Electric field distribution analysis method and system in strong electric field scene

Abstract

The invention discloses an electric field distribution analysis method and system in a strong electric field scene. The method comprises the steps of obtaining first point cloud data of a target operation site, and performing first preprocessing on the first point cloud data; constructing a target three-dimensional tower pole model according to the first point cloud data after the first preprocessing; presetting a simulation constraint, and establishing a first network model based on the simulation constraint and the target three-dimensional tower pole model; and solving target parameters of the first network model, wherein the target parameters at least comprise the electric field intensity and the shortest safety path. Through the method, the electric field distribution in the strong electric field scene can be quickly and accurately analyzed, and reliable data support is provided for the safety, stability and environmental influence evaluation of power equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical engineering, and particularly to a method and system for analyzing the electric field distribution in a strong electric field scenario. Background Art

[0002] With the rapid development of power, electronic devices, and communication technologies, the application scenarios of electromagnetic fields, especially strong electric fields, are increasing day by day. The analysis of the electric field distribution in a strong electric field scenario, as an important link in electrical engineering, electronic device design, and electromagnetic environment control, is of great significance for ensuring the safety and stability of power equipment and evaluating the environmental impact. Traditional methods for analyzing electric field distribution mainly rely on experimental measurements and numerical simulations. In particular, the electric field analysis based on the finite element method (FEM) has become one of the core technologies in research and practical applications. With its powerful computing ability and high precision, the finite element method is widely used in the simulation and analysis of complex electric field distributions, and can accurately predict the distribution state of the electric field by discretizing the space and solving the corresponding electric field equations.

[0003] Although the finite element method has achieved remarkable application results in the analysis of electric field distribution, the existing technologies still have certain deficiencies when facing the actual complex electric field environment. Traditional methods for analyzing electric fields usually rely on idealized models and static data, and it is difficult to effectively handle the dynamic, complex structures, and large data volume problems at the strong electric field operation site. The accurate modeling of the electric field distribution depends on accurate scene modeling and geometric data. However, traditional measurement methods usually cannot obtain sufficiently accurate and detailed scene geometric information, resulting in the accuracy and reliability of electric field analysis being affected. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for analyzing the electric field distribution in a strong electric field scenario, which can solve the problems mentioned in the background art.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for analyzing the electric field distribution in a strong electric field scenario, including:

[0009] Obtaining the first point cloud data of the target operation site and performing a first preprocessing on the first point cloud data;

[0010] Construct a target three-dimensional tower model based on the first preprocessed point cloud data;

[0011] Preset simulation constraints and establish a first network model based on the simulation constraints and the target three-dimensional tower model;

[0012] Solve for target parameters of the first network model, where the target parameters at least include electric field strength and the shortest safe path.

[0013] As a preferred solution of the method for analyzing the electric field distribution in a strong electric field scenario according to the present invention, wherein: the constructing a target three-dimensional tower model based on the first preprocessed point cloud data includes:

[0014] Perform a first clustering operation on the first point cloud data;

[0015] Perform a first segmentation operation on the first point cloud data after the first clustering operation to obtain second point cloud data, and the first segmentation operation is a vertical direction segmentation operation

[0016] Obtain first correlation parameters of the second point cloud data, and construct a target three-dimensional tower model according to the first correlation parameters, where the first correlation parameters at least include the width, length, and inclination of the second point cloud data.

[0017] As a preferred solution of the method for analyzing the electric field distribution in a strong electric field scenario according to the present invention, wherein: the solving for target parameters of the first network model includes:

[0018] Perform a simulation analysis on the first network model and obtain the electric field strength;

[0019] Preset a first region judgment criterion, where the first region judgment criterion is used to judge the region to which the electric field strength belongs;

[0020] Establish a first path objective function according to the electric field strength and the first region judgment criterion;

[0021] Solve the first path objective function to obtain the shortest safe path.

[0022] As a preferred solution of the method for analyzing the electric field distribution in a strong electric field scenario according to the present invention, wherein: the preset simulation constraints include:

[0023] The preset simulation constraints include cell size constraints and physical field boundary constraints;

[0024] The cell size constraints are obtained through the electric field gradient and an adjustment coefficient.

[0025] As a preferred solution of the method for analyzing the electric field distribution in the strong electric field scenario of the present invention, wherein: establishing the first path objective function according to the electric field strength and the first region determination criterion includes:

[0026] The first path objective function is the total path cost function;

[0027] Set the electric field strength as the path cost and calculate the actual cost;

[0028] Preset to construct a heuristic function, and calculate the total path cost function according to the actual cost and the heuristic function.

[0029] As a preferred solution of the method for analyzing the electric field distribution in the strong electric field scenario of the present invention, wherein: the first preprocessing includes:

[0030] The first preprocessing includes performing a registration operation on the first point cloud data;

[0031] And perform denoising processing on the first point cloud data after the matching operation;

[0032] Then thin the denoised first point cloud data to obtain the thinned first point cloud data;

[0033] Then perform normalization processing on the thinned first point cloud data.

[0034] As a preferred solution of the method for analyzing the electric field distribution in the strong electric field scenario of the present invention, wherein: calculating the total path cost function includes:

[0035] Iterate for each node, and use the total path cost function to calculate the total cost function value of each node;

[0036] Select the path with the minimum cost of the current node as the next node to be calculated, and calculate the total cost function values of all nodes to stop the iteration;

[0037] Reconstruct by backtracking the minimum cost node to obtain the shortest safe path.

[0038] In a second aspect, the present invention provides a system for analyzing the electric field distribution in a strong electric field scenario, including:

[0039] A data acquisition and processing module, configured to acquire the first point cloud data of the target work site and perform the first preprocessing on the first point cloud data;

[0040] A first model establishment module, configured to construct a target three-dimensional tower model according to the first point cloud data after the first preprocessing;

[0041] A second model building module, configured to preset simulation constraints and build a first network model based on the simulation constraints and a target three-dimensional tower model;

[0042] A solving module, configured to solve target parameters for the first network model, where the target parameters at least include electric field strength and the shortest safe path.

[0043] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a method and system for analyzing the electric field distribution in a strong electric field scenario, obtains first point cloud data of a target operation site, and performs a first preprocessing on the first point cloud data; constructs a target three-dimensional tower model according to the first point cloud data after the first preprocessing; presets simulation constraints and builds a first network model based on the simulation constraints and the target three-dimensional tower model; solves target parameters for the first network model, where the target parameters at least include electric field strength and the shortest safe path. Through this method, the electric field distribution in a strong electric field scenario can be analyzed quickly and accurately, providing reliable data support for the evaluation of the safety, stability, and environmental impact of power equipment.

[0046] Specifically, first, by obtaining and preprocessing the first point cloud data of the target operation site, the high-precision geometric information obtained by modern measurement technologies can be fully utilized, improving the accuracy and reliability of electric field analysis. Compared with traditional measurement methods, point cloud data provides more detailed and accurate scene geometric information, making the modeling of the electric field distribution more precise.

[0047] Secondly, constructing a target three-dimensional tower model according to the preprocessed point cloud data can truly reflect the complex structure and dynamic changes of the strong electric field operation site. This step fully considers the geometric features in the actual scenario, providing a solid foundation for subsequent electric field analysis.

[0048] Furthermore, presetting simulation constraints and building a first network model based on the simulation constraints and the target three-dimensional tower model can ensure the accuracy and efficiency of electric field analysis. By setting reasonable simulation constraints, a real electric field environment can be simulated, effectively reducing the amount of calculation and improving the analysis efficiency.

[0049] Finally, the target parameters of the first network model are solved to obtain key parameters such as the electric field strength and the shortest safe path, providing reliable data support for the assessment of the safety, stability, and environmental impact of power equipment. This step comprehensively considers the complexity of the electric field distribution and the requirements of practical applications, making the analysis results closer to the actual situation and having higher practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts. Among them:

[0051] Figure 1 is a method flow chart of a method for analyzing the electric field distribution in a strong electric field scenario and a system provided by an embodiment of the present invention;

[0052] Figure 2 is an internal structure diagram of a computer device of a method for analyzing the electric field distribution in a strong electric field scenario and a system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0054] Embodiment 1

[0055] Referring to Figure 1 - Figure 2 , the first embodiment of the present invention provides a method and system for analyzing the electric field distribution in a strong electric field scenario, including:

[0056] In the existing related technologies, there are some problems,

[0057] such as the accuracy of the electric field distribution analysis being limited by the accuracy of the measurement data, it being difficult to process electric field environments with large scales and complex structures, and the analysis process being cumbersome and time-consuming. These problems limit the wide application of the electric field distribution analysis in strong electric field scenarios.

[0058] This application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail how to implement the method for analyzing the electric field distribution in a strong electric field scenario;

[0059] Figure 1 The method flowchart of a method and system for analyzing the electric field distribution in a strong electric field scenario is shown, including:

[0060] S101, obtaining the first point cloud data of the target work site and performing a first preprocessing on the first point cloud data;

[0061] In an optional embodiment, the target work site can be a strong electric field environment such as an electric field work site, a substation, a high-voltage transmission line, etc. The first point cloud data can be obtained by modern measurement devices such as a laser scanner and a 3D camera. These devices can capture the three-dimensional geometric information of the target work site and generate high-precision point cloud data.

[0062] It should be noted that the purpose of the first preprocessing step is to clean and optimize the original point cloud data to improve the efficiency and accuracy of subsequent processing.

[0063] In an optional embodiment, the first preprocessing may include operations such as denoising, registration, thinning, and standardization of the point cloud data, removing noise points, filling in missing data, reducing the number of data points to reduce the computational complexity, and standardizing the data for subsequent processing.

[0064] In the embodiment of the present application, the target work site selected is an electric field work site.

[0065] In the embodiment of the present application, the first preprocessing includes:

[0066] The first preprocessing includes performing a registration operation on the first point cloud data;

[0067] And performing a denoising process on the first point cloud data after the matching operation;

[0068] Then thinning the denoised first point cloud data to obtain the thinned first point cloud data;

[0069] Then performing a standardization process on the thinned first point cloud data.

[0070] In an optional embodiment, the registration operation can be implemented by a point cloud registration algorithm, such as the ICP (Iterative Closest Point) algorithm, the NDT (Normal Distributions Transform) algorithm, etc. These algorithms can align the point cloud data obtained from different perspectives or at different time points to generate a complete three-dimensional model.

[0071] In an optional embodiment, the denoising process can be performed by methods such as statistical filtering and radius filtering to remove noise points generated due to factors such as equipment errors and environmental interference, and improve the accuracy of the point cloud data.

[0072] In an optional embodiment, point cloud thinning is to reduce the number of data points on the premise of ensuring the accuracy of point cloud data, so as to reduce the computational complexity of subsequent processing. Methods such as voxel grid method and random sampling method can be used for point cloud thinning.

[0073] In an optional embodiment, normalization processing is to convert the data into a unified coordinate system and unit for subsequent processing and analysis. Methods such as affine transformation and rigid body transformation can be used for data normalization. Through the first preprocessing step, optimized point cloud data can be obtained, providing an accurate and reliable basis for subsequent 3D modeling and electric field analysis.

[0074] Specifically, this application uses a ground lidar to collect the first point cloud data of the strong electric field operation site and perform the first preprocessing;

[0075] In this application, the first preprocessing uses the Iterative Closest Point (ICP) algorithm to register the point cloud data, uses a voxel grid filter to denoise the point cloud data, uses the Voxel Grid thinning method to thin the denoised point cloud data to obtain the thinned point cloud data, and performs normalization processing on the thinned point cloud data.

[0076] It should be noted that the ground lidar can quickly and efficiently collect the 3D point cloud data of the site in a complex and dangerous strong electric field environment, avoiding the high risk and inefficiency of manual measurement. At the same time, it has higher spatial resolution and a wider coverage range, meeting the requirements of large-scale power sites. The ICP algorithm optimizes the relative positions of point clouds through an iterative process, improving the accuracy of point cloud alignment, reducing the inconsistencies caused by measurement errors, and enhancing the accuracy of subsequent electric field distribution analysis. The voxel grid filter can effectively remove these noise points by spatially meshing the point cloud data and selecting representative points in each grid, thus retaining the structural features of the point cloud. Through the Voxel Grid thinning method, the point cloud data can remove redundant points and reduce the data volume on the premise of ensuring the integrity of the data structure, effectively accelerating the subsequent electric field analysis process and improving the operation efficiency of the system. Especially in a large-scale or dynamic electric field environment, it can significantly improve the processing speed and real-time performance. The normalized data has a consistent scale and range, which is very important for the calculation and analysis of complex electric field distributions, helping to improve the accuracy and comparability of the results.

[0077] It should be noted that obtaining the first point cloud data of the target work site and performing the first preprocessing on the first point cloud data can ensure the accuracy of the three-dimensional model and electric field analysis in the subsequent steps. By registering, denoising, thinning, and normalizing the point cloud data, errors and redundancies in the original data can be eliminated, the accuracy and consistency of the data can be improved, and a reliable basis for subsequent three-dimensional modeling and electric field analysis can be provided. This can not only improve the accuracy of the electric field distribution analysis, but also optimize the calculation process, reduce the calculation complexity, and improve the analysis efficiency. Therefore, this step plays a crucial role in the electric field distribution analysis method in the entire strong electric field scenario.

[0078] S102. Construct a target three-dimensional tower model according to the first point cloud data after the first preprocessing;

[0079] In the embodiment of the present application, constructing a target three-dimensional tower model according to the first point cloud data after the first preprocessing includes:

[0080] Perform a first clustering operation on the first point cloud data;

[0081] Perform a first segmentation operation on the first point cloud data after the first clustering operation to obtain second point cloud data. The first segmentation operation is a vertical segmentation operation

[0082] Obtain the first relevant parameters of the second point cloud data, and construct a target three-dimensional tower model according to the first relevant parameters. The first relevant parameters at least include the width, length, and inclination of the second point cloud data.

[0083] In an optional embodiment, the first clustering operation is to group similar points or adjacent points in the point cloud data for subsequent processing and analysis. Methods such as K-means clustering and DBSCAN clustering can be used for the first clustering operation. These methods can automatically group based on the characteristics of the point cloud data, improving the processing efficiency.

[0084] It should be noted that after the first clustering operation, it is necessary to segment the clustered point cloud data to extract the various part structures of the target three-dimensional tower model.

[0085] In the embodiment of the present application, a vertical segmentation operation is used for the first segmentation operation, and the point cloud data is segmented in the vertical direction to obtain point cloud data segments at different heights, that is, the second point cloud data. This step can segment based on the actual structural characteristics of the tower, ensuring that the subsequent constructed three-dimensional model is more accurate.

[0086] In an alternative embodiment, the first segmentation operation is to extract different parts of the tower pole, such as the tower body, cross arm, insulator, etc., so as to facilitate subsequent 3D modeling based on the structural characteristics of these parts. Obtain the first relevant parameters of the second point cloud data, which at least include the width, length, and inclination of the second point cloud data, and they can reflect the actual geometric dimensions and posture of the tower pole.

[0087] It should be noted that after obtaining the second point cloud data, it is necessary to extract its relevant parameters to construct the target 3D tower pole model.

[0088] In the embodiment of the present application, the first relevant parameters of the extracted second point cloud data at least include width, length, and inclination. These parameters can reflect the actual dimensions and shape characteristics of the tower pole, providing accurate data support for subsequent 3D modeling. According to these parameters, tools such as 3D modeling software can be used to construct the target 3D tower pole model, generating a 3D model with a sense of reality and geometric accuracy, providing a reliable basis for subsequent electric field analysis.

[0089] Specifically, constructing a 3D tower pole model according to the first preprocessed point cloud data in the present application means setting a judgment threshold using the empirical method, setting the number of clusters K using the elbow method, randomly selecting K points as the initial cluster centers from the preprocessed point cloud data, calculating the Euclidean distance from each point to the K distance centers using the Euclidean distance method, assigning the points in the preprocessed point cloud data to the nearest cluster center, and recalculating the K cluster centers after each point assignment. When the calculated cluster centers are less than the judgment threshold, stop the iteration to obtain K classified cluster regions;

[0090] Furthermore, obtain the point cloud data of the pole tower according to the cluster regions;

[0091] Furthermore, segment the point cloud data of the pole tower along the vertical direction (z-axis direction) using the height-based layering method to obtain cross-sectional point cloud data;

[0092] Furthermore, collect historical cross-sectional point cloud data and calculate the Gaussian kernel function The formula is:

[0093]

[0094] where ∈ is the smoothing parameter, controlling the influence range of the distance on the function value, x i is the position of the cross-sectional point cloud data, and x is the cross-sectional point cloud data to be interpolated;

[0095] Furthermore, use the RBF interpolation formula to construct a linear equation system, and the formula is:

[0096]

[0097] where N is the total number of cross-section point cloud data, λ i is the coefficient of RBF interpolation, f(x) is the value of the surface function to be fitted, and x i is the position of the cross-section point cloud data, and x is the cross-section point cloud data to be interpolated. is the Gaussian kernel function, and j is the index;

[0098] Furthermore, the Gaussian elimination method is used to solve the linear equations to obtain the coefficient λ of RBF interpolation i ;

[0099] Furthermore, calculate the average position x of the cross-section point cloud data cen ;

[0100] Furthermore, use the average position x cen , the coefficient λ of RBF interpolation i and the Gaussian kernel function to calculate the fitted surface value f(x) of the cross-section point cloud data. The formula is:

[0101]

[0102] where N is the total number of cross-section point cloud data;

[0103] It should be noted that in the cross-section point cloud data, due to the complex shape of the tower pole, the existence of noise, and the uneven distribution of data, other methods such as simple linear interpolation or polynomial fitting cannot accurately reflect the complex surface. The combination of RBF interpolation and the Gaussian kernel function has strong non-linear fitting ability, especially suitable for processing complex point cloud data in strong electric field scenarios. Using the Gaussian elimination method to solve the linear equations can efficiently determine the RBF interpolation coefficients, which is suitable for the calculation of large-scale point cloud data. Existing cross-section data fitting techniques mostly use polynomial interpolation or linear fitting, but these methods cannot accurately model irregular and uneven-density data. Through the advantages of the combination of RBF interpolation and the Gaussian kernel function, high-precision non-linear surface fitting is achieved, ensuring the smoothness and continuity of the fitted surface. Traditional techniques cannot fully obtain the accurate width, length, and inclination of the cross-section point cloud data, resulting in errors in the three-dimensional tower pole model. By calculating the fitted surface value through RBF interpolation and combining with the minimum circumscribed rectangle method, key parameters (width, length, inclination) are accurately extracted, providing a high-precision data basis for subsequent model construction. Through the combination of the Gaussian kernel function and RBF interpolation, noise data can be adaptively smoothed, improving the accuracy and reliability of the fitting.

[0104] In the embodiment of the present application, the orthogonal projection method is used to project the fitted surface value onto the XY plane to obtain a projection point set;

[0105] Further, the minimum bounding rectangle algorithm is used to calculate the minimum bounding rectangle of the projection point set, and the differences between the maximum and minimum values of the X and Y coordinates of the minimum bounding rectangle are calculated respectively to obtain the width and length of the cross-sectional point cloud data;

[0106] Further, by calculating the gradient of the fitted surface value of the cross-sectional point cloud data, the normal vector n of the cross-sectional point cloud data is obtained;

[0107] Further, according to the Z-axis, the unit vector method is used to obtain the vertical direction vector z;

[0108] Further, according to the angle between the normal vector n and the vertical direction vector z, the inclination θ of the cross-sectional point cloud data is calculated, and the formula is:

[0109]

[0110] Further, according to the width, length and inclination θ of the cross-sectional point cloud data, the progressive geometric body construction method is used to connect the cross-sectional point cloud data to obtain a three-dimensional tower pole model.

[0111] It should also be noted that by clustering and classifying the point cloud data in an iterative manner, the data allocation is optimized, so that each class of data corresponds to a specific part of the tower pole. Using the elbow method and the Euclidean distance method makes the clustering process efficient and accurate, avoiding the errors caused by artificial experience, improving the accuracy and calculation efficiency of the model, and being able to provide higher-quality basic data for subsequent electric field distribution analysis. The point cloud data of the tower pole is segmented layer by layer along the vertical direction, so that the point cloud data of each layer can better represent the shape of the tower pole at different heights, and the electric field distribution characteristics at different heights can be clearly distinguished, thereby improving the electric field simulation accuracy. By collecting historical cross-sectional point cloud data and using the Gaussian kernel function for calculation, more accurate weight coefficients can be provided for the interpolation of the tower pole model, which helps to improve the accuracy of interpolation and can effectively cope with the possible noise and irregular distribution in the point cloud data. By constructing a linear equation system and using the Gaussian elimination method to solve the interpolation coefficients, it helps to provide a smooth and accurate result for the fitting of the point cloud data, significantly improving the geometric reconstruction accuracy of the point cloud data. The generated model is smoother and has physical authenticity, and is applicable to the electric field distribution analysis and other engineering applications in complex scenarios. By calculating the average position of each cross-sectional point cloud data, the central position of the tower pole cross-section can be extracted, providing useful reference information for subsequent electric field analysis. Calculating the cross-sectional point cloud data has significant practical value, especially when conducting strong electric field analysis, which can ensure that the geometric shape of the tower pole is accurately reflected, thus providing important support for the accurate calculation of the electric field distribution.

[0112] S103, preset simulation constraints, and establish a first network model based on the simulation constraints and the target three-dimensional tower pole model;

[0113] In an optional embodiment, the preset simulation constraints are to simulate a real strong electric field environment to ensure the accuracy and reliability of the electric field distribution analysis. These simulation constraints may include physical parameters such as the electric field strength range, electric field direction, conductivity, and dielectric constant of the tower pole material, as well as boundary conditions, such as the influence of the presence of the ground, air, or other obstacles on the electric field.

[0114] It should be noted that by establishing these simulation constraints, the variables in the simulation process can be restricted, making the simulation results closer to the actual situation. At the same time, based on the preprocessed point cloud data and the constructed target three-dimensional tower pole model, a first network model is established. This model will be used to simulate the distribution of the electric field in the tower pole and its surrounding environment.

[0115] In the embodiment of the present application, the preset simulation constraints include:

[0116] The preset simulation constraints include cell size constraints and physical field boundary constraints;

[0117] The cell size constraints are obtained through the electric field gradient and the adjustment coefficient.

[0118] Specifically, in the present application, according to the target three-dimensional tower pole model, a geometric modeling and physical field boundary condition setting method is used to construct an electric field simulation region;

[0119] Furthermore, the grid scale estimation method is used to calculate the basic grid cell size Δx of the electric field simulation region base ;

[0120] Furthermore, the numerical difference method is used to calculate the electric field gradient of the electric field simulation region The electric field gradients are sorted from largest to smallest, and the maximum electric field gradient is marked According to the electric field gradient The dynamic grid adjustment method is used to construct an adjustment coefficient calculation formula to calculate the adjustment coefficient k, and the formula is:

[0121]

[0122] where k is the adjustment coefficient;

[0123] Furthermore, according to the electric field gradient and the adjustment coefficient k, the final grid cell size Δx is calculated new , and the formula is:

[0124]

[0125] It should be noted that in strong electric field scenarios, the electric field gradient is unevenly distributed, the electric field gradient in some areas is large, and electric field concentration phenomena (such as edge effect and tip discharge area) are prone to occur. If a fixed grid division method is used, it is difficult to capture local changes in the electric field gradient, resulting in a decrease in simulation accuracy. By introducing the electric field gradient and adjustment coefficient, adaptive adjustment of the grid unit size is achieved, so that the grid is automatically refined in the high electric field gradient area, while a coarser grid is maintained in the low electric field gradient area. This dynamic adjustment method is a necessary means to achieve high-precision electric field simulation. Other fixed grid division methods cannot achieve the same effect. In finite element simulation, the number of grid units directly affects the consumption of computing resources and the solution efficiency. Using fixed-scale grid division will lead to too many grid units and a sharp increase in the amount of calculation, especially in strong electric field scenarios, which is difficult to meet the real-time simulation requirements. Through this formula, the grid units in high-gradient areas are adaptively reduced, while the low-gradient areas maintain larger grids, effectively balancing the simulation accuracy and calculation efficiency, ensuring the optimal configuration of computing resources, and making the grid division more accurate through the grid refinement strategy directly related to the electric field gradient, especially in areas where the electric field changes dramatically, which can better reflect the actual electric field distribution and make the simulation results more reliable;

[0126] In an optional embodiment, according to the final grid cell size Δx new The grid of the electric field simulation area is refined, and the three-dimensional tower model is embedded into the electric field simulation area using the grid embedding method to obtain a grid model of the electric field simulation area.

[0127] In an optional embodiment, by using geometric modeling and physical field boundary condition setting methods, the spatial model of the electric field simulation area can be accurately constructed, which not only ensures that the shape of the simulation area meets the actual requirements, but also can reasonably set the initial conditions and boundary conditions of the electric field according to the boundary conditions of the physical field, thereby providing a stable foundation for subsequent electric field simulation.

[0128] It should be noted that the use of the grid scale estimation method to dynamically calculate the size of the grid unit in the electric field simulation area can ensure that there are sufficiently small grid units in the area where the electric field changes dramatically, and use larger grid units in the area where the change is slow. This method effectively reduces the amount of calculation and improves the simulation efficiency while ensuring the simulation accuracy. The calculation of the electric field gradient can help identify the area where the electric field changes dramatically. The dynamic grid adjustment method adjusts the size of the grid according to the electric field gradient. This method can use a higher-precision grid in the area where the electric field changes greatly, thereby improving the accuracy of the electric field calculation;

[0129] It should also be noted that larger grids are used in areas with relatively small changes in the electric field to optimize the calculation time and resource utilization. This refined grid adjustment method can effectively address the deficiencies of traditional static grid methods in complex electric field scenarios, improving the simulation efficiency and accuracy. Through the grid embedding method, the three-dimensional tower model can be accurately embedded into the grid of the electric field simulation area, ensuring that the interaction between the tower and the electric field is accurately simulated. This is of great significance for optimizing the design of power facilities and evaluating the impact of the electromagnetic environment. The accurate grid model helps to conduct a more detailed analysis of the electric field distribution and supports the design optimization and safety assessment of power facilities.

[0130] S104. Solve the target parameters for the first network model, where the target parameters at least include the electric field strength and the shortest safe path.

[0131] In the embodiment of the present application, solving the target parameters for the first network model includes:

[0132] Conduct a simulation analysis on the first network model and obtain the electric field strength;

[0133] Preset the first area judgment criterion, which is used to judge the area to which the electric field strength belongs;

[0134] Establish the first path objective function according to the electric field strength and the first area judgment criterion;

[0135] Solve the first path objective function to obtain the shortest safe path.

[0136] In the embodiment of the present application, establishing the first path objective function according to the electric field strength and the first area judgment criterion includes:

[0137] The first path objective function is the total path cost function;

[0138] Set the electric field strength as the path cost and calculate the actual cost;

[0139] Preset a construction heuristic function, and calculate the total path cost function according to the actual cost and the heuristic function.

[0140] In the embodiment of the present application, calculating the total path cost function includes:

[0141] Iterate for each node, and use the total path cost function to calculate the total cost function value of each node;

[0142] Select the path with the minimum cost of the current node as the next node to be calculated, and calculate the total cost function values of all nodes to stop the iteration;

[0143] Reconstruct by backtracking the minimum cost node to obtain the shortest safe path.

[0144] In an embodiment of the present application, the present application defines the electric field distribution equation as the Poisson equation according to the grid model, discretizes the Poisson equation using the finite element method, and solves the discretized Poisson equation using the conjugate gradient method to obtain the electric potential distribution;

[0145] Furthermore, the electric field strength E is calculated using the electric potential gradient method according to the electric potential distribution i ;

[0146] Furthermore, the safety threshold is set using the International Electrotechnical Commission standard, the electric field strength is compared with the safety threshold, the electric field strength greater than or equal to the safety threshold is judged as a dangerous area, and the electric field strength less than the safety threshold is judged as a safe area, that is, the first area judgment criterion;

[0147] Furthermore, path planning is performed using the A* algorithm according to the electric field strength and the dangerous area to obtain a safe operation path;

[0148] Furthermore, the electric field strength E i is set as the path cost, and the actual cost g(s) is calculated. The formula is:

[0149]

[0150] where s is the node, the node refers to the grid point in the electric field simulation area, M is the total number of nodes, and Δd i is the spatial distance from the node to the next node, and i is the index used to index each node in the path;

[0151] Furthermore, the Euclidean distance is used to calculate the distance from the current node to the target node, and the heuristic function h(s) is constructed. The formula is:

[0152]

[0153] where u tar and v tar are the coordinates of the current node, and u and v are the coordinates of the target node;

[0154] Furthermore, according to the actual cost g(s) and the heuristic function h(s), the total path cost R(s) is calculated. The formula is:

[0155] R(s) = g(s) + h(s)

[0156] Iterate through each node, calculate the total cost function value of each node using the total path cost R(s), select the path with the minimum cost of the current node as the next node to be calculated, calculate the total cost function values of all nodes to stop the iteration, and reconstruct by backtracking the minimum cost node to obtain the shortest path.

[0157] It should be noted that by discretizing the Poisson equation and combining the finite element method with the conjugate gradient method, the potential distribution of the electric field can be accurately and efficiently solved, enabling the electric field analysis to be not only theoretically verified but also capable of dealing with complex three-dimensional electric field distributions and multi-physical field coupling situations. By calculating the electric field strength and combining it with the safety threshold set by the International Electrotechnical Commission standard, the safe area and the dangerous area can be effectively distinguished, the electric field strength distribution at the strong electric field operation site can be monitored in real time, providing real-time safety warnings for the staff and guiding safe operations. By combining the A* algorithm and the dynamic programming method, not only the shortest path can be calculated, but also the "path cost" brought by the electric field strength can be considered, thus calculating the optimal safe operation path. Through the combination of the Euclidean distance and the heuristic function, the A* algorithm can flexibly adjust the path calculation according to the actual situation, ensuring that the path planning is both safe and efficient. Further optimizing the path through dynamic programming not only improves the calculation efficiency but also enhances the robustness and adaptability of the path.

[0158] In summary, the present invention proposes a method for analyzing the electric field distribution in a strong electric field scenario, which obtains the first point cloud data of the target operation site and performs a first preprocessing on the first point cloud data; constructs a target three-dimensional tower model according to the first point cloud data after the first preprocessing; presets simulation constraints and establishes a first network model based on the simulation constraints and the target three-dimensional tower model; solves the target parameters of the first network model, and the target parameters at least include the electric field strength and the shortest safe path. Through this method, the electric field distribution in a strong electric field scenario can be analyzed quickly and accurately, providing reliable data support for the evaluation of the safety, stability and environmental impact of power equipment.

[0159] Specifically, first of all, by obtaining the first point cloud data of the target operation site and performing preprocessing, the high-precision geometric information obtained by modern measurement techniques can be fully utilized, improving the accuracy and reliability of the electric field analysis. Compared with traditional measurement methods, the point cloud data provides more detailed and accurate scene geometric information, making the modeling of the electric field distribution more precise.

[0160] Secondly, constructing a target three-dimensional tower model according to the preprocessed point cloud data can truly reflect the complex structure and dynamic changes of the strong electric field operation site. This step fully considers the geometric features in the actual scene, providing a solid foundation for subsequent electric field analysis.

[0161] Furthermore, presetting simulation constraints and establishing a first network model based on the simulation constraints and the target three-dimensional tower model can ensure the accuracy and efficiency of the electric field analysis. By setting reasonable simulation constraints, the real electric field environment can be simulated, effectively reducing the calculation amount and improving the analysis efficiency.

[0162] Finally, the target parameters of the first network model are solved to obtain key parameters such as electric field strength and the shortest safe path, providing reliable data support for the assessment of the safety, stability, and environmental impact of power equipment. This step comprehensively considers the complexity of the electric field distribution and the requirements of practical applications, making the analysis results closer to the actual situation and having higher practical value.

[0163] Embodiment 2

[0164] In a preferred embodiment, the shortest safe path can also be displayed by constructing a visualization interface, enabling the operator to intuitively understand the electric field distribution and the position of the safe path. The visualization interface can include information such as the electric field strength distribution map, the division of dangerous and safe areas, and the identification of the shortest safe path. By means of colors, lines, or animations, the electric field strength, safety thresholds, and path planning results are visually presented, allowing the operator to quickly grasp the key information of the electric field distribution and make accurate safety judgments. In addition, the visualization interface can also provide interactive functions, allowing the operator to adjust simulation parameters, view the electric field analysis results under different scenarios, and save and export the analysis results, further enhancing the practicality and flexibility of the method.

[0165] Specifically, in this application, the front-end framework React.js is used to construct the visualization interface, including the main chart area and the top information bar;

[0166] Furthermore, the shortest safe path is displayed in the main chart area, and the total path cost is displayed in the top information bar;

[0167] In an alternative embodiment, users who pass real-name verification are allowed to view.

[0168] Furthermore, React.js optimizes the page rendering speed through the virtual DOM. When the shortest safe path and the total path cost need to be updated in real time, it can efficiently reflect the changes. The total path cost (including factors such as the safety and efficiency of the path) is displayed in the top information bar, which can help users instantly understand the performance indicators of the path. The shortest safe path is displayed in the main chart area, which can visually present the optimal passage route in the electric field operation area. This helps users understand the relationship between the electric field strength distribution and the operation path, so as to make more reasonable operation decisions. Real-name verification ensures that only authorized personnel can access sensitive information, preventing unauthorized users from viewing or tampering with data. The real-time update ability of React.js enables the system to maintain a smooth operation experience even when multiple users use it simultaneously, avoiding affecting safety due to data lag or operation delay.

[0169] In an optional embodiment, storing the collected and analyzed point cloud data means sorting the collected point cloud data and the shortest safe path generated during the analysis process according to timestamps and storing them in a database. The database is sorted in chronological order, and the stored data is marked;

[0170] In an optional embodiment, the stored data is synchronously backed up regularly, and the stored data and the backup data are regularly detected for security and integrity, and a detection report is generated.

[0171] It should be noted that sorting the point cloud data and the analysis results in chronological order can not only achieve the standardized storage of data, but also facilitate subsequent query and processing. Regularly backing up the stored data is a necessary means to ensure data security. Especially in the process of processing long-term and large-scale point cloud data, backup can avoid the risk of data loss or damage. By regularly detecting the security and integrity of data, the system can monitor the security status of data in real time. Integrity detection can ensure that the data has not been damaged during transmission or storage, prevent analysis errors caused by data errors, and enhance user trust and system reliability.

[0172] Embodiment 3

[0173] This embodiment also provides an electric field distribution analysis system in a strong electric field scenario, including:

[0174] A data acquisition and processing module, configured to acquire first point cloud data of a target operation site and perform first preprocessing on the first point cloud data;

[0175] A first model establishment module, configured to construct a target three-dimensional tower model according to the first point cloud data after the first preprocessing;

[0176] A second model establishment module, configured to preset simulation constraints and establish a first network model based on the simulation constraints and the target three-dimensional tower model;

[0177] A solution module, configured to solve target parameters for the first network model, where the target parameters at least include electric field strength and the shortest safe path.

[0178] The above-mentioned each unit module can be embedded in the processor of the computer device in a hardware form or be independent of it, or can be stored in the memory of the computer device in a software form, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned each module.

[0179] This embodiment also provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 2As shown in the figure. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a method for analyzing the electric field distribution in a strong electric field scenario. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0180] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, the following steps are realized:

[0181] Obtain the first point cloud data of the target work site and perform a first preprocessing on the first point cloud data;

[0182] Construct a target three-dimensional tower model according to the first point cloud data after the first preprocessing;

[0183] Preset simulation constraints and establish a first network model based on the simulation constraints and the target three-dimensional tower model;

[0184] Solve the target parameters for the first network model, and the target parameters at least include electric field strength and the shortest safe path.

[0185] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0186] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0187] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0188] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0190] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0191] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A method for analyzing the electric field distribution in a strong electric field scenario, characterized in that Including: Obtain the first point cloud data of the target work site, and perform a first preprocessing on the first point cloud data; Construct a target three-dimensional tower model based on the first point cloud data after the first preprocessing; Preset simulation constraints, and establish a first network model based on the simulation constraints and the target three-dimensional tower model; Solve the target parameters of the first network model, where the target parameters at least include electric field strength and the shortest safe path.

2. The method for analyzing the electric field distribution in a strong electric field scenario according to claim 1, wherein The constructing the target three-dimensional tower model based on the first point cloud data after the first preprocessing includes: Perform a first clustering operation on the first point cloud data; Perform a first segmentation operation on the first point cloud data after the first clustering operation to obtain second point cloud data, and the first segmentation operation is a vertical direction segmentation operation Obtain the first relevant parameters of the second point cloud data, and construct a target three-dimensional tower model according to the first relevant parameters, where the first relevant parameters at least include the width, length, and inclination of the second point cloud data.

3. The method for analyzing the internal electric field distribution in a strong electric field scenario according to claim 2, characterized in that The solving the target parameters of the first network model includes: Perform a simulation analysis on the first network model, and obtain the electric field strength; Preset a first region judgment criterion, where the first region judgment criterion is used to judge the region to which the electric field strength belongs; Establish a first path objective function according to the electric field strength and the first region judgment criterion; Solve the first path objective function to obtain the shortest safe path.

4. The method for analyzing the electric field distribution in a strong electric field scenario according to claim 3, wherein, The preset simulation constraints include: The preset simulation constraints include cell size constraints and physical field boundary constraints; The cell size constraints are obtained through the electric field gradient and the adjustment coefficient.

5. The method for analyzing the electric field distribution in a strong electric field scenario according to claim 4, characterized in that, The establishing the first path objective function according to the electric field strength and the first region judgment criterion includes: The first path objective function is the total path cost function; Set the electric field strength as the path cost, and calculate the actual cost; Preset a construction heuristic function, and calculate the total path cost function according to the actual cost and the heuristic function.

6. The method for analyzing the electric field distribution in a strong electric field scenario according to claim 5, characterized in that, The first preprocessing includes: The first preprocessing includes performing a registration operation on the first point cloud data; And perform a denoising process on the first point cloud data after the matching operation; Then thin the denoised first point cloud data to obtain the thinned first point cloud data; Then perform a normalization process on the thinned first point cloud data.

7. The method for analyzing the electric field distribution in a strong electric field scenario according to claim 6, characterized in that The calculating the total path cost function includes: Iterate for each node, and calculate the total cost function value of each node using the total path cost function; Select the path with the minimum cost of the current node as the next node to be calculated, and calculate the total cost function values of all nodes to stop the iteration; Reconstruct by backtracking the minimum cost node to obtain the shortest safe path.

8. An electric field distribution analysis system within a strong electric field scenario, characterized in that, Including: A data acquisition and processing module, configured to obtain the first point cloud data of the target work site, and perform a first preprocessing on the first point cloud data; A first model establishment module, configured to construct a target three-dimensional tower model based on the first point cloud data after the first preprocessing; A second model establishment module, configured to preset simulation constraints, and establish a first network model based on the simulation constraints and the target three-dimensional tower model; A solution module for solving target parameters of the first network model, where the target parameters at least include electric field strength and the shortest safe path.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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