Large-scale space electromagnetic field modeling simulation method

Abstract

The invention provides a large-scale space electromagnetic field modeling simulation method, and belongs to the field of electromagnetic field modeling simulation, and the method comprises the steps: scanning a target large-scale space scene through a laser radar, and obtaining a transmission tower point cloud model in the large-scale space scene; the method comprises the following steps of: obtaining a transmission tower wire frame model through a DBSCAN algorithm, extracting edges and vertexes through a DBSCAN algorithm to obtain a transmission tower wire frame model, obtaining a transmission tower entity model through edge shortening, direct extrusion and rotation translation matching extrusion, and carrying out adaptive grid division by creating a distance field and a threshold field to obtain a transmission tower grid model; and finally, material attributes and design boundary conditions are defined based on actual working conditions of the transmission tower, electromagnetic field distribution of the transmission tower is solved based on Maxwell equations, and electromagnetic field modeling simulation is completed. According to the method, the problems that simulation modeling cannot be realized by a three-dimensional scanning technology due to complex and changeable large-scale space scenes in the existing electromagnetic field simulation modeling, and the simulation modeling accuracy is low due to dependence on artificial experience are solved.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic field modeling and simulation, and in particular relates to a method for large-scale spatial electromagnetic field modeling and simulation. Background Technology

[0002] In the field of electromagnetic field simulation of power systems, especially in the scenario of foreign object intrusion monitoring of transmission lines, it is necessary to accurately construct electromagnetic field simulation models that include conductors, towers and various intruding foreign objects in order to analyze the electric field distortion characteristics caused by foreign objects and provide theoretical support for foreign object identification and risk warning.

[0003] Existing electromagnetic field simulation modeling methods mainly rely on manual modeling: first, data such as the geometric parameters of transmission lines and the physical characteristics of foreign objects are obtained through on-site surveys; then, three-dimensional models of conductors, towers and foreign objects are manually drawn using simulation software; and finally, parameters such as dielectric constant, conductivity and boundary conditions are manually set to complete the modeling.

[0004] However, existing electromagnetic field simulation modeling methods have the following drawbacks when dealing with large-scale spatial scenarios: First, large-scale spatial scenarios include complex conductor structures, tower layouts, and diverse foreign objects. The process of manual drawing and parameter setting is cumbersome and the modeling cycle is long, making it difficult to meet the needs of real-time monitoring. Second, the modeling accuracy depends on human experience. The geometric shape and physical parameters of foreign objects are complex, and manual modeling cannot accurately reproduce their true characteristics, resulting in large errors in the electric field distortion simulation results. Finally, because there is no efficient conversion channel between the physical parameters obtained from the field survey and the parameters of the simulation model, parameter mismatch is likely to occur, affecting the accuracy of the simulation.

[0005] In recent years, the development of 3D scanning technology has made it possible to quickly acquire spatial data of real scenes. However, existing technical approaches usually remain at the level of "geometric reconstruction", which only converts point clouds into 3D mesh models. It is difficult to realize the transformation from "geometric model" to "simulation model" that can be used for calculation. This includes key steps such as determining the computational domain and applying boundary conditions. These tasks still rely heavily on human experience and have become the main bottleneck to achieving automated simulation. Summary of the Invention

[0006] To address the aforementioned shortcomings in existing technologies, this invention provides a method for estimating surface evapotranspiration that considers land-atmosphere coupling. This method solves the problem of low accuracy in existing electromagnetic field simulation modeling methods, which rely heavily on manual experience for drawing and parameter setting due to the complexity and variability of large-scale spatial scenes and the inability of 3D scanning technology to achieve simulation modeling. It enables rapid and automatic conversion from real physical scenes to initial electromagnetic field simulation models and then to high-precision electromagnetic field simulation models, significantly improving modeling efficiency and simulation realism. In particular, it provides core technical support for real-time electric field analysis and risk assessment of foreign object intrusion into power transmission lines.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a large-scale spatial electromagnetic field modeling and simulation method, characterized by comprising the following steps: S1. Scan the large-scale spatial scene of the target using lidar to obtain the point cloud model of the power transmission tower in the large-scale spatial scene; S2. Based on the point cloud model of the transmission tower, extract the edges and vertices of the transmission tower and perform vertex clustering using the DBSCAN algorithm to obtain the wireframe model of the transmission tower. S3. Based on the transmission tower wireframe model, each side is converted into a solid with a rectangular cross-section by side shortening, direct extrusion, and rotational translation matching extrusion. Excess conductor solids are identified and removed to obtain the transmission tower solid model. S4. Adaptively mesh the solid model of the transmission tower and create a distance field and a threshold field to obtain the mesh model of the transmission tower. S5. Based on the transmission tower mesh model and the actual working conditions of the transmission tower, the electromagnetic field distribution of the transmission tower is obtained by defining material properties and design boundary conditions and solving Maxwell's equations, thus completing the large-scale spatial electromagnetic field modeling and simulation.

[0008] Furthermore, S2 specifically includes: S201. Based on the point cloud model of the transmission tower, extract all edges and the coordinates of the start and end points of each edge, construct the mapping relationship between vertices and edges, and obtain the initial wireframe model. S202. Based on the initial wireframe model, the vertices are aggregated using DBSCAN clustering to obtain several clusters. S203. Calculate the coordinates of the center point of each cluster, and replace the coordinates of all vertices of the original cluster with the coordinates of the center point to obtain the set of vertices after clustering. S204. Based on the clustered vertex set, construct the edge connection relationship and remove duplicate edges to obtain the wireframe model of the transmission tower.

[0009] The further beneficial effects mentioned above are: by merging spatially adjacent vertices through DBSCAN clustering, the number of model vertices is reduced from hundreds of thousands to thousands, which can effectively eliminate redundant data, improve the processing efficiency of point cloud data, and lay a lightweight data foundation for subsequent entity conversion and mesh generation, thereby improving the efficiency of the entire simulation process.

[0010] Furthermore: the clustering radius and minimum number of samples for the DBSCAN clustering are adaptively determined based on the transmission tower point cloud model, and the expressions for the clustering radius and minimum number of samples are as follows:

[0011]

[0012] in, The cluster radius is 1. The maximum dimension of the model bounding box. For adaptive coefficients, Minimum number of samples; The total number of vertices; This is the sample proportion coefficient. To find the maximum value function, This is the floor function.

[0013] Furthermore, S3 specifically includes: S301. By using the principle of edge shortening, each edge in the transmission tower wireframe model is shortened to obtain the shortened new edge; S302. Based on each shortened new edge, convert each edge into a solid with a rectangular cross section through direct extrusion and rotation-translation matching extrusion to obtain several solids, and combine them to obtain a preliminary solid model. S303. Based on the entity ID, spatial coordinates, and cross-sectional dimension parameters of each entity, match according to the corresponding preset threshold, identify and remove redundant conductor entities in the preliminary entity model, and obtain the transmission tower entity model.

[0014] The further beneficial effects mentioned above are as follows: by using edge shortening, direct extrusion and rotational translation matching extrusion, a high success rate and geometric correctness in the conversion of complex geometry into solid models are ensured. Furthermore, by intelligently eliminating redundant wires based on multiple feature matching, the automation level and purity of modeling are improved, providing a reliable and structured geometric foundation for subsequent high-precision electromagnetic simulation.

[0015] Furthermore: the solid transformation operation through direct extrusion and rotational translation matching extrusion specifically includes: Direct extrusion: Create a rectangular cross section in a plane perpendicular to the side along the direction vector of the side, and perform an extrusion operation along the length of the side to obtain the first solid. Rotation and translation matching extrusion: Create a line segment and rectangular cross section with the same length as the edge in the preset reference plane and extrude them to generate a reference solid. Then, through rotation and translation transformation, match the spatial position and orientation of the reference solid with the edge to obtain the second solid.

[0016] The further beneficial effects mentioned above are: the dual-scheme extrusion mechanism ensures the transformation of conventional spatial pose edges through the main scheme, while using the backup scheme to deal with the algorithm problems that edges are prone to in complex spaces, ensuring automatic and high success rate conversion from wireframe to 3D solid, and improving the robustness and automation of the modeling process.

[0017] Furthermore, S4 specifically includes: S401. Divide the solid model of the transmission tower into components, resulting in air boxes, conductors and insulators, and the tower body. S402. Based on the component division results, differentiated grid size parameters are set to obtain a preliminary grid model. The grid size parameters of the conductors and insulators are set to the minimum, the grid size parameters of the air boxes are set to the maximum, and the grid size parameters of the tower body are set between the conductors, insulators, and air boxes. S403. Create distance field and threshold field to adaptively adjust the mesh size of the initial mesh model, and obtain the transmission tower mesh model.

[0018] The further beneficial effects mentioned above are: by classifying components and setting differentiated parameters, and by combining distance field and threshold field, continuous and stable control of mesh size can be achieved, automatic mesh densification in key areas to ensure accuracy, and automatic mesh sparsening in the vast air domain to improve computational efficiency, thereby reducing computational load while ensuring simulation accuracy.

[0019] Further: the distance field is used to calculate the distance from the initial mesh model to the surface of the conductor and insulator entity; the threshold field is used to adjust the mesh size based on the distance calculated by the distance field, and its expression is as follows:

[0020] in, This is the adjustment value for the grid size. This is the minimum grid size for conductors and insulators. This is the maximum grid size of the air box. The distance calculated for the distance field. The first distance threshold, This is the second distance threshold.

[0021] The further beneficial effects mentioned above are: by controlling the piecewise function of the threshold field, a linear and smooth transition of the mesh size from the wire surface to the air domain is achieved, reducing numerical errors caused by abrupt changes in mesh size, and improving the overall computational efficiency and numerical stability while ensuring the simulation accuracy of key areas.

[0022] Furthermore, S5 specifically includes: S501. Based on the entity category corresponding to each component in the transmission tower mesh model, define electromagnetic material properties for each component. The components include air boxes, conductors, insulators, and the tower body. S502. Based on the actual working conditions of the transmission towers, set natural boundaries and Robin boundaries for the transmission tower mesh model; S503. Based on Maxwell's equations, combined with electromagnetic material properties, natural boundaries, and Robin boundaries, a complex variational form of the governing equations for electric potential is constructed. S504. Solve the complex variational form of the governing equations concerning electric potential to calculate the electric field intensity distribution and electric field distortion rate, thus completing the electromagnetic field modeling and simulation.

[0023] The further beneficial effects mentioned above are: by setting natural boundaries and absorbing Robin boundary conditions, non-physical reflections of the electric field at the simulation boundary are suppressed, and at the same time, a complex variational equation is constructed to accurately describe the electromagnetic coupling under the time harmonic field, ensuring the stability of mesh calculation in large-scale scenes and improving simulation accuracy.

[0024] Furthermore, the complex variational form of the governing equation with respect to electric potential is expressed as follows:

[0025]

[0026] in, Where is the dielectric constant. For electrical conductivity, For complex potentials, For the test function, Angular frequency, Let be the gradient of the real part of the complex potential. To test the gradient of the real part of the function, To solve for the domain, This represents the imaginary part of the complex potential. This is the real part of the test function. The gradient is the imaginary part of the complex potential. To test the gradient of the imaginary part of the function, Let be the real part of the complex potential. This is the imaginary part of the test function; The expression for the electric field strength is as follows:

[0027] in, The amplitude of the total electric field intensity. Let be the real part of the electric field strength. This represents the imaginary part of the electric field strength. The expression for the electric field distortion rate is as follows:

[0028] in, The electric field distortion rate, The maximum electric field strength on the surface of the conductor. Let be the average electric field strength of the air box.

[0029] The beneficial effects of this invention are: This invention simplifies large-scale spatial electromagnetic field modeling and simulation. First, a point cloud model of the transmission tower is obtained by scanning the tower with a lidar scanner. Based on the original point cloud model, edge and vertex data are extracted. This invention uses DBSCAN clustering to reduce the number of vertices, remove redundant information, and generate a lightweight wireframe model. Based on the wireframe model, each edge is converted into a solid with a rectangular cross-section to simulate the tower's poles, conductors, and other structures. Edge shortening is used to avoid self-intersections of the solids. Addressing the difficulty in accurately identifying redundant components in traditional modeling, this invention uses triple matching of entity ID, spatial coordinates, and dimensional parameters to automatically identify and remove redundant conductor entities from the model, resulting in a complete model. The entire transmission tower model is designed with adaptive mesh generation: Based on the complete solid model, differentiated mesh size parameters are set for each component of the transmission tower to generate a high-quality adaptive mesh for the solid model. Distance field and threshold field are created to achieve smooth transition between meshes of different components. The electromagnetic field distribution of the transmission tower is solved based on Maxwell's equations, and natural boundaries and Robin boundaries are integrated to avoid simulation distortion caused by electric field reflection at the boundaries. Based on Maxwell's equations, considering the dielectric constant and conductivity of the material, variational equations are constructed to solve for electric field strength and distortion rate. This fully realizes the rapid construction of the simulation model and the solution of the electric field without the need for manual model construction and boundary condition setting, which greatly shortens the model construction time. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of a large-scale space electromagnetic field modeling and simulation method. Detailed Implementation

[0031] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0032] Example 1 like Figure 1 As shown, a large-scale space electromagnetic field modeling and simulation method includes the following steps: S1. Scan the large-scale spatial scene of the target using lidar to obtain the point cloud model of the power transmission tower in the large-scale spatial scene; S2. Based on the point cloud model of the transmission tower, extract the edges and vertices of the transmission tower and perform vertex clustering using the DBSCAN algorithm to obtain the wireframe model of the transmission tower. S3. Based on the transmission tower wireframe model, each side is converted into a solid with a rectangular cross-section by side shortening, direct extrusion, and rotational translation matching extrusion. Excess conductor solids are identified and removed to obtain the transmission tower solid model. S4. Adaptively mesh the solid model of the transmission tower and create a distance field and a threshold field to obtain the mesh model of the transmission tower. S5. Based on the transmission tower mesh model and the actual working conditions of the transmission tower, the electromagnetic field distribution of the transmission tower is obtained by defining material properties and design boundary conditions and solving Maxwell's equations, thus completing the large-scale spatial electromagnetic field modeling and simulation.

[0033] In one embodiment of the present invention, a large-scale spatial scene of the target is scanned by a lidar to obtain a point cloud model of the transmission towers in the large-scale spatial scene; the lidar scanning principle is to determine the spatial position by measuring the round-trip time difference of the laser pulse and combining it with the scanning angle; in the present invention, the three-dimensional coordinates of each scanning point of the transmission tower can be calculated by the following formula:

[0034]

[0035]

[0036] in, The x-coordinate of the scan point, The vertical coordinate of the scan point. The vertical coordinates of the scan point, This refers to the distance between the lidar and the scanning point on the power transmission tower. The vertical scanning angle, This is the horizontal scanning angle.

[0037] After obtaining each scan point of the transmission tower through lidar scanning, point cloud data of the transmission tower is obtained. In order to remove interference information and retain the core geometric features of the transmission tower, the point cloud data needs to be preprocessed by denoising, registration, and simplification to obtain the point cloud model of the transmission tower, specifically including: For noise reduction, statistical filtering algorithms can be used to identify outliers and remove environmental noise points based on the statistical characteristics of the local neighborhood of the point cloud. For registration, when the scene requires multi-view scanning, the Iterative Closest Point (ICP) algorithm is used to unify the point clouds from multiple views to the same coordinate system, and the optimal transformation matrix is ​​solved by minimizing the distance error between the source point cloud and the target point cloud. Simplified preprocessing: Voxel mesh filtering is used to divide the point cloud space into uniform voxels, with only one centroid point retained in each voxel, which greatly reduces the amount of data while maintaining geometric features.

[0038] In one embodiment of the present invention, although the point cloud model of the transmission tower can completely display the transmission tower, its data volume is huge, the computational load is large, and the computational cost is high. Therefore, the present invention simplifies the number of vertices in the point cloud model of the transmission tower by DBSCAN clustering, removes redundant information, reduces the number of vertices in the tower model from 100,000 to several thousand, reduces the data volume by 90%, and extracts and combines the edges and vertices of the transmission tower to obtain a lightweight wireframe model of the transmission tower.

[0039] This invention first extracts all edges of the transmission tower point cloud model and the start and end coordinates of each edge to obtain the mapping relationship between vertices and edges. Then, DBSCAN clustering is used to merge and simplify spatially similar redundant vertices, further reducing the data volume of the transmission tower point cloud model. The DBSCAN algorithm is a commonly used density-driven clustering algorithm that can automatically identify clusters based on the spatial distribution of data and filter noise points. In this invention, the bounding box size of the transmission tower point cloud model is calculated to adapt the DBSCAN clustering parameters, and the clustering radius and the minimum number of samples required to form clusters are dynamically adjusted according to the complexity of the model. This achieves accurate clustering of vertices in the transmission tower point cloud model, while ensuring significant data simplification and maximizing the preservation of the core geometric features of the transmission tower. The expression is as follows:

[0040]

[0041] in, The cluster radius is 1. The maximum dimension of the model bounding box. For adaptive coefficients, Minimum number of samples; The total number of vertices; This is the sample proportion coefficient. To find the maximum value function, This is the floor function.

[0042] After clustering, the original point cloud data is divided into several clusters, each cluster representing a group of spatially dense vertices, representing the simplified point cloud model of the power transmission towers. The coordinates of the center point of each cluster are calculated and represented as follows:

[0043] in, For the first The coordinates of the center point of each cluster For the first The number of vertices in a cluster. For the first The first cluster vertex coordinates The coordinates of the center points of each cluster are used as the serial numbers. Finally, the coordinates of all vertices of the original cluster are replaced with the coordinates of the center points of each cluster obtained by calculation, resulting in a set of vertices after clustering. The number of vertices is significantly reduced. Based on the set of vertices after clustering, the edge connection relationship is constructed and duplicate edges are removed to obtain a lightweight wireframe model of the transmission tower. This model can provide a simple and stable geometric data foundation for subsequent solid model conversion and electromagnetic field numerical simulation.

[0044] In one embodiment of the present invention, the wireframe model of the transmission tower can maintain the core geometric features of the original transmission tower while achieving lightweight design. However, it is only a geometric description framework, which can only describe the geometric topology of the transmission tower and cannot be used for electromagnetic field simulation. Therefore, it needs to be converted into a solid model. However, for solid models that need to be simulated for electromagnetic fields, the overlapping of components in traditional modeling can lead to geometric invalidity. It is necessary to manually adjust the position of the components according to the actual situation, such as converting the wireframe model to a solid model in CAD software. The conversion process is prone to errors due to the complex spatial posture of the components or geometric interference at the connection, which reduces the purity and computational efficiency of the subsequent simulation model. The present invention adopts a dual approach to convert each side of the wireframe model of the transmission tower into a solid with a rectangular cross section to simulate the actual structure of the transmission tower, such as the pole and conductor. At the same time, the edge shortening process is used to avoid self-intersection of the solid model. The dual approach includes the main approach of direct extrusion and the backup approach of rotation and translation matching extrusion, which can improve the modeling success rate and convert it into a solid model.

[0045] In a specific embodiment of the present invention, extrusion refers to extending a two-dimensional plane along a direction to form a three-dimensional solid model; all generated entities are preferentially merged using the union operation; if merging fails, it is automatically downgraded to assembly mode; if assembly mode still fails, it is exported as a separate part file, that is, each entity corresponds to a STEP file to ensure the integrity of the model.

[0046] Edge shortening principle: To avoid self-intersection at component connection points, this invention shortens both ends of each edge according to a preset ratio to obtain a new edge. The shortening amount at both ends of each edge is as follows:

[0047] in, Let the length of the new edge be . This is the original length of the edge. The shortening ratio of the edge, default. ; At the same time, calculate the starting point of the shortened new edge. and the finish line Its expression is as follows;

[0048]

[0049]

[0050] in, As the starting point of the new border, The end point of the new border, As the original starting point of the edge, Let be the original endpoint of the edge. The unit direction vector; for the shortened new edge, a dual-scheme extrusion is used to convert each edge into a solid with a rectangular cross-section.

[0051] The main scheme is direct extrusion: directly extruding a rectangle along the edge, creating a rectangular cross section in a plane perpendicular to the edge along the direction vector of the edge, and extruding a solid along the edge length; the alternative scheme is rotation and translation matching extrusion: first create a line in a preset reference plane, such as the XY plane, then generate a rectangular extruded body, and match the spatial orientation of the original edge by rotation and translation.

[0052] In a specific embodiment of the present invention, each side is converted into a solid with a rectangular cross-section through direct extrusion, rotational translation matching extrusion, and edge shortening to obtain an initial transmission tower solid model. This model includes redundant conductor entities and components, which can interfere with the simulation results and reduce the accuracy of the electromagnetic field simulation. Therefore, the present invention identifies and removes redundant conductor entities to obtain a more accurate transmission tower solid model. Specifically, this involves automatically identifying and removing redundant conductor entities from the initial transmission tower solid model using the entity ID, spatial coordinates, and size parameters. Specifically, the identification and removal can be performed by comparing and judging based on the pre-set entity ID, spatial coordinates, and size parameters to be deleted. Manual identification or supervision can also be added to ensure clean removal without accidental deletion, providing a clean modeling foundation for subsequent standardized component assembly and forming a complete and clean transmission tower solid model.

[0053] In one embodiment of the present invention, after obtaining the physical model of the transmission tower, electromagnetic field simulation is performed using the physical model. However, existing methods rely on manual experience, manually setting the mesh size for different regions. This process is complex and inconsistent, making it difficult to control the overall mesh size, resulting in wasted computational resources or affecting the accuracy of the electromagnetic field simulation. Therefore, the present invention performs adaptive mesh generation on the physical model of the transmission tower and creates distance and threshold fields to obtain a mesh model of the transmission tower. Specifically, it includes: The solid model of the transmission tower is divided into three components: air box, conductors and insulators, and tower body. The division is based on the following criteria: the mass of all volumetric entities in the solid model of the transmission tower is calculated, and the entity with the largest volume is the air box; the number of surfaces of each entity is counted, and entities with 8 surfaces are conductors and insulators; the remaining entities are the tower body; and differentiated mesh size parameters are set according to different components.

[0054] In this invention, due to the small diameter of the conductors and the complex structure of the insulators, a sufficiently small mesh is needed to capture curvature changes and accurately resolve the electric field. The towers are relatively large, and the overall focus is not on the entire tower; using a too-fine mesh would result in excessive computation. The air domain is used to simulate boundary conditions and does not require fine resolution. This invention sets the mesh size parameters of the conductors and insulators to the minimum, the mesh size parameters of the air box to the maximum, and the mesh size parameters of the tower body to be set between the conductors, insulators, and air box. The specific parameter settings are as follows:

[0055]

[0056]

[0057] in, This is the minimum grid size for the air box. This is the maximum grid size of the air box. This is the minimum grid size for conductors and insulators. This refers to the maximum mesh size for the conductor and insulator. This represents the minimum grid size for the tower body. This represents the maximum grid size of the tower body.

[0058] After mesh generation, to enable adaptive behavior and avoid solution errors caused by abrupt changes in mesh size, this invention creates a distance field and a threshold field to achieve smooth mesh transition. The distance field calculates the distance from the mesh to the surface of the conductor and insulator, while the threshold field adjusts the mesh size based on the distance from the distance field, satisfying the formula:

[0059] in, This is the adjustment value for the grid size. This is the minimum grid size for conductors and insulators. This is the maximum grid size of the air box. The distance calculated for the distance field. The first distance threshold, This is the second distance threshold; and All of these are pre-set thresholds. , 8 is a transition factor that controls the width of the transition band from fine to coarse. The distance calculated for the distance field is the shortest distance from a point in space to the surface of the conductor and the insulator; Minimum grid size for conductor and insulator areas; The minimum grid size for the air box. This indicates that the point is located on the surface or inside the insulator or conductor, therefore the minimum grid size is used. This indicates that the point is located in the 0-40mm area outside the surface of the conductor and insulator. This grid size is related to the distance and will not cause abrupt changes in the grid. This indicates that the point is far from the conductor and insulator, and the chosen grid size for the air domain is a coarse grid to describe the slowly changing electromagnetic field.

[0060] Based on the electromagnetic characteristics of each component of the transmission tower, including air boxes, conductors, insulators, and the tower itself, differentiated mesh size parameters are set to generate high-quality adaptive meshes for the solid model. Distance fields and threshold fields are also created, and the mesh size is dynamically adjusted through mathematical formulas to achieve a smooth transition between meshes of different components.

[0061] In one embodiment of the present invention, a grid model of a transmission tower is obtained. Before performing electromagnetic field simulation, it needs to be given realistic physical meaning so that it can represent a real transmission tower and simulate the actual working conditions of the transmission tower. Based on the grid model of the transmission tower and the actual working conditions of the transmission tower, the present invention defines material properties and design boundary conditions, and obtains the electromagnetic field distribution of the transmission tower by solving Maxwell's equations, thus completing large-scale spatial electromagnetic field modeling and simulation; specifically including: Based on the entity category corresponding to each component in the transmission tower mesh model, electromagnetic material properties are defined for each component. The components of the transmission tower include: air box, conductor, insulator and tower body. Based on the electromagnetic characteristics of each component, the electromagnetic parameters of the material are quantified, including dielectric constant and conductivity, and a function space adapted to AC characteristics is constructed.

[0062] Based on the actual operating conditions of 110kV transmission towers, two types of core boundary conditions are set: natural boundary and Robin boundary. The natural boundary condition sets the tower grounding, i.e., the surface potential is 0; a 110kV AC current is applied to the conductor surface, in complex form:

[0063]

[0064]

[0065] in This is the voltage of phase A. This is the voltage of phase B. This is the C-phase voltage, with a phase difference of 120°.

[0066] Robin boundary is set for the outer boundary of the air cell. It is an absorbing boundary to avoid simulation distortion caused by electric field reflection at the boundary. The expression is:

[0067] in, The absorption coefficient is adapted to the characteristics of the air domain. The gradient coefficient controls the rate of electric field decay at the boundary. The boundary normal vector. The gradient of the electric potential along the normal direction, It represents the electric potential.

[0068] Based on Maxwell's equations, and considering electromagnetic material properties, natural boundaries, and Robin boundaries, a complex variational governing equation for the electric potential is constructed. For a 50Hz power frequency AC scenario, its time-harmonic field has a time factor of... Ignoring higher-order effects other than displacement current, the expression for the core control equation is as follows:

[0069] in, Where is the dielectric constant. For electrical conductivity, For complex potentials, Angular frequency, It is the power frequency; due to the electric potential Since it is a complex number containing amplitude and phase, it is decomposed into a real part. and the virtual part , represented as:

[0070] in, Let be the imaginary part symbol. Substituting it into the governing equations yields two coupled real governing equations, which are used to construct the variational equations. The expression for the variational equations is as follows:

[0071]

[0072] The variational equation was then derived using the weighted residual method, by multiplying both sides of the real governing equation by the test function. and in the solution domain Integrate upwards and combine with Green's theorem to transform it into a weak form.

[0073] For any real-number governing equation The variational equation is in the form of:

[0074] in, For test functions that satisfy boundary conditions, The potential to be determined is denoted as .

[0075] Finally, substituting the real and imaginary control equations into the variational equations, we obtain the real and imaginary variational equations, which are the complex variational form of the control equations concerning the potential. Their expressions are as follows:

[0076]

[0077] in, Where is the dielectric constant. For electrical conductivity, For complex potentials, For the test function, Angular frequency, Let be the gradient of the real part of the complex potential. To test the gradient of the real part of the function, To solve for the domain, This represents the imaginary part of the complex potential. This is the real part of the test function. The gradient is the imaginary part of the complex potential. To test the gradient of the imaginary part of the function, Let be the real part of the complex potential. This is the imaginary part of the test function.

[0078] The complex variational form of the governing equations concerning electric potential is solved to calculate the electric field intensity distribution and electric field distortion rate, thus completing the electromagnetic field modeling and simulation.

[0079] The expression for electric field strength is as follows:

[0080] in, The amplitude of the total electric field intensity. Let be the real part of the electric field strength. This represents the imaginary part of the electric field strength. The expression for the electric field distortion rate is as follows:

[0081] in, The electric field distortion rate, The maximum electric field strength on the conductor surface is used to determine whether there is a risk of corona discharge. The average electric field strength of the air box is used to evaluate the uniformity of the electric field distribution.

[0082] The beneficial effects of this invention are as follows: This invention simplifies the modeling and simulation of large-scale spatial electromagnetic fields. First, a point cloud model of the transmission tower is obtained by scanning the tower with a lidar. Based on the original point cloud model, edge and vertex data of the tower are extracted. This invention simplifies the number of vertices and removes redundant information through DBSCAN clustering, generating a lightweight wireframe model. Based on the wireframe model, each edge of the wireframe model is converted into a solid with a rectangular cross-section to simulate the structure of the tower, such as the pole and conductors. Self-intersection of solids is avoided by shortening the edges. Addressing the problem of difficult accurate identification of redundant components in traditional modeling, redundant conductor solids in the model are automatically identified and removed through triple matching of entity ID, spatial coordinates, and size parameters. A complete tower model is formed; adaptive mesh generation: based on the complete solid model, differentiated mesh size parameters are set for each component of the transmission tower to generate a high-quality adaptive mesh for the solid model, and distance field and threshold field are created to achieve smooth transition of mesh between different components; the electromagnetic field distribution of the transmission tower is solved based on Maxwell's equations, and natural boundaries and Robin boundaries are integrated to avoid simulation distortion caused by electric field reflection at the boundary. Based on Maxwell's equations, considering the dielectric constant and conductivity of the material, variational equations are constructed to solve for electric field strength and distortion rate, realizing the rapid construction of simulation model and electric field solution without the need for manual model construction and boundary condition setting, which greatly shortens the model construction time.

Claims

1. A method for modeling and simulating large-scale spatial electromagnetic fields, characterized in that, Includes the following steps: S1. Scan the large-scale spatial scene of the target using lidar to obtain the point cloud model of the power transmission tower in the large-scale spatial scene; S2. Based on the point cloud model of the transmission tower, extract the edges and vertices of the transmission tower and perform vertex clustering using the DBSCAN algorithm to obtain the wireframe model of the transmission tower. S3. Based on the transmission tower wireframe model, each side is converted into a solid with a rectangular cross-section by side shortening, direct extrusion, and rotational translation matching extrusion. Excess conductor solids are identified and removed to obtain the transmission tower solid model. S4. Adaptively mesh the solid model of the transmission tower and create a distance field and a threshold field to obtain the mesh model of the transmission tower. S5. Based on the transmission tower mesh model and the actual working conditions of the transmission tower, the electromagnetic field distribution of the transmission tower is obtained by defining material properties and design boundary conditions and solving Maxwell's equations, thus completing the large-scale spatial electromagnetic field modeling and simulation.

2. The large-scale space electromagnetic field modeling and simulation method according to claim 1, characterized in that, S2 specifically includes: S201. Based on the point cloud model of the transmission tower, extract all edges and the coordinates of the start and end points of each edge, construct the mapping relationship between vertices and edges, and obtain the initial wireframe model. S202. Based on the initial wireframe model, the vertices are aggregated using DBSCAN clustering to obtain several clusters. S203. Calculate the coordinates of the center point of each cluster, and replace the coordinates of all vertices of the original cluster with the coordinates of the center point to obtain the set of vertices after clustering. S204. Based on the clustered vertex set, construct the edge connection relationship and remove duplicate edges to obtain the wireframe model of the transmission tower.

3. The large-scale space electromagnetic field modeling and simulation method according to claim 2, characterized in that, The clustering radius and minimum number of samples for DBSCAN clustering are adaptively determined based on the transmission tower point cloud model. The expressions for the clustering radius and minimum number of samples are as follows: in, The cluster radius is 1. The maximum dimension of the model bounding box. For adaptive coefficients, Minimum number of samples; The total number of vertices; This is the sample proportion coefficient. To find the maximum value function, This is the floor function.

4. The large-scale space electromagnetic field modeling and simulation method according to claim 1, characterized in that, S3 specifically includes: S301. By using the principle of edge shortening, each edge in the transmission tower wireframe model is shortened to obtain the shortened new edge; S302. Based on each shortened new edge, convert each edge into a solid with a rectangular cross section through direct extrusion and rotation-translation matching extrusion to obtain several solids, and combine them to obtain a preliminary solid model. S303. Based on the entity ID, spatial coordinates, and cross-sectional dimension parameters of each entity, match according to the corresponding preset threshold, identify and remove redundant conductor entities in the preliminary entity model, and obtain the transmission tower entity model.

5. The large-scale space electromagnetic field modeling and simulation method according to claim 4, characterized in that, The solid transformation operation through direct extrusion and rotational translation matching extrusion specifically includes: Direct extrusion: Create a rectangular cross section in a plane perpendicular to the side along the direction vector of the side, and perform an extrusion operation along the length of the side to obtain the first solid. Rotation and translation matching extrusion: Create a line segment and rectangular cross section with the same length as the edge in the preset reference plane and extrude them to generate a reference solid. Then, through rotation and translation transformation, match the spatial position and orientation of the reference solid with the edge to obtain the second solid.

6. The large-scale space electromagnetic field modeling and simulation method according to claim 1, characterized in that, S4 specifically includes: S401. Divide the solid model of the transmission tower into components, resulting in air boxes, conductors and insulators, and the tower body. S402. Based on the component division results, differentiated grid size parameters are set to obtain a preliminary grid model. The grid size parameters of the conductors and insulators are set to the minimum, the grid size parameters of the air boxes are set to the maximum, and the grid size parameters of the tower body are set between the conductors, insulators, and air boxes. S403. Create distance field and threshold field to adaptively adjust the mesh size of the initial mesh model, and obtain the transmission tower mesh model.

7. The large-scale space electromagnetic field modeling and simulation method according to claim 6, characterized in that, The distance field is used to calculate the distance from the initial mesh model to the surface of the conductor and the insulator entity; the threshold field is used to adjust the mesh size based on the distance calculated by the distance field, and its expression is as follows: in, This is the adjustment value for the grid size. This is the minimum grid size for conductors and insulators. This is the maximum grid size of the air box. The distance calculated for the distance field. The first distance threshold, This is the second distance threshold.

8. The large-scale space electromagnetic field modeling and simulation method according to claim 1, characterized in that, S5 specifically includes: S501. Based on the entity category corresponding to each component in the transmission tower mesh model, define the electromagnetic material properties for each component. The components include air boxes, conductors, insulators, and the tower body. S502. Based on the actual working conditions of the transmission towers, set natural boundaries and Robin boundaries for the transmission tower mesh model; S503. Based on Maxwell's equations, combined with electromagnetic material properties, natural boundaries, and Robin boundaries, a complex variational form of the governing equations for electric potential is constructed. S504. Solve the complex variational form of the governing equations concerning electric potential to calculate the electric field intensity distribution and electric field distortion rate, thus completing the electromagnetic field modeling and simulation.

9. The large-scale space electromagnetic field modeling and simulation method according to claim 8, characterized in that, The complex variational form of the governing equation with respect to electric potential is expressed as follows: in, Where is the dielectric constant. For electrical conductivity, For complex potentials, For the test function, Angular frequency, Let be the gradient of the real part of the complex potential. To test the gradient of the real part of the function, To solve for the domain, This represents the imaginary part of the complex potential. This is the real part of the test function. The gradient is the imaginary part of the complex potential. To test the gradient of the imaginary part of the function, Let be the real part of the complex potential. This is the imaginary part of the test function; The expression for the electric field strength is as follows: in, The amplitude of the total electric field intensity. Let be the real part of the electric field strength. This represents the imaginary part of the electric field strength. The expression for the electric field distortion rate is as follows: in, The electric field distortion rate, The maximum electric field strength on the surface of the conductor. Let be the average electric field strength of the air box.

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