus separating the point clouds of different structural components of the tower, such as the tower body and crossarms. Using three-dimensional surface reconstruction technology, the tower point cloud is processed using the moving least squares method to construct an accurate three-dimensional surface model of the tower, obtaining its shape, size, and other morphological features. For conductor nodes, based on the continuity of the conductor point cloud in space and the shape characteristics of the catenary, a curve fitting method is used. Cubic spline curves are used to fit the conductor point cloud, extracting the centerline and outer contour of the conductor, and recording morphological parameters such as sag and tension of the conductor. The extracted node cloud morphological features are integrated to generate channel point cloud node morphological data containing information such as node coordinates, point cloud structure model, and morphological parameters.

[0147] Preferably, the point cloud morphology and structure deviation of the power grid line is evaluated based on the power transmission channel deviation data, and point cloud morphology and structure deviation data is generated.

[0148] In this embodiment of the invention, based on the transmission channel deviation data of the power grid line, the point cloud node morphology data of the channel is evaluated for point cloud morphology structural deviation. Spatial position deviation information (such as tower tilt angle deviation, conductor sag deviation, etc.) in the transmission channel deviation data is compared with the initial morphological parameters in the channel point cloud node morphology data. For towers, the tilt angle of the tower is obtained by calculating the offset between the actual center of gravity position of the tower point cloud and the initial vertical state, combined with the tower height. If the difference between the actual tilt angle and the initial angle exceeds 2 degrees, the tower is determined to have a structural deviation. For conductors, the actual fitted sag value is compared with the designed sag value; if the difference exceeds 0.3 meters, the conductor sag is considered to have a deviation. For insulator strings, the difference between the number of point clouds and the initial installed number is statistically analyzed; if the number deviation exceeds 10%, the insulator string structure is considered abnormal. By quantifying these deviation values, point cloud morphology structural deviation data containing information such as the deviation type, deviation value, and deviation location of each node is generated. At the same time, a severity level is set for each deviation item, such as slight deviation (deviation value within 1-2 times the allowable range), moderate deviation (within 2-5 times), and severe deviation (more than 5 times), for subsequent analysis and processing.

[0149] Preferably, point cloud morphology offset feature analysis of power grid lines is performed based on point cloud morphology and structure deviation data to generate point cloud morphology offset feature data.

[0150] In this embodiment of the invention, point cloud morphological offset feature analysis is performed based on point cloud morphological structure deviation data. Point cloud morphological structure deviation data for multiple time periods are collected, with a time interval set to 1 hour. For each node, the change in deviation value between adjacent time periods is calculated to obtain the offset rate. For example, if a tower's tilt angle deviation is 3 degrees in the first time period and 3.2 degrees in the second time period, its tilt angle offset rate is 0.2 degrees / hour. By analyzing the offset data for multiple time periods, the least squares method is used to fit an offset trend curve to determine whether the offset exhibits a linear, non-linear, or periodic change pattern. For conductor sag deviation, its offset under different environmental conditions is observed, and a correlation model between sag deviation and environmental factors is established. If a positive correlation is found between sag deviation and temperature, and the correlation coefficient reaches 0.8 or higher, then temperature is determined to be the main factor affecting conductor sag deviation. The calculated offset rate, offset trend, influencing factors, and other information are summarized to generate point cloud morphological offset feature data containing node offset dynamic characteristics, influencing factor weights, etc.

[0151] Preferably, point cloud morphology offset feature data is used to identify the misalignment of the power transmission channel morphology point cloud in the optical fiber-power link spatial configuration data, thereby generating power transmission channel morphology point cloud misalignment data.

[0152] In this embodiment of the invention, point cloud morphology offset feature data is used to identify point cloud misalignment in the spatial configuration data of the fiber-to-power link. The offset position and direction information in the point cloud morphology offset feature data are compared with the standard position and shape in the fiber-to-power link spatial configuration data. A misalignment judgment threshold is set: for the distance between the fiber and the power conductor, if the actual distance is less than 0.5 meters, a spatial misalignment is determined; for point cloud intersections, when the fiber point cloud and the power conductor point cloud have an overlap area exceeding 10% in three-dimensional space, it is considered a point cloud intersection misalignment. For example, if the overlap volume between a segment of fiber point cloud and the power conductor point cloud after offset accounts for 15% of the total volume of the fiber point cloud, then a point cloud intersection misalignment is determined to have occurred at this point. Simultaneously, combining the offset trend in the point cloud morphology offset feature data, the offset position of the point cloud in the future is predicted, and the possibility of further deterioration of the misalignment is assessed. If the prediction shows that the offset of a certain node will cause the distance between the fiber and the power conductor to drop below 0.3 meters within the next 24 hours, a warning message is issued. Ultimately, point cloud misalignment data of the transmission channel morphology is generated, containing detailed information such as misalignment location coordinates, misalignment type, misalignment severity, and future offset prediction, providing accurate basis for power grid line maintenance and fault handling.

[0153] Furthermore, step S4 includes the following steps:

[0154] Step S41: Based on the misalignment data of the power transmission channel morphology point cloud, perform axial structure analysis of the power transmission point cloud attitude of the power grid line to generate axial structure data of the power transmission point cloud attitude.

[0155] In this embodiment of the invention, after acquiring the misalignment data of the transmission channel morphology point cloud, the attitude axis structure of the power grid line's transmission point cloud is analyzed. Taking the tower point cloud as an example, its attitude axis is determined using Principal Component Analysis (PCA). First, the mean of the three-dimensional coordinates of all points in the tower point cloud is calculated, and the point cloud data is translated to a coordinate system with the mean point as the origin. Next, the covariance matrix of the point cloud data is calculated, which reflects the degree of dispersion of the point cloud distribution in various directions. By performing eigenvalue decomposition on the covariance matrix, three eigenvalues ​​and corresponding eigenvectors are obtained. The direction of the eigenvector with the largest eigenvalue is the primary attitude axis of the tower point cloud, usually corresponding to the vertical direction of the tower; the direction of the eigenvector with the second largest eigenvalue is the secondary attitude axis, generally perpendicular to the primary attitude axis and pointing towards the line direction; the direction of the eigenvector with the smallest eigenvalue is the third attitude axis, forming a right-handed coordinate system with the first two axes. For the conductor point cloud, based on its catenary shape, the tangent direction of the curve at each sampling point is calculated as the local attitude axis, and then the overall attitude axis trend of the conductor is determined by statistically analyzing the tangent directions of all sampling points. The attitude axial information of the point cloud of power transmission components such as towers and conductors, including axial direction vectors and starting point coordinates, is integrated to generate power transmission point cloud attitude axial structure data containing the attitude axial structural features of each component.

[0156] Step S42: Analyze the rotation axis angle of the power grid line's transmission point cloud based on the transmission response point cloud offset data, and generate the transmission point cloud rotation axis angle data;

[0157] In this embodiment of the invention, the rotation axis angle of the transmission point cloud is analyzed based on the transmission response point cloud offset data of the power grid line. For the conductor point cloud, a 50-meter-long area with significant offset is selected, and 10 sampling points are selected at equal intervals within this area. Point cloud data within a radius of 0.5 meters is extracted with each sampling point as the center. For the point cloud data of each sampling point, the current moment point cloud data is compared with the initial moment point cloud data using a quaternion-based rotation calculation method. First, the reference coordinate system of the initial moment point cloud data is determined. Using this coordinate system as a reference, the rotation angle of the current moment point cloud relative to the initial moment point cloud on the three rotation axes (X-axis, Y-axis, and Z-axis) is obtained by calculating the rotation parameters of the quaternion. For example, if the calculated rotation angle around the X-axis is 5 degrees, the rotation angle around the Y-axis is -3 degrees, and the rotation angle around the Z-axis is 2 degrees, then the rotation axis angle of that sampling point is recorded. Statistical analysis is performed on the rotation axis angles of all sampling points, and the average value and standard deviation are calculated to eliminate the influence of local noise, thus obtaining the overall rotation axis angle data of the conductor point cloud segment. For the tower point cloud, by comparing the changes in its attitude axis at different times, the rotation angle of the attitude axis in space is calculated, and power transmission point cloud rotation axis angle data containing the rotation axis angle information of each power transmission component is generated.

[0158] Step S43: Based on the transmission point cloud attitude axial structure data and the transmission point cloud rotation axial angle data, design the transmission point cloud matching matrix for the power grid line and generate the transmission point cloud matching matrix data;

[0159] In this embodiment of the invention, firstly, a 4×4 homogeneous transformation matrix framework is established, which is used to describe the translation and rotation transformations of the point cloud in three-dimensional space. Based on the attitude axial direction vectors of each component in the transmission point cloud attitude axial structure data, the element values ​​of the rotation matrix are determined. The direction vectors of the primary attitude axis, secondary attitude axis, and third attitude axis are respectively used as column vectors of the rotation matrix for filling. Then, based on the rotation axis angle data of the transmission point cloud, the rotation matrix is ​​further adjusted. Through trigonometric function calculations, the rotation angles are converted into specific element values ​​of the rotation matrix, achieving a precise description of the rotation transformation of the point cloud. For the translation part, based on the actual offset of each component's point cloud, its translation distance in the X-axis, Y-axis, and Z-axis directions is used as a translation vector and filled into the fourth column of the homogeneous transformation matrix. For example, if a tower's point cloud has translated 2 meters in the X-axis direction, -1 meter in the Y-axis direction, and 0.5 meters in the Z-axis direction, then [2, -1, 0.5, 1]ᵀ is used as the translation vector to fill the matrix. The transformation matrices of all power transmission components are integrated to generate a power transmission point cloud matching matrix data containing the point cloud transformation information of each component. This matrix can accurately describe the spatial transformation relationship between power transmission point clouds at different times.

[0160] Step S44: Based on the power transmission point cloud matching matrix data and the power grid line point cloud hierarchical structure separation data, perform power grid line point cloud registration to generate power grid line point cloud registration data.

[0161] In this embodiment of the invention, power grid line point cloud registration is performed based on transmission line point cloud matching matrix data and power grid line point cloud hierarchical structure separation data. For tower point clouds in the power grid line point cloud hierarchical structure separation data, the tower point cloud data at the current moment is translated and rotated according to the transformation matrix corresponding to the tower in the transmission line point cloud matching matrix data. Specifically, the homogeneous coordinates of each point in the point cloud data are multiplied by the transformation matrix to obtain the transformed point coordinates, thereby adjusting the spatial position and attitude of the tower point cloud. For other component point clouds such as conductor point clouds and insulator point clouds, the same transformation operation is performed according to the transformation matrix corresponding to the matching matrix data. After completing the transformation of all component point clouds, the transformed component point cloud data are merged and unified into the same coordinate system. By calculating the distance error between point clouds, an improved version of the nearest point iteration (ICP) algorithm is used to further optimize the point cloud registration accuracy. During the iteration process, the nearest point pair between the transformed point cloud and the target point cloud is continuously searched, the error is calculated and the transformation matrix is ​​updated until the distance error between the point clouds is less than a set threshold (such as 0.05 meters). Finally, the precisely aligned power grid line point cloud registration data is generated, providing an accurate data foundation for the status analysis and fault diagnosis of power grid lines.

[0162] Furthermore, step S44 includes the following steps:

[0163] Step S441: Based on the point cloud hierarchical structure separation data of the power grid lines, set the transmission matching constraint conditions for the power grid lines and generate transmission matching constraint condition data;

[0164] In this embodiment of the invention, after obtaining the hierarchical structure separation data of the power grid line point cloud, the transmission matching constraint conditions are set. For the tower point cloud, spatial position constraints are set according to its structural characteristics and engineering standards. Taking the center point of the tower bottom as a reference, it is stipulated that the horizontal offset of the center point of the bottom of the same tower in the point cloud data at different times shall not exceed 0.3 meters, and the vertical offset shall not exceed 0.2 meters. This takes into account the minimal range of tower displacement under normal operating conditions and the allowable error in engineering. At the same time, attitude constraints are set, the overall tilt angle change of the tower shall not exceed 3 degrees, which is determined by calculating the angle change between the main attitude axis and the vertical direction of the tower point cloud. For the conductor point cloud, according to its physical characteristics and safe operation requirements, sag constraints are set, under the condition that the change in ambient temperature does not exceed 10°C, the change in conductor sag within the same span shall not exceed 0.5 meters; spacing constraints are set, the horizontal distance deviation between adjacent conductors shall not exceed 0.2 meters, and the vertical distance deviation shall not exceed 0.15 meters. For insulator point clouds, quantity and distribution constraints are set. The number of insulator segments in an insulator string should remain constant, and the spacing deviation between adjacent insulator strings should not exceed 0.1 meters. These constraints, including spatial location, attitude, and physical parameters, set for different levels of point clouds (towers, conductors, insulators, etc.), are summarized to generate transmission matching constraint data containing information such as constraint type and specific numerical range.

[0165] Step S442: Design a multi-level point cloud structure compatibility matrix for power grid lines based on transmission matching constraint data, and generate multi-level point cloud structure compatibility matrix data;

[0166] In this embodiment of the invention, based on the hierarchical structure of the power grid line point cloud, the towers, conductors, insulators, etc., are divided into three layers according to the number of point clouds and geometric complexity: Layer 1 is the tower point cloud (complex structure, number of point clouds > 5000), Layer 2 is the conductor point cloud (linear structure, number of point clouds 1000-5000), and Layer 3 is the insulator point cloud (small components, number of point clouds < 1000). A three-dimensional matrix is ​​then constructed. The third dimension of the matrix This represents the total number of equipment nodes in a transmission line. For any two levels... device nodes and Compatibility is calculated using the following formula: ,in Weighting coefficients (e.g.) Corresponding to the spatial position constraints of the tower, (corresponding to conductor sag constraint), determined by the importance of transmission matching constraint conditions; The three-dimensional Euclidean distance between the geometric centers of the two device nodes is given by: and They are nodes and The coordinates; hierarchical and Safety distance thresholds for equipment (e.g., between towers) Between the tower and the conductor ); To account for the difference in attitude angles between the two devices, principal component analysis is used to calculate the angle between the eigenvectors of the tower point cloud, such as the difference in tower tilt angles. ; This is an indicator function; it takes the value 1 if the condition is met, and 0 otherwise. Taking a two-tower node at level 1 as an example, if the horizontal distance between the bottom center points... And tilt angle difference ,but This indicates high compatibility; however, if the conductor node is not correctly connected to the corresponding tower or the spacing exceeds [the specified value], it indicates high compatibility. ,but By iterating through all device node pairs, if a condition is met, the corresponding matrix element is assigned a value of 1; otherwise, it is assigned a value of 0. Similarly, other layer combinations are evaluated and assigned values. By iterating through the relationships between all layer point clouds, the entire 3D matrix is ​​filled. Simultaneously, weight coefficients are added to the matrix elements, set according to the importance of the constraints, ultimately generating a multi-layered point cloud structure compatibility matrix data containing layer topology relationships, compatibility values, and weight parameters.

[0167] Step S443: Based on the transmission matching constraint data, transmission point cloud matching matrix data, and multi-level point cloud structure compatibility matrix data, perform power grid line point cloud registration to generate power grid line point cloud registration data.

[0168] In this embodiment of the invention, the transformation parameters in the transmission line point cloud matching matrix data are first applied to the power grid line point cloud hierarchical structure separation data to perform preliminary translation and rotation transformations on the point clouds of each component. For the tower point cloud, based on the spatial position constraints in the transmission matching constraint data, the coordinates of the center point at the bottom of the tower after transformation are checked to see if they are within the allowable offset range. If they are outside the range, the transformation matrix is ​​adjusted according to the constraints, and the position of the tower point cloud is gradually corrected using linear interpolation until the constraints are met. For the conductor point cloud, based on the sag and spacing constraints, the differences between the sag and spacing of the conductor after transformation and the standard values ​​are compared. If the sag deviation exceeds 0.2 meters, the vertical position and curve shape of the conductor point cloud are adjusted to meet the sag requirement; if the spacing is not met, the horizontal position of the conductor point cloud is fine-tuned. During the adjustment process, the multi-level point cloud structure compatibility matrix data is referenced to ensure that the adjustment operation does not disrupt the compatibility relationship between different levels of point clouds. For example, when adjusting the position of a tower point cloud, if it leads to a decrease in the compatibility of the connected conductor point cloud with other component point clouds (e.g., the corresponding element in the matrix changes from 1 to 0), the adjustment scheme is re-evaluated. Through continuous iterative adjustments, until all point clouds satisfy both the transmission matching constraints and maintain good hierarchical compatibility, the final accurate registered power grid line point cloud registration data is generated.

[0169] Furthermore, step S5 includes the following steps:

[0170] Step S51: Analyze the power supply demand of the power grid lines based on the power grid line point cloud registration data, and generate power supply demand data for the power grid lines;

[0171] In this embodiment of the invention, after acquiring the power grid line point cloud registration data, a power supply demand analysis of the power grid lines is conducted. First, based on the spatial location and connection relationship of components such as poles and conductors in the point cloud registration data, combined with power topology data, the power supply area of ​​the power grid lines is determined. For each power supply area, the number, type, and rated power of electrical equipment within the area are statistically analyzed. Considering the difference between peak and off-peak electricity consumption, historical electricity consumption data is introduced for correction. Electricity consumption data for different times of day in the past year for this area are retrieved, and it is analyzed that the peak electricity load during peak hours (e.g., 7-9 pm) is 1.5 times the total load, and the off-peak electricity load during off-peak hours (e.g., 2-4 am) is 0.3 times the total load. This electricity load data is combined with geographic information to determine the power supply demand of different areas at different times, generating power grid line power supply demand data containing information such as the coordinate range of the power supply area, electrical equipment parameters, peak load, and off-peak load.

[0172] Step S52: Perform power distribution time-link analysis on the power grid lines based on the power supply demand data of the power grid lines to generate power distribution time-link data of the power grid lines;

[0173] In this embodiment of the invention, a power distribution time-link analysis of the power grid lines is performed based on the power supply demand data. Using peak and off-peak periods in the power demand data as time nodes, a day is divided into six time periods (0-4 AM, 4-8 AM, 8-12 PM, 12-4 PM, 4-8 PM, and 8-12 AM). For each time period, the power distribution link from the substation to each power consumption area is analyzed based on the power load of the power supply area and the power grid line topology. Taking a certain transmission line as an example, this line starts from substation A, passes through 3 towers, and supplies power to residential area B and commercial area C. During the peak power consumption period (4-8 PM), the power load of residential area B is 800 kW, and the power load of commercial area C is 1200 kW. Based on the conductor specifications (e.g., aluminum stranded wire with a cross-sectional area of ​​240 square millimeters) and length (2 kilometers from substation A to residential area B, and 3 kilometers to commercial area C), the current, voltage drop, and power loss of each segment of the line are calculated using Ohm's law and Joule's law. For example, the line from substation A to the first tower is calculated to have a current of 800 amperes, a voltage drop of 20 volts, and a power loss of 16 kilowatts. Simultaneously, considering the operating status and capacity limitations of power distribution equipment (such as transformers and circuit breakers), the power distribution sequence and priority on the power distribution link are determined for each time period.

[0174] Step S53: Based on the power supply demand data and the power distribution time sequence-link data of the power grid lines, perform power transmission conversion matrix analysis on the power grid lines to generate power transmission conversion matrix data of the power grid lines;

[0175] In this embodiment of the invention, a two-dimensional matrix framework is constructed, where rows represent different power supply areas and columns represent different power distribution time periods. Matrix elements are used to represent the power transmission relationships and conversion parameters between power supply areas within a specific time period. Taking three power supply areas (area 1, area 2, and area 3) and six power distribution time periods as an example, in time period 1 (0-4 o'clock), area 1 transmits 50 kW of power to area 2 and 30 kW of power to area 3, while there is no power transmission between area 2 and area 3. Therefore, in the matrix, the element corresponding to the intersection of the row of area 1 and time period 1 and the column of area 2 and time period 1 is assigned a value of 50, the element corresponding to the intersection of the row of area 1 and time period 1 and the column of area 3 and time period 1 is assigned a value of 30, and other corresponding elements are assigned a value of 0. For each matrix element, combining the line parameters in the power distribution time sequence-link data and the power load in the power supply demand data, the transmission parameters such as voltage drop and power loss corresponding to that element are calculated using the power system power flow calculation method, and these parameters are then appended to the matrix element. By traversing all power supply areas and distribution periods, the entire power transmission conversion matrix is ​​filled, generating power grid line power transmission conversion matrix data containing information such as power transmission relationships and transmission parameters.

[0176] Step S54: Based on the power transmission conversion matrix data of the power grid lines, perform pose error correction between multiple frames of power grid line point cloud registration data to generate pose error corrected power grid line point cloud registration data.

[0177] In this embodiment of the invention, the power transmission relationships and parameters in the power transmission conversion matrix data are combined with the positions of components such as towers and conductors in the point cloud registration data. Since the electrodynamic force generated during power transmission affects the line components, it causes slight changes in the point cloud pose. Taking a certain tower as an example, according to the power transmission conversion matrix data, during peak electricity consumption hours (4-8 PM), the electrodynamic force generated by the conductors connected to this tower is 50 Newtons, directed horizontally to the right. Using mechanical analysis methods, the electrodynamic force is treated as an external force acting on the mechanical model of the tower point cloud. Through finite element analysis, it is found that the tower point cloud will produce a displacement of 0.05 meters in the horizontal direction and 0.01 meters in the vertical direction. For multiple frames of point cloud data, the position of the tower point cloud in different frames is compared. If there is a deviation between the actual displacement and the calculated displacement, the deviation correction parameter is calculated using the least squares fitting method. If the actual horizontal displacement of the tower point cloud in the current frame is 0.06 meters, which differs from the calculated displacement by 0.01 meters, then the tower point cloud is moved 0.01 meters in the opposite horizontal direction by adjusting the translation matrix of the point cloud. For the conductor point cloud, the curve shape and position of the conductor point cloud are adjusted according to the sag and tension changes caused by electrodynamic forces. Through continuous iterative calculation and adjustment, the pose of each component point cloud in the multi-frame point cloud data is matched with the physical changes caused by power transmission in the power transmission conversion matrix data, ultimately generating the grid line point cloud registration data after pose error correction.

[0178] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0179] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for automatic registration of power grid line point clouds, characterized in that, The method comprises the following steps: Step S1: obtaining power grid line three-dimensional point cloud data; performing power grid line point cloud hierarchical structure separation on the power grid line three-dimensional point cloud data to generate power grid line point cloud hierarchical structure separation data; Step S2: performing power transmission response point cloud offset detection on the power grid line according to the power grid line point cloud hierarchical structure separation data to generate power transmission response point cloud offset data of the power grid line; Step S3: performing power transmission channel form point cloud dislocation identification on the power grid line according to the power transmission response point cloud offset data of the power grid line to generate power transmission channel form point cloud dislocation data; Step S4: performing power grid line point cloud registration based on the power transmission channel form point cloud dislocation data, the power transmission response point cloud offset data of the power grid line, and the power grid line point cloud hierarchical structure separation data to generate power grid line point cloud registration data; Step S5: performing pose error correction between multiple frames of power grid line point cloud according to the power grid line point cloud registration data to generate power grid line point cloud registration data after pose error correction.

2. The power grid line point cloud automatic registration method according to claim 1, characterized in that, Step S1 comprises the following steps: Step S11: obtaining power grid line three-dimensional point cloud data; Step S12: performing power topology structure analysis on the power grid line according to the power grid line three-dimensional point cloud data to generate power topology structure data; Step S13: performing structure feature analysis on the power grid line according to the power topology structure data to generate power grid line structure feature data; Step S14: performing power grid line point cloud hierarchical structure separation on the power grid line three-dimensional point cloud data based on the power grid line structure feature data to generate power grid line point cloud hierarchical structure separation data.

3. The power grid line point cloud automatic registration method according to claim 1, characterized in that, Step S2 comprises the following steps: Step S21: performing electrical parameter extraction on the power grid line according to the power grid line point cloud hierarchical structure separation data to generate electrical parameter data of the power grid line; Step S22: performing electric field distribution state detection on the power grid line according to the electrical parameter data of the power grid line to generate electric field distribution state data; Step S23: performing electric field sensitivity analysis on the power grid line according to the electric field distribution state data to generate electric field sensitivity data; Step S24: performing electric field response shock evaluation on the power grid line according to the electric field sensitivity data to generate electric field response shock data; Step S25: performing power transmission response point cloud offset detection on the power grid line according to the electric field response shock data to generate power transmission response point cloud offset data of the power grid line.

4. The power grid line point cloud automatic registration method according to claim 1, characterized in that, Step S3 comprises the following steps: Step S31: performing fiber-optic power layout analysis on the power grid line according to the power transmission response point cloud offset data of the power grid line to generate fiber-optic power layout data of the power grid line; Step S32: performing fiber-optic power hybrid channel collaborative transmission on the power grid line according to the fiber-optic power layout data of the power grid line to generate fiber-optic power hybrid channel collaborative transmission data of the power grid line; Step S33: performing power transmission channel deviation detection on the power grid line according to the fiber-optic power hybrid channel collaborative transmission data of the power grid line to generate power transmission channel deviation data of the power grid line; Step S34: performing power transmission channel form point cloud dislocation identification on the power grid line according to the power transmission channel deviation data of the power grid line to generate power transmission channel form point cloud dislocation data.

5. The power grid line point cloud automatic registration method according to claim 4, characterized in that, Step S32 comprises the following steps: Step S321: Fiber laying channel identification of the power grid line is performed according to the fiber-power layout data of the power grid line, and fiber laying channel data is generated; Step S322: Fiber channel candidate section setting of the power grid line is performed according to the fiber laying channel data, and fiber channel candidate section data is generated; Step S323: Wire network collaborative path analysis of the power grid line is performed according to the fiber channel candidate section data, and wire network collaborative path data is generated; Step S324: Fiber-power composite link planning of the power grid line is performed according to the wire network collaborative path data, and fiber-power composite link planning data is generated; Step S325: Fiber-power hybrid channel collaborative transmission of the power grid line is performed based on the fiber-power composite link planning data and the wire network collaborative path data, and fiber-power hybrid channel collaborative transmission data of the power grid line is generated.

6. The power grid line point cloud automatic registration method according to claim 5, characterized in that, Step S34 includes the following steps: Step S341: Fiber-power link spatial configuration analysis of the power grid line is performed according to the fiber-power composite link planning data, and fiber-power link spatial configuration data is generated; Step S342: Power transmission path node integration of the power grid line is performed according to the fiber-power link spatial configuration data, and path node integration data of the power grid line is generated; Step S343: Link connection relationship analysis of the power grid line is performed according to the path node integration data of the power grid line, and link connection relationship data of the power grid line is generated; Step S344: Channel linkage response identification of the power grid line is performed according to the link connection relationship data of the power grid line, and channel linkage response data of the power grid line is generated; Step S345: Power transmission channel form point cloud dislocation identification of the power grid line is performed based on the channel linkage response data of the power grid line, the power transmission channel deviation data of the power grid line, and the fiber-power link spatial configuration data, and power transmission channel form point cloud dislocation data is generated.

7. The power grid line point cloud automatic registration method according to claim 6, characterized in that, Step S345 includes the following steps: Channel point cloud node form extraction of the power grid line is performed according to the channel linkage response data of the power grid line, and channel point cloud node form data is generated; Point cloud form structure deviation evaluation of the power grid line is performed on the channel point cloud node form data based on the power transmission channel deviation data of the power grid line, and point cloud form structure deviation data is generated; Point cloud form offset feature analysis of the power grid line is performed according to the point cloud form structure deviation data, and point cloud form offset feature data is generated; Power transmission channel form point cloud dislocation identification of the power grid line is performed on the fiber-power link spatial configuration data based on the point cloud form offset feature data, and power transmission channel form point cloud dislocation data is generated.

8. The power grid line point cloud automatic registration method of claim 1, wherein, Step S4 includes the following steps: Step S41: Power transmission point cloud posture axial structure analysis of the power grid line is performed according to the power transmission channel form point cloud dislocation data, and power transmission point cloud posture axial structure data is generated; Power transmission point cloud rotation axial angle analysis of the power grid line is performed according to the power transmission response point cloud offset data of the power grid line, and power transmission point cloud rotation axial angle data is generated; Step S43: Power transmission point cloud matching matrix design of the power grid line is performed based on the power transmission point cloud posture axis structure data and the power transmission point cloud rotation axis angle data, and power transmission point cloud matching matrix data is generated; Step S44: Power grid line point cloud registration is performed based on the power transmission point cloud matching matrix data and the power grid line point cloud hierarchical structure separation data, and power grid line point cloud registration data is generated.

9. The power grid line point cloud automatic registration method according to claim 8, characterized in that, Step S44 includes the following steps: Step S441: Power transmission matching constraint condition setting of the power grid line is performed according to the power grid line point cloud hierarchical structure separation data, and power transmission matching constraint condition data is generated; Step S442: Multi-level point cloud structure compatibility matrix design of the power grid line is performed according to the power transmission matching constraint condition data, and multi-level point cloud structure compatibility matrix data is generated; Step S443: Power grid line point cloud registration is performed based on the power transmission matching constraint condition data, the power transmission point cloud matching matrix data, and the multi-level point cloud structure compatibility matrix data, and power grid line point cloud registration data is generated.

10. The power grid line point cloud automatic registration method of claim 1, wherein, Step S5 includes the following steps: Step S51: Power supply demand analysis of the power grid line is performed according to the power grid line point cloud registration data, and power supply demand data of the power grid line is generated; Step S52: Power distribution timing-link analysis of the power grid line is performed according to the power supply demand data of the power grid line, and power distribution timing-link data of the power grid line is generated; Step S53: Power transmission conversion matrix analysis of the power grid line is performed based on the power supply demand data of the power grid line and the power distribution timing-link data of the power grid line, and power transmission conversion matrix data of the power grid line is generated; Step S54: Pose error correction between multi-frame power grid line point clouds is performed on the power grid line point cloud registration data based on the power transmission conversion matrix data of the power grid line, and power grid line point cloud registration data after pose error correction is generated.

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