Conductor reverse modeling method and system for all-outdoor transformer substation

Abstract

This invention discloses a method and system for reverse modeling conductors in fully outdoor substations, relating to the field of data processing technology. The method includes: reading conductor distribution design data of the target outdoor substation; analyzing the conductor morphology variation range under the influence of the external environment and constructing curve fitting constraints; performing virtual overlap avoidance analysis of conductors in the same scanning direction and planning the scanning path; scanning multiple conductor segments to obtain a point cloud scanning dataset; optimizing the fitting of multiple conductor segments in the point cloud scanning dataset to generate multiple actual fitted conductor segments; and performing three-dimensional reconstruction of the conductors of the target outdoor substation using the multiple actual fitted conductor segments. This solves the technical problem in existing technologies where conductor morphology is greatly affected by the external environment in a fully outdoor substation environment, leading to low accuracy and efficiency in reverse modeling. It achieves the technical effect of improving the accuracy and efficiency of conductor reverse modeling and realizing high-precision three-dimensional reconstruction of conductors.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for reverse modeling conductors in fully outdoor substations. Background Technology

[0002] In the construction and operation of fully outdoor substations, accurate modeling of the spatial distribution and morphology of conductors is crucial for equipment layout, operation monitoring, and safety assessment. However, due to the influence of external environmental factors (such as wind load, temperature changes, and electromagnetic effects), the actual morphology of conductors often deviates from the design data, making it difficult for traditional modeling methods to meet high-precision requirements. Existing reverse modeling techniques typically rely on manual measurement or reconstruction based on limited scanned point clouds, resulting in problems such as incomplete data acquisition, low modeling accuracy, and low computational efficiency. Summary of the Invention

[0003] This application provides a method and system for reverse modeling conductors in fully outdoor substations, which solves the technical problem in the prior art that the conductor morphology is greatly affected by the external environment in a fully outdoor substation environment, resulting in low reverse modeling accuracy and low modeling efficiency.

[0004] The first aspect of this application provides a method for reverse modeling conductors in fully outdoor substations, the method comprising:

[0005] The process involves: reading the conductor distribution design data of the target outdoor substation, which includes design data for multiple conductor segments, each segment being fixed at both ends by hardware components; analyzing the conductor morphology variation range under the influence of the external environment for these multiple conductor segments and constructing curve fitting constraints; performing virtual overlap avoidance analysis of conductors in the same scanning direction based on the conductor distribution design data and planning the scanning path; activating a laser scanning device to scan the multiple conductor segments according to the scanning path, denoising and stitching the obtained point cloud data to obtain a point cloud scan dataset; invoking the curve fitting constraints to optimize the fitting of the multiple conductor segments in the point cloud scan dataset, generating multiple actual fitted conductor segments; and using these multiple actual fitted conductor segments to perform three-dimensional reconstruction of the conductors of the target outdoor substation.

[0006] A second aspect of this application provides a reverse modeling system for conductors in fully outdoor substations, the system comprising:

[0007] The system includes a data acquisition module for reading conductor distribution design data of a target outdoor substation, wherein the conductor distribution design data includes design data for multiple conductor segments, each segment being fixed at both ends by hardware components; a first analysis module for analyzing the conductor morphology variation range under the influence of the external environment for the multiple conductor segments and constructing curve fitting constraints; a second analysis module for performing virtual overlap avoidance analysis of conductors in the same scanning direction based on the conductor distribution design data and planning the scanning path; a scanning module for activating a laser scanning device to scan the multiple conductor segments according to the scanning path, denoising and stitching the obtained point cloud data to obtain a point cloud scan dataset; an optimization module for calling the curve fitting constraints to perform fitting optimization of the multiple conductor segments in the point cloud scan dataset, generating multiple actual fitted conductor segments; and a 3D reconstruction module for performing 3D reconstruction of the conductors of the target outdoor substation using the multiple actual fitted conductor segments.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the conductor distribution design data of the target outdoor substation is read. This data includes design data for multiple conductor segments, each connected and fixed at both ends by hardware components. Next, the conductor morphology variation range under the influence of the external environment is analyzed for these segments, and curve fitting constraints are constructed. Then, based on the conductor distribution design data, virtual overlap avoidance analysis of conductors in the same scanning direction is performed, and a scanning path is planned. Further, a laser scanning device is activated to scan the multiple conductor segments according to the scanning path. The resulting point cloud data is denoised and stitched to obtain a point cloud scan dataset. Next, the curve fitting constraints are invoked to optimize the fitting of the multiple conductor segments in the point cloud scan dataset, generating multiple actual fitted conductor segments. Finally, the 3D reconstruction of the conductors of the target outdoor substation is performed using these actual fitted conductor segments. This method solves the technical problem in existing technologies where conductor morphology is greatly affected by the external environment in a fully outdoor substation environment, leading to low accuracy and efficiency in reverse modeling. It achieves the technical effect of improving the accuracy and efficiency of conductor reverse modeling and realizing high-precision 3D reconstruction of conductors. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1A schematic diagram of the reverse modeling method for conductors in fully outdoor substations provided in this application embodiment;

[0012] Figure 2 This is a schematic diagram of the conductor reverse modeling system for fully outdoor substations provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached figures: Data acquisition module 11, First analysis module 12, Second analysis module 13, Scanning module 14, Optimization module 15, 3D reconstruction module 16. Detailed Implementation

[0014] This application provides a method and system for reverse modeling conductors in fully outdoor substations, which solves the technical problem in the prior art that the conductor morphology is greatly affected by the external environment in the fully outdoor substation environment, resulting in low reverse modeling accuracy and low modeling efficiency.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides a method for reverse modeling conductors in fully outdoor substations, wherein the method includes:

[0018] Read the conductor distribution design data of the target outdoor substation, wherein the conductor distribution design data includes the design data of multiple conductor segments, and the two ends of each conductor segment are connected and fixed by hardware components.

[0019] Read the conductor distribution design data of the target outdoor substation from the construction drawings and CAD design files of the substation, and analyze the conductor geometric information, including conductor number, three-dimensional coordinates (X, Y, Z) of conductor endpoints, conductor material parameters (such as material type, diameter, tension, allowable sag, etc.), and conductor connection hardware information, including hardware type (such as tension clamp, suspension clamp) and its fixing method (such as bolt fixing, welding fixing).

[0020] An analysis of the morphological change range of the multiple conductor segments under the influence of the external environment was conducted, and curve fitting constraints were constructed.

[0021] Based on the conductor distribution design data, structural information of each conductor segment is extracted, including conductor material, diameter, initial tension, suspension length, and fixing method. Simultaneously, the service life of the corresponding conductor is obtained to ensure that the morphological change analysis accurately reflects the actual state of the conductor. Then, the geographical location information of the target outdoor substation is read, and external influence parameters of the conductor's environment are constructed through historical environmental data retrieval. These parameters include temperature changes, wind load, rainfall, humidity, and electromagnetic influences, some of which exhibit periodic characteristics, such as daily and seasonal variations in temperature and wind force. Next, based on the conductor structural information, service life, and environmental parameters, a pre-trained digital simulation model of conductor motion is invoked. This model simulates the dynamic morphological changes of the conductor under different environmental conditions through finite element analysis or physical modeling to calculate the possible deviation range of the conductor trajectory. First, a static simulation is performed, loading the initial state of the conductor and historical environmental data at the current laser scanning moment to calculate the static conductor trajectory. Then, a dynamic simulation is performed, collecting current environmental data in real time and loading it into the simulation model to calculate the dynamic deviation of the current conductor trajectory. Finally, by comparing and analyzing static and dynamic trajectories, the trajectory range of the conductor shape change is determined, and curve fitting constraints are constructed based on this to ensure that the actual conductor shape can be accurately fitted during reverse modeling, thereby improving modeling accuracy and stability.

[0022] Furthermore, an analysis of the morphological change range of the multiple conductor segments under the influence of the external environment is conducted, and curve fitting constraints are constructed, including:

[0023] Extract the first conductor segment from the multiple conductor segments; read the first conductor structure information and first service duration of the first conductor segment based on the conductor distribution design data; combine the first conductor structure information and the first service duration to perform conductor morphology change simulation under preset external environmental parameters, construct the first curve fitting constraint, and add it to the curve fitting constraint.

[0024] First, the first conductor segment from a multi-segment conductor is extracted. Based on the conductor distribution design data, the structural information and service duration of the first conductor segment are retrieved. The structural information includes parameters such as material type, diameter, initial tension, suspension length, and fixing method. The service duration is used to assess changes in the conductor's physical properties due to long-term operation, such as decreased elastic modulus or fatigue damage. Next, morphological changes of the conductor are simulated under preset external environmental parameters, including but not limited to temperature, wind speed, wind direction, humidity, rainfall, and electromagnetic influences, constructed using historical environmental data combined with geographical location parameters. Then, a pre-trained digital simulation model of conductor motion is invoked. This model, based on finite element analysis or dynamic modeling methods, simulates the morphological changes of the conductor under different environmental conditions. First, a static simulation is performed at the current laser scanning moment, loading the initial state of the conductor and historical environmental parameters to calculate the static conductor trajectory. Then, a dynamic simulation is performed, loading the current environmental parameters and calculating the dynamic trajectory offset of the conductor. By analyzing the range of change between the static and dynamic trajectories, the trajectory change interval of the first conductor segment is generated, and this is used to construct the first curve fitting constraint to ensure the optimization of the conductor's fitting accuracy during reverse modeling. Finally, the first curve fitting constraint is added to the curve fitting constraint set to support the fitting optimization of subsequent multiple traverse segments, thereby improving the accuracy and stability of the entire traverse 3D reconstruction.

[0025] Furthermore, combining the first conductor structure information and the first service duration, a conductor morphology change simulation is performed under preset external environmental parameters to construct a first curve fitting constraint, including:

[0026] The geographical location of the target outdoor substation is read; based on the geographical location, the preset external environment parameters are constructed by retrieving historical environmental data; a digital simulation model of conductor motion based on conductor structure, environmental conditions, and service duration is trained; the first conductor structure information, the first service duration, and the preset external environment parameters are loaded into the digital simulation model of conductor motion to perform simulation and generate the conductor trajectory change range; the first curve fitting constraint is constructed based on the conductor trajectory change range.

[0027] Based on the structural information and service duration of the first conductor, the geographical location information of the target outdoor substation is first read to obtain the environmental characteristics of the area where the conductor is located. Based on the geographical location, a historical environmental database is accessed to retrieve long-term accumulated environmental data, including temperature, wind speed, wind direction, humidity, precipitation, and electromagnetic radiation levels, and their changing trends are analyzed to construct preset external environmental parameters that conform to the actual situation. Some environmental factors (such as temperature and wind speed) exhibit periodic variation characteristics; their periodic patterns can be extracted using statistical analysis methods (such as time series analysis or wavelet transform) to improve simulation accuracy. Subsequently, based on the conductor structure, environmental conditions, and service duration, a digital simulation model of conductor motion is trained. This model can employ finite element analysis (FEA), multibody dynamics (MBD), or a machine learning-based regression model to simulate the influence of different environmental factors on the conductor morphology. After training, the digital simulation model of conductor motion is invoked, and the structural information, service duration, and constructed preset external environmental parameters of the first conductor are loaded to execute simulation calculations. In the simulation process, static analysis is first performed to calculate the traverse morphology under the absence of external dynamic interference. Then, based on dynamic simulation methods, external environmental influences are applied to obtain the traverse trajectory at different time points, and the trajectory range of traverse morphology changes is deduced from this. Finally, a first curve fitting constraint is constructed based on the calculated traverse trajectory change range. This constraint is used to guide the fitting optimization of subsequent point cloud data, ensuring accurate reconstruction of the traverse morphology and environmental adaptability during the reverse modeling process.

[0028] Furthermore, the preset external environment parameters include periodic environmental parameters.

[0029] Preset external environmental parameters include periodic environmental parameters, mainly referring to environmental factors that change regularly over time. These factors have a long-term cumulative impact on conductor morphology changes and exhibit periodic fluctuations on a specific time scale. For example, daily and seasonal temperature variations cause conductor morphology changes due to thermal expansion and contraction, and their periodicity can usually be derived from historical meteorological data analysis. Daily variations in wind speed and direction, and seasonal strong winds (such as monsoons or typhoons) also have significant periodic characteristics, which can affect the conductor's swing amplitude and stress state. In addition, environmental factors such as rainfall, humidity, and electromagnetic radiation (such as solar radiation intensity) may also have periodic characteristics, affecting the conductor's physical properties and long-term stability.

[0030] When constructing the preset external environmental parameters, based on the geographical location of the target outdoor substation, periodic factors are analyzed through historical environmental data retrieval. Time series modeling (such as Fourier transform, ARIMA model, or wavelet transform) is used to extract key periodic features, and statistical analysis methods (such as moving average and maximum / minimum normalization) are combined to obtain the range and fluctuation patterns of the periodic environmental parameters. Finally, these periodic environmental parameters are input into the conductor motion digital simulation model to more accurately simulate the conductor's morphological changes at different time points and provide a reliable basis for subsequent curve fitting constraint construction.

[0031] Furthermore, the conductor motion digital simulation model is loaded with the first conductor structure information, the first service duration, and the preset external environment parameters to perform simulation, generating the conductor trajectory change range, including:

[0032] The first conductor structure information, the first service duration, and the preset external environment parameters are simulated and loaded into the conductor motion digital simulation model. Static simulation at the current laser scanning moment is performed to generate a static conductor trajectory. Based on the static conductor trajectory, real-time environmental information at the current laser scanning moment is collected and loaded into the conductor motion digital simulation model for dynamic simulation to generate a dynamic conductor trajectory. The conductor trajectory change range is constructed with the static conductor trajectory as the starting point and the dynamic conductor trajectory as the ending point.

[0033] In the process of simulating the conductor's trajectory variation range by loading the structural information of the first conductor, its first service duration, and preset external environmental parameters into the conductor motion digital simulation model, the structural information of the first conductor (including the conductor's material, diameter, tension, and fixing method), service duration (used to evaluate the conductor's fatigue characteristics and deformation trend), and the constructed preset external environmental parameters (such as historical meteorological data and periodic environmental influencing factors) are first loaded into the conductor motion digital simulation model, and a static simulation at the current laser scanning moment is executed. During the static simulation, the simulation model calculates the stable shape of the conductor without current dynamic environmental disturbances based on the conductor's initial installation state and morphological evolution after long-term service, combined with long-term historical environmental data, and generates a static conductor trajectory. Subsequently, while the laser scanning is being executed, real-time environmental information at the current moment is collected, including current temperature, wind speed, wind direction, humidity, rainfall, electromagnetic influences, etc., and this real-time environmental data is loaded into the conductor motion digital simulation model to perform dynamic simulation calculations. During dynamic simulation, the system calculates the conductor's shape adjustment under current environmental conditions based on the additional influence of real-time environmental factors on the conductor's morphology, such as the sway amplitude under wind and stress changes caused by sudden temperature changes, and generates a dynamic conductor trajectory. Finally, using the static conductor trajectory as the starting point and the dynamic conductor trajectory as the ending point, the system comprehensively analyzes the range of conductor morphological changes under the influence of different environmental factors, and constructs the conductor trajectory change interval to provide more accurate morphological constraints, ensuring the accuracy of subsequent point cloud data fitting optimization and conductor 3D modeling.

[0034] Based on the conductor distribution design data, perform virtual overlap avoidance analysis of conductors in the same scanning direction and plan the scanning path.

[0035] During laser scanning, conductors at different heights or angles may project and overlap from the same viewpoint, leading to aliasing in point cloud data. Therefore, virtual overlap avoidance analysis is necessary to ensure the integrity and accuracy of the scanned data. Specifically, a virtual overlap laser scanning feature model is established, simulating potential overlap areas based on the spatial coordinates and angular relationships of the conductors and the scanning angle of the laser scanning equipment. Through ray projection analysis, the projection overlap rate between different conductors in the same scanning direction is calculated. Combined with the height difference and spacing of the conductors, as well as the parameters of the scanning equipment (such as laser beam divergence angle and scanning resolution), it is determined which areas may have a risk of data aliasing. After completing the overlap analysis, the scanning path is optimized based on the scanning parameters of the laser scanning equipment. Specifically, the installation angle, movement trajectory, or scanning sequence of the scanning equipment can be adjusted to avoid overlap problems in critical areas. For example, if severe projection overlap of conductors is found in a specific area, the scanning angle can be adjusted so that different conductors are in different scanning areas, reducing aliasing. Finally, the optimized scanning path is output to guide the laser scanning equipment to perform data acquisition according to the planned trajectory, ensuring high-precision, low-interference conductor point cloud data.

[0036] Furthermore, based on the aforementioned conductor distribution design data, a virtual overlap avoidance analysis of conductors in the same scanning direction is performed to plan the scanning path, including:

[0037] A virtual overlapping laser scanning feature is established; based on the conductor distribution design data, the virtual overlapping laser scanning feature is used as a scanning avoidance factor, and the scanning parameters of the laser scanning equipment are combined to perform scanning planning for multiple conductor segments, thereby generating the scanning path.

[0038] In the process of performing virtual overlap avoidance analysis and planning scanning paths for conductors in the same scanning direction based on conductor distribution design data, the virtual overlap laser scanning features are first established. Specifically, the conductor distribution design data of the target outdoor substation is read, including the spatial coordinates, height, direction, and connection method of each conductor. Combined with the scanning parameters of the laser scanning equipment (such as scanning angle, point cloud density, and laser beam divergence angle), a three-dimensional spatial model for analyzing conductor overlap is constructed. Through geometric projection and ray tracing techniques, the effective range of laser scanning at different angles is simulated, and the projection area of ​​each conductor is calculated to identify conductor areas where point cloud data may overlap in the same scanning direction, thereby establishing a virtual overlap laser scanning feature model. After establishing the virtual overlap features, based on the conductor distribution design data, using these virtual overlap laser scanning features as scanning avoidance factors, and combined with the specific scanning parameters of the laser scanning equipment, scanning planning for multiple conductor segments is performed. First, the spatial distribution pattern of the conductors is analyzed to determine high-risk areas where overlap may occur, and appropriate scanning strategies are adapted to these areas. For example, for conductors with similar heights and directions, different scanning angles or positions can be selected for multi-view scanning to avoid point cloud data overlap caused by the same scanning direction. Secondly, while meeting the working range and accuracy requirements of the scanning equipment, the scanning path is optimized to ensure efficient coverage of all conductors while minimizing blind spots. Finally, based on the conductor overlap avoidance analysis results and the operating characteristics of the scanning equipment, an optimized scanning path is generated and provided to the scanning equipment for execution. Through reasonable path planning, the aliasing of point cloud data can be minimized during laser scanning, improving the integrity and accuracy of the scanned data and providing high-quality foundational data for subsequent conductor point cloud data processing, curve fitting optimization, and 3D reconstruction.

[0039] The laser scanning device is activated to scan the multiple wire segments according to the scanning path. The point cloud data obtained by scanning is denoised and stitched together to obtain a point cloud scanning dataset.

[0040] During the process of activating the laser scanning device and scanning multiple wire segments according to the planned scanning path, the scanning mode of the laser scanning device is first set according to the pre-generated scanning path parameters, including scanning angle, scanning resolution, scanning range, and data acquisition frequency. Then, the laser scanning device is controlled to move along the planned path and emit laser beams in real time to perform a comprehensive spatial scan of the multiple wire segments.

[0041] During the scanning process, the laser beam emitted by the laser scanning equipment is reflected upon encountering the surface of the conductor. The receiving end records the reflected signal and calculates the corresponding spatial coordinate values, thereby generating preliminary conductor point cloud data. After scanning, the acquired raw point cloud data undergoes denoising processing to eliminate the influence of environmental interference factors (such as slight swaying of the conductor due to wind, background noise points, reflections from other structures, etc.) on the scanning results. Denoising methods include outlier removal based on statistical analysis, noise point filtering based on density analysis, and local smoothing based on spatial topology. After denoising, the point cloud data obtained from multiple scanning perspectives are stitched together. First, coordinate transformation is performed on the data from different scanning frames using methods such as feature point matching and global registration to align them to the same coordinate system. Then, a point cloud fusion algorithm is used to eliminate stitching boundary errors and improve the continuity and consistency of the point cloud. Finally, a complete point cloud scanning dataset is obtained, providing a high-precision data foundation for subsequent conductor fitting optimization and 3D modeling.

[0042] The curve fitting constraints are invoked to perform fitting optimization on the multiple traverse segments in the point cloud scan dataset, generating multiple actual fitted traverse segments.

[0043] By invoking curve fitting constraints, multiple conductor segments are fitted and optimized in the point cloud scan dataset, generating multiple actual fitted conductor segments. Specifically, an initial fitting curve is constructed based on the curve fitting constraints, followed by iterative analysis in the point cloud data to optimize the fitting accuracy. Finally, multiple fitting results are generated, and the optimal fitting curve is selected to ensure accurate reproduction of the conductor shape.

[0044] Furthermore, by invoking the curve fitting constraints, the fitting optimization of the multiple traverse segments is performed on the point cloud scan dataset to generate multiple actual fitted traverse segments, including:

[0045] Step a: Using the first curve fitting constraint in the curve fitting constraints as the first construction space, construct the first fitting curve of the first segment; Step b: Perform a density-consistent iterative analysis on the first fitting curve in the point cloud scan dataset to generate the first optimal fitting curve; Step c: In the first construction space, construct the second fitting curve of the first segment, and perform a density-consistent iterative analysis on the point cloud scan dataset to generate the second optimal fitting curve; Repeat steps a to c, traversing the first curve fitting constraints to generate the first optimal fitting curve, the second optimal fitting curve, and up to the Nth optimal fitting curve; Perform a density-consistent analysis on the first optimal fitting curve, the second optimal fitting curve, and up to the Nth optimal fitting curve to generate the first actual fitting guideline; Add the first actual fitting guideline to the multiple actual fitting guidelines.

[0046] In the process of optimizing traverse fitting on a point cloud scan dataset using curve fitting constraints, the process begins by extracting a first curve fitting constraint based on factors such as the traverse's physical characteristics, service life, and external environmental influences. This first constraint forms the initial fitting range for the first traverse segment, which is then used as the construction space. Within this construction space, based on the distribution characteristics of the point cloud scan data, methods such as spline curve fitting, polynomial regression, or B-spline curves are used to generate a first fitting curve, which serves as the baseline for subsequent optimization calculations. After the initial fitting is completed, iterative optimization analysis is performed on the first fitting curve based on the density continuity of the point cloud data. This process includes calculating the error distribution between the curve and the point cloud data, adjusting the fitting parameters to make the curve fit the point cloud data as closely as possible while maintaining its smoothness and continuity. After multiple optimization iterations, the first optimal fitting curve is obtained, achieving the best state in terms of error range, smoothness, and structural rationality. Subsequently, within the same construction space, fitting calculations are performed again based on different optimization constraints (such as different fitting algorithms or different constraint weights) to generate a second fitting curve. This second curve undergoes the same density continuity consistency analysis to ultimately obtain the second optimal fitting curve. This process can be repeated multiple times to generate the third, fourth, and so on, up to the Nth best-fit curve. After obtaining all the best-fit curves, a density continuity analysis is performed on them to calculate the distribution characteristics, mean error, and density change trend of each fitted curve in the point cloud data. The curve that best matches the physical characteristics is selected as the actual fitted traverse for the first segment of the traverse and added to the set of multiple actual fitted traverse segments.

[0047] Furthermore, the first fitted curve is subjected to a density-consistent iterative analysis in the point cloud scan dataset to generate a first optimal fitted curve, including:

[0048] The first fitted curve is fitted in multiple directions in the point cloud scan dataset to generate curve fitting results in multiple directions; the point cloud density uniformity within the curve is identified in the curve fitting results in multiple directions, and the curve fitting result with the largest point cloud density uniformity index is selected to generate the first optimal fitted curve.

[0049] In the process of generating the first optimal fitting curve by performing iterative analysis on the point cloud scan dataset based on the first fitted curve as a benchmark, firstly, curve fitting analysis in different directions is performed on the point cloud scan dataset using the first fitted curve as the initial input. This process includes selecting multiple fitting directions (e.g., based on the direction of the conductor in space, combining the horizontal direction, vertical direction, and possible tilt angle), and performing curve fitting calculations in each direction to obtain curve fitting results in multiple directions. Next, the point cloud density uniformity is identified on the obtained curve fitting results in multiple directions. Specifically, the density distribution of each fitted curve in the point cloud data is calculated, and parameters such as the coverage of the point cloud data in the curve fitting area, the uniformity of the point cloud spacing, and the overall matching degree between the curve and the point cloud are analyzed. By comparing the point cloud density uniformity index of different fitting results, the curve fitting result with the highest point cloud density uniformity is selected. Finally, the selected curve is taken as the first optimal fitting curve. This curve can maintain the distribution characteristics of the point cloud data to the greatest extent while ensuring fitting accuracy, and ensure the continuity and consistency of the fitted curve in the entire conductor shape, thereby improving the accuracy and stability of conductor reverse modeling.

[0050] The target outdoor substation's conductors are reconstructed in three dimensions using the multiple actual fitted conductor segments.

[0051] After generating multiple actual fitted conductor segments, these fitted conductors are used to perform three-dimensional reconstruction of the conductors of the target outdoor substation.

[0052] Furthermore, the three-dimensional reconstruction of the conductors of the target outdoor substation using the multiple actual fitted conductor segments includes:

[0053] The multiple actual fitted conductor segments are connected to generate a three-dimensional reconstruction model of the conductors of the target outdoor substation; the conductor anomaly analysis and maintenance reminders are performed using the three-dimensional reconstruction model of the conductors.

[0054] First, the generated multiple actual fitted conductor segments are connected according to their actual layout in the target substation. Precise connection of these fitted conductors ensures consistency with the actual conductor connections in the substation. Based on this, a complete 3D reconstruction model of the conductors is constructed. This model not only accurately represents the spatial location, direction, and connection method of the conductors but also considers potential morphological changes due to environmental factors and service life, ensuring the model's realism and reliability. Next, based on the constructed 3D reconstruction model, conductor anomaly analysis is performed. This step identifies potential anomalies, such as conductor bending, stretching, and loosening, by analyzing the conductor's morphology, orientation, and operating environment. Simultaneously, using data from the model, combined with the substation's operation and maintenance requirements and historical maintenance data, intelligent monitoring and fault prediction of the conductors are performed. Based on these analysis results, the system can automatically generate maintenance reminders, prompting maintenance personnel to conduct timely inspections and repairs, thereby effectively preventing potential faults and improving the substation's safety and stability.

[0055] In summary, the embodiments of this application have at least the following technical effects:

[0056] First, the conductor distribution design data of the target outdoor substation is read. This data includes design data for multiple conductor segments, each connected and fixed at both ends by hardware components. Next, the conductor morphology variation range under the influence of the external environment is analyzed for these segments, and curve fitting constraints are constructed. Then, based on the conductor distribution design data, virtual overlap avoidance analysis of conductors in the same scanning direction is performed, and a scanning path is planned. Further, a laser scanning device is activated to scan the multiple conductor segments according to the scanning path. The resulting point cloud data is denoised and stitched to obtain a point cloud scan dataset. Next, the curve fitting constraints are invoked to optimize the fitting of the multiple conductor segments in the point cloud scan dataset, generating multiple actual fitted conductor segments. Finally, the 3D reconstruction of the conductors of the target outdoor substation is performed using these actual fitted conductor segments. This method solves the technical problem in existing technologies where conductor morphology is greatly affected by the external environment in a fully outdoor substation environment, leading to low accuracy and efficiency in reverse modeling. It achieves the technical effect of improving the accuracy and efficiency of conductor reverse modeling and realizing high-precision 3D reconstruction of conductors.

[0057] Example 2, based on the same inventive concept as the reverse modeling method for conductors in the aforementioned examples for fully outdoor substations, such as... Figure 2 As shown, this application provides a reverse modeling system for conductors in fully outdoor substations, wherein the system includes:

[0058] The data acquisition module 11 is used to read the conductor distribution design data of the target outdoor substation, wherein the conductor distribution design data includes the design data of multiple conductor segments, and the two ends of each conductor segment are connected and fixed by hardware components; the first analysis module 12 is used to analyze the conductor morphology change range under the influence of the external environment for the multiple conductor segments and construct curve fitting constraints; the second analysis module 13 is used to perform virtual overlap avoidance analysis of conductors in the same scanning direction based on the conductor distribution design data and plan the scanning path; the scanning module 14 is used to activate the laser scanning device to scan the multiple conductor segments according to the scanning path, and to denoise and stitch the point cloud data obtained by scanning to obtain a point cloud scanning dataset; the optimization module 15 is used to call the curve fitting constraints and perform fitting optimization of the multiple conductor segments in the point cloud scanning dataset to generate multiple actual fitted conductor segments; the three-dimensional reconstruction module 16 is used to perform three-dimensional reconstruction of the conductors of the target outdoor substation using the multiple actual fitted conductor segments.

[0059] Furthermore, the first analysis module 12 is used to perform the following methods:

[0060] Extract the first conductor segment from the multiple conductor segments; read the first conductor structure information and first service duration of the first conductor segment based on the conductor distribution design data; combine the first conductor structure information and the first service duration to perform conductor morphology change simulation under preset external environmental parameters, construct the first curve fitting constraint, and add it to the curve fitting constraint.

[0061] Furthermore, the first analysis module 12 is used to perform the following methods:

[0062] The geographical location of the target outdoor substation is read; based on the geographical location, the preset external environment parameters are constructed by retrieving historical environmental data; a digital simulation model of conductor motion based on conductor structure, environmental conditions, and service duration is trained; the first conductor structure information, the first service duration, and the preset external environment parameters are loaded into the digital simulation model of conductor motion to perform simulation and generate the conductor trajectory change range; the first curve fitting constraint is constructed based on the conductor trajectory change range.

[0063] Furthermore, the first analysis module 12 is used to perform the following methods:

[0064] The preset external environmental parameters include periodic environmental parameters.

[0065] Furthermore, the first analysis module 12 is used to perform the following methods:

[0066] The first conductor structure information, the first service duration, and the preset external environment parameters are simulated and loaded into the conductor motion digital simulation model. Static simulation at the current laser scanning moment is performed to generate a static conductor trajectory. Based on the static conductor trajectory, real-time environmental information at the current laser scanning moment is collected and loaded into the conductor motion digital simulation model for dynamic simulation to generate a dynamic conductor trajectory. The conductor trajectory change range is constructed with the static conductor trajectory as the starting point and the dynamic conductor trajectory as the ending point.

[0067] Furthermore, the optimization module 15 is used to perform the following method:

[0068] Step a: Using the first curve fitting constraint in the curve fitting constraints as the first construction space, construct the first fitting curve of the first segment; Step b: Perform a density-consistent iterative analysis on the first fitting curve in the point cloud scan dataset to generate the first optimal fitting curve; Step c: In the first construction space, construct the second fitting curve of the first segment, and perform a density-consistent iterative analysis on the point cloud scan dataset to generate the second optimal fitting curve; Repeat steps a to c, traversing the first curve fitting constraints to generate the first optimal fitting curve, the second optimal fitting curve, and up to the Nth optimal fitting curve; Perform a density-consistent analysis on the first optimal fitting curve, the second optimal fitting curve, and up to the Nth optimal fitting curve to generate the first actual fitting guideline; Add the first actual fitting guideline to the multiple actual fitting guidelines.

[0069] Furthermore, the optimization module 15 is used to perform the following method:

[0070] The first fitted curve is fitted in multiple directions in the point cloud scan dataset to generate curve fitting results in multiple directions; the point cloud density uniformity within the curve is identified in the curve fitting results in multiple directions, and the curve fitting result with the largest point cloud density uniformity index is selected to generate the first optimal fitted curve.

[0071] Furthermore, the second analysis module 13 is used to perform the following methods:

[0072] A virtual overlapping laser scanning feature is established; based on the conductor distribution design data, the virtual overlapping laser scanning feature is used as a scanning avoidance factor, and the scanning parameters of the laser scanning equipment are combined to perform scanning planning for multiple conductor segments, thereby generating the scanning path.

[0073] Furthermore, the three-dimensional reconstruction module 16 is used to perform the following methods:

[0074] The multiple actual fitted conductor segments are connected to generate a three-dimensional reconstruction model of the conductors of the target outdoor substation; the conductor anomaly analysis and maintenance reminders are performed using the three-dimensional reconstruction model of the conductors.

[0075] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0076] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0077] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for reverse modeling conductors in fully outdoor substations, characterized in that, The method includes: Read the conductor distribution design data of the target outdoor substation, wherein the conductor distribution design data includes the design data of multiple conductor segments, and the two ends of each conductor segment are connected and fixed by hardware components; For the multiple conductor segments, the range of conductor morphology changes under the influence of the external environment is analyzed, and curve fitting constraints are constructed. Based on the conductor distribution design data, perform virtual overlap avoidance analysis of conductors in the same scanning direction and plan the scanning path; The laser scanning device is activated to scan the multiple wire segments according to the scanning path. The point cloud data obtained by scanning is denoised and stitched together to obtain a point cloud scanning dataset. The curve fitting constraints are invoked to optimize the fitting of the multiple traverse segments in the point cloud scan dataset, generating multiple actual fitted traverse segments. The conductors of the target outdoor substation are reconstructed in three dimensions using the multiple actual fitted conductor segments. The curve fitting constraints are invoked to perform fitting optimization on the multiple traverse segments in the point cloud scan dataset, generating multiple actual fitted traverse segments, including: Step a: Using the first curve fitting constraint in the curve fitting constraints as the first construction space, construct the first fitting curve of the first segment of the curve; Step b: Perform a density-consistent iterative analysis on the point cloud scan dataset using the first fitted curve to generate the first optimal fitted curve; Step c: In the first construction space, construct the second fitting curve of the first segment curve, and perform iterative analysis with continuous and consistent density in the point cloud scan dataset to generate the second optimal fitting curve; Repeat steps a to c to iterate through the first curve fitting constraints and generate the first optimal fitting curve, the second optimal fitting curve, and so on up to the Nth optimal fitting curve. Density continuity and consistency analysis is performed on the first optimal fitting curve, the second optimal fitting curve and up to the Nth optimal fitting curve to generate the first segment of actual fitting wire. Add the first segment of the actual fitted wire to the plurality of actual fitted wire segments; The first fitted curve is subjected to a density-consistent iterative analysis in the point cloud scan dataset to generate a first optimal fitted curve, including: The first fitted curve is fitted in multiple directions in the point cloud scan dataset to generate curve fitting results in multiple directions. The uniformity of point cloud density within the curves is identified for the curve fitting results in the multiple directions. The curve fitting result with the largest point cloud density uniformity index is selected to generate the first optimal fitting curve.

2. The reverse modeling method for conductors in fully outdoor substations as described in claim 1, characterized in that, For the multiple conductor segments, an analysis of the conductor morphology variation range under the influence of the external environment is conducted, and curve fitting constraints are constructed, including: Extract the first segment of the multiple conductor segments; Based on the conductor distribution design data, the first conductor structure information and first service duration of the first conductor segment are read; Combining the first conductor structure information and the first service duration, a conductor morphology change simulation is performed under preset external environmental parameters to construct a first curve fitting constraint, which is then added to the curve fitting constraint.

3. The reverse modeling method for conductors in fully outdoor substations as described in claim 2, characterized in that, Combining the first conductor structure information and the first service duration, a conductor morphology change simulation is performed under preset external environmental parameters to construct a first curve fitting constraint, including: Read the geographical location of the target outdoor substation; Based on the geographical location, the preset external environment parameters are constructed by retrieving historical environmental data. Training a digital simulation model of conductor motion based on conductor structure, environmental conditions, and service duration; The first conductor structure information, the first service duration, and the preset external environment parameters are loaded into the conductor motion digital simulation model to perform simulation and generate the conductor trajectory change range. The first curve fitting constraint is constructed based on the range of change in the conductor trajectory.

4. The reverse modeling method for conductors in fully outdoor substations as described in claim 3, characterized in that, The preset external environmental parameters include periodic environmental parameters.

5. The reverse modeling method for conductors in fully outdoor substations as described in claim 3, characterized in that, The conductor motion digital simulation model is loaded with the first conductor structure information, the first service duration, and the preset external environment parameters to perform simulation, generating the conductor trajectory change range, including: The first conductor structure information, the first service duration, and the preset external environment parameters are simulated and loaded into the conductor motion digital simulation model, and a static simulation at the current laser scanning moment is performed to generate a static conductor trajectory. Based on the static conductor trajectory, real-time environmental information at the current laser scanning moment is collected and loaded into the conductor motion digital simulation model for dynamic simulation to generate a dynamic conductor trajectory. The range of change of the conductor trajectory is constructed by taking the static conductor trajectory as the starting point and the dynamic conductor trajectory as the ending point.

6. The reverse modeling method for conductors in fully outdoor substations as described in claim 1, characterized in that, Based on the conductor distribution design data, perform virtual overlap avoidance analysis of conductors in the same scanning direction, and plan the scanning path, including: Establish virtual overlapping laser scanning features; Based on the conductor distribution design data, the virtual overlapping laser scanning characteristics are used as scanning avoidance factors, and the scanning parameters of the laser scanning equipment are combined to perform scanning planning for multiple conductor segments, thereby generating the scanning path.

7. The reverse modeling method for conductors in fully outdoor substations as described in claim 1, characterized in that, The three-dimensional reconstruction of the conductors of the target outdoor substation is performed using the multiple actual fitted conductor segments, including: The multiple actual fitted conductor segments are connected to generate a three-dimensional reconstruction model of the conductor of the target outdoor substation. The three-dimensional reconstruction model of the conductor is used to perform conductor anomaly analysis and maintenance alerts.

8. A reverse modeling system for conductors in fully outdoor substations, characterized in that, The system is used to implement the reverse modeling method for conductors in fully outdoor substations according to any one of claims 1-7, the system comprising: The data acquisition module is used to read the conductor distribution design data of the target outdoor substation, wherein the conductor distribution design data includes the design data of multiple conductor segments, and the two ends of each conductor segment are connected and fixed by hardware components; The first analysis module is used to analyze the range of conductor morphology changes under the influence of the external environment for the multiple conductor segments and to construct curve fitting constraints. The second analysis module is used to perform virtual overlap avoidance analysis of conductors in the same scanning direction based on the conductor distribution design data, and to plan the scanning path. The scanning module is used to activate the laser scanning device to scan the multiple wire segments according to the scanning path, and to denoise and stitch the point cloud data obtained by scanning to obtain a point cloud scanning dataset. The optimization module is used to call the curve fitting constraints to perform fitting optimization of the multiple traverse segments in the point cloud scan dataset, and generate multiple actual fitted traverse segments. The three-dimensional reconstruction module is used to perform three-dimensional reconstruction of the conductors of the target outdoor substation using the multiple actual fitted conductor segments.

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