A power transmission line simulation modeling method, device and system

Abstract

The application discloses a power transmission line simulation modeling method, device and system, and belongs to the field of power transmission line modeling. Since only the key point positions of devices such as towers are extracted, all point cloud data need not be processed, and the data amount is greatly reduced. Subsequently, the data of non-key points is supplemented through linear interpolation and nonlinear interpolation, the corresponding model is obtained, and the data processing amount is reduced. Then, an air geometric model is established, a human body model is imported into the structure model to obtain a human body electrical detection three-dimensional entity model, and finally, regional segmentation and connection are performed to obtain a finite element model, so that subsequent analysis and calculation are facilitated. In the modeling and simulation, the application scheme is combined with point cloud data, parameterization, modularization and a RAM virtual external memory, so that the model is consistent with the actual object, the data amount to be processed is greatly reduced, the modeling and simulation efficiency is improved, and in addition, the human body model is introduced, and is suitable for data measurement in various environments.

Description

Technical Field

[0001] This invention relates to the field of power transmission line modeling and simulation, and in particular, to a method, apparatus and system for power transmission line simulation modeling. Background Technology

[0002] Electric shock is one of the main risks faced by electrical workers. Statistics show that in the past 10 years, electric shock accounted for 81% of all personal injury accidents, with insufficient safe distance being the direct cause. Developing reliable and user-friendly emergency power warning equipment is crucial for reducing electric shock accidents. Using computer-aided engineering simulations in the development of such equipment can shorten the construction period, accelerate project progress, and improve project quality.

[0003] When the space surrounding transmission lines in a distribution network is complex, including towers, lines, people, transformers, circuit breakers, and disconnectors, the resulting spatial electric field often differs significantly from that of a single conductor. Due to variations in phase, material, spatial location, and shape of different equipment and lines, the electric field around energized objects is distorted. If the temporary power warning algorithm is not adequately trained and validated, it can easily lead to false alarms from temporary power warning sensors, posing a safety hazard to distribution network maintenance personnel. Relying solely on on-site measurements from temporary power warning sensors is insufficient to encompass all spatial electric field locations and cannot comprehensively verify the reliability of the temporary power warning algorithm.

[0004] Therefore, existing technologies employ simulation modeling of transmission lines and then verify the reliability of the power outage warning algorithm through data in the model. However, existing methods for modeling transmission lines either involve a large amount of data, making modeling difficult, or the resulting model does not match the actual object and cannot be applied to measurements in various environments. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a method, apparatus, and system for simulating and modeling transmission lines, which solves the problems that existing methods for modeling transmission lines either involve large amounts of data, making modeling difficult, or the resulting models do not match the actual objects and are not applicable to measurements in various environments.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] Firstly, a method for simulating and modeling transmission lines is provided, including the following steps:

[0008] Acquire point cloud data of power transmission lines obtained from lidar scanning;

[0009] Extract the key locations of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnect switches from the point cloud data of the transmission line;

[0010] Based on the key point locations, modular models of the tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector are constructed collaboratively using linear and nonlinear interpolation, and the structural model is obtained by assembling each modular model.

[0011] Human models in different postures and in different spatial positions are imported into the structural model, and an air model is established to contain other entity models through Boolean operations to obtain a three-dimensional entity model for human body electrical detection.

[0012] The three-dimensional solid model for human body electrical detection is divided into linear and nonlinear structural regions. Node coupling or constraint equations are established between the nonlinear structural regions and the adjacent nodes with the smallest distance from the linear structural regions to obtain the finite element models of each module in the three-dimensional solid model for human body electrical detection.

[0013] Furthermore, the modular model of the tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector, constructed collaboratively based on the key point locations using linear and nonlinear interpolation, includes:

[0014] Establish a corresponding geometric model based on the location of the key points;

[0015] The geometric model is subjected to linear interpolation and nonlinear interpolation in combination to obtain a modular model, which has the same shape as the corresponding physical object.

[0016] Furthermore, establishing the corresponding geometric model based on the key point locations includes:

[0017] Key points of poles, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches are extracted as reference points for parametric modeling, and components are defined for each.

[0018] The geometric model of the electrical detection simulation is classified by modularity, and the modeling order of points, lines, surfaces and volumes is determined according to the relationship between the local coordinates and the global coordinates of the modules.

[0019] Based on the modeling sequence, corresponding geometric models are established for each of the following components: tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector.

[0020] Furthermore, based on the key point locations of the round rod or iron tower, a geometric model is established using the parameterized loop command, with parameters including the diameter and height of the upper and lower ends of the round rod or the cross-sectional configuration, dimensions, length, and installation angle of the supporting beam in the iron tower.

[0021] Based on the key point locations of the drainage line and the conductor, the geometric model of the drainage line and the conductor is established by combining linear interpolation and nonlinear interpolation, using the sag curvature, cross-sectional configuration and size of the drainage line and the conductor as parameters.

[0022] Based on the key point locations of the insulator, a cross-sectional model of the insulator is established, and the three-dimensional geometric model of the insulator is obtained by using the rotation command.

[0023] Based on the key point locations of the ground wire, a three-dimensional geometric model of the ground wire is established through linear interpolation and nonlinear interpolation, using the curvature, cross-sectional shape, and size of the ground wire as parameters.

[0024] Based on the key locations of the transformer and disconnector, and using the appearance and dimensions of the transformer and disconnector as parameters, a surface model of the transformer and disconnector is established through a combination of linear and nonlinear interpolation. Then, based on the potential, the corresponding three-dimensional solid geometric models of different components of the transformer and disconnector are obtained by solid filling.

[0025] Furthermore, the assembly of the various module models to obtain the structural model includes:

[0026] Based on the correlation between the local coordinates of each module model and the overall coordinates of the structural model, the module models are sequentially called for assembly using the superposition principle and parameterization commands.

[0027] Further, the process of segmenting the three-dimensional solid model for detecting near-field electrical activity in the human body includes:

[0028] The three-dimensional solid model for detecting human electrical activity is divided into linear and nonlinear structural regions based on the linearity of each module model.

[0029] The linear structural region and the nonlinear structural region are respectively divided into units;

[0030] A global finite element model of the insulator is established using the parametric rotation command based on the already divided auxiliary surface elements.

[0031] Further, the step of dividing the linear structural region and the nonlinear structural region into units includes:

[0032] For linear structural regions, hexahedral finite elements are generated using auxiliary surface meshing and scanning methods.

[0033] For nonlinear regions, local mesh refinement is used to generate tetrahedral, pentahedral, or hexahedral elements.

[0034] Furthermore, establishing node coupling or constraint equations between the nonlinear structural region and the adjacent nodes with the smallest distance to the linear structural region includes:

[0035] A distance search function is established using nodes within the nonlinear or linear structural regions.

[0036] The distance search function is used to find the two adjacent nodes with the smallest distance between the nonlinear structural region and the linear structural region.

[0037] Couple the two nodes with the smallest distance or establish constraint equations.

[0038] Secondly, a power transmission line simulation modeling device is provided, comprising:

[0039] The point cloud data acquisition module is used to acquire point cloud data of power transmission lines obtained by lidar scanning;

[0040] The key point extraction module is used to extract the key point locations of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnect switches from the point cloud data of the transmission line.

[0041] The structural model acquisition module is used to construct modular models of the tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector based on the key point locations using linear and nonlinear interpolation, and to assemble the modular models to obtain the structural model.

[0042] The solid model acquisition module is used to import human body models in different poses and in different spatial positions into the structural model, and to create an air model based on Boolean operations to contain other solid models and obtain a three-dimensional solid model for human body electrical detection.

[0043] The finite element model construction module is used to divide the three-dimensional solid model of the human body near-electric shock detection into linear and nonlinear structural regions, and to establish node coupling or constraint equations for the adjacent nodes with the smallest distance between the nonlinear and linear structural regions, so as to obtain the finite element model of each module in the three-dimensional solid model of the human body near-electric shock detection.

[0044] Thirdly, a power transmission line simulation modeling system is provided, comprising: virtualizing a portion of the computer's RAM as an external memory for installing temporary power detection simulation software and mirroring the installed software to the computer's external memory; periodically mirroring user data temporarily stored in the RAM virtual external memory to the computer's external memory during computer operation; after the computer is powered on, the temporary power detection simulation software and user data are mirrored from the external memory to the RAM virtual external memory; the periodically mirrored user data in the computer's external memory includes at least the data mirrored at the previous time point and the data at the current time point; and an uninterruptible power supply for the computer ensures that the data in the RAM virtual external memory is non-volatile.

[0045] The portion of RAM that is not virtualized as external memory is used to store the processor's executable instructions and data;

[0046] The computer's processor, the portion of RAM not virtualized as external storage, the external storage virtualized by RAM, and the external storage configured to perform the method described in any one of the technical solutions provided in the first aspect.

[0047] Beneficial effects:

[0048] This application provides a method, device, and system for simulating and modeling transmission lines. After acquiring point cloud data of the transmission line, key point locations are extracted, and linear and nonlinear interpolation is used to construct modular models of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnectors. These modular models are then assembled to obtain a structural model. Since only key point locations of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnectors are extracted, processing of all point cloud data is unnecessary, significantly reducing the data volume. Subsequently, non-key point data is supplemented through linear and nonlinear interpolation, resulting in the corresponding model and further reducing data processing. A human body model is then imported into the structural model, and an air-based three-dimensional geometric model encompassing other entity models is established to obtain a three-dimensional entity model for human body electrical detection. Finally, the three-dimensional entity model is segmented and connected to obtain a finite element model for subsequent analysis and calculation. This application's solution combines point cloud data and parametric collaboration during modeling and simulation. While ensuring that the obtained model is consistent with the actual object, it greatly reduces the amount of data that needs to be processed and improves the efficiency of simulation modeling. It introduces human body models and air geometry models, making it suitable for data measurement in various environments. In addition, virtualizing a portion of the computer's large-capacity RAM as an external storage device to install the power detection simulation software and store user data can significantly improve the software's running speed and greatly reduce the time required for modeling and simulation. This enables large-scale, large-volume modeling and simulation calculations that are difficult to perform with conventional computers. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a power transmission line simulation modeling method provided in an embodiment of the present invention;

[0051] Figure 2 This is a geometric model diagram of a tower provided in an embodiment of the present invention;

[0052] Figure 3 A finite element model diagram of a pole tower provided in an embodiment of the present invention;

[0053] Figure 4 The geometric model diagram of the tower and insulator provided in the embodiment of the present invention;

[0054] Figure 5 Finite element model diagrams of towers and insulators provided in embodiments of the present invention;

[0055] Figure 6 This is a cross-sectional model diagram of an insulator provided in an embodiment of the present invention;

[0056] Figure 7 This is a finite element model diagram of an insulator cross-section provided in an embodiment of the present invention;

[0057] Figure 8 This is a schematic diagram of a three-dimensional finite element model of an insulator provided in an embodiment of the present invention;

[0058] Figure 9 This is a node coupling model diagram provided in an embodiment of the present invention;

[0059] Figure 10 This is a schematic diagram of a wire model provided in an embodiment of the present invention;

[0060] Figure 11 This is a schematic diagram of the structure of a power transmission line simulation modeling device provided in an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] The first embodiment, referred to Figure 1 This invention provides a method for simulating and modeling power transmission lines, comprising the following steps:

[0063] S11: Acquire point cloud data of power transmission lines obtained from lidar scanning;

[0064] S12: Extract the key point locations of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnect switches from the point cloud data of transmission lines;

[0065] S13: Based on the key point locations, modular models of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches are constructed collaboratively using linear and nonlinear interpolation, and the structural model is obtained by assembling each modular model;

[0066] S14: Import human body models in different postures and in different spatial positions into the structural model, and further establish an air model based on Boolean operations to encompass other entity models to obtain a three-dimensional entity model for human body electrical detection.

[0067] S15: The three-dimensional solid model of human body electrical detection is divided into linear and nonlinear structural regions. The adjacent nodes with the smallest distance between the nonlinear and linear structural regions are used to establish node coupling or constraint equations to obtain the finite element model of each module in the three-dimensional solid model of human body electrical detection.

[0068] The transmission line simulation modeling method provided in this invention involves acquiring point cloud data of the transmission line, then extracting key point locations and using linear and nonlinear interpolation to construct modular models of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnectors, which are then assembled to obtain a structural model. Since only key point locations of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnectors are extracted, there is no need to process all point cloud data, significantly reducing the data volume. Subsequently, non-key point data is supplemented through linear and nonlinear interpolation, resulting in the corresponding model and further reducing data processing. Human models in different postures and spatial locations are then imported into the structural model. Further, an air model is established based on Boolean operations to encompass other entity models, resulting in a three-dimensional entity model for human electrical detection. Finally, the three-dimensional entity model is segmented and connected to obtain a finite element model for subsequent analysis and calculation. This application's solution combines point cloud data and parametric collaboration during modeling and simulation, ensuring consistency between the obtained model and the actual object while significantly reducing the amount of data to be processed and improving simulation modeling efficiency. In addition, a human body model was introduced, making it suitable for data measurement in various environments.

[0069] In a second embodiment, as a supplementary explanation to the first embodiment, the present invention provides a specific method for modeling and simulating transmission lines, which specifically includes the following steps:

[0070] S1. In view of the large amount of data in the simulation geometric model of the temporary power detection, the simulation software and user data are installed in the computer RAM virtual external memory to improve the software running speed. The contents of the RAM virtual external memory are saved by setting a mirror address in the computer external memory. In addition, the computer is powered by an uninterruptible power supply to improve the non-volatility of the data in RAM.

[0071] S2. Based on the point cloud data of the transmission line obtained by LiDAR scanning, key points of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnectors are extracted as reference points for parametric modeling, and components are defined for each. Key points are the locations of the geometric shape and position information of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnectors. In practice, the laser point cloud data not only includes the point cloud data of the geometric structure and position information of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnectors, but also includes the interference data of the environment during scanning. Therefore, if the obtained point cloud data is directly processed for modeling, the model will be inaccurate due to interference data. Moreover, the point cloud data of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnectors is large, the data processing volume is large, and the modeling is slow. Since towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches all have fixed and unique structures, it is only necessary to select a few key points on the fixed structure to determine its approximate geometry. The remaining points can be obtained by interpolation. This ensures accurate modeling while reducing the amount of data processing and improving modeling speed.

[0072] S3. The geometric model of the temporary power detection simulation is classified in a modular way. The modeling order of points, lines, surfaces and volumes is determined according to the correlation between the position information of the local coordinates and the overall coordinates of the module. The corresponding models are established based on the functions of the tower, crossarm, insulator, drain line, ground wire, conductor, transformer and disconnector.

[0073] S4. Based on the correlation between the local coordinates of each module model and the overall coordinates of the global model, the module models are sequentially called for assembly and graphic interference checks are performed using the superposition principle and parameterization commands; functions of air under different weather conditions are defined based on parameterization commands; specifically, the corresponding positions of each module in the global model are changed for assembly.

[0074] S5. For human models in different postures, the human body function is obtained by using a data fitting algorithm based on the point cloud data obtained by LiDAR scanning, and the change of human body spatial position is realized according to the local coordinate transformation of the human body in the electrical detection scenario.

[0075] S6. Based on the linearity of the components in the model, the simulation model for electrical detection is divided into regions. Linear structural regions are obtained by scanning with auxiliary surface elements to obtain hexahedral elements. Nonlinear structural regions are divided into elements separately based on local mesh refinement technology, and seamless links are achieved with the elements of adjacent regions through node coupling or constraint equations. The linearity of the components in the model is determined by the degree of abrupt change of the lines of the components in the model. When the change amplitude of a certain line or a group of lines exceeds the threshold, it is judged as a nonlinear structure.

[0076] It is understandable that in step S2 above, since there are many key points extracted, it is necessary to define each component, and the names of each component need to be distinctive to effectively distinguish the information of each key point.

[0077] In step S3 above, the specific steps are as follows:

[0078] S31. Based on the key point locations of the round pole or iron tower, using the diameter and height at both ends of the round pole, or the cross-sectional configuration, dimensions, length, and installation angle of the supporting beams in the iron tower as parameters, use the parametric loop command to establish the tower's geometric model; such as Figure 2 As shown;

[0079] S32. Based on the key point locations of the drainage line and conductor, establish the geometric model of the drainage line and conductor through linear and nonlinear interpolation, using the sag curvature, cross-sectional shape, and dimensions of the drainage line and conductor as parameters; the conductor model is as follows: Figure 10 As shown;

[0080] S33. Based on the key point locations of the insulator, establish an insulator cross-sectional model. Use the rotation command to obtain the three-dimensional geometric model of the insulator. The geometric models of the tower and insulator are as follows: Figure 4 As shown;

[0081] S34. Based on the key point locations of the ground wire, a three-dimensional geometric model of the ground wire is established using linear interpolation and nonlinear interpolation in conjunction with the curvature, cross-sectional shape and size of the ground wire as parameters.

[0082] S35. Using the appearance and dimensions of the transformer and disconnector as parameters, establish the appearance surface model of the transformer and disconnector through linear interpolation and nonlinear interpolation, and obtain the corresponding three-dimensional solid geometric model by solid filling for different components of the transformer and disconnector according to the potential.

[0083] It should be noted that steps S31 to S35 above are only for establishing different models for different modules, and do not limit the order in which the relevant models are established.

[0084] In step S4 above, the specific steps are as follows:

[0085] S41. Define the critical points of the tower as numbered from i to i+n;

[0086] S42. Define the key points of the drainage line as (i+n)+1 to i+j;

[0087] S43. Define the key points of the insulator as i+j+1 to i+k;

[0088] S44. Define the key points of the ground wire as i+k+1 to i+m;

[0089] S45. Define the critical points of the traverse as i+m+1 to i+b;

[0090] S46. Using the positional relationship between the local X, Y, and Z axes of the tower and the overall X, Y, and Z axes of the overall model as parameters, use the calling command to position the tower at the corresponding position in the overall model;

[0091] S47. Using the positional relationship between the local coordinates X, Y, and Z axes of the drainage line and the overall coordinates of the overall model as parameters, use the calling command to place the drainage line at the corresponding position in the overall model;

[0092] S48. Using the positional relationship between the X, Y, and Z axes of the insulator's local coordinates and the overall coordinates of the global model as parameters, use the calling command to position the insulator at the corresponding position in the global model to form an insulator string;

[0093] S49. Using the positional relationship between the local X, Y, and Z axes of the ground line and the overall coordinates of the global model as parameters, use the calling command to place the ground line at the corresponding position in the global model;

[0094] S50. Using the positional relationship between the X, Y, and Z axes of the traverse and the overall coordinates of the global model as parameters, use the calling command to place the traverse at the corresponding position in the global model;

[0095] S51. Using the positional relationship between the X, Y, and Z axes of the transformer's local coordinates and the overall coordinates of the global model as parameters, use the calling command to position the transformer at the corresponding position in the global model.

[0096] S52. Using the positional relationship between the local X, Y, and Z axes of the disconnecting switch and the overall X, Y, and Z axes of the overall model as parameters, use the calling command to position the disconnecting switch at the corresponding position in the overall model;

[0097] S53. Based on the size of the air model range, establish an air model that includes all other models. Based on the parameterization command, define the function of air under different environmental conditions using air dielectric constant, conductivity, and resistivity as parameters to simulate different weather conditions.

[0098] Boolean operations in finite element software are used to segment and differentiate the models of air, towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches to prevent interference between modules;

[0099] It should be noted that steps S41 to S45 above are only for defining different key point numbers for each module, and do not restrict the order in which the key point numbers of related models are defined; steps S46 to S53 above are only for calling different models in sequence, and do not restrict the order in which the related models are called.

[0100] In step S5 above, the human body model is imported into the finite element analysis software using third-party software. The positional relationship between the local coordinates X, Y, and Z axes of the human body in the electrical detection scenario and the overall coordinates of the model is used as parameters. The spatial position change of the human body is realized by calling the command.

[0101] In step S6 above, the specific steps are as follows:

[0102] The S61, air, drain line, conductor, ground wire, pole, transformer, and disconnector regular model (linear region structure) are divided into hexahedral meshes using a scanning method;

[0103] S62. Select the axisymmetric section of the insulator, such as... Figure 6 As shown, quadrilateral auxiliary surface elements are used to divide the axisymmetric section of the insulator into two-dimensional elements, resulting in the following: Figure 7 The finite element model of the cross section shown is then used with the rotation command to obtain the following: Figure 8 The three-dimensional finite element model of the insulator is shown below;

[0104] S63. Select one node from each adjacent region element of each module, ensuring that the two selected nodes are closest to each other. Couple the selected two nodes using node coupling or constraint equations so that the degrees of freedom solutions of the two nodes can be mutually transferred. The coupling formula is as follows:

[0105] Where U(I) is the degree of freedom term, N is the term number in the equation, Coefficient(I) is the coefficient of U(I), Constant is a constant, and the constraint equation is to make the Constant of different elements equal. The node coupling model is as follows: Figure 9 As shown.

[0106] To ensure the rapid selection of the nearest neighboring node, a distance search function is established for nodes within either a nonlinear or linear structural region. The nearest node is determined by this distance search function, which works as follows: using the first node within the nonlinear structural region as a fixed point, the distance from this node to each node in the adjacent linear structural region is calculated. Then, the second node with the smallest distance in the adjacent linear structural region is obtained. At this point, the first and second nodes are the two adjacent nodes with the smallest distance between the nonlinear and linear structural regions that we need.

[0107] The finite element model of the tower obtained after node coupling is as follows Figure 3 As shown, the finite element models of the tower and insulator are as follows: Figure 5 As shown.

[0108] This invention proposes a method using computer RAM as a virtual external storage device (RAM) to install simulation software and set a working directory. This reduces the fatigue damage to the computer's external storage due to frequent read / write operations caused by excessive data throughput. Since the read / write speed in RAM is much higher than in external storage, this improves the modeling efficiency of geometric and finite element models and shortens finite element calculation time. By setting a mirror address in the external storage device and reserving storage space to back up the simulation software and working directory installed in the RAM virtual external storage, the simulation software and user data mirrored in the external storage device can be automatically mirrored to the RAM virtual external storage device after each power-on. Data stored in the working directory of the simulation software in the RAM virtual external storage device can be periodically saved to the mirror address set in the external storage device, reducing the loss of simulation data due to sudden power outages during calculations. The uninterruptible power supply (UPS) provided to the computer further enhances the non-volatility of data stored in the RAM external storage working directory.

[0109] This invention provides a method for extracting key points of components such as towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnect switches from laser imaging point cloud data. Based on the configuration functions and parameterized collaboration of these components, a simulation model for temporary power detection is established, which significantly improves the efficiency and accuracy of temporary power detection simulation modeling.

[0110] This invention further simplifies the modeling process and improves the modeling speed of electric field simulation in large-scale spatial ranges for temporary power detection by using modularization and associating the local coordinates of components such as towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches with the overall coordinates of the temporary power detection simulation model. It also incorporates the modeling concepts of graphic overlay and graphic parameterization.

[0111] This invention further proposes a comprehensive approach that combines 3D modeling and numerical computation modeling, parametric modeling, model insertion, auxiliary surface elements, node coupling, and constraint equations to solve the numerical calculation of alternating electric fields in large-scale spaces. Based on the structural characteristics of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, disconnect switches, and human bodies, the temporary power simulation model is divided into linear and nonlinear structural regions, ensuring the overall model is physically continuous and meeting the accuracy requirements of spatial electric field simulation for temporary power detection of transmission lines.

[0112] In a third embodiment, the present invention provides a simulation modeling device for power transmission and distribution lines, such as... Figure 11 As shown, it includes:

[0113] Point cloud data acquisition module 41 is used to acquire point cloud data of power transmission and distribution lines obtained by lidar scanning;

[0114] The key point extraction module 42 is used to extract the key point locations of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnect switches from the point cloud data of power transmission and distribution lines.

[0115] The structural model acquisition module 43 is used to construct modular models of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches based on key point locations using linear and nonlinear interpolation, and assemble the modular models to obtain the structural model. Specifically, the structural model acquisition module 43 establishes corresponding geometric models based on key point locations; linear and nonlinear interpolation is performed on the geometric models to obtain modular models, and the modular models are identical in shape to the corresponding physical objects.

[0116] The process involves establishing corresponding geometric models based on key point locations, including: extracting key points from towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches as reference points for parametric modeling and defining components for each; classifying the temporary power detection simulation geometric model in a modular manner, and determining the modeling order of points, lines, surfaces, and volumes based on the relationship between the local coordinates and the overall coordinates of the modules; and establishing corresponding geometric models based on the configurations of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches according to the modeling order. Specifically, based on the key point locations of the round rod or iron tower, a geometric model is established using the diameter and height of the top and bottom ends of the round rod, or the cross-sectional configuration, dimensions, length, and installation angle of the supporting beam in the iron tower, and a parametric loop command is used to create a geometric model; based on the key point locations of the drain wire and conductor, a geometric model of the drain wire and conductor is established using the sag curvature and cross-sectional dimensions of the drain wire and conductor; based on the key point locations of the ground wire, a geometric model of the conductor is established using the curvature and cross-sectional dimensions of the ground wire; based on the key point locations of the insulator, a cross-sectional model of the insulator is established, and a three-dimensional geometric model of the insulator is obtained using a rotation command; based on the key point locations of the transformer and disconnector, a geometric model of the transformer and disconnector is established using the configuration parameters of the transformer and disconnector.

[0117] The structural model acquisition module 43 is also used to associate the local coordinates of each module model with the total coordinates of the structural model, and to assemble each module model sequentially by using the superposition principle and parameterization commands.

[0118] The solid model acquisition module 44 is used to establish an air geometry model that includes other entities and to import human body models in different postures and in different spatial positions into the structural model to obtain a three-dimensional solid model for human body electrical detection.

[0119] The finite element model construction module 45 is used to segment the three-dimensional solid model of human body electric shock detection into linear and nonlinear structural regions. It establishes node coupling or constraint equations between adjacent nodes with the smallest distance between the nonlinear and linear structural regions to obtain the finite element models of each module in the three-dimensional solid model of human body electric shock detection. Specifically, the three-dimensional solid model of human body electric shock detection is segmented into linear and nonlinear structural regions according to the linearity of each module model; element partitioning is performed on the linear and nonlinear structural regions respectively; and a parametric rotation command is used to obtain the overall finite element model of the insulator. The element partitioning for the linear and nonlinear structural regions includes: generating hexahedral finite elements for the linear structural regions using auxiliary surface meshing and a scanning method; and dividing the nonlinear regions into tetrahedral, pentahedral, or hexahedral elements using local mesh refinement.

[0120] Furthermore, establishing node coupling or constraint equations between adjacent nodes of the nonlinear structural region and the linear structural region includes: establishing a distance search function for nodes within the nonlinear or linear structural region; obtaining the two adjacent nodes with the smallest distance between the nonlinear and linear structural regions based on the distance search function; and coupling or establishing constraint equations between the two nodes with the smallest distance.

[0121] The power transmission and distribution line simulation modeling device provided in this embodiment of the invention comprises: a point cloud data acquisition module that acquires point cloud data of the power transmission and distribution line obtained by laser radar scanning; a key point extraction module that extracts the key point positions of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnectors from the power transmission and distribution line point cloud data; a structural model acquisition module that uses linear and nonlinear interpolation to collaboratively construct modular models of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnectors based on the key point positions, and assembling the modular models to obtain a structural model; a solid model acquisition module that imports human models in different postures and spatial positions into the structural model, and further establishes an air 3D model based on Boolean operations to encompass other solid models to obtain a 3D solid model for human body electric shock detection; and a finite element model construction module that performs region segmentation on the 3D solid model for human body electric shock detection to obtain linear and nonlinear structural regions, and establishes node coupling or constraint equations at the adjacent nodes with the smallest distance between the nonlinear and linear structural regions to obtain the finite element models of each module in the 3D solid model for human body electric shock detection. Because only the key points of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches are extracted, there is no need to process all point cloud data, greatly reducing the data volume. Subsequently, linear and nonlinear interpolation are used to supplement the data of non-key points, thus obtaining the corresponding model and further reducing the data processing volume. Then, the air geometry model and human body model are imported into the structural model to obtain a three-dimensional solid model of the human body for electrical detection. Finally, the three-dimensional solid model is segmented and connected to obtain a finite element model for subsequent analysis and calculation. The device provided in this embodiment combines point cloud data and parametric collaboration during modeling and simulation, ensuring that the obtained model is consistent with the actual object while greatly reducing the amount of data that needs to be processed and improving the efficiency of simulation modeling.

[0122] Fourth embodiment: The present invention provides a power transmission line simulation modeling system, comprising:

[0123] Processors for computation; random access memory (RAM) for storing processor-executable instructions; RAM for storing system files and user data; virtual external memory and external storage; units for displaying graphics and simulation results, etc.

[0124] The processor, RAM, RAM virtual external memory, and external memory, along with the graphics processing and graphics display units, are configured to execute the power transmission line simulation modeling method provided in the first or second embodiment.

[0125] The power transmission line simulation modeling system provided in this invention stores executable instructions for the processor in a memory. When the processor executes these instructions, it acquires point cloud data of the power transmission line, extracts key point locations, and then uses linear and nonlinear interpolation to construct modular models of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnectors. These modular models are then assembled to obtain a structural model. Since only key point locations of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnectors are extracted, there is no need to process all point cloud data, significantly reducing the data volume. Subsequently, non-key point data is supplemented through linear and nonlinear interpolation, resulting in the corresponding model and further reducing data processing. A human body model is then imported into the structural model, and an air model encompassing other entity models is created to obtain a three-dimensional entity model of the human body for electrical detection. Finally, the three-dimensional entity model is segmented and connected to obtain a finite element model for subsequent analysis and calculation. This application's solution combines point cloud data and parametric collaboration during modeling and simulation, ensuring consistency between the obtained model and the actual object while significantly reducing the amount of data that needs to be processed and improving simulation modeling efficiency.

[0126] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0127] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.

[0128] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0129] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0130] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0131] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0132] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0133] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0134] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for simulating and modeling transmission lines, characterized in that, Includes the following steps: Acquire point cloud data of power transmission lines obtained from lidar scanning; Extract the key locations of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnect switches from the point cloud data of the transmission line; Based on the key point locations, modular models of the tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector are constructed collaboratively using linear and nonlinear interpolation, and the structural model is obtained by assembling each modular model. Human models in different postures and in different spatial positions are imported into the structural model, and an air model that encompasses other entities is established through Boolean operations to obtain a three-dimensional solid model of human body electrical detection. The three-dimensional solid model for human body electrical detection is divided into linear and nonlinear structural regions. Node coupling or constraint equations are established between adjacent nodes in the nonlinear and linear structural regions to obtain a finite element model in which each module of the three-dimensional solid model for human body electrical detection is interconnected.

2. The method according to claim 1, characterized in that: The modular model of the tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector, constructed based on the key point locations through linear and nonlinear interpolation, includes: Establish a corresponding geometric model based on the location of the key points; Linear and nonlinear interpolation are performed on the geometric model to obtain a module model, which has the same shape as the corresponding physical object.

3. The method according to claim 2, characterized in that: The step of establishing a corresponding geometric model based on the key point locations includes: Key points of poles, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnect switches are extracted as reference points for parametric modeling, and components are defined for each. The geometric model of the electrical detection simulation is classified by modularity, and the modeling order of points, lines, surfaces and volumes is determined according to the relationship between the local coordinates and the global coordinates of the modules. Based on the modeling sequence, corresponding geometric models are established for each of the following components: tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector.

4. The method according to claim 3, characterized in that: The process involves establishing corresponding geometric models based on the configurations of towers, crossarms, insulators, drain wires, ground wires, conductors, transformers, and disconnectors according to the modeling order, including: Based on the key point locations of the round pole or iron tower, the geometric model of the pole is established using the diameter and height of the upper and lower ends of the round pole or the cross-sectional configuration, size, length and installation angle of the supporting beam in the iron tower as parameters, and the parametric loop command is used. Based on the key point locations of the drainage line and the conductor, the geometric model of the drainage line and the conductor is established by combining linear interpolation and nonlinear interpolation, using the sag curvature, cross-sectional configuration and size of the drainage line and the conductor as parameters. Based on the key point locations of the insulator, a cross-sectional model of the insulator is established, and the three-dimensional geometric model of the insulator is obtained by using the rotation command. Based on the key point locations of the ground wire, a three-dimensional geometric model of the ground wire is established through linear interpolation and nonlinear interpolation, using the curvature, cross-sectional shape, and size of the ground wire as parameters. Based on the key locations of the transformer and disconnector, and using the appearance and dimensions of the transformer and disconnector as parameters, a surface model of the transformer and disconnector is established through a combination of linear and nonlinear interpolation. Then, based on the potential, the corresponding three-dimensional solid geometric models of different components of the transformer and disconnector are obtained by solid filling.

5. The method according to claim 1, characterized in that: The assembly of the module models to obtain the structural model includes: Based on the correlation between the local coordinates of each module model and the overall coordinates of the structural model, the module models are sequentially called for assembly using the superposition principle and parameterization commands.

6. The method according to claim 1, characterized in that: The process of segmenting the three-dimensional solid model for detecting near-electrical activity in the human body includes: The three-dimensional solid model for detecting human electrical activity is divided into linear and nonlinear structural regions based on the linearity of each module model. The linear structural region and the nonlinear structural region are respectively divided into units; The overall finite element model of the insulator is obtained by using the parametric rotation command based on the auxiliary surface elements that have already been divided.

7. The method according to claim 6, characterized in that: The step of dividing the linear structural region and the nonlinear structural region into units includes: For linear structural regions, hexahedral finite elements are generated using auxiliary surface meshing and scanning methods. For nonlinear regions, local mesh refinement is used to generate tetrahedral, pentahedral, or hexahedral elements.

8. The method according to claim 1, characterized in that: The establishment of node coupling or constraint equations between adjacent nodes in the nonlinear and linear structural regions includes: A distance search function is established using nodes within the nonlinear or linear structural regions. The distance search function is used to find the two adjacent nodes with the smallest distance between the nonlinear structural region and the linear structural region. Couple or establish constraint equations between the two adjacent nodes with the smallest distance.

9. A transmission line simulation modeling device, characterized in that, include: The point cloud data acquisition module is used to acquire point cloud data of power transmission lines obtained by lidar scanning; The key point extraction module is used to extract the key point locations of towers, crossarms, insulators, lead wires, ground wires, conductors, transformers, and disconnect switches from the point cloud data of the transmission line. The structural model acquisition module is used to construct modular models of the tower, crossarm, insulator, drain wire, ground wire, conductor, transformer, and disconnector based on the key point locations using linear and nonlinear interpolation, and to assemble the modular models to obtain the structural model. The solid model acquisition module is used to import human body models in different postures and in different spatial positions into the structural model and then establish an air model that includes other entities to obtain a three-dimensional solid model of human body electrical detection. The finite element model construction module is used to divide the three-dimensional solid model of the human body near-electric shock detection into linear and nonlinear structural regions, and to establish node coupling or constraint equations for the adjacent nodes with the smallest distance between the nonlinear and linear structural regions, so as to obtain the finite element model of each module in the three-dimensional solid model of the human body near-electric shock detection.

10. A power transmission line simulation modeling system, characterized in that, include: A portion of the computer's RAM is virtualized as external storage for installing temporary power detection simulation software and storing temporary data. Set the image address and image data storage address of the temporary power detection simulation software in the computer's external storage; The power-on detection simulation software only mirrors data from external storage to RAM virtual external storage during computer startup. User data running on the power-on detection simulation software installed in RAM virtual external storage is backed up to the mirror address of external storage at set intervals, and the backed-up data includes at least the data backed up at the previous time point and the data at the current time point; the uninterruptible power supply provided to the computer makes the data in RAM virtual external storage non-volatile. The computer's processor, RAM, and RAM-virtual external storage are configured to perform the method according to any one of claims 1-8, wherein the external storage is used to mirror the power-on simulation software and user data installed in the RAM-virtual external storage.

Images