Power transmission tower three-dimensional reconstruction method and device based on standard model library

Abstract

The invention discloses a power transmission tower three-dimensional reconstruction method and device based on a standard model library, and relates to the technical field of three-dimensional reconstruction or other related fields, and the method comprises the steps: collecting the point cloud data of a power transmission tower through a mobile scanning device, carrying out the filtering and coordinate transformation of the point cloud data, and obtaining the point cloud data of a unified coordinate system, the method comprises the following steps: obtaining different types of tower standard information, establishing a standard model library, classifying point cloud data, capturing tower key points, obtaining a matching model from the standard model library based on the key points, carrying out geometric transformation on the matching model to obtain a preliminary three-dimensional model, and obtaining a final three-dimensional model based on physical and prior constraint conditions of the tower. And refining the preliminary three-dimensional model by adopting a random iterative algorithm to obtain a refined three-dimensional model, aligning and fusing the preliminary three-dimensional model and the refined three-dimensional model to obtain a target three-dimensional model, and generating a formatted file. According to the invention, the technical problems of low accuracy, low applicability and low automation degree of three-dimensional reconstruction of the power transmission tower in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional reconstruction or other related fields, and in particular to a method and device for three-dimensional reconstruction of a transmission tower based on a standard model library. Background Art

[0002] The stable operation of the power system is the guarantee for the smooth operation of the social economy. The losses caused by the aging and damage of transmission equipment are immeasurable. Therefore, as an important part of the power system, the transmission corridor is indispensable for regular monitoring and maintenance. With the increasing expansion of the scale of the power grid, the length of the transmission line is growing rapidly, and the inspection demand is also growing exponentially. As the key facilities in the transmission corridor, the transmission tower is the main object of refined inspection.

[0003] In the related art, the 3D reconstruction of transmission towers mainly relies on drawings and uses 3D modeling software for modeling. However, the 3D model constructed by this strategy only has visualization function and lacks important spatial physical information. The semi-automatic reconstruction strategy implemented on lidar point cloud data can improve the spatial geometric accuracy of the 3D model, but it requires complex interaction, annotation and surface creation process. Therefore, whether it is a reconstruction strategy using 3D modeling software or a semi-automatic reconstruction strategy using point cloud data, it is a very time-consuming and labor-intensive strategy, and the availability of the 3D model is low. Image-based 3D reconstruction strategy is also one of the choices for 3D reconstruction strategy. The traditional image-based 3D reconstruction strategy includes a multi-view stereo vision module, which mainly extracts image geometric features and performs feature matching. The existing multi-view stereo vision module is difficult to accurately reconstruct the fine structure of the modeling object. With the development of deep learning methods, by combining deep learning with traditional image-based 3D reconstruction strategies, fine structures can be reconstructed to a certain extent. However, since the scene size and reconstruction accuracy are limited by computing resources and training data, the combination of deep learning can only reconstruct relatively small scenes, but cannot accurately reconstruct larger scenes. For image-based 3D reconstruction strategies, the number of viewpoints has a great influence on the accuracy of 3D reconstruction, especially for complex and special geometric structures such as transmission towers, which require very high redundant observations, and this places high demands on the collection of image data. Therefore, the current image-based 3D reconstruction strategies still have defects when facing the 3D reconstruction task of transmission towers.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiment of the present invention provides a method and device for three-dimensional reconstruction of a transmission tower based on a standard model library, so as to at least solve the technical problems of low accuracy and applicability and low degree of automation in the three-dimensional reconstruction of a transmission tower in the related art.

[0006] According to one aspect of an embodiment of the present invention, a method for three-dimensional reconstruction of a transmission tower based on a standard model library is provided, comprising: collecting point cloud data of a transmission tower by a mobile scanning device, filtering and coordinate transformation processing the point cloud data, and obtaining point cloud data of a unified coordinate system; acquiring standard information of different types of transmission towers, establishing a standard model library, classifying and processing the point cloud data, and capturing key points of the transmission tower, wherein the standard information at least includes: design specification files and manufacturing standard files of the transmission tower, and the key points at least include: grounding points, cross arm points, and tower body points of the transmission tower; Based on the key points of the transmission tower, a matching model is obtained from the standard model library, and the matching model is geometrically transformed to obtain a preliminary three-dimensional model of the transmission tower; based on the physical constraints and prior constraints of the transmission tower, a random iterative algorithm is used to refine the preliminary three-dimensional model to obtain a refined three-dimensional model, wherein the physical constraints reflect the actual physical characteristics of the transmission tower, and the prior constraints reflect the historical three-dimensional reconstruction information of the transmission tower; the preliminary three-dimensional model and the refined three-dimensional model are aligned and fused to obtain a target three-dimensional model, and a standard format file of the target three-dimensional model is generated.

[0007] Optionally, before collecting point cloud data of transmission towers by mobile scanning equipment, the method also includes: using a geographic information system to analyze the terrain and landforms of the location of the transmission towers, and using a path optimization algorithm to plan the flight path of the mobile scanning equipment based on the terrain, landforms and geographic information of the transmission lines; setting flight parameters of the mobile scanning equipment, and the flight parameters include at least: flight altitude, flight speed and flight attitude.

[0008] Optionally, the step of filtering and coordinate transforming the point cloud data to obtain point cloud data in a unified coordinate system includes: performing ground point separation processing on the point cloud data to remove ground point data; performing denoising processing on the point cloud data to filter out noise data; performing histogram equalization processing and edge detection processing on the point cloud data to obtain a uniform distribution of the histogram of the point cloud data; obtaining a unified coordinate system of the associated transmission tower, and performing coordinate transformation processing on the point cloud data based on the unified coordinate system to obtain point cloud data in the unified coordinate system.

[0009] Optionally, the step of obtaining standard information of different types of transmission towers and establishing a standard model library includes: based on the standard information of different types of transmission towers, using three-dimensional modeling software to establish a three-dimensional model of the transmission tower; using a parametric design strategy to define a set of parameters for each type of three-dimensional model, wherein each set of parameters includes at least: the height, width and node spacing of the transmission tower; and establishing a standard model library, which includes three-dimensional models of all types of transmission towers.

[0010] Optionally, the step of classifying and processing the point cloud data to capture the key points of the transmission tower includes: calculating the covariance matrix of the point cloud data to obtain multiple eigenvalues ​​of the covariance matrix; determining the dominant direction of the point cloud data based on the multiple eigenvalues, wherein the dominant direction reflects the distribution direction of the point cloud data; based on the multiple eigenvalues ​​and the dominant direction, using a clustering algorithm to classify the point cloud data to obtain multiple local areas, wherein each local area includes multiple point cloud data; using a surface fitting algorithm to obtain the surface equation of the local area, and based on the parameters of the surface equation, calculating the local curvature of all the point cloud data; based on the local curvature, using a feature point detection algorithm to capture the key points of the transmission tower, and recording the coordinates of the key points in a unified coordinate system.

[0011] Optionally, the step of obtaining a matching model from a standard model library based on the key points of the transmission tower includes: obtaining simulation key points of the three-dimensional model of each type of transmission tower in the standard model library; calculating the Euclidean distance between the key points of the transmission tower and the simulation key points of each type, and determining the matching model based on multiple Euclidean distances.

[0012] Optionally, the step of refining the preliminary three-dimensional model using a random iterative algorithm includes: initializing the random iterative algorithm, determining a first objective function and an algorithm iteration number, the first objective function including a data fidelity term and a regularization term, wherein the data fidelity term is the difference value between the point cloud data of the transmission tower and the point cloud data of the matching model, and the regularization term is a priori constraint condition; based on the actual physical characteristics of the transmission tower, obtaining the physical constraint conditions of the transmission tower, wherein the actual physical characteristics at least include: material property data, force condition data, and the physical constraint conditions at least include: tower stress limit, tower strain limit and tower displacement limit; based on the physical constraint conditions, obtaining a physical constraint term, adding the physical constraint term to the first objective function to obtain a second objective function; executing the iterative process of the random iterative algorithm, and obtaining a refined three-dimensional model when the algorithm iteration number is reached.

[0013] Optionally, after aligning and fusing the preliminary three-dimensional model and the refined three-dimensional model to obtain the target three-dimensional model, the method includes: calculating the three-dimensional reconstruction error based on the key points of the transmission tower and the key points of the target three-dimensional model; adjusting the geometric features and edge details of the target three-dimensional model based on the three-dimensional reconstruction error, wherein the edge details include: edge smoothness and edge clarity; generating a three-dimensional reconstruction report of the transmission tower based on the target three-dimensional model, wherein the three-dimensional reconstruction report includes at least: reconstruction process, reconstruction parameters and error analysis.

[0014] According to another aspect of an embodiment of the present invention, a three-dimensional reconstruction device for a transmission tower based on a standard model library is provided, comprising: an acquisition unit, used to acquire point cloud data of a transmission tower by means of a mobile scanning device, and to perform filtering and coordinate transformation processing on the point cloud data to obtain point cloud data of a unified coordinate system; a classification unit, used to obtain standard information of different types of transmission towers, establish a standard model library, classify the point cloud data, and capture key points of the transmission tower, wherein the standard information at least includes: design specification documents and manufacturing standard documents of the transmission tower, and the key points at least include: grounding points, cross arm points, and tower body points of the transmission tower; and a transformation unit. The conversion unit obtains the matching model from the standard model library based on the key points of the transmission tower, performs geometric transformation on the matching model, and obtains a preliminary three-dimensional model of the transmission tower; the processing unit uses a random iterative algorithm to refine the preliminary three-dimensional model based on the physical constraints and prior constraints of the transmission tower to obtain a refined three-dimensional model, wherein the physical constraints reflect the actual physical characteristics of the transmission tower, and the prior constraints reflect the historical three-dimensional reconstruction information of the transmission tower; the generation unit is used to align and fuse the preliminary three-dimensional model and the refined three-dimensional model to obtain a target three-dimensional model, and generate a standard format file of the target three-dimensional model.

[0015] Optionally, the acquisition unit includes: a planning module, which is used to use a geographic information system to analyze the terrain and landforms of the location of the transmission tower, and based on the geographical information of the terrain, landforms and transmission lines, use a path optimization algorithm to plan the flight path of the mobile scanning device; a setting module, which is used to set the flight parameters of the mobile scanning device, and the flight parameters include at least: flight altitude, flight speed and flight attitude.

[0016] Optionally, the acquisition unit also includes: a separation module, which is used to perform ground point separation processing on the point cloud data to remove the ground point data; a denoising module, which is used to perform denoising processing on the point cloud data to filter out noise data; an equalization module, which is used to perform histogram equalization processing and edge detection processing on the point cloud data to obtain a uniform distribution of the histogram of the point cloud data; a coordinate transformation module, which is used to obtain a unified coordinate system of the associated transmission tower, and perform coordinate transformation processing on the point cloud data based on the unified coordinate system to obtain point cloud data under the unified coordinate system.

[0017] Optionally, the classification unit includes: a first establishment module, which uses three-dimensional modeling software to establish a three-dimensional model of a transmission tower based on standard information of different types of transmission towers; a definition module, which uses a parametric design strategy to define a set of parameters for each type of three-dimensional model, wherein each set of parameters includes at least: the height, width and node spacing of the transmission tower; and a second establishment module, which is used to establish a standard model library, which includes three-dimensional models of all types of transmission towers.

[0018] Optionally, the classification unit also includes: a first calculation module, used to calculate the covariance matrix of the point cloud data and obtain multiple eigenvalues ​​of the covariance matrix; a determination module, based on multiple eigenvalues, determines the dominant direction of the point cloud data, wherein the dominant direction reflects the distribution direction of the point cloud data; a data classification module, based on multiple eigenvalues ​​and dominant directions, uses a clustering algorithm to classify the point cloud data to obtain multiple local areas, wherein each local area includes multiple point cloud data; a fitting module, used to use a surface fitting algorithm to obtain the surface equation of the local area, and based on the parameters of the surface equation, calculate the local curvature of all point cloud data; a recording module, based on the local curvature, uses a feature point detection algorithm to capture the key points of the transmission tower, and records the coordinates of the key points in a unified coordinate system.

[0019] Optionally, the transformation unit includes: a first acquisition module, used to obtain simulation key points of the three-dimensional model of each type of transmission tower in the standard model library; a second calculation module, used to calculate the Euclidean distance between the key points of the transmission tower and each type of simulation key points, and determine the matching model based on multiple Euclidean distances.

[0020] Optionally, the processing unit includes: an initialization module, used to initialize the random iterative algorithm, determine the first objective function and the number of algorithm iterations, the first objective function includes a data fidelity term and a regularization term, wherein the data fidelity term is the difference value between the point cloud data of the transmission tower and the point cloud data of the matching model, and the regularization term is a priori constraint condition; a second acquisition module, based on the actual physical characteristics of the transmission tower, to obtain the physical constraint conditions of the transmission tower, wherein the actual physical characteristics at least include: material property data, force condition data, and the physical constraint conditions at least include: tower stress limit, tower strain limit and tower displacement limit; a third calculation module, based on the physical constraint conditions, to obtain the physical constraint terms, add the physical constraint terms to the first objective function, and obtain the second objective function; an execution module, used to execute the iterative process of the random iterative algorithm, and obtain the refined three-dimensional model when the number of algorithm iterations is reached.

[0021] Optionally, the generation unit includes: a fourth calculation module, which calculates the three-dimensional reconstruction error based on the key points of the transmission tower and the key points of the target three-dimensional model; an adjustment module, which adjusts the geometric features and edge details of the target three-dimensional model based on the three-dimensional reconstruction error, wherein the edge details include: edge smoothness and edge clarity; a report generation module, which generates a three-dimensional reconstruction report of the transmission tower based on the target three-dimensional model, wherein the three-dimensional reconstruction report includes at least: reconstruction process, reconstruction parameters and error analysis.

[0022] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for three-dimensional reconstruction of transmission towers based on a standard model library.

[0023] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned methods for three-dimensional reconstruction of transmission towers based on a standard model library.

[0024] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for three-dimensional reconstruction of transmission towers based on a standard model library.

[0025] In the present disclosure, point cloud data of a transmission tower is collected by a mobile scanning device, and the point cloud data is filtered and transformed to obtain point cloud data of a unified coordinate system. Standard information of different types of transmission towers is obtained to establish a standard model library, and the point cloud data is classified to capture key points of the transmission tower. Based on the key points of the transmission tower, a matching model is obtained from the standard model library, and the matching model is geometrically transformed to obtain a preliminary three-dimensional model of the transmission tower. Based on the physical constraints and prior constraints of the transmission tower, a random iterative algorithm is used to refine the preliminary three-dimensional model to obtain a refined three-dimensional model. Finally, the preliminary three-dimensional model and the refined three-dimensional model are aligned and fused to obtain a target three-dimensional model, and a standard format file of the target three-dimensional model is generated. The present invention adopts mobile scanning equipment to collect point cloud data of transmission towers, realizes automatic and efficient data collection, and greatly improves the efficiency and adaptability of generating preliminary 3D models by establishing a standard model library. By combining the physical constraints and prior constraints of the transmission towers, a random iterative algorithm is adopted to refine the preliminary 3D model, which significantly improves the accuracy of the 3D model, thereby realizing 3D reconstruction of transmission towers of different scales and complexities, and further solves the technical problems of low accuracy and applicability and low degree of automation of 3D reconstruction of transmission towers in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0027] Figure 1 is a flowchart of an optional method for 3D reconstruction of a transmission tower based on a standard model library according to an embodiment of the present invention;

[0028] Figure 2 is a flowchart of an optional method for 3D reconstruction of a transmission tower based on a standard model library according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of an optional three-dimensional reconstruction device for a transmission tower based on a standard model library according to an embodiment of the present invention;

[0030] Figure 4 The present invention is a hardware structure block diagram of an electronic device (or mobile device) for a method for three-dimensional reconstruction of a transmission tower based on a standard model library according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] It should be noted that the three-dimensional reconstruction method and device of the transmission tower based on the standard model library in the present disclosure can be used in the field of three-dimensional reconstruction technology. When the transmission tower is three-dimensionally reconstructed based on the standard model library, it can also be used in any field except the field of three-dimensional reconstruction technology. When the transmission tower is three-dimensionally reconstructed based on the standard model library, the present disclosure does not limit the application field of the three-dimensional reconstruction method and device of the transmission tower based on the standard model library.

[0034] The following embodiments of the present invention may be applied to various systems / applications / devices for three-dimensional reconstruction of transmission towers based on a standard model library. The present invention collects point cloud data of a transmission tower through a mobile scanning device, performs filtering processing and coordinate transformation processing on the point cloud data to obtain point cloud data of a unified coordinate system, establishes a standard model library by acquiring standard information of different types of transmission towers, classifies the point cloud data, captures key points of the transmission tower, and then obtains a matching model from the standard model library based on the key points of the transmission tower, performs geometric transformation on the matching model to obtain a preliminary three-dimensional model of the transmission tower, and then refines the preliminary three-dimensional model based on the physical constraints and prior constraints of the transmission tower by using a random iterative algorithm to obtain a refined three-dimensional model, finally aligns and fuses the preliminary three-dimensional model and the refined three-dimensional model to obtain a target three-dimensional model, and generates a standard format file of the target three-dimensional model, thereby realizing automated and efficient data collection, greatly improving the efficiency and adaptability of generating the preliminary three-dimensional model, and significantly improving the accuracy of the three-dimensional model, thereby realizing three-dimensional reconstruction of the transmission tower with high efficiency, high accuracy, high adaptability and high level of automation.

[0035] The present invention is described in detail below in conjunction with various embodiments.

[0036] Embodiment 1

[0037] According to an embodiment of the present invention, an embodiment of a method for three-dimensional reconstruction of a transmission tower based on a standard model library is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] Figure 1 is a flowchart of an optional method for 3D reconstruction of a transmission tower based on a standard model library according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0039] Step S101, collecting point cloud data of the transmission tower by a mobile scanning device, performing filtering processing and coordinate transformation processing on the point cloud data, and obtaining point cloud data of a unified coordinate system.

[0040] Point cloud data can present rich spatial information and is widely used in city-level 3D reconstruction tasks, including 3D reconstruction tasks of buildings, vegetation, roads and bridges, etc., providing strong support for urban planning and development, emergency rescue and other aspects. In the embodiment of the present invention, point cloud data of transmission towers are collected by mobile scanning equipment. The mobile scanning equipment can be a drone. By carrying a high-resolution camera, laser scanner or radar airborne laser detection and ranging system on the drone, the transmission tower is scanned and photographed in all directions to obtain point cloud data of the transmission tower. In the process of collecting data, the sensors on the drone monitor the flight status of the drone and the quality of the collected data in real time to ensure the integrity and accuracy of the point cloud data.

[0041] After collecting the point cloud data of the transmission tower through the mobile scanning equipment, the point cloud data is filtered to remove the noise data, and then the coordinate transformation of the point cloud data is performed to ensure that all point cloud data are in a unified coordinate system. Finally, the point cloud data of the unified coordinate system is obtained for the subsequent 3D reconstruction process.

[0042] Optionally, before collecting point cloud data of transmission towers by mobile scanning equipment, the method also includes: using a geographic information system to analyze the terrain and landforms of the location of the transmission towers, and using a path optimization algorithm to plan the flight path of the mobile scanning equipment based on the terrain, landforms and geographic information of the transmission lines; setting flight parameters of the mobile scanning equipment, and the flight parameters include at least: flight altitude, flight speed and flight attitude.

[0043] In an embodiment of the present invention, before collecting point cloud data of a transmission tower by a mobile scanning device, it is first necessary to plan the flight path of the mobile scanning device. The terrain and topography of the location of the transmission tower are analyzed by a geographic information system, and a path optimization algorithm is used to plan the flight path of the mobile scanning device based on the terrain, topography and geographic information of the transmission line. The path optimization algorithm may be a particle swarm optimization algorithm, which combines geographic information to enable the flight path of the mobile scanning device to cover the transmission line to the greatest extent, thereby reducing the flight time and energy loss of the mobile scanning device.

[0044] At the same time, the embodiment of the present invention needs to set the flight parameters of the mobile scanning device in advance, including the flight altitude, flight speed and flight attitude of the mobile scanning device, to ensure that the flight path of the mobile scanning device can adapt to different terrains and environmental conditions. The embodiment of the present invention realizes efficient and automatic data collection by using mobile scanning devices and geographic information systems.

[0045] Optionally, the step of filtering and coordinate transforming the point cloud data to obtain point cloud data in a unified coordinate system includes: performing ground point separation processing on the point cloud data to remove ground point data; performing denoising processing on the point cloud data to filter out noise data; performing histogram equalization processing and edge detection processing on the point cloud data to obtain a uniform distribution of the histogram of the point cloud data; obtaining a unified coordinate system of the associated transmission tower, and performing coordinate transformation processing on the point cloud data based on the unified coordinate system to obtain point cloud data in the unified coordinate system.

[0046] In the embodiment of the present invention, after the point cloud data of the transmission tower is collected, the point cloud data needs to be preprocessed, and the preprocessing includes filtering processing and coordinate transformation processing, and finally the point cloud data of the unified coordinate system is obtained.

[0047] Before filtering the point cloud data, it is first necessary to perform ground point separation processing on it to remove the ground point data, so that the embodiment of the present invention can focus on other data features besides the ground points. Then, the point cloud data without the ground points is filtered, i.e., denoised, to filter out the noise data in the point cloud data to improve the quality of the point cloud data. During the filtering process, the filter can select a Gaussian filter, which is a linear filter that can effectively remove noise in the data and retain the edge details of the data, making the data clearer.

[0048] After filtering the point cloud data, perform histogram equalization on it to enhance the contrast of the point cloud data. Histogram equalization adjusts the image grayscale distribution of the point cloud data so that the originally concentrated pixel values ​​are evenly distributed, thereby enhancing the image contrast of the data. In order to make the histogram distribution of the point cloud data more uniform and improve the recognizability of data features, after performing histogram equalization on the point cloud data, edge detection can also be performed on it to extract and enhance the edge information in the point cloud data so that the histogram distribution of the point cloud data is more uniform.

[0049] After performing histogram equalization and edge detection processing on the point cloud data to obtain a uniformly distributed histogram of the point cloud data, an embodiment of the present invention performs coordinate transformation processing on the point cloud data. All point cloud data are transformed into a unified coordinate system by means of coordinate transformation such as translation, rotation, and scaling of the point cloud data. The unified coordinate system is a pre-acquired unified coordinate system of the associated transmission tower. Transforming all point cloud data into the unified coordinate system can provide a consistent and accurate coordinate framework for subsequent three-dimensional reconstruction of the transmission tower.

[0050] Step S102, obtaining standard information of different types of transmission towers, establishing a standard model library, classifying and processing the point cloud data, and capturing key points of the transmission towers, wherein the standard information at least includes: design specification documents and manufacturing standard documents of the transmission towers, and the key points at least include: grounding points, cross arm points, and tower body points of the transmission towers.

[0051] In the embodiment of the present invention, it is necessary to establish a standard model library, which stores three-dimensional models of different types of transmission towers. By acquiring standard information of different types of transmission towers, three-dimensional models of different types of transmission towers are established using three-dimensional modeling software. Types of transmission towers include drum-shaped towers, goat-shaped towers, "JG"-shaped towers, cup-shaped towers, cathead towers, and steel pipe towers. All types of transmission tower three-dimensional models are stored in the standard model library to meet the needs of different modeling scenarios.

[0052] It should be noted that the standard information of transmission towers includes the design specification documents and manufacturing standard documents of transmission towers. The design specification documents stipulate the drawing and construction standards of transmission towers, including detailed provisions on tower segmentation, component numbering, tower structure, combined components, steel pipe structure, etc. The manufacturing standard documents stipulate the manufacturing process of towers and similar steel structural parts, including detailed provisions on raw material requirements, manufacturing process, quality assurance, etc.

[0053] This step also includes classifying the point cloud data and capturing the key points of the transmission tower according to the classification results. The key points of the transmission tower include the grounding point, cross arm point and tower body point of the transmission tower, among which the grounding point is the part of the transmission tower that contacts the ground, usually referring to the base of the transmission tower; the cross arm point is an important component of the transmission tower, and its function is to install insulating equipment, and the cross arm point is located between the two cross arms and the tower body; the tower body point is the main part of the transmission tower, including the tower head, tower body and tower legs. The position of the tower body point covers the main structure of the entire transmission tower, and is a key part of supporting and fixing transmission lines such as conductors and lightning arresters. The embodiment of the present invention captures these key points of the transmission tower by classifying the point cloud data, which provides an important reference for the subsequent three-dimensional reconstruction process.

[0054] Optionally, the step of obtaining standard information of different types of transmission towers and establishing a standard model library includes: based on the standard information of different types of transmission towers, using three-dimensional modeling software to establish a three-dimensional model of the transmission tower; using a parametric design strategy to define a set of parameters for each type of three-dimensional model, wherein each set of parameters includes at least: the height, width and node spacing of the transmission tower; and establishing a standard model library, which includes three-dimensional models of all types of transmission towers.

[0055] In an embodiment of the present invention, when establishing a standard model library, it is first necessary to obtain standard information of different types of transmission towers, and the standard information includes design specification files and manufacturing standard files of transmission towers. Based on these files, three-dimensional modeling software is used to establish three-dimensional models of different types of transmission towers. At the same time, the embodiment of the present invention adopts a parametric design strategy to define a set of parameters for each type of three-dimensional model, and each set of parameters at least includes the height, width and node spacing of the transmission tower, so that variants of the three-dimensional model can be quickly generated by adjusting these parameters in the subsequent three-dimensional modeling process. Finally, all types of transmission tower three-dimensional models and their corresponding parameters are stored in the standard model library. The standard model library provides support for the subsequent rapid generation of preliminary three-dimensional models, which can not only shorten the time of three-dimensional reconstruction, but also improve the accuracy of the preliminary three-dimensional model, laying a solid foundation for the refinement and optimization of the subsequent model.

[0056] Optionally, the step of classifying and processing the point cloud data to capture the key points of the transmission tower includes: calculating the covariance matrix of the point cloud data to obtain multiple eigenvalues ​​of the covariance matrix; determining the dominant direction of the point cloud data based on the multiple eigenvalues, wherein the dominant direction reflects the distribution direction of the point cloud data; based on the multiple eigenvalues ​​and the dominant direction, using a clustering algorithm to classify the point cloud data to obtain multiple local areas, wherein each local area includes multiple point cloud data; using a surface fitting algorithm to obtain the surface equation of the local area, and based on the parameters of the surface equation, calculating the local curvature of all the point cloud data; based on the local curvature, using a feature point detection algorithm to capture the key points of the transmission tower, and recording the coordinates of the key points in a unified coordinate system.

[0057] In the embodiment of the present invention, when classifying the point cloud data, it is first necessary to calculate the covariance matrix of the point cloud data, obtain multiple eigenvalues ​​in the covariance matrix, and then select the largest eigenvalue from the multiple eigenvalues. The process can be expressed as:

[0058] λ max =max(λ1,λ2,λ3);

[0059] Among them, λ1, λ2, and λ3 are the three eigenvalues ​​of the covariance matrix of the point cloud data, λ max is the maximum eigenvalue. The maximum eigenvalue is used to identify the dominant direction of the point cloud data. The dominant direction of the point cloud data is the direction in which the data points are most significantly distributed along a specific direction, reflecting the main expansion direction and shape characteristics of the point cloud data. The dominant direction can reflect the distribution characteristics of the point cloud data and provide higher accuracy and efficiency for the 3D reconstruction process.

[0060] After obtaining the eigenvalues ​​and dominant directions of the covariance matrix of the point cloud data, the embodiment of the present invention uses a clustering algorithm to classify the point cloud data to obtain multiple local areas of the point cloud data. The clustering algorithm can select the K-means algorithm or the DBSCAN algorithm. After classifying the point cloud data, a surface fitting algorithm is used to fit the local area of ​​the point cloud data to obtain the surface equation of the local area, and the equation parameters are extracted therefrom to calculate the local curvature of the point cloud data. The calculation formula of the local curvature is:

[0061]

[0062] Among them, A, B, C, D, E, and F are parameters of the surface equation. When fitting a local area of ​​point cloud data, the surface fitting algorithm can use the least squares method. The least squares method finds the best surface match for the data by minimizing the sum of squares of errors. Its goal is to find a surface that minimizes the sum of squares of distances from all point cloud data in the local area to the curve. The equation corresponding to the surface is the surface equation of the local area. The embodiment of the present invention extracts A, B, C, D, E, and F parameters from the surface equation to calculate the local curvature of the point cloud data.

[0063] Then, based on the local curvature, the feature point detection algorithm is used to capture the key points of the transmission tower and record the coordinates of the key points in the unified coordinate system. Before capturing the key points, it is necessary to first formulate the key point capture rules. For the grounding point, since the grounding point is near the ground, the key point is captured in the area with small local curvature; for the cross arm point, in the area with large local curvature, the key point is captured by detecting whether there is a significant horizontal protruding line segment; for the tower body point, the key point is captured by finding an area with vertical extension and checking the change of local curvature. The feature point detection algorithm can accurately capture each key point of the transmission tower according to the formulated key point capture rules, and finally record the coordinates of all key points in the unified coordinate system.

[0064] Step S103, based on the key points of the transmission tower, a matching model is obtained from a standard model library, and a geometric transformation is performed on the matching model to obtain a preliminary three-dimensional model of the transmission tower.

[0065] In the embodiment of the present invention, the key points of the transmission tower are obtained according to the collected point cloud data, and a standard model library is established at the same time. This step obtains a matching model from the standard model library based on the key points of the transmission tower, and then performs geometric transformation on the matching model to obtain a preliminary three-dimensional model of the transmission tower. The matching model is a three-dimensional model of the transmission tower that is closest to the task object. By performing geometric transformation operations such as translation, rotation, and scaling on the matching model, the error between the matching model and the task object is minimized, thereby obtaining a three-dimensional model of the transmission tower that is closer to the task object, that is, a preliminary three-dimensional model.

[0066] Optionally, the step of obtaining a matching model from a standard model library based on the key points of the transmission tower includes: obtaining simulation key points of the three-dimensional model of each type of transmission tower in the standard model library; calculating the Euclidean distance between the key points of the transmission tower and the simulation key points of each type, and determining the matching model based on multiple Euclidean distances.

[0067] In the embodiment of the present invention, the matching model is obtained from the standard model library based on the key points of the transmission tower, which is achieved by determining the Euclidean distance between the key points of the transmission tower and the simulation key points in the standard model library.

[0068] The standard model includes multiple types of transmission tower 3D models. Each type of transmission tower 3D model has its own set of key points. In the embodiment of the present invention, each set of key points is called simulation key points. The Euclidean distance between the key points of the transmission tower to be reconstructed (i.e., the task object) and the simulation key points is:

[0069]

[0070] Among them, k i is the key point set of the task object, k i Include key points x i ,y i 、z i , m j is a set of simulated key points, including the key point x j ,y j 、z j , d(k i ,m j ) is the Euclidean distance between the key points of the task object and the simulation key points. After calculating the Euclidean distance between the key points of the task object and all types of simulation key points, the transmission tower model corresponding to the set of simulation key points with the smallest Euclidean distance is determined as the matching model.

[0071] Finally, the matching model is subjected to geometric transformation operations such as translation, rotation, and scaling to minimize the Euclidean distance between the key points of the matching model and the key points of the task object, thereby minimizing the error between the matching model and the task object, thereby obtaining a three-dimensional model of the transmission tower that is closer to the task object, namely the preliminary three-dimensional model.

[0072] Step S104, based on the physical constraints and a priori constraints of the transmission tower, a random iterative algorithm is used to refine the preliminary three-dimensional model to obtain a refined three-dimensional model, wherein the physical constraints reflect the actual physical characteristics of the transmission tower, and the a priori constraints reflect the historical three-dimensional reconstruction information of the transmission tower.

[0073] In the embodiment of the present invention, after obtaining the preliminary three-dimensional model, it is necessary to refine it. The refinement process first needs to obtain the physical constraints and prior constraints of the transmission tower, the physical constraints reflect the actual physical characteristics of the transmission tower, and the prior constraints reflect the historical three-dimensional reconstruction information of the transmission tower, integrate the physical constraints and prior constraints into the random iterative algorithm, and use the integrated random iterative algorithm to refine the preliminary three-dimensional model to obtain a refined three-dimensional model.

[0074] The embodiment of the present invention integrates physical constraints and prior constraints into a random iterative algorithm, and uses the integrated random iterative algorithm to refine the preliminary three-dimensional model, thereby optimizing the preliminary three-dimensional model and significantly improving the accuracy of the three-dimensional model.

[0075] Optionally, the step of refining the preliminary three-dimensional model using a random iterative algorithm includes: initializing the random iterative algorithm, determining a first objective function and an algorithm iteration number, the first objective function including a data fidelity term and a regularization term, wherein the data fidelity term is the difference value between the point cloud data of the transmission tower and the point cloud data of the matching model, and the regularization term is a priori constraint condition; based on the actual physical characteristics of the transmission tower, obtaining the physical constraint conditions of the transmission tower, wherein the actual physical characteristics at least include: material property data, force condition data, and the physical constraint conditions at least include: tower stress limit, tower strain limit and tower displacement limit; based on the physical constraint conditions, obtaining a physical constraint term, adding the physical constraint term to the first objective function to obtain a second objective function; executing the iterative process of the random iterative algorithm, and obtaining a refined three-dimensional model when the algorithm iteration number is reached.

[0076] When the embodiment of the present invention refines the preliminary three-dimensional model, the first step is to initialize the random iterative algorithm, determine the first objective function and the number of algorithm iterations, the number of algorithm iterations determines the convergence condition of the random iterative algorithm, the first objective function is the optimization function of the random iterative algorithm, and the refinement of the preliminary three-dimensional model is achieved by minimizing the first objective function. The first objective function is:

[0077]

[0078] Among them, ￡(x) is the first objective function, x represents the parameters of the transmission tower model to be reconstructed, is the data assurance term, y is the point cloud data of the transmission tower, Ax is the point cloud data of the matching model, λR(x) is the regularization term, and λ is the regularization parameter. The data fidelity term is the difference between the point cloud data of the transmission tower and the point cloud data of the matching model, which is used to measure the difference between the real data and the simulated data. The regularization term represents the prior constraint condition, which is used to introduce prior knowledge to prevent the first objective function from overfitting. The regularization parameter is used to control the weights of the data fidelity term and the regularization term to adapt to different 3D reconstruction scenarios.

[0079] The second step is to obtain the physical constraints of the transmission tower according to the actual physical characteristics of the transmission tower. The transmission tower is the task object. The actual physical characteristics of the transmission tower include material property data and force condition data. These actual physical characteristics are converted into physical constraints and integrated into the random iterative algorithm. This step converts the physical properties and mechanical behaviors reflected by the actual physical characteristics into physical constraints of mathematical nature, so that the preliminary three-dimensional model can take into account the physical constraints during the refinement process, ensuring that the model gradually approaches the actual structure. Among them, the physical constraints include tower stress limit, tower strain limit and tower displacement limit.

[0080] The third step is to obtain the physical constraint terms based on the physical constraint conditions, and then add the physical constraint terms to the first objective function to obtain the second objective function, that is, to integrate the physical constraint terms into the random iterative algorithm. The second objective function is:

[0081]

[0082] Among them, ￡(x) is the first objective function, x represents the parameters of the transmission tower model to be reconstructed, is the data assurance item, y is the point cloud data of the transmission tower, Ax is the point cloud data of the matching model, λR(x) is the regularization item, λ is the regularization parameter, ηP(x) is the physical constraint item, η is the physical constraint parameter, which is used to adjust the weight of the physical constraint item. The embodiment of the present invention integrates the physical constraint item into the random iterative algorithm, so that the preliminary three-dimensional model can simultaneously consider data fidelity, regularization and physical constraints during the refinement process, ensuring that the model gradually approaches the actual structure.

[0083] Finally, the iterative process of the random iterative algorithm is executed. In each iteration, the model is adjusted according to the physical constraints and prior constraints to gradually reduce the difference between the model and the actual structure. In order to speed up the convergence of the algorithm, the model can also be updated in a random order to avoid the algorithm falling into the dilemma of local optimal solution. At the same time, a substitution function strategy is used to evaluate and optimize the update of the model, thereby improving the accuracy of the model. Finally, when the number of algorithm iterations is reached, the execution of the random iterative algorithm is stopped and the execution result is obtained, that is, the three-dimensional model is refined.

[0084] Step S105, aligning and fusing the preliminary 3D model and the refined 3D model to obtain a target 3D model, and generating a standard format file of the target 3D model.

[0085] In the embodiment of the present invention, after obtaining the preliminary 3D model and the refined 3D model respectively, an alignment algorithm is used to accurately align the preliminary 3D model and the refined 3D model to ensure that the position and posture of the target 3D model outputted in the end match the actual structure. The alignment algorithm can select the iterative closest point algorithm. After achieving precise alignment, a fusion algorithm is used to fuse the preliminary 3D model and the refined 3D model to ensure the integrity and detail accuracy of the target 3D model outputted in the end. The fusion algorithm can select Boolean operation.

[0086] After the preliminary 3D model and the refined 3D model are aligned and fused, the target 3D model can be output, which is the 3D reconstruction result of the task object (i.e., the transmission tower to be reconstructed) according to the embodiment of the present invention. The embodiment of the present invention can also generate a standard format file of the target 3D model, such as an STL or OBJ format file, so that it can be used in 3D printing, virtual reality display or other engineering analysis work, ensuring that the output standard format file has compatibility and data integrity.

[0087] Optionally, after aligning and fusing the preliminary three-dimensional model and the refined three-dimensional model to obtain the target three-dimensional model, the method includes: calculating the three-dimensional reconstruction error based on the key points of the transmission tower and the key points of the target three-dimensional model; adjusting the geometric features and edge details of the target three-dimensional model based on the three-dimensional reconstruction error, wherein the edge details include: edge smoothness and edge clarity; generating a three-dimensional reconstruction report of the transmission tower based on the target three-dimensional model, wherein the three-dimensional reconstruction report includes at least: reconstruction process, reconstruction parameters and error analysis.

[0088] In the embodiment of the present invention, after obtaining the target three-dimensional model, it can also be fine-tuned. The accuracy of the target three-dimensional model is evaluated by calculating the error between the key points of the transmission tower and the key points of the target three-dimensional model. The error calculation formula is:

[0089]

[0090] Among them, Error is the error between the key points of the transmission tower and the key points of the target 3D model, p i is the key point set of the transmission tower, q i is the set of key points of the target three-dimensional model, and N is the number of key points. By calculating the error, the accuracy of the target three-dimensional model can be evaluated, and then the geometric features and edge details of the target three-dimensional model can be adjusted according to the accuracy evaluation result. For example, the total variation regularization algorithm can be used to further balance the edge smoothness and edge clarity of the target three-dimensional model, so that the target three-dimensional model can avoid excessively smooth edges while maintaining high edge clarity. Finally, based on the target three-dimensional model, a three-dimensional reconstruction report of the transmission tower is generated. The three-dimensional reconstruction report records in detail the reconstruction process, reconstruction parameters and error analysis of the embodiment of the present invention, and retains a complete document record for the embodiment of the present invention.

[0091] Figure 2 is a flowchart of an optional method for 3D reconstruction of a transmission tower based on a standard model library according to an embodiment of the present invention. Figure 2In the process, after collecting the point cloud data of the transmission tower, the data preprocessing and path planning module is entered. In this module, the mobile scanning device is path-planned and the point cloud data is preprocessed. The preprocessing includes filtering, coordinate transformation of the point cloud data, and unification of the coordinate system of all point cloud data. Then the standard model library and preliminary model establishment module are entered. In this module, a standard model library is built, and the point cloud data is classified and processed. By matching the key points of the point cloud data with the simulation key points in the standard model library, a matching model is obtained, thereby establishing a preliminary three-dimensional model. Then the model refinement and optimization module is entered. By initializing the random iteration algorithm, physical constraints are established, and the prior constraints and physical constraints are integrated into the random iteration algorithm. The integrated random iteration algorithm is used to iteratively refine the preliminary three-dimensional model to obtain a refined three-dimensional model. Finally, the model accuracy verification and output module is entered. In this module, the preliminary three-dimensional model and the refined three-dimensional model are aligned and fused to obtain the target three-dimensional model. Then the target three-dimensional model is verified for accuracy, and the target three-dimensional model is carefully adjusted to finally generate a standard format file.

[0092] The above-mentioned three-dimensional reconstruction method of the transmission tower based on the standard model library provides the steps of collecting point cloud data of the transmission tower through a mobile scanning device, filtering and coordinate transformation processing the point cloud data to obtain point cloud data of a unified coordinate system, establishing a standard model library by acquiring standard information of different types of transmission towers, classifying the point cloud data, capturing the key points of the transmission tower, and then obtaining a matching model from the standard model library based on the key points of the transmission tower, performing geometric transformation on the matching model to obtain a preliminary three-dimensional model of the transmission tower, and then based on the physical constraints and prior constraints of the transmission tower, using a random iterative algorithm to refine the preliminary three-dimensional model to obtain a refined three-dimensional model, and finally aligning and fusion processing the preliminary three-dimensional model and the refined three-dimensional model to obtain a target three-dimensional model, and generating a standard format file of the target three-dimensional model.

[0093] The embodiment of the present invention realizes automated and efficient data collection by using a mobile scanning device to collect point cloud data of a transmission tower. By establishing a standard model library, the efficiency and adaptability of generating a preliminary three-dimensional model are greatly improved. By combining the physical constraints and prior constraints of the transmission tower, a random iterative algorithm is used to refine the preliminary three-dimensional model, which significantly improves the accuracy of the three-dimensional model, thereby realizing three-dimensional reconstruction of transmission towers of different scales and complexities, thereby achieving the technical effect of improving the efficiency, accuracy and applicability of the three-dimensional reconstruction of transmission towers, and improving the level of automation of three-dimensional reconstruction.

[0094] The following is a detailed description in conjunction with another embodiment.

[0095] Embodiment 2

[0096] The three-dimensional reconstruction device for a transmission tower based on a standard model library provided in this embodiment includes a plurality of implementation units, each of which corresponds to each implementation step in the above-mentioned first embodiment.

[0097] Figure 3 is a schematic diagram of an optional transmission tower 3D reconstruction device based on a standard model library according to an embodiment of the present invention, such as Figure 3 As shown, the transmission tower 3D reconstruction device based on the standard model library may include: a collection unit 30 , a classification unit 31 , a transformation unit 32 , a processing unit 33 , and a generation unit 34 .

[0098] The acquisition unit 30 is used to acquire point cloud data of the transmission tower through a mobile scanning device, and to perform filtering and coordinate transformation on the point cloud data to obtain point cloud data of a unified coordinate system.

[0099] The classification unit 31 is used to obtain standard information of different types of transmission towers, establish a standard model library, classify and process the point cloud data, and capture the key points of the transmission towers, wherein the standard information at least includes: design specification documents and manufacturing standard documents of the transmission towers, and the key points at least include: grounding wire points, cross arm points, and tower body points of the transmission towers.

[0100] The transformation unit 32 obtains a matching model from a standard model library based on key points of the transmission tower, performs geometric transformation on the matching model, and obtains a preliminary three-dimensional model of the transmission tower.

[0101] The processing unit 33 refines the preliminary three-dimensional model based on the physical constraints and prior constraints of the transmission tower using a random iterative algorithm to obtain a refined three-dimensional model, wherein the physical constraints reflect the actual physical characteristics of the transmission tower, and the prior constraints reflect the historical three-dimensional reconstruction information of the transmission tower.

[0102] The generating unit 34 is used to perform alignment processing and fusion processing on the preliminary 3D model and the refined 3D model to obtain a target 3D model and generate a standard format file of the target 3D model.

[0103] The above-mentioned transmission tower three-dimensional reconstruction device based on the standard model library can collect point cloud data of the transmission tower through the collection unit 30 mobile scanning device, filter and coordinate transform the point cloud data to obtain point cloud data of a unified coordinate system, obtain standard information of different types of transmission towers through the classification unit 31, establish a standard model library, classify the point cloud data, capture the key points of the transmission tower, and then obtain the matching model from the standard model library based on the key points of the transmission tower through the transformation unit 32, perform geometric transformation on the matching model, and obtain a preliminary three-dimensional model of the transmission tower, and then refine the preliminary three-dimensional model based on the physical constraints and prior constraints of the transmission tower through the processing unit 33 using a random iterative algorithm to obtain a refined three-dimensional model, and finally align and fuse the preliminary three-dimensional model and the refined three-dimensional model through the generation unit 34 to obtain a target three-dimensional model, and generate a standard format file of the target three-dimensional model. This embodiment realizes automated and efficient data collection by using mobile scanning equipment to collect point cloud data of transmission towers. By establishing a standard model library, the efficiency and adaptability of generating preliminary three-dimensional models are greatly improved. By combining the physical constraints and prior constraints of the transmission towers, a random iterative algorithm is used to refine the preliminary three-dimensional model, which significantly improves the accuracy of the three-dimensional model, thereby realizing three-dimensional reconstruction of transmission towers of different scales and complexities, and further solving the technical problems of low accuracy and applicability and low degree of automation in the three-dimensional reconstruction of transmission towers in related technologies.

[0104] Optionally, the acquisition unit 30 includes: a planning module, which is used to use a geographic information system to analyze the terrain and landforms of the location of the transmission tower, and based on the geographical information of the terrain, landforms and transmission lines, use a path optimization algorithm to plan the flight path of the mobile scanning device; a setting module, which is used to set the flight parameters of the mobile scanning device, and the flight parameters include at least: flight altitude, flight speed and flight attitude.

[0105] Optionally, the acquisition unit 30 also includes: a separation module, which is used to perform ground point separation processing on the point cloud data to remove the ground point data; a denoising module, which is used to perform denoising processing on the point cloud data to filter out noise data; an equalization module, which is used to perform histogram equalization processing and edge detection processing on the point cloud data to obtain a uniform distribution of the histogram of the point cloud data; a coordinate transformation module, which is used to obtain a unified coordinate system of the associated transmission tower, and perform coordinate transformation processing on the point cloud data based on the unified coordinate system to obtain the point cloud data under the unified coordinate system.

[0106] Optionally, the classification unit 31 includes: a first establishment module, which uses three-dimensional modeling software to establish a three-dimensional model of a transmission tower based on standard information of different types of transmission towers; a definition module, which uses a parametric design strategy to define a set of parameters for each type of three-dimensional model, wherein each set of parameters includes at least: the height, width and node spacing of the transmission tower; and a second establishment module, which is used to establish a standard model library, which includes three-dimensional models of all types of transmission towers.

[0107] Optionally, the classification unit 31 also includes: a first calculation module, used to calculate the covariance matrix of the point cloud data and obtain multiple eigenvalues ​​of the covariance matrix; a determination module, based on multiple eigenvalues, determines the dominant direction of the point cloud data, wherein the dominant direction reflects the distribution direction of the point cloud data; a data classification module, based on multiple eigenvalues ​​and dominant directions, uses a clustering algorithm to classify the point cloud data to obtain multiple local areas, wherein each local area includes multiple point cloud data; a fitting module, used to use a surface fitting algorithm to obtain the surface equation of the local area, and based on the parameters of the surface equation, calculate the local curvature of all point cloud data; a recording module, based on the local curvature, uses a feature point detection algorithm to capture the key points of the transmission tower, and records the coordinates of the key points in a unified coordinate system.

[0108] Optionally, the transformation unit 32 includes: a first acquisition module, used to obtain the simulation key points of the three-dimensional model of each type of transmission tower in the standard model library; a second calculation module, used to calculate the Euclidean distance between the key points of the transmission tower and each type of simulation key points, and determine the matching model based on multiple Euclidean distances.

[0109] Optionally, the processing unit 33 includes: an initialization module, used to initialize the random iterative algorithm, determine the first objective function and the number of algorithm iterations, the first objective function includes a data fidelity term and a regularization term, wherein the data fidelity term is the difference value between the point cloud data of the transmission tower and the point cloud data of the matching model, and the regularization term is a priori constraint condition; a second acquisition module, based on the actual physical characteristics of the transmission tower, to obtain the physical constraint conditions of the transmission tower, wherein the actual physical characteristics at least include: material property data, force condition data, and the physical constraint conditions at least include: tower stress limit, tower strain limit and tower displacement limit; a third calculation module, based on the physical constraint conditions, to obtain the physical constraint terms, add the physical constraint terms to the first objective function, and obtain the second objective function; an execution module, used to execute the iterative process of the random iterative algorithm, and obtain the refined three-dimensional model when the number of algorithm iterations is reached.

[0110] Optionally, the generation unit 34 includes: a fourth calculation module, which calculates the three-dimensional reconstruction error based on the key points of the transmission tower and the key points of the target three-dimensional model; an adjustment module, which adjusts the geometric features and edge details of the target three-dimensional model based on the three-dimensional reconstruction error, wherein the edge details include: edge smoothness and edge clarity; a report generation module, which generates a three-dimensional reconstruction report of the transmission tower based on the target three-dimensional model, wherein the three-dimensional reconstruction report includes at least: reconstruction process, reconstruction parameters and error analysis.

[0111] The above-mentioned transmission tower three-dimensional reconstruction device based on the standard model library may also include a processor and a memory. The above-mentioned acquisition unit 30, classification unit 31, transformation unit 32, processing unit 33, generation unit 34, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0112] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the transmission tower can be reconstructed in three dimensions by adjusting kernel parameters.

[0113] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0114] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for three-dimensional reconstruction of transmission towers based on a standard model library in the above-mentioned embodiment 1.

[0115] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the three-dimensional reconstruction method of a transmission tower based on a standard model library according to any one of the above-mentioned embodiments.

[0116] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for three-dimensional reconstruction of a transmission tower based on a standard model library described in each embodiment of the present application.

[0117] The present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for three-dimensional reconstruction of a transmission tower based on a standard model library described in each embodiment of the present application are implemented.

[0118] Figure 4 1 is a hardware structure block diagram of an electronic device (or mobile device) for a method for 3D reconstruction of a transmission tower based on a standard model library according to an embodiment of the present invention. Figure 4 As shown, the electronic device may include one or more ( Figure 4 402a, 402b, ..., 402n are used to illustrate) processor 402 (processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), memory 404 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components as shown, or with Figure 4 Different configurations are shown.

[0119] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0120] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0123] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0124] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0125] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A three-dimensional reconstruction method for transmission towers based on a standard model library, characterized in that: include: Collecting point cloud data of the transmission tower by a mobile scanning device, filtering and coordinate transformation the point cloud data, and obtaining point cloud data of a unified coordinate system; Acquire standard information of different types of transmission towers, establish a standard model library, classify and process the point cloud data, and capture key points of the transmission towers, wherein the standard information at least includes: design specification files and manufacturing standard files of the transmission towers, and the key points at least include: grounding wire points, cross arm points, and tower body points of the transmission towers; Based on the key points of the transmission tower, a matching model is obtained from the standard model library, and a geometric transformation is performed on the matching model to obtain a preliminary three-dimensional model of the transmission tower; Based on the physical constraints and a priori constraints of the transmission tower, the preliminary three-dimensional model is refined by using a random iterative algorithm to obtain a refined three-dimensional model, wherein the physical constraints reflect the actual physical characteristics of the transmission tower, and the a priori constraints reflect the historical three-dimensional reconstruction information of the transmission tower; The preliminary three-dimensional model and the refined three-dimensional model are aligned and fused to obtain a target three-dimensional model, and a standard format file of the target three-dimensional model is generated.

2. The method for three-dimensional reconstruction of a transmission tower according to claim 1, characterized in that: Before collecting point cloud data of transmission towers through mobile scanning equipment, it also includes: Using a geographic information system to analyze the topography and landforms of the location of the transmission tower, and using a path optimization algorithm to plan the flight path of the mobile scanning device based on the topography, landforms and geographic information of the transmission line; The flight parameters of the mobile scanning device are set, and the flight parameters at least include: flight altitude, flight speed and flight attitude.

3. The method for three-dimensional reconstruction of a transmission tower according to claim 1, characterized in that: The step of performing filtering and coordinate transformation on the point cloud data to obtain point cloud data in a unified coordinate system includes: Performing ground point separation processing on the point cloud data to remove ground point data; Performing denoising on the point cloud data to filter out noise data; Performing histogram equalization processing and edge detection processing on the point cloud data to obtain a uniform distribution of the histogram of the point cloud data; A unified coordinate system associated with the transmission tower is acquired, and coordinate transformation processing is performed on the point cloud data based on the unified coordinate system to obtain the point cloud data in the unified coordinate system.

4. The method for three-dimensional reconstruction of a transmission tower according to claim 1, characterized in that: The steps of obtaining standard information of different types of transmission towers and establishing a standard model library include: Based on the standard information of different types of transmission towers, a three-dimensional model of the transmission tower is established using three-dimensional modeling software; A parametric design strategy is adopted to define a set of parameters for each type of the three-dimensional model, wherein each set of parameters at least includes: the height, width and node spacing of the transmission tower; The standard model library is established, and the standard model library includes three-dimensional models of all types of transmission towers.

5. The method for 3D reconstruction of a transmission tower according to claim 1, characterized in that: The step of classifying the point cloud data and capturing the key points of the transmission tower comprises: Calculating a covariance matrix of the point cloud data, and obtaining a plurality of eigenvalues ​​of the covariance matrix; Determining a dominant direction of the point cloud data based on the multiple eigenvalues, wherein the dominant direction reflects a distribution direction of the point cloud data; Based on the multiple feature values ​​and the dominant directions, a clustering algorithm is used to classify the point cloud data to obtain multiple local areas, wherein each local area includes multiple point cloud data; A surface fitting algorithm is used to obtain a surface equation of the local area, and based on the parameters of the surface equation, the local curvature of all the point cloud data is calculated; Based on the local curvature, a feature point detection algorithm is used to capture key points of the transmission tower, and the coordinates of the key points in the unified coordinate system are recorded.

6. The method for three-dimensional reconstruction of a transmission tower according to claim 1, characterized in that: The step of acquiring a matching model from the standard model library based on the key points of the transmission tower comprises: Acquire simulation key points of each type of three-dimensional model of transmission tower in the standard model library; The Euclidean distances between the key points of the transmission tower and the simulation key points of each type are calculated, and the matching model is determined based on a plurality of the Euclidean distances.

7. The method for three-dimensional reconstruction of a transmission tower according to claim 1, characterized in that: The step of using the random iterative algorithm to refine the preliminary three-dimensional model includes: Initializing the random iterative algorithm, determining a first objective function and the number of algorithm iterations, wherein the first objective function includes a data fidelity term and a regularization term, wherein the data fidelity term is a difference value between the point cloud data of the transmission tower and the point cloud data of the matching model, and the regularization term is the prior constraint condition; Based on the actual physical characteristics of the transmission tower, obtaining physical constraints of the transmission tower, wherein the actual physical characteristics at least include: material property data, force condition data, and the physical constraints at least include: tower stress limit, tower strain limit, and tower displacement limit; Based on the physical constraint condition, a physical constraint term is obtained, and the physical constraint term is added to the first objective function to obtain a second objective function; The iterative process of the random iterative algorithm is executed, and when the number of algorithm iterations is reached, the refined three-dimensional model is obtained.

8. The method for 3D reconstruction of a transmission tower according to claim 1, characterized in that: After the preliminary three-dimensional model and the refined three-dimensional model are aligned and fused to obtain a target three-dimensional model, the method includes: Based on the key points of the transmission tower and the key points of the target three-dimensional model, a three-dimensional reconstruction error is calculated; Based on the 3D reconstruction error, adjusting the geometric features and edge details of the target 3D model, wherein the edge details include: edge smoothness and edge clarity; Based on the target three-dimensional model, a three-dimensional reconstruction report of the transmission tower is generated, wherein the three-dimensional reconstruction report at least includes: a reconstruction process, reconstruction parameters and error analysis.

9. A three-dimensional reconstruction device for transmission towers based on a standard model library, characterized in that: include: A collection unit, used to collect point cloud data of the transmission tower through a mobile scanning device, and perform filtering and coordinate transformation on the point cloud data to obtain point cloud data of a unified coordinate system; A classification unit is used to obtain standard information of different types of transmission towers, establish a standard model library, classify the point cloud data, and capture key points of the transmission towers, wherein the standard information at least includes: design specification files and manufacturing standard files of the transmission towers, and the key points at least include: grounding wire points, cross arm points, and tower body points of the transmission towers; A transformation unit, based on the key points of the transmission tower, obtains a matching model from the standard model library, performs geometric transformation on the matching model, and obtains a preliminary three-dimensional model of the transmission tower; A processing unit, based on the physical constraints and a priori constraints of the transmission tower, uses a random iterative algorithm to refine the preliminary three-dimensional model to obtain a refined three-dimensional model, wherein the physical constraints reflect the actual physical characteristics of the transmission tower, and the a priori constraints reflect the historical three-dimensional reconstruction information of the transmission tower; The generating unit is used to align and fuse the preliminary three-dimensional model and the refined three-dimensional model to obtain a target three-dimensional model, and generate a standard format file of the target three-dimensional model.

10. An electronic device, characterized in that: The method comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the three-dimensional reconstruction method of a transmission tower based on a standard model library as described in any one of claims 1 to 7.

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