Method for identifying tower structure defects and three-dimensional spatial positioning based on multi-source fusion

Abstract

The present invention discloses a method for identifying defects in a pole tower structure and for three-dimensional spatial positioning based on multi-source fusion, comprising the following steps: constructing multi-dimensional acquisition data; unifying the multi-dimensional data into the same coordinate system; establishing an enhanced data set based on the multi-dimensional data after the unified coordinate system; constructing a point cloud model of an independent single pole tower and reverse modeling to form a vectorized single pole tower three-dimensional DXF format model; using a projection dimensionality reduction comparison method to detect and analyze the differences between the vectorized single pole tower three-dimensional DXF format model and the manually designed pole tower three-dimensional DXF format model; outputting the spatial positions of the pole tower structure differences found during the matching process, and mapping and associating them with the enhanced data set to realize the identification and positioning of the pole tower structure defects; the present invention breaks through the subjectivity of traditional methods and the one-sidedness of single-source data, and constructs a full-process automation system of "data fusion-reverse modeling-intelligent comparison-visual positioning", thereby realizing the accurate diagnosis of three-dimensional deviations of the pole tower before operation.

Description

Technical Field

[0001] The present invention relates to the technical field of target recognition and positioning, and specifically to a method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion. Background Art

[0002] With the rapid development of ultra-high voltage power transmission networks and smart grids, the efficient identification and accurate three-dimensional positioning of tower structure defects have become the core challenges for ensuring the reliable operation of the power system. However, traditional detection methods and existing technical systems face multiple bottlenecks under complex working conditions. Currently, the detection is still mainly dominated by manual inspection, which has defects such as low efficiency (the efficiency of conventional inspection is less than 3 towers per day), high risk, and large omission rate (it is impossible to perform three-dimensional tower structure detection). Although multi-sensor collaborative acquisition can improve the integrity of information, the spatio-temporal alignment of heterogeneous data and the excavation of feature complementarity are still technical difficulties. Since the current identification based solely on images cannot effectively identify defects in tower structure characteristics and lacks an adaptive fusion algorithm for tower structure characteristics, it seriously affects the quality of defect identification. The development trend of the industry's technology focuses on multi-sensor fusion, in-depth application of artificial intelligence, and high-precision three-dimensional reconstruction. It is urgent to break through key technologies such as deep fusion of multi-source data, highly robust defect identification models, and centimeter-level three-dimensional positioning, and build an intelligent solution for the entire "detection-identification-positioning" chain. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides a method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion. The purpose is to overcome technical bottlenecks through multi-source data fusion and intelligent algorithms, promote the upgrade of power facility operation and maintenance towards precision and unmanned operation, and provide core support for the safe operation of ultra-high voltage power grids.

[0004] To achieve the above object, the present invention provides the following technical solution: A method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion, characterized by including the following steps:

[0005] Step S1: Use a drone carrying sensors to perform an all-round scan of the tower to construct multi-dimensional acquisition data, where the multi-dimensional acquisition data includes tower point cloud, tower visible light image, and tower panoramic image;

[0006] Step S2: Remove the scattered points and isolated points in the tower point cloud, simplify the tower point cloud, and unify the simplified tower point cloud, tower visible light image, and tower panoramic image into the same coordinate system;

[0007] Step S3: Perform spherical projection de - distortion on the panoramic image of the pole tower after unifying the coordinate system, and calculate the mapping relationship from the spherical longitude - latitude coordinates to the pixel coordinates of the de - distorted image; capture the panoramic images of the four sides of the pole tower, divide each panoramic image into pixel blocks, and store the mapping relationship between the pixel coordinates of the pixel blocks and the spherical longitude - latitude coordinates to the pixel coordinates of the de - distorted image.

[0008] Step S4: Use an adaptive clustering method to extract features of the pole tower to form an independent single - body pole - tower point cloud model.

[0009] Step S5: Reverse - model the extracted independent single - body pole - tower point cloud model to form a vectorized single - body pole - tower 3D DXF - format model.

[0010] Step S6: Use the projection dimensionality reduction comparison method to detect and analyze the differences between the vectorized single - body pole - tower 3D DXF - format model and the artificial - design pole - tower 3D DXF - format model.

[0011] Step S7: Output the spatial positions of the pole - tower structure differences found during the matching process, and map - associate them with the mapping relationship between the pixel coordinates of the pixel blocks and the spherical longitude - latitude coordinates to the pixel coordinates of the de - distorted image in Step S3, so as to realize the defect identification and positioning of the pole - tower structure.

[0012] Furthermore, the specific process of Step S3 is as follows:

[0013] Step S3.1: Based on the internal parameters of the panoramic camera for collecting the panoramic image of the pole tower, perform spherical projection transformation on the panoramic image of the pole tower after unifying the coordinate system to eliminate lens distortion and generate an undistorted equidistant cylindrical projection image of the pole tower.

[0014] Among them, the internal parameters of the panoramic camera include: the focal length of the panoramic camera on the axis and the focal length on the axis, the principal - point coordinates and the distortion coefficients , , , ; represents the quadratic - term coefficient of radial distortion, represents the quartic - term coefficient of radial distortion, , both represent the parameters of tangential distortion;

[0015] Step S3.2: Calculate the mapping relationship from the spherical longitude - latitude coordinates to the pixel coordinates of the equidistant cylindrical projection image of the pole tower, expressed as:

[0016] ;

[0017] ;

[0018] In the formula, represents the pixel coordinates of the equidistant columnar projection image of the pole tower; represents the spherical longitude and latitude coordinates; represents the principal point coordinates; and respectively represent the focal lengths of the panoramic camera on the axis and axis ;

[0019] Step S3.3: Set observation points on the four sides of the pole tower, take panoramic images, divide the panoramic image of each observation point into pixel blocks, and store the mapping relationship between the pixel coordinates of the pixel blocks and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image at the same time.

[0020] Furthermore, the specific process of step S4 is as follows:

[0021] Step S4.1: Collect the point cloud of the single pole tower to be processed through the sensor carried by the UAV;

[0022] Step S4.2: Separate the ground point cloud and the non-ground point cloud of the point cloud of the single pole tower to be processed. Specifically:

[0023] Generate an elevation constraint surface through a simulation filtering algorithm, combined with the elevation information of the point cloud of the single pole tower:

[0024] ;

[0025] In the formula, represents the elevation value of the elevation constraint surface at the position ; and respectively represent the second-order partial derivatives of the elevation in the , directions; represents the gradient of the elevation ; represents the preset slope threshold;

[0026] Remove the ground point cloud less than the preset distance, and output the non-ground point cloud belonging to the pole tower structure, expressed as:

[0027] ;

[0028] In the formula, represents the th non-ground point cloud point obtained after screening; represents the The elevation value of a point; Indicates the elevation value at the horizontal position of the th point of the single-tower point cloud on the elevation constraint surface; ;

[0029] Step S4.3: Calculate the average distance from the th non-ground point cloud point to other points within its own neighborhood:

[0030] ;

[0031] In the formula, indicates the average distance from to other points within its own neighborhood; indicates 's neighborhood, that is, the set of all non-ground point cloud points within the sphere with as the center and a radius of ; indicates the preset neighborhood radius; indicates the number of non-ground point cloud points within 's neighborhood; indicates the

[0032] th point in ; [[ID=A]] Step S4.4: Based on , calculate the average distance

[0033] of each point in the non-ground point cloud and the standard deviation :

[0034] ;

[0035] In the formula, indicates the number of points in the non-ground point cloud;

[0036] Step S4.5: Based on the average distance and the standard deviation calculate the adaptive threshold, and use the adaptive threshold to screen the non-ground point cloud to obtain candidate clustering point clouds similar to the tower structure characteristics; the adaptive threshold is expressed as:

[0037] ;

[0038] In the formula, indicates the set coefficient used to adjust the adaptive threshold Size;

[0039] Step S4.6: Calculate the covariance matrix of the candidate clustering point cloud And perform eigenvalue decomposition:

[0040] ;

[0041] [[ID=1...]];

[0042] In the formula, Represents the center point of the candidate clustering point cloud; Represents the transpose; Represents the th eigenvector of the covariance matrix; Represents the th eigenvalue of the candidate clustering point cloud;

[0043] Step S4.7: When Is less than the preset value, it is determined that the corresponding candidate clustering point cloud meets the eigenvalue and is retained. Otherwise, it is marked as a candidate clustering point cloud that does not meet the eigenvalue; when there is a candidate clustering point cloud that does not meet the eigenvalue, adjust Value and repeat steps S4.3 - S4.5;

[0044] Step S4.8: Merge all candidate clustering point clouds that meet the eigenvalue to form an independent single - body pole and tower point cloud model.

[0045] Furthermore, the specific process of step S5 is as follows:

[0046] Step S5.1: Identify the feature edges of the independent single - body pole and tower point cloud model;

[0047] For the edges of the independent single - body pole and tower point cloud model Perform single - adjacent - edge screening, Represent the two vertices of the independent single - body pole and tower point cloud model respectively. When The number of adjacent edges of And it is located at the end of the rod, it is determined as the section boundary edge; at the same time, determine the feature edges of the gusset plate of the independent single - body pole and tower point cloud model:

[0048] ;

[0049] In the formula, Represents the angle between two normal vectors; , Represent the normal vectors of two adjacent edges of the independent single - body pole and tower point cloud model respectively; Represents the arccosine function;

[0050] When , it is determined that is the characteristic edge of the gusset plate;

[0051] Step S5.2: Construct the contour of the point cloud model of the independent single-tower pole;

[0052] For the point cloud at the end of the pole of the point cloud model of the independent single-tower pole perform plane fitting , and optimize the objective:

[0053] ;

[0054] In the formula, represents the plane equation of the fitting; are all coefficients of the plane equation; represents a point in , with coordinates

[0055] Step S5.3: Construct the axis of the pole of the point cloud model of the independent single-tower pole;

[0056] Extract the axis of the pole of the point cloud model of the independent single-tower pole according to the axis equation : , represents any point on the axis of the pole; represents the midpoint of the axis of the pole; represents the distance along the axis direction; represents the direction vector of the axis;

[0057] Step S5.4: Construct the cross-section of the pole of the point cloud model of the independent single-tower pole;

[0058] Calculate the maximum distance between the contour vertices of the pole of the point cloud model of the independent single-tower pole in the cross-section plane:

[0059] ;

[0060] In the formula, represents the maximum distance between the contour vertices of the pole in the cross-section plane; represents the th vertex on the contour of the pole, represents the th vertex on the contour of the pole, ;

[0061] Calculate the minimum distance from the contour vertices of the pole to the opposite side:

[0062] ;

[0063] In the formula, represents the edge thickness of the pole, that is, the minimum distance from the contour vertices of the pole to the opposite side; Indicates the vertex of the rod profile

[0064] Step S5.5: Construct the gusset plate of the single-tower point cloud model

[0065] Based on the gusset plate feature profile point cloud Determine the reference point , and calculate except for among the other points relative to the polar angle , expressed as:

[0066] ;

[0067] ;

[0068] ;

[0069] In the formula, represents the th point in ; represents the total number of points in ; represents the coordinate component of the standard two-dimensional arctangent function, used to calculate the angle of point relative to the reference point ; represents the coordinate difference of on the axis; represents the coordinate difference of on the axis; represents the coordinate difference of on the axis;

[0070] Sort in ascending order of the polar angle . When >the corresponding polar angles of two points in are the same, then sort them in ascending order of the distance between these two points and the reference point . After traversing all the points in , connect the remaining points to form the gusset plate of the single-tower point cloud model

[0071] Step S5.6: Perform vector modeling of the connection relationship of tower components through graph theory

[0072] Establish the component connection diagram , the vertex set consists of bar vertices and gusset plate vertices ; the edge set consists of the coplanar connection constraint set and the coaxial connection constraint set ;

[0073] ;

[0074] ;

[0075] In the formula, represents the direction vector of the bar; represents the normal vector of the gusset plate; respectively represent the vertices of the first bar and the vertices of the second bar; respectively represent the direction vector of the first bar and the direction vector of the second bar;

[0076] Step S5.7: Output the vectorized single-tower three-dimensional DXF format model;

[0077] Store the bar axis of the independent single-tower point cloud model, the bar cross-section of the independent single-tower point cloud model, and the gusset plate of the single-tower point cloud model as entities to establish a vectorized single-tower three-dimensional DXF format model.

[0078] Furthermore, the specific process of step S6 is as follows:

[0079] Step S6.1: Transform the vectorized single-tower DXF model to the coordinate system consistent with the designed output tower DXF model through the transformation matrix;

[0080] Take the point coordinates in the manually designed tower three-dimensional DXF format model as , represents 's coordinate vector, represents the transpose;

[0081] Take the point coordinates in the vectorized single-tower three-dimensional DXF format model as , represents 's coordinate vector;

[0082] Through the rotation matrix and the translation vector transform the vectorized single-tower three-dimensional DXF format model so that the point coordinates of the transformed vectorized single-tower three-dimensional DXF format model In the same coordinate system as the point coordinates of the 3D DXF format model of the manually designed pole tower:

[0083] ;

[0084] Let be the point set of the 3D DXF format model of the manually designed pole tower, represent the th point in the point set of the 3D DXF format model of the manually designed pole tower, represent the number of points in the point set of the 3D DXF format model of the manually designed pole tower;

[0085] Let be the point set of the 3D DXF format model of the vectorized single pole tower, represent the th point in the 3D DXF format model of the vectorized single pole tower, represent the number of points in the point set of the 3D DXF format model of the vectorized single pole tower;

[0086] For each , find such that is minimized, and the objective function to be minimized is:

[0087] ;

[0088] In the formula, represents and the square of the Euclidean distance between the closest point in the DXF model of the vectorized single pole tower;

[0089] Continuously iterate and update the rotation matrix and the translation vector until the objective function converges to the set threshold condition;

[0090] Output the point set of the transformed 3D DXF format model of the vectorized single pole tower. At this time, the 3D DXF format model of the vectorized single pole tower and the 3D DXF format model of the manually designed pole tower are in the same coordinate system;

[0091] Step S6.2: Project the points of the DXF model of the vectorized single pole tower and the 3D DXF format model of the manually designed pole tower onto three mutually perpendicular two-dimensional planes;

[0092] Step S6.3: On the projected two-dimensional plane, extract the two endpoints of the line segments representing the poles in the vectorized single-pole DXF model and the three-dimensional DXF format model of the manually designed pole respectively, and extract the polygons representing the gusset plates in the vectorized single-pole DXF model and the three-dimensional DXF format model of the manually designed pole respectively.

[0093] Step S6.4: Perform two-dimensional geometric matching on the extracted line segments of the poles.

[0094] Compare the line segments of the poles in the three-dimensional DXF format model of the manually designed pole and the projected vectorized single-pole three-dimensional DXF format model, and calculate the average value of the distances between the line segments of the poles. and determine whether they match.

[0095] ;

[0096] In the formula, , represent the two endpoints of , represent the two endpoints of represents the line segment of the pole in the projected three-dimensional DXF format model of the manually designed pole; represents the line segment of the pole in the projected vectorized single-pole three-dimensional DXF format model.

[0097] When is less than the set threshold , then it is determined that , match;

[0098] Step S6.5: Perform two-dimensional geometric matching on the extracted gusset plates.

[0099] Compare the polygons in the three-dimensional DXF format model of the manually designed pole and the projected vectorized single-pole three-dimensional DXF format model, and calculate the average value of the distances between the corresponding vertices. and determine whether they match.

[0100] ;

[0101] In the formula, , respectively represent and the th vertex of respectively represent the polygons in the projected three-dimensional DXF format model of the manually designed pole and the projected vectorized single-pole three-dimensional DXF format model; represents or the number of vertices of

[0102] When is less than the set threshold , it is determined that and match.

[0103] Furthermore, the specific process of step S7 is as follows: When a certain geometric element in the three-dimensional DXF format model of the manually designed pole tower, i.e., a rod segment or a polygon, has no matching geometric element in the projection of the vectorized single pole tower three-dimensional DXF format model, record the information of these missing geometric elements and their positions in the three-dimensional DXF format model of the manually designed pole tower, and associate and correspond them with the mapping relationship between the pixel coordinates of the pixel block in step S3.3 and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image; When the difference in the size parameters of the matching geometric elements between the three-dimensional DXF format model of the manually designed pole tower and the vectorized single pole tower three-dimensional DXF format model exceeds the set threshold, record the information of these geometric elements with inconsistent sizes and the size difference values, and associate and correspond them with the mapping relationship between the pixel coordinates of the pixel block in step S3.3 and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image, so as to realize the defect identification and positioning of the pole tower structure.

[0104] Furthermore, the specific process of unifying the simplified pole tower point cloud, the pole tower visible light image, and the pole tower panoramic image into the same coordinate system is as follows:

[0105] Make the world coordinate system of the simplified pole tower point cloud and the coordinate system of the high-definition camera in the external parameters of the pole tower visible light image satisfy orthogonality through a rotation matrix, so as to realize the unification of the coordinate systems;

[0106] Perform a spherical projection transformation on the pole tower panoramic image, establish a mapping relationship between the pole tower panoramic image and the simplified pole tower point cloud, so as to realize the unification of the coordinate systems.

[0107] An electronic device includes a processor, a memory, and a bus. The processor and the memory are connected through the bus. Among them, the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory to execute the method for identifying pole tower structure defects and three-dimensional space positioning based on multi-source fusion.

[0108] A non-volatile computer storage medium stores computer-executable instructions, and these computer-executable instructions execute the method for identifying pole tower structure defects and three-dimensional space positioning based on multi-source fusion.

[0109] Compared with the existing technologies, the present invention has the following beneficial effects: Through the multi-source fusion of point cloud, visible light, panoramic image, and design data, the present invention realizes the lightweight reverse vector construction of the pole tower. By comparing with the artificial pole tower model, the defect visualization positioning of the pole tower is completed. The present invention breaks through the subjectivity of traditional manual detection and the one-sidedness of single-source data, constructs a full-process automation system of "data fusion - reverse modeling - intelligent comparison - visualization positioning", realizes the accurate diagnosis of three-dimensional deviation before the operation of the pole tower, and provides a new technical solution for the digital delivery and full-life cycle management of transmission facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0111] As Figure 1 shown, the present invention provides a technical solution: A method for identifying structural defects of a pole tower and three-dimensional space positioning based on multi-source fusion, including the following steps:

[0112] Step S1: Use a drone equipped with sensors to perform an all-round scan of the pole tower to construct multi-dimensional acquisition data, where the multi-dimensional acquisition data includes pole tower point cloud, pole tower visible light image, and pole tower panoramic image.

[0113] Among them, the sensors carried by the drone include a three-dimensional laser scanner, a 20-megapixel high-definition camera, and a dual-fisheye lens panoramic camera.

[0114] Among them, the three-dimensional laser scanner obtains the pole tower point cloud with millimeter-level accuracy at an angular resolution of 0.1°; the high-definition camera synchronously captures the pole tower visible light image with a ground resolution of 0.5 cm; the dual-fisheye lens panoramic camera (including horizontal 360° and pitch ±60° coverage) generates the pole tower panoramic image and performs real-time positioning through RTK technology.

[0115] Establish a sensor coordinate system transformation matrix, and calculate the space propagation delay by calibrating the position offset of each sensor (three-dimensional laser scanner, 20-megapixel high-definition camera, dual-fisheye lens panoramic camera) relative to the inertial measurement unit of the drone to ensure the consistency of the multi-dimensional acquisition data collected by the sensors in time and space, expressed as:

[0116] (1);

[0117] In the formula, represents the time delay, that is, the time required for the signal to travel from the sensor to the inertial measurement unit of the drone; represents the electromagnetic wave speed; , , respectively represent at , , The position offset on the coordinate axis, that is, the position change of the sensor relative to the inertial measurement unit of the UAV in three-dimensional space.

[0118] Step S2: Remove the hash points and isolated points in the tower point cloud, simplify the tower point cloud, and unify the simplified tower point cloud, tower visible light image, and tower panoramic image into the same coordinate system.

[0119] Among them, the specific process of unifying the simplified tower point cloud, tower visible light image, and tower panoramic image into the same coordinate system is as follows:

[0120] Make the world coordinate system of the simplified tower point cloud and the coordinate system of the high-definition camera in the external parameters of the tower visible light image satisfy orthogonality through the rotation matrix to achieve coordinate system unification, which is expressed as:

[0121] (2);

[0122] In the formula, represents the rotation matrix, which is a 3×3 matrix used to describe the rotation operation in three-dimensional space; represents the transpose of; represents the identity matrix.

[0123] Perform a spherical projection transformation on the tower panoramic image, establish a mapping relationship between the tower panoramic image and the simplified tower point cloud, and achieve coordinate system unification.

[0124] Step S3: Perform spherical projection de-distortion on the tower panoramic image in the unified coordinate system, calculate the mapping relationship from the spherical longitude and latitude coordinates to the pixel coordinates of the de-distorted image; take panoramic images of the four sides of the tower, divide each panoramic image into pixel blocks, and store the mapping relationship between the pixel coordinates of the pixel blocks and the spherical longitude and latitude coordinates to the pixel coordinates of the de-distorted image.

[0125] The specific process of Step S3 is as follows:

[0126] Step S3.1: Based on the internal parameters of the panoramic camera, perform a spherical projection transformation on the tower panoramic image in the unified coordinate system to eliminate lens distortion and generate a distortion-free equidistant cylindrical projection image of the tower.

[0127] Among them, the internal parameters of the panoramic camera include: the focal length of the panoramic camera on the axis and the focal length on the axis, the principal point coordinates , , , ; represents the quadratic term coefficient of radial distortion, represents the quartic term coefficient of radial distortion, , respectively represent different parameters of tangential distortion, which are used to correct the geometric distortion caused by tangential distortion in the image.

[0128] Step S3.2: Calculate the mapping relationship from spherical longitude and latitude coordinates to pixel coordinates of the equal-distance cylindrical projection image of the tower pole, which is expressed as:

[0129] (3);

[0130] (4);

[0131] In the formula, represents the pixel coordinates of the equal-distance cylindrical projection image of the tower pole; represents the spherical longitude and latitude coordinates; represents the principal point coordinates; and respectively represent the focal lengths of the panoramic camera on the axis and axis .

[0132] Step S3.3: Set observation points on the four faces of the tower pole and take panoramic images. Cut each panoramic image of the observation point into 256×256 pixel blocks, and at the same time store the mapping relationship between the pixel coordinates of the pixel block and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image.

[0133] Step S4: Use the adaptive clustering method to extract the features of the tower pole to form an independent single-tower pole point cloud model.

[0134] The specific process of Step S4 is as follows:

[0135] Step S4.1: Collect the point cloud of the single tower pole to be processed through the sensor (3D laser scanner) carried by the UAV.

[0136] Step S4.2: Separate the ground point cloud and non-ground point cloud of the single tower pole to be processed. Specifically:

[0137] Generate an elevation constraint surface through the simulated filtering (CSF) algorithm, combined with the elevation information of the single tower pole point cloud:

[0138] (5);

[0139] In the formula, represents the elevation value of the elevation constraint surface at the position ; and respectively represent the second-order partial derivatives in the elevation in the and directions, which are used to measure the curvature of the elevation surface in the corresponding directions; represents the gradient of the elevation ; represents a preset slope threshold, which is used to limit the maximum slope of the generated elevation constraint surface.

[0140] Remove the ground point clouds with a distance less than the preset distance (0.5 m), and output the non-ground point clouds belonging to the tower structure, which is expressed as:

[0141] (6);

[0142] In the formula, represents the th non-ground point cloud point obtained after screening; represents the elevation value of the th point of the single-tower point cloud; represents the elevation value at the horizontal position of the th point of the elevation constraint surface in the single-tower point cloud ;

[0143] The ground point clouds occupy a large proportion. Filtering the ground point clouds can reduce the complexity of subsequent extraction.

[0144] Step S4.3: Calculate the average distance from the th non-ground point cloud point to other points within its own neighborhood:

[0145] (7);

[0146] In the formula, represents the average distance from to other points within its own neighborhood; represents the neighborhood of , that is, the set of all non-ground point cloud points within a sphere with as the center and a radius of ; represents the preset neighborhood radius, and in this embodiment ; represents the number of non-ground point cloud points within the neighborhood of ; represents the

[0147] Step S4.4: Based on , calculate the average distance of each point of the non-ground point cloud and the standard deviation :

[0148] (8);

[0149] (9);

[0150] In the formula, represents the number of points in the non-ground point cloud.

[0151] Step S4.5: Based on the average distance and the standard deviation calculate the adaptive threshold. Through the adaptive threshold screen the non-ground point cloud (for example, if it exceeds , then mark the corresponding point as a noise point or an abnormal point and eliminate it), and obtain the candidate clustering point cloud similar to the tower structure characteristics; the adaptive threshold is expressed as:

[0152] (10);

[0153] In the formula, represents the set coefficient, which is used to adjust the size of the adaptive threshold . In this embodiment, setting can cover approximately 99% of the normally distributed points.

[0154] Step S4.6: Calculate the covariance matrix of the candidate clustering point cloud and perform eigenvalue decomposition:

[0155] (11);

[0156] (12);

[0157] In the formula, represents the center point of the candidate clustering point cloud; represents the transpose; represents the th eigenvector of the covariance matrix, ; represents the th eigenvalue of the candidate clustering point cloud, that is, the variance size of the covariance matrix in the direction of the corresponding eigenvector. In this embodiment, , , is the maximum eigenvalue, representing the variance along the tower height direction, is the intermediate eigenvalue, representing the variance in the width direction of the pole tower. is the minimum eigenvalue, representing the variance in the thickness direction of the pole tower.

[0158] Step S4.7: When is less than the preset value (0.25), it is determined that the corresponding candidate clustered point cloud conforms to the eigenvalue (pole tower structure feature) and is retained; otherwise, it is marked as a candidate clustered point cloud that does not conform to the eigenvalue. When there are candidate clustered point clouds that do not conform to the eigenvalue, adjust the value and repeat Steps S4.3 - S4.5.

[0159] Step S4.8: Merge all candidate clustered point clouds that conform to the eigenvalue to form an independent single - body pole tower point cloud model.

[0160] Step S5: Reverse - model the extracted independent single - body pole tower point cloud model to form a vectorized three - dimensional DXF - format model of the single - body pole tower.

[0161] The specific process of Step S5 is as follows:

[0162] Step S5.1: Identify the feature edges of the independent single - body pole tower point cloud model.

[0163] For the edges of the independent single - body pole tower point cloud model perform single - adjacent - edge screening. respectively represent two vertices of the independent single - body pole tower point cloud model. If the number of adjacent edges of and it is located at the end of the rod, it is determined as the cross - section boundary edge. At the same time, determine the feature edges of the gusset plate of the independent single - body pole tower point cloud model (the edge contour line of the tower gusset plate (the component connecting different rods)):

[0164] (13);

[0165] In the formula, represents the angle between two normal vectors; , respectively represent the normal vectors of two adjacent edges of the independent single - body pole tower point cloud model; represents the inverse cosine function.

[0166] If , then it is determined that is the feature edge of the gusset plate (the angle between the normal vectors in the connection area of the tower gusset plate and the rod is usually greater than 90°).

[0167] Step S5.2: Construct the contour of the independent single - body pole tower point cloud model.

[0168] For the point cloud at the end of the rod of the independent single - body pole tower point cloud model Perform plane fitting , and optimize the objective:

[0169] (14);

[0170] In the formula, represents the plane equation of the fitting; are all coefficients of the plane equation; represents a point in, with coordinates .

[0171] Step S5.3: Construct the axis of the pole and tower member in the point cloud model of an independent single pole and tower

[0172] Extract the axis of the pole and tower member in the point cloud model of an independent single pole and tower according to the axis equation : , represents any point on the axis of the member; represents the midpoint of the axis of the member; represents the distance along the axis direction; represents the direction vector of the axis.

[0173] Step S5.4: Construct the cross-section of the pole and tower member in the point cloud model of an independent single pole and tower

[0174] Calculate the maximum distance between the vertexes of the pole and tower member contour in the cross-section plane:

[0175] (15);

[0176] In the formula, represents the maximum distance between the vertexes of the pole and tower member contour in the cross-section plane; represents the th vertex on the pole and tower member contour, represents the th vertex on the pole and tower member contour, .

[0177] Calculate the minimum distance from the vertex of the pole and tower member contour to the opposite side (the thickness of the pole and tower member edge):

[0178] (16);

[0179] In the formula, represents the thickness of the pole and tower member edge, that is, the minimum distance from the vertex of the pole and tower member contour to the opposite side; represents the vertex of the pole and tower member contour.

[0180] Step S5.5: Construct the gusset plate in the point cloud model of an independent single pole and tower

[0181] According to the characteristic contour point cloud of the gusset plate Determine the reference point , and calculate the polar angles of other points relative to the reference point , expressed as:

[0182] (17);

[0183] (18);

[0184] (19);

[0185] In the formula, represents the th point in ; represents the total number of points in ; represents the coordinate component of ; represents the standard two-dimensional arctangent function, used to calculate the angle of point relative to the reference point ; represents the coordinate difference of on the axis; represents the coordinate difference of on the axis; represents the coordinate difference of on the[[ID=……]] axis; represents the coordinate difference of

[0186] Sort in ascending order of the polar angle . If the corresponding polar angles of two points in are the same, then sort them in ascending order of the distance between these two points and the reference point ; . After traversing all the points in , connect the remaining points to form the nodal plate of the single-tower point cloud model

[0187] Step S5.6: Perform vector modeling of the connection relationship of tower components through graph theory methods

[0188] Establish a component connection graph , where the vertex set is composed of the rod vertices and the nodal plate vertices , and the edge set is composed of the coplanar connection constraint set and the coaxial connection constraint set consist of;

[0189] (20);

[0190] (21);

[0191] In the formula, represents the direction vector of the rod; represents the normal vector of the gusset plate; respectively represent the vertex of the first rod and the vertex of the second rod; respectively represent the direction vector of the first rod and the direction vector of the second rod.

[0192] Step S5.7: Output the vectorized single-tower three-dimensional DXF format model.

[0193] Store the axis of the rod of the independent single-tower point cloud model, the cross-section of the rod of the independent single-tower point cloud model, and the gusset plate of the single-tower point cloud model as Line entities, LwpolyLine entities, and Polygon entities respectively, and establish a vectorized single-tower three-dimensional DXF format model.

[0194] Step S6: Use the projection dimensionality reduction comparison method to detect and analyze the differences between the vectorized single-tower three-dimensional DXF format model and the manually designed tower three-dimensional DXF format model.

[0195] The specific process of Step S6 is as follows:

[0196] Step S6.1: Transform the vectorized single-tower DXF model to the coordinate system consistent with the designed output tower DXF model through the transformation matrix.

[0197] Take the point coordinates in the manually designed tower three-dimensional DXF format model as , represents the coordinate vector of, represents the transpose.

[0198] Take the point coordinates in the vectorized single-tower three-dimensional DXF format model as , represents the coordinate vector of.

[0199] Through the rotation matrix and the translation vector transform the vectorized single-tower three-dimensional DXF format model so that the point coordinates of the transformed vectorized single-tower three-dimensional DXF format model and the point coordinates of the manually designed tower three-dimensional DXF format model are in the same coordinate system:

[0200] (22).

[0201] Let be the point set of the 3D DXF format model of the manually designed pole tower, represent the th point in the point set of the 3D DXF format model of the manually designed pole tower, represent the number of points in the point set of the 3D DXF format model of the manually designed pole tower.

[0202] Let be the point set of the 3D DXF format model of the vectorized single pole tower, represent the th point in the 3D DXF format model of the vectorized single pole tower, represent the number of points in the point set of the 3D DXF format model of the vectorized single pole tower.

[0203] For each , find such that is minimized. The objective function to be minimized is:

[0204] (23);

[0205] In the formula, represents the square of the Euclidean distance between and the closest point

[0206] in the DXF model of the vectorized single pole tower. Continuously iterate and update the rotation matrix and the translation vector until the objective function

[0207] converges to a smaller value that meets the set threshold condition.

[0208] Output the point set of the 3D DXF format model of the transformed vectorized single pole tower. At this time, the 3D DXF format model of the vectorized single pole tower and the 3D DXF format model of the manually designed pole tower are in the same coordinate system.

[0209] Step S6.2: Project the points of the DXF model of the vectorized single pole tower and the 3D DXF format model of the manually designed pole tower onto three mutually perpendicular two-dimensional planes (xy plane, xz plane, and yz plane) to reflect the structural information of the pole tower from different angles.Step S6.3: On the projected two-dimensional plane, extract the two endpoints of the rod segments represented by the vectorized single-tower DXF model and the three-dimensional DXF format model of the manually designed tower respectively, and extract the polygons of the gusset plates represented by the vectorized single-tower DXF model and the three-dimensional DXF format model of the manually designed tower respectively.

[0210] Step S6.4: Perform two-dimensional geometric matching on the extracted rod segments.

[0211] Compare the rod segments after projection of the three-dimensional DXF format model of the manually designed tower and the three-dimensional DXF format model of the vectorized single-tower, and calculate the average value of the distances between the rod segments , and determine whether they match;

[0212] (24);

[0213] In the formula, , represent the two endpoints of; , represent the two endpoints of; represents the rod segment after projection of the three-dimensional DXF format model of the manually designed tower; represents the rod segment after projection of the three-dimensional DXF format model of the vectorized single-tower.

[0214] If is less than the set threshold , then these two rod segments are considered to match.

[0215] Step S6.5: Perform two-dimensional geometric matching on the extracted gusset plates.

[0216] Compare the polygons after projection of the three-dimensional DXF format model of the manually designed tower and the three-dimensional DXF format model of the vectorized single-tower, and calculate the average value of the distances between the corresponding vertices , and determine whether they match;

[0217] (25);

[0218] In the formula, , respectively represent and the th vertex of; respectively represent the polygons after projection of the three-dimensional DXF format model of the manually designed tower and the three-dimensional DXF format model of the vectorized single-tower; represents or the number of vertices of.

[0219] If is less than the set threshold , then it is considered that these two polygons match.

[0220] Step S7: Output the spatial positions of the differences in the pole tower structures found during the matching process, and map and associate them with the mapping relationship between the pixel coordinates of the pixel blocks in step S3 and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image, so as to realize the defect identification and positioning of the pole tower structure, facilitating the viewing by relevant personnel.

[0221] The specific process of step S7 is as follows: If a certain geometric element (rod segment or polygon) in the three-dimensional DXF format model of the manually designed pole tower has no matching geometric element in the projection of the vectorized single-pole tower three-dimensional DXF format model, record the information of these missing geometric elements and their positions in the three-dimensional DXF format model of the manually designed pole tower, and perform an associated correspondence with the mapping relationship between the pixel coordinates of the pixel blocks in step S3.3 and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image; for the geometric elements that match in the three-dimensional DXF format model of the manually designed pole tower and the vectorized single-pole tower three-dimensional DXF format model, if the difference in their dimensional parameters (length, radius, area, etc.) exceeds the set threshold, record the information of these geometric elements with inconsistent dimensions and the specific dimensional difference values, and perform an associated correspondence with the mapping relationship between the pixel coordinates of the pixel blocks in step S3.3 and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image, so as to realize the defect identification and positioning of the pole tower structure.

[0222] An electronic device, comprising a processor, a memory, and a bus, where the processor and the memory are connected through the bus. Among them, the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory to execute the method for identifying defects in pole tower structures and three-dimensional spatial positioning based on multi-source fusion.

[0223] A non-volatile computer storage medium stores computer-executable instructions, and these computer-executable instructions execute the method for identifying defects in pole tower structures and three-dimensional spatial positioning based on multi-source fusion.

[0224] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying tower structure defects and three-dimensional spatial positioning based on multi-source fusion, characterized in that, It includes the following steps: Step S1: Use a drone carrying a sensor to conduct an all-round scan of the pole tower, and construct multi-dimensional acquisition data, where the multi-dimensional acquisition data includes pole tower point clouds, pole tower visible light images, and pole tower panoramic images; Step S2: Eliminate the hash points and isolated points in the pole tower point cloud, simplify the pole tower point cloud, and unify the simplified pole tower point cloud, pole tower visible light image, and pole tower panoramic image into the same coordinate system; Step S3: Perform spherical projection de-distortion on the pole tower panoramic image after unifying the coordinate system, and calculate the mapping relationship from the spherical longitude and latitude coordinates to the pixel coordinates of the de-distorted image; Take panoramic images of the four sides of the pole tower, divide each panoramic image into pixel blocks, and store the mapping relationship between the pixel coordinates of the pixel blocks and the mapping relationship from the spherical longitude and latitude coordinates to the pixel coordinates of the de-distorted image; Step S4: Use an adaptive clustering method to extract features of the pole tower to form an independent single-pole tower point cloud model; Step S5: Reverse model the extracted independent single-pole tower point cloud model to form a vectorized single-pole tower three-dimensional DXF format model; Step S6: Use the projection dimensionality reduction comparison method to detect and analyze the differences between the vectorized single-pole tower three-dimensional DXF format model and the manually designed pole tower three-dimensional DXF format model; Step S7: Output the spatial positions of the pole tower structure differences found during the matching process, and map and associate them with the mapping relationship between the pixel coordinates of the pixel blocks and the mapping relationship from the spherical longitude and latitude coordinates to the pixel coordinates of the de-distorted image in Step S3 to achieve defect identification and positioning of the pole tower structure.

2. The method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion according to claim 1, wherein: The specific process of Step S3 is as follows: Step S3.1: Based on the internal parameters of the panoramic camera for collecting the pole tower panoramic image, perform spherical projection transformation on the pole tower panoramic image after unifying the coordinate system to eliminate lens distortion and generate an undistorted pole tower equidistant cylindrical projection image; Among them, the internal parameters of the panoramic camera include: the focal length of the panoramic camera on the axis and the focal length of the axis , the principal point coordinates and the distortion coefficients , , , ; represents the quadratic term coefficient of the radial distortion, represents the quartic term coefficient of the radial distortion, , both represent the parameters of the tangential distortion; Step S3.2: Calculate the mapping relationship from the spherical longitude and latitude coordinates to the pixel coordinates of the pole tower equidistant cylindrical projection image, expressed as: ； ； In the formula, represents the pixel coordinates of the equally-spaced columnar projection image of the pole tower; represents the spherical longitude and latitude coordinates; represents the principal point coordinates; and respectively represent the focal lengths of the panoramic camera on the axis and axis ; Step S3.3: Set observation points on the four sides of the pole tower, take panoramic images, divide each panoramic image of each observation point into pixel blocks, and at the same time store the mapping relationship between the pixel coordinates of the pixel blocks and the mapping relationship from the spherical longitude and latitude coordinates to the pixel coordinates of the de-distorted image.

3. The method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion according to claim 2, wherein: The specific process of Step S4 is as follows: Step S4.1: Use a drone carrying a sensor to collect the point cloud of the single pole tower to be processed; Step S4.2: Separate the ground point cloud and non-ground point cloud of the single pole tower to be processed. Specifically: Generate an elevation constraint surface through a simulation filtering algorithm in combination with the elevation information of the single pole tower point cloud; ； In the formula, represents the elevation value of the elevation constraint surface at the position ; and respectively represent the second-order partial derivatives of the elevation in the , directions; represents the gradient of the elevation ; represents the preset slope threshold; Remove the ground point cloud less than the preset distance and output the non-ground point cloud belonging to the pole tower structure, expressed as: ； In the formula, represents the th non-ground point cloud point obtained after screening; represents the elevation value of the th point of the single-tower point cloud; represents the elevation value at the horizontal position of the th point of the elevation constraint surface on the single-tower point cloud ; Step S4.3: Calculate the average distance from the non-ground point cloud point to other points within its own neighborhood: ； In the formula, represents the average distance to other points within its neighborhood; represents the neighborhood of i.e., the set of all non-ground point cloud points within the sphere centered at with a radius of ; represents the preset neighborhood radius; represents the number of non-ground point cloud points within the neighborhood of represents the th point in Step S4.4: Based on , calculate the average distance and standard deviation of each point of the non-ground point cloud: ； ； In the formula, represents the number of points in the non-ground point cloud; Step S4.5: Based on the average distance and the standard deviation calculate the adaptive threshold, and through the adaptive threshold screen the non-ground point cloud to obtain the candidate clustering point cloud; Adaptive threshold It is expressed as: ； In the formula, represents the set coefficient for adjusting the size of the adaptive threshold [[ID= Step S4.6: Calculate the covariance matrix of the candidate clustered point cloud and perform eigenvalue decomposition: ； ； In the formula, represents the center point of the candidate clustering point cloud; represents the transpose; represents the th eigenvector of the covariance matrix; represents the th eigenvalue of the candidate clustering point cloud; Step S4.7: When is less than the preset value, it is determined that the corresponding candidate clustered point cloud conforms to the eigenvalue and is retained; otherwise, it is marked as a candidate clustered point cloud that does not conform to the eigenvalue. When there is a candidate clustered point cloud that does not conform to the eigenvalue, adjust the value and repeat Steps S4.3 - S4.5; Step S4.8: Merge all candidate clustering point clouds that meet the eigenvalue to form an independent single-pole tower point cloud model.

4. The method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion according to claim 3, wherein: The specific process of Step S5 is as follows: Step S5.1: Identify the feature edges of the independent single-pole tower point cloud model; Edges of the point cloud model of an independent single-tower Perform single adjacent edge screening, respectively represent two vertices of the point cloud model of an independent single-tower. When the number of adjacent edges and is located at the end of the member, it is determined as a cross-section boundary edge; at the same time, determine the characteristic edges of the gusset plate of the point cloud model of the independent single-tower: ； In the formula, represents the angle between two normal vectors; , respectively represent the normal vectors of two adjacent sides of the point cloud model of the independent single-tower pole; represents the inverse cosine function; When , it is determined that is the characteristic edge of the gusset plate; Step S5.2: Construct the contour of the independent single-pole tower point cloud model; For the point cloud of the end of the pole of the independent single-pole tower point cloud model Perform plane fitting , and optimize the objective: ； In the formula, represents the fitted plane equation; are all coefficients of the plane equation; represents a point in, with coordinates ; Step S5.3: Construct the axis of the rod of the independent single-pole tower point cloud model; Extract the axis of the pole and tower point cloud model of an independent monomer according to the axis equation : , represents any point on the axis of the rod; represents the midpoint of the axis of the rod; represents the distance along the axis direction; represents the direction vector of the axis; Step S5.4: Construct the cross-section of the rod of the independent single-pole tower point cloud model; Calculate the maximum distance between the vertexes of the rod profile in the cross-sectional plane of the point cloud model of an independent single-pole tower: ； In the formula, represents the maximum distance between the vertexes of the bar profile within the section plane; represents the th vertex on the bar profile, represents the th vertex on the bar profile, ; Calculate the minimum distance from the vertex of the rod profile to the opposite side: ； In the formula, represents the edge thickness of the rod, that is, the minimum distance from the vertex of the rod profile to the opposite side; represents the vertex of the rod profile; Step S5.5: Construct the gusset plate of the point cloud model of the independent single-pole tower; According to the characteristic contour point cloud of the gusset plate Determine the reference point , and calculate The polar angles of other points in except relative to are expressed as: ； ； ； In the formula, represents the th point, ; represents the total number of midpoints; represents the coordinate component of represents the standard two-dimensional arctangent function, used to calculate the angle of point relative to the reference point ; represents the coordinate difference of on the axis; represents the coordinate difference of on the axis; represents the coordinate difference of on the axis; Sort in ascending order according to the polar angle For When The corresponding polar angles of two points in Are the same, then sort in ascending order according to the distances of these two points from the reference point After traversing all the points in Connect the remaining points to form the node plate of the single-tower point cloud model; Step S5.6: Perform vector modeling of the connection relationship of the tower components by the graph theory method; Establish a component connection diagram , the vertex set consists of bar vertices and gusset plate vertices , and the edge set consists of a coplanar connection constraint set and a coaxial connection constraint set ; ； ； In the formula, represents the direction vector of the rod member; represents the normal vector of the gusset plate; respectively represent the vertex of the first rod member and the vertex of the second rod member; respectively represent the direction vector of the first rod member and the direction vector of the second rod member; Step S5.7: Output the vectorized 3D DXF format model of the single-pole tower; Store the axis of the rod of the point cloud model of the independent single-pole tower, the cross-section of the rod of the point cloud model of the independent single-pole tower, and the gusset plate of the point cloud model of the single-pole tower as entities, and establish a vectorized 3D DXF format model of the single-pole tower.

5. The method for identifying structural defects of a pole tower and three-dimensional space positioning based on multi-source fusion according to claim 4, characterized in that: The specific process of Step S6 is as follows: Step S6.1: Convert the vectorized single-pole tower DXF model to the coordinate system consistent with the DXF model of the designed output tower through the transformation matrix; Take the point coordinates in the 3D DXF format model of the manually designed pole tower as , denote as the coordinate vector of denote the transpose; Take the point coordinates in the vectorized 3D DXF format model of a single pole tower as , denote as the coordinate vector; By means of a rotation matrix and a translation vector transform the vectorized 3D DXF format model of a single-tower pole, so that the point coordinates of the transformed vectorized 3D DXF format model of a single-tower pole are in the same coordinate system as the point coordinates of the 3D DXF format model of the manually designed tower pole: ； Let be the point set of the 3D DXF format model of the manually designed pole tower, represent the th point in the point set of the 3D DXF format model of the manually designed pole tower, represent the number of points in the point set of the 3D DXF format model of the manually designed pole tower; Let be the point set of the vectorized single-tower three-dimensional DXF format model, represent the th point in the vectorized single-tower three-dimensional DXF format model, represent the number of points in the point set of the vectorized single-tower three-dimensional DXF format model; For each , find such that is minimized, minimizing the objective function which is ； In the formula, represents the square of the Euclidean distance between and the closest point in the vectorized single-tower DXF model; Continuously iterate and update the rotation matrix and the translation vector until the objective function converges to the set threshold condition; Output the point set of the transformed vectorized 3D DXF format model of the single-pole tower. At this time, the vectorized 3D DXF format model of the single-pole tower and the 3D DXF format model of the manually designed tower are in the same coordinate system; Step S6.2: Project the points of the vectorized single-pole tower DXF model and the 3D DXF format model of the manually designed tower onto three mutually perpendicular two-dimensional planes; Step S6.3: On the projected two-dimensional planes, respectively extract the two endpoints of the line segments representing the rods of the vectorized single-pole tower DXF model and the 3D DXF format model of the manually designed tower, and respectively extract the polygons representing the gusset plates of the vectorized single-pole tower DXF model and the 3D DXF format model of the manually designed tower; Step S6.4: Perform two-dimensional geometric matching on the extracted rod line segments; Compare the pole segments after projection of the 3D DXF format model of manually designed poles and towers and the 3D DXF format model of vectorized single poles and towers, and calculate the average value of the distances between the pole segments , and determine whether they match; ； In the formula, and represent both endpoints of; and represent both endpoints of; represents the rod segment after the projection of the three-dimensional DXF format model of the manually designed pole tower; represents the rod segment after the projection of the three-dimensional DXF format model of the vectorized single pole tower; When is less than the set threshold , it is determined that and match; Step S6.5: Perform two-dimensional geometric matching on the extracted gusset plates; Compare the polygons after projection of the 3D DXF format model of the manually designed pole tower and the 3D DXF format model of the vectorized single pole tower, and calculate the average value of the distances between the corresponding vertices , and determine whether they match; ； In the formula, , respectively represent and the th vertex; respectively represent the polygons after projection of the artificially designed three-dimensional DXF format model of the pole tower and the vectorized single-pole tower three-dimensional DXF format model; represents or the number of vertices; When is less than the set threshold , it is determined that , matches.

6. The method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion according to claim 5, wherein: The specific process of Step S7 is as follows: When there are no matching geometric elements in the projection of the geometric elements in the 3D DXF format model of the manually designed tower in the vectorized 3D DXF format model of the single-pole tower, record the information of these missing geometric elements and their positions in the 3D DXF format model of the manually designed tower, and associate and correspond them with the mapping relationship between the pixel coordinates of the pixel block and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image in Step S3.3; When the difference in the size parameters of the matching geometric elements between the 3D DXF format model of the manually designed tower and the vectorized 3D DXF format model of the single-pole tower exceeds the set threshold, record the information of these geometric elements with inconsistent sizes and the size difference values, and associate and correspond them with the mapping relationship between the pixel coordinates of the pixel block and the spherical longitude and latitude coordinates to the pixel coordinates of the undistorted image in Step S3.3 to realize the defect identification and positioning of the tower structure; The geometric elements are rod line segments or polygons.

7. The method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion according to claim 1, characterized in that: The specific process of unifying the simplified tower point cloud, the tower visible light image, and the tower panoramic image into the same coordinate system is as follows: Make the world coordinate system of the simplified tower point cloud and the coordinate system of the high-definition camera in the external parameters of the tower visible light image satisfy orthogonality through the rotation matrix to realize the unification of the coordinate systems; Perform spherical projection transformation on the tower panoramic image to establish the mapping relationship between the tower panoramic image and the simplified tower point cloud to realize the unification of the coordinate systems.

8. An electronic device, characterized in that, It includes a processor, a memory, and a bus. The processor and the memory are connected through the bus. Among them, the memory is used to store a set of program codes, and the processor is used to call the program codes stored in the memory and execute the method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion according to any one of claims 1-7.

9. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions execute the method for identifying tower structure defects and three-dimensional space positioning based on multi-source fusion according to any one of claims 1-7.

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