A power transmission tower load dynamic analysis method, system, device and medium

Abstract

The application discloses a kind of power transmission tower load dynamic analysis method, system, equipment and medium, belong to power transmission line engineering technical field, including to the lightweight modification and semantic information enhancement of power transmission tower GIM model, obtain multi-level tower line information model;Characteristic point extraction is carried out to multi-level tower line information model, establishes initial pose relationship, obtains the benchmark model of coordinate system one;According to working condition combination forms load envelope;Based on benchmark model, establish finite element model, coupling load vector is applied to finite element model in steps, carries out nonlinear solution, obtains the internal force distribution of member, member stress and node displacement;Stress ratio grading early warning, displacement threshold early warning and residual life assessment are executed, and the output tower health state assessment result is obtained.The application can reduce the deviation between design model and actual environment in field, make tower position, terrain foundation and component spatial relationship closer to actual operating state.

Description

Technical Field

[0001] This invention relates to the field of power transmission line engineering technology, specifically to a method, system, equipment, and medium for dynamic analysis of power transmission tower loads. Background Technology

[0002] With the widespread adoption of 3D design for transmission lines, Grid Information Models (GIMs) can now express 3D design information for towers, conductors, insulators, fittings, terrain, and foundations. However, existing GIM models for transmission towers are typically used primarily for design demonstration and construction delivery. These models have large data volumes, low efficiency in spatial data transfer between components, and incomplete relationships between component materials, connection methods, specifications, and design numbers. Furthermore, when performing stress analysis on transmission towers, manual processing of member properties, connection relationships, and analysis models is still required. It is difficult to directly generate a computable finite element model from the transmission tower GIM model, impacting the modeling efficiency and calculation accuracy of transmission tower load analysis.

[0003] Meanwhile, there are discrepancies between the actual operating state of transmission lines and the design model. The location of towers, the terrain foundation, and the spatial state of conductors are all affected by the on-site environment. Existing methods often process the 3D design model, laser point cloud data, oblique photogrammetry model, wind load, temperature load, and icing load separately, making it difficult to complete model registration, coupled load application, nonlinear stress solution, and health warning under the same coordinate reference. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, equipment and medium for dynamic analysis of power transmission tower loads.

[0005] Therefore, the technical problem solved by this invention is: how to solve the problem that existing methods often process three-dimensional design models, laser point cloud data, oblique photogrammetry models, wind loads, temperature loads and icing loads separately, making it difficult to complete model registration, coupled load application, nonlinear force solution and health warning under the same coordinate reference.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for dynamic load analysis of transmission towers, comprising: lightweighting and semantic information enhancement of the transmission tower GIM model; partitioning the model space using an octree; constructing a multi-level detail hierarchy model and R-tree spatial index for data blocks; associating component information in the transmission tower GIM model with an engineering attribute map library to obtain a multi-level tower line information model; extracting feature points from the multi-level tower line information model based on laser point cloud data and oblique photogrammetry real-scene model; establishing initial pose relationships; iteratively registering the initial pose relationships to obtain a reference model of coordinate system one; generating coupled load vectors based on dynamic wind load, temperature load, and icing load; forming a load envelope according to the combination of working conditions; establishing a finite element model based on the reference model; applying the coupled load vectors to the finite element model step by step; performing nonlinear solutions to obtain the internal force distribution, stress, and nodal displacement of the members; and performing stress ratio classification early warning, displacement threshold early warning, and remaining life assessment based on the internal force distribution, stress, and nodal displacement of the members, outputting the tower health status assessment results.

[0007] As a preferred embodiment of the dynamic load analysis method for transmission towers described in this invention, the following steps are included: lightweighting and semantic information enhancement of the transmission tower GIM model; partitioning the model space using an octree; constructing a multi-level detail model of data blocks and an R-tree spatial index; and obtaining a multi-level tower line information model based on the component information associated with the engineering attribute map library in the transmission tower GIM model. This includes: parsing the transmission tower GIM model to obtain component geometric information and component topological relationships; and performing non-uniform recursive partitioning of the model space using an octree based on the local structural complexity and component density of the tower structure, forming a multi-level tower line information model based on the tower's local structure. The system divides data into blocks based on structural complexity and component density. For each data block, a multi-level detail model is constructed: the first detail model is formed by preserving the original mesh data; the second detail model is formed by edge folding; and the third detail model is formed by sampling and reconstruction. An R-tree spatial index is established using the spatial bounding range of the data block as the index key. The target data block and target detail model are then called based on the viewpoint distance and viewpoint range. The transmission tower GIM model is converted into a component node graph structure. Based on the component information in the transmission tower GIM model, component attributes are injected from the engineering attribute graph library to obtain a multi-level tower line information model.

[0008] As a preferred embodiment of the dynamic load analysis method for power transmission towers described in this invention, the following steps are included: Based on laser point cloud data and oblique photogrammetry real-world models, feature points are extracted from a multi-level tower line information model to establish initial pose relationships. Iterative registration of these initial pose relationships yields a reference model for coordinate system one. This includes: acquiring laser point cloud data of the power transmission line corridor; calculating the flight trajectory and attitude information during the point cloud data acquisition process; transforming the laser point cloud data from the sensor coordinate system to the geodetic coordinate system based on the flight trajectory and attitude information; filtering, classifying, and reconstructing the surface of the transformed laser point cloud data to generate a laser point cloud model; acquiring oblique photogrammetry images; performing aerial triangulation, point cloud generation, irregular triangular network construction, and texture mapping on the oblique photogrammetry images to generate an oblique photogrammetry real-world model; extracting shared structural feature points from the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real-world model; establishing initial pose relationships based on these shared structural feature points; iterative registration of these initial pose relationships to obtain a reference model for coordinate system one.

[0009] This preferred solution improves the consistency between the baseline model and the field environment by registering point cloud models, real-world models, and multi-level tower line information models.

[0010] As a preferred embodiment of the dynamic load analysis method for transmission towers described in this invention, the step of generating a coupled load vector based on dynamic wind load, temperature load, and icing load, and forming a load envelope according to the combination of operating conditions, includes: establishing an average wind speed varying along height based on wind field parameters; generating a fluctuating wind speed time history based on a fluctuating wind simulation method; calculating the conductor wind load and the tower wind load based on the average wind speed and the fluctuating wind speed time history; the conductor wind load is determined based on the superposition result of the calculated outer diameter of the conductor and the wind speed; and the tower wind load... The load is applied to the centroid of each segment based on the tower height segmentation results; the temperature load is determined based on the difference between the ambient temperature and the reference temperature; the conductor icing load and the tower icing load are determined based on the icing thickness, icing density, and icing distribution; the conductor wind load, tower wind load, temperature load, conductor icing load, and tower icing load are integrated according to time steps and spatial points to generate a coupled load vector; the coupled load vector is combined and calculated based on high wind conditions, icing conditions, low temperature conditions, and uneven icing wind conditions to form a load envelope.

[0011] This preferred scheme integrates wind, temperature, and icing loads to make the load analysis more closely resemble the actual operating conditions of transmission lines.

[0012] As a preferred embodiment of the dynamic load analysis method for transmission towers described in this invention, the step of establishing a finite element model based on a benchmark model, applying coupled load vectors to the finite element model step by step, and performing nonlinear solutions to obtain the internal force distribution, stress, and nodal displacement of the members includes: establishing a finite element model based on the spatial location of the tower, component attributes, and component connection relationships in the benchmark model; configuring the main members and crossarms as beam elements, and the diagonal members and auxiliary members as rod elements; setting finite element constraint relationships for the connection points between the main members and diagonal members, the connection points between the main members and the main members, and the tower foot boundary based on the connection methods in the multi-level tower line information model; applying the coupled load vectors in the load envelope to the finite element model step by step according to the time step, and performing stress calculations using a nonlinear iterative solution method that considers geometric nonlinearity and material nonlinearity; judging the convergence state of the load step based on the force residual and displacement increment, and extracting the internal force distribution, stress, and nodal displacement of the members in response to the load step satisfying the convergence condition.

[0013] This preferred solution improves the accuracy of the stress analysis of the members by combining the component properties, connection relationships and tower foot boundaries for nonlinear solution.

[0014] As a preferred embodiment of the dynamic load analysis method for transmission towers described in this invention, the method involves: based on the internal force distribution of members, member stress, and node displacement, performing stress ratio classification early warning, displacement threshold early warning, and remaining life assessment, and outputting tower health status assessment results. This includes: determining the maximum stress ratio of each member based on member stress, member axial force, material strength design value, and member cross-sectional area under the most unfavorable load combination; generating stress ratio classification early warning results based on the range of the maximum stress ratio; extracting the horizontal displacement at the tower top, the vertical displacement of the crossarm, and the inter-story lateral displacement angle from the node displacement; and adjusting the tower... The top horizontal displacement, crossarm vertical displacement, and inter-story lateral displacement angle are compared with preset limits, and displacement threshold warning results are generated based on the proportion of exceeding the limits. Based on historical meteorological data and member stress, the stress time history of key members is formed, and rainflow is counted on the stress time history of key members to obtain a fatigue load spectrum including stress amplitude and stress mean. Based on the fatigue load spectrum and material life curve, cumulative damage is calculated, and the remaining life assessment result is generated based on the cumulative damage result and annual average load spectrum. The stress ratio classification warning result, displacement threshold warning result, and remaining life assessment result are integrated to output the tower health status assessment result.

[0015] This preferred solution achieves multi-dimensional early warning of the health status of the tower by integrating stress, displacement and life assessment results.

[0016] As a preferred embodiment of the dynamic load analysis method for transmission towers described in this invention, the step of extracting shared structural feature points from a multi-level tower line information model, a laser point cloud model, and an oblique photogrammetry real-scene model, establishing an initial pose relationship based on the shared structural feature points, and iteratively registering the initial pose relationship to obtain a reference model of coordinate system one includes: determining matching point pairs between the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real-scene model based on the matching relationship between the shared structural feature points; calculating the initial rotation matrix and translation vector between the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real-scene model based on the matching point pairs, and forming an initial pose relationship from the initial rotation matrix and translation vector; using the initial pose relationship as the initial registration value, iteratively registering the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real-scene model; and outputting the reference model of coordinate system one in response to the satisfaction of the iteration termination condition.

[0017] To address the aforementioned technical problems, this invention provides the following technical solution: a dynamic load analysis system for power transmission towers, comprising: a model modification module, a data fusion module, a load analysis module, a structural analysis module, and an evaluation and early warning module; the model modification module is used to perform lightweight modification and semantic information enhancement on the GIM model of the power transmission tower, using an octree to partition the model space, constructing a multi-level detail hierarchy model of data blocks and an R-tree spatial index, and obtaining a multi-level tower line information model based on the component information associated with the engineering attribute map library in the GIM model of the power transmission tower; the data fusion module is used to perform special analysis on the multi-level tower line information model based on laser point cloud data and oblique photogrammetry real-scene model. The system extracts reference points, establishes initial pose relationships, and iteratively registers these relationships to obtain a reference model for coordinate system one. The load analysis module generates coupled load vectors based on dynamic wind loads, temperature loads, and icing loads, forming a load envelope according to the combination of working conditions. The structural analysis module establishes a finite element model based on the reference model, applies the coupled load vectors to the finite element model step by step, performs nonlinear solutions, and obtains the internal force distribution, stress, and nodal displacement of the members. The evaluation and early warning module performs stress ratio grading early warning, displacement threshold early warning, and remaining life assessment based on the internal force distribution, stress, and nodal displacement of the members, outputting the tower health status assessment results.

[0018] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the dynamic load analysis method for power transmission towers.

[0019] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for dynamic analysis of load on power transmission towers.

[0020] The beneficial effects of this invention are as follows: By lightweighting and enhancing the semantic information of the GIM model of the transmission tower, this invention enables the originally design-oriented 3D model to further incorporate component attributes, connection relationships, and spatial index information, providing a directly usable model foundation for subsequent load analysis. Simultaneously, by constructing a reference model based on laser point cloud data and oblique photogrammetry real-world models, the deviation between the design model and the actual on-site environment can be reduced, making the tower positions, terrain foundations, and component spatial relationships closer to the actual operating state.

[0021] This invention also couples dynamic wind load, temperature load and icing load for analysis, and performs nonlinear solution by combining finite element model, which can obtain analysis results such as internal force distribution of members, member stress and nodal displacement. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 The above is a flowchart of a dynamic load analysis method for power transmission towers provided in one embodiment of the present invention.

[0024] Figure 2 The flowchart illustrates the lightweight retrofitting and semantic information enhancement of the GIM model of a power transmission tower, which is provided as an embodiment of the present invention for a dynamic load analysis method of power transmission towers.

[0025] Figure 3 The flowchart of the tower health status assessment method is provided in an embodiment of the present invention for a dynamic analysis method of power transmission tower load. Detailed Implementation

[0026] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0027] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for dynamic analysis of the load on power transmission towers, including: S1. Lightweight transformation and semantic information enhancement of the transmission tower GIM model are carried out. The model space is divided by octree, and a multi-level detail hierarchy model and R-tree spatial index of data blocks are constructed. Based on the component information in the transmission tower GIM model, an engineering attribute map library is associated to obtain a multi-level tower line information model.

[0028] S2. Based on laser point cloud data and oblique photogrammetry real scene model, feature points are extracted from multi-level tower line information model, initial pose relationship is established, and the initial pose relationship is iteratively registered to obtain the reference model of coordinate system one.

[0029] S3. Generate coupled load vectors based on dynamic wind load, temperature load and icing load, and form load envelopes according to the combination of working conditions.

[0030] S4. Based on the benchmark model, establish a finite element model, apply the coupled load vector to the finite element model step by step, and perform nonlinear solution to obtain the internal force distribution of the members, the stress of the members, and the nodal displacement.

[0031] S5. Based on the internal force distribution of the members, the stress of the members and the displacement of the nodes, perform stress ratio classification early warning, displacement threshold early warning and remaining life assessment, and output the health status assessment results of the tower.

[0032] It should be noted that although the GIM model of the transmission tower can express the three-dimensional structural information of the tower, there are still problems in load dynamic analysis, such as large data volume, insufficient correlation of component attributes, and deviation between the design model and the site environment.

[0033] Therefore, this embodiment forms a multi-level tower line information model and a reference model of coordinate system one through steps S1-S5, performs nonlinear solution by combining coupled load vectors, and finally outputs the tower health status assessment result, providing a basis for power transmission tower safety early warning and remaining life assessment.

[0034] Example 2, refer to Figure 2 and Figure 3 As an embodiment of the present invention, based on the previous embodiment, a method for dynamic analysis of transmission tower loads is provided, including: Furthermore, in step S1, the transmission tower GIM model is subjected to lightweight modification and semantic information enhancement. An octree is used to partition the model space, constructing a multi-level detail hierarchy model of data blocks and an R-tree spatial index. Based on the component information in the transmission tower GIM model, an engineering attribute map library is associated to obtain a multi-level tower line information model, including the following steps A1-A4: A1. Analyze the GIM model of the power transmission tower to obtain the geometric information and topological relationship of the components. Based on the local structural complexity and component density of the tower structure, perform non-uniform recursive partitioning of the model space using an octree to form data blocks based on the local structural complexity and component density of the tower.

[0035] A2. Construct a multi-level detail model for the data block, retain the original grid data to form the first level of detail model, use edge folding to form the second level of detail model, and use sampling reconstruction to form the third level of detail model.

[0036] A3. Build an R-tree spatial index using the spatial bounding range of the data block as the index key, and call the target data block and the target level of detail model according to the viewpoint distance and viewpoint range.

[0037] A4. Convert the GIM model of the transmission tower into a component node diagram structure. Based on the component information in the GIM model of the transmission tower, inject the component attributes from the engineering attribute map library to obtain a multi-level tower line information model.

[0038] Specifically, the standard format GIM model file of the power transmission tower, such as .GIM or .IFC, is parsed to extract the geometric information and topological relationships of all components. Components include main members, diagonal members, crossarms, auxiliary members, etc., and the geometric information includes edges, faces, and vertex coordinates. After parsing, a component list is generated, which records the component identifier, component name, design number, geometric information, and topological relationships.

[0039] Preferably, based on the spatial distribution of the tower structure, an octree algorithm is used to perform non-uniform recursive partitioning of the model space. The partitioning criteria are as follows: areas with complex structures and dense components, such as the tower head and crossarm connections, have a greater partitioning depth (up to 8 layers); areas with sparse components, such as the middle section of the tower body, have a smaller partitioning depth (4-5 layers). Each leaf node corresponds to a data block. Each data block records the data block number, spatial range, number of components, and a list of the components it belongs to.

[0040] Build a Level of Detail (LOD) model for each data block.

[0041] LOD0 offers the highest accuracy, preserving the original mesh data for fine-grained local analysis.

[0042] LOD1 has medium accuracy and uses a quadratic error metric (QEM) edge folding algorithm to reduce the number of triangles in the original mesh by 50%-70% while maintaining the tower material's profile characteristics. A folding cost threshold is set, prioritizing the folding of edges in flat or low-curvature regions.

[0043] LOD2 has the lowest precision and uses a random sampling method to randomly retain 10%-20% of the points from the LOD1 vertex set. Then, Poisson surface reconstruction is performed to generate a rough block model, which is only used for macroscopic deformation display and fast scene roaming.

[0044] The first level of detail model refers to LOD0, the second level of detail model refers to LOD1, and the third level of detail model refers to LOD2.

[0045] Furthermore, an R-tree spatial index is built for all data blocks, with the index key being the minimum bounding box (MBR) of the data block. During loading, the data blocks to be loaded and their corresponding Level of Detail (LOD) are quickly retrieved based on the viewpoint distance and range. The R-tree spatial index records the minimum bounding box, data block number, and level of detail of each data block.

[0046] Referring to the technical specifications for three-dimensional design of transmission lines and the structural design drawings of iron towers, three types of core semantic material properties, connection methods and component specifications are defined.

[0047] Material properties include steel grade, standard yield strength, design tensile / compressive / bending strength, elastic modulus (e.g., 206 GPa), Poisson's Law (e.g., 0.3), and density (e.g., 7850 kg / m³). 3 The coefficient of linear expansion is, for example, 1.2 × 10⁻⁶. -5 / ℃; steel grades such as Q235, Q345, Q420, etc.

[0048] Connection methods include distinguishing between hinged or bolted connections and rigid or welded connections. For each member node, record the connection type, bolt grade / weld strength, number of bolts, and their arrangement.

[0049] For angle steel components, the specifications include recording the side width, side thickness, cross-sectional area, and radius of gyration. Section modulus For steel pipe components, record the outer diameter, wall thickness, and cross-sectional area.

[0050] A graph database-based mapping method is used to parse the GIM model of the power transmission tower into a "component-node" graph structure, where components are nodes and connections between components are edges. Component nodes record the component identifier, component name, design number, and component specifications; edges record the connection relationships and connection methods between components.

[0051] Develop an attribute injection script that automatically links to a predefined attribute library based on existing fuzzy identifiers in the transmission tower GIM model, such as "L-50×5" corresponding to angle steel with a side width of 5mm and a thickness of 5mm, or a design number. The attribute injection script then writes the material properties, connection methods, and component specifications from the attribute library into the component node diagram structure.

[0052] The final result is a multi-level tower line information model containing geometry, topology, and enhanced semantics, saved as extended JSON or a custom binary format for efficient reading and writing by the program. The multi-level tower line information model includes a component list, data blocks, a multi-level detail hierarchy model, an R tree spatial index, and a component node graph structure.

[0053] like Figure 2 As shown in the figure, the process of extracting component geometric information and component topological relationships from the GIM model of the transmission tower, performing lightweight transformation and semantic information enhancement, and then integrating the multi-level detail model, R-tree spatial index, and component node graph structure to generate a multi-level tower line information model.

[0054] Furthermore, in step S2, based on the laser point cloud data and the oblique photogrammetry real-scene model, feature points are extracted from the multi-layer tower line information model to establish an initial pose relationship. The initial pose relationship is then iteratively registered to obtain the reference model of coordinate system one, including the following steps B1-B4: B1. Acquire laser point cloud data of the power transmission line corridor and calculate the flight trajectory and attitude information during the point cloud data acquisition process.

[0055] B2. Based on flight trajectory and attitude information, the laser point cloud data is transformed from the sensor coordinate system to the geodetic coordinate system. The transformed laser point cloud data is then filtered, classified, and reconstructed to generate a laser point cloud model.

[0056] B3. Acquire oblique photogrammetry images, perform aerial triangulation, point cloud generation, irregular triangular mesh construction, and texture mapping on the oblique photogrammetry images to generate an oblique photogrammetry real-world model.

[0057] B4. Extract shared structural feature points from the multi-level tower line information model, laser point cloud model and oblique photogrammetry real scene model, establish initial pose relationship based on the shared structural feature points, and perform iterative registration on the initial pose relationship to obtain the reference model of coordinate system one.

[0058] Furthermore, in step B4, shared structural feature points are extracted from the multi-layered tower line information model, the laser point cloud model, and the oblique photogrammetry real-world model. An initial pose relationship is established based on these shared structural feature points, and iterative registration is performed on the initial pose relationship to obtain the reference model for coordinate system one, including the following steps B41-B44: B41. Based on the matching relationship between shared structural feature points, determine the matching point pairs between the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real scene model.

[0059] B42. Calculate the initial rotation matrix and translation vector between the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real scene model based on the matching point pairs, and form the initial pose relationship from the initial rotation matrix and translation vector.

[0060] B43. Using the initial pose relationship as the initial value for registration, iteratively register the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real scene model.

[0061] B44. In response to the satisfaction of the iteration termination condition, output the reference model of coordinate system one.

[0062] Specifically, using drones equipped with LiDAR, a zigzag flight path is planned, with a forward overlap rate of no less than 60% and a lateral overlap rate of no less than 30%, to acquire point cloud data of the power transmission line corridor. The forward and lateral overlap rates are determined based on the continuous coverage requirements of the power transmission line corridor, the LiDAR point cloud sampling density, and the integrity of adjacent flight paths.

[0063] By performing tight-coupled filtering on data from the UAV's Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data, high-precision flight trajectory and attitude information can be obtained, with high precision referring to centimeter-level accuracy.

[0064] Using the calculated trajectory and attitude, each frame of laser scanning data is transformed from the sensor coordinate system to a geodetic coordinate system such as CGCS2000 to generate an initial point cloud.

[0065] Preferably, a progressively encrypted triangular mesh filtering algorithm is used to classify the point cloud into ground points, vegetation points, power line points, tower points, etc. Iterative angle thresholds and distance thresholds are set to distinguish between ground and non-ground points. The iterative angle thresholds and distance thresholds are determined based on the terrain undulation of the transmission line corridor, the point cloud density, and the spatial distribution differences between ground and non-ground points. In this embodiment, the iterative angle threshold is set to 6°, and the distance threshold is set to 1m.

[0066] The classified point clouds of towers and power lines are used to generate meshes using the Poisson surface reconstruction algorithm, which serve as components of the high-precision baseline model. Specifically, tower point clouds are used to generate tower meshes, and power line point clouds are used to generate power line meshes.

[0067] The drone is equipped with a five-lens oblique camera to acquire images from five angles: vertical, front, back, left, and right. The flight overlap is set to 80% in the forward direction and 70% in the lateral direction. The forward and lateral overlaps are determined based on the extraction of corresponding points in the oblique photogrammetry 3D reconstruction, the stability of aerial triangulation, and texture continuity.

[0068] The images are imported into modeling software, such as ContextCapture or similar software. First, aerial triangulation is performed to extract feature points and solve for camera position and pose, generating a sparse point cloud. Then, dense matching is performed to generate a dense point cloud. Finally, an irregular triangular network (TIN) is constructed and textures are mapped to form a textured OSGB format real-world 3D model.

[0069] In the modified transmission tower GIM model and reality / point cloud model, the 3D scale-invariant feature transform (SIFT) algorithm is used to extract shared salient feature points, such as the center of the tower leg anchor bolts, the crossarm hanging point, and the turning point of the main tower material. Each feature point is described by a 128-dimensional feature vector. The 128-dimensional feature vector represents the spatial gradient distribution of the neighborhood of the salient feature point.

[0070] The Random Sample Consensus (RANSAC) algorithm is used to find the best matching point pair in the feature point sets of the two models, and the initial rotation matrix R and translation vector T are calculated to achieve coarse alignment of the models. The initial rotation matrix R and translation vector T constitute the initial pose relationship.

[0071] Using the initial registration result as the initial value, the Iterative Closest Point (ICP) algorithm is employed for fine-grained registration. The iteration termination condition is set as follows: the change in root mean square error (RMSE) between two consecutive iterations is less than 1 × 10⁻⁶. -6 The iteration count can be set to a maximum of 200 times. The final output is a fused, high-precision benchmark model with a unified coordinate system. This high-precision benchmark model is formed by combining the modified transmission tower GIM model, the OSGB format realistic 3D model, the tower mesh, and the power line mesh under a unified coordinate system.

[0072] Furthermore, in step S3, a coupled load vector is generated based on the dynamic wind load, temperature load, and icing load, and a load envelope is formed according to the combination of working conditions, including the following steps C1-C5: C1. Establish the average wind speed along the height based on the wind field parameters, and generate the time history of the fluctuating wind speed based on the fluctuating wind simulation method.

[0073] C2. Calculate the wind load on the conductor and the wind load on the tower based on the time history of the average wind speed and the fluctuating wind speed. The wind load on the conductor is determined by superimposing the calculated outer diameter of the conductor and the wind speed. The wind load on the tower is applied to the centroid of the segment based on the segmented height of the tower.

[0074] C3. Determine the temperature load based on the difference between the ambient temperature and the reference temperature, and determine the conductor icing load and tower icing load based on the icing thickness, icing density and icing distribution.

[0075] C4. Integrate the conductor wind load, tower wind load, temperature load, conductor icing load, and tower icing load according to time steps and spatial points to generate a coupled load vector.

[0076] C5. Based on the conditions of strong wind, icing, low temperature and uneven icing wind, the coupled load vector is combined and calculated to form the load envelope.

[0077] Specifically, to perform dynamic wind load calculations, the average wind speed is input. Wind angle Surface roughness category (AF type), turbulence intensity Wind angle It ranges from 0° to 360°, with 0° being perpendicular to the line direction. Wind direction angle. Used to determine the direction of wind load relative to conductors and towers.

[0078] Wind profiles follow an exponential law: in, For height The average wind speed at that location The baseline average wind speed is at a height of 10m. For height, This is the surface roughness index. It can be determined based on the design wind speed of the line, the wind speed observed at the meteorological station, or the meteorological zoning data of the project.

[0079] In the simulation of fluctuating wind, the Davenport or Kaimal spectrum is selected as the target wind speed spectrum based on the surface roughness type, turbulence intensity, and wind speed statistical characteristics. Time-dependent fluctuating wind speed time history curves are generated using either the harmonic superposition method or the linear filtering method. The linear filtering method employs an autoregressive model. The time step is determined based on the accuracy of the dynamic load analysis and the computational complexity; in this embodiment, it is taken as 0.1 s to 0.5 s. The simulation duration is determined based on the statistical stability of the wind speed time history; in this embodiment, it is taken as 10 min.

[0080] Calculation of wind load on conductor / ground wire, based on quasi-steady assumptions, wind load acting on a unit length of conductor. for: in, This refers to the wind load acting on a unit length of conductor. For air density, take 1.225 kg / m³. 3 ; The drag coefficient is 1.0-1.2 for conductors and 1.1-1.3 for icy areas; The calculated outer diameter of the conductor / ground wire is doubled when there is ice. For a moment The pulsating wind speed.

[0081] The wind load calculation for the iron tower involves dividing the tower into several segments along its height and applying a concentrated wind force to the centroid of each segment. For the tower body, the reduction factor for the leeward wind load and the tower body's windbreak coefficient must be considered. In this embodiment, the reduction factor is taken as 0.7~0.8, and the reduction factor is determined based on the shading relationship between the windward and leeward sides of the tower body; the tower body's windbreak coefficient is set according to the tower structure.

[0082] Furthermore, temperature load calculations are performed, inputting the ambient temperature at various points along the line. (°C) and rate of change. The reference temperature is the temperature at the time of installation. The temperature is usually set at 15℃ or 20℃.

[0083] Temperature strain and stress, when temperature changes At that time, the free thermal expansion strain of the rod is ,in, Let be the coefficient of linear expansion of the material. In statically indeterminate structures, thermal strain is constrained and transformed into temperature stress: in, For temperature stress, The elastic modulus of the material is given, and the temperature stress is positive under tension.

[0084] Furthermore, for icing load calculation, the design icing thickness is input. (mm) and ice density Typically 900 kg / m 3 Based on meteorological zones, a non-uniform icing coefficient of 0.6 to 1.3 is considered. The design icing thickness b and the non-uniform icing coefficient are determined according to the meteorological zone where the line is located and the design documents.

[0085] The weight of ice per unit length of conductor is expressed as: in, The weight of ice per unit length of conductor. The density of the ice layer. It is the acceleration due to gravity. This refers to the thickness of the ice layer.

[0086] Calculate the surface area of ​​each member, multiply it by the ice thickness and ice density to obtain the ice weight of the member, and apply it to the corresponding node.

[0087] When ice forms, the outer diameter of the conductor becomes D+2b, the projected area increases, and the wind load is recalculated based on the new outer diameter.

[0088] The dynamic wind load, temperature load, and icing load mentioned above are integrated according to time steps and spatial points to form a load vector. .

[0089] In this embodiment, operating condition combinations are established based on the load verification requirements of transmission lines, and envelope analysis is performed on the calculation results under different operating conditions. The operating condition combinations include high wind conditions, icing conditions, low temperature conditions, and uneven icing and wind conditions.

[0090] High wind load: The first load vector is formed by 1.5 times the design wind load, 1.0 times the self-weight load, 0.0 times the icing load and 0.6 times the minimum temperature load.

[0091] Icing load case: The second load vector is formed by 0.75 times the design wind load, 1.0 times the self-weight load, 1.4 times the icing load and 0.8 times the minimum temperature load.

[0092] Low temperature condition: The third condition load vector is formed by 0.0 times wind load, 1.0 times self-weight load, 0.0 times ice load and 1.0 times minimum temperature load.

[0093] Uneven icing and wind conditions: Apply an uneven icing load at the most unfavorable location and superimpose 50% of the design wind load to form the fourth load vector; wherein, the uneven icing load can be set to the ice thickness on one side being 1.5 times the ice thickness on the other side.

[0094] The load vectors for the first, second, third, and fourth working conditions are input into the finite element model for calculation. The internal forces, stresses, and nodal displacements of the members under each working condition are extracted, and the unfavorable results in the calculation results of each working condition are taken as the load envelope results.

[0095] The load partial factors in the above-mentioned load combination are determined based on the transmission line load combination verification requirements, line design conditions, and engineering meteorological zoning parameters. Among them, the combination factors for design wind load, icing load, minimum temperature load, and self-weight load are used to characterize the relationship between the dominant load and accompanying load under different operating scenarios. In this embodiment, the above partial factors represent one load combination value; in other embodiments, they can be adjusted according to transmission line design specifications, design documents, or engineering verification requirements.

[0096] In this embodiment, the wind load is the dominant load for the high wind condition, the icing load is the dominant load for the icing condition, the lowest temperature load is the dominant load for the low temperature condition, and the non-uniform icing wind condition uses the non-uniform icing load and part of the design wind load as a combined load. The combination coefficients can be adjusted according to the transmission line design specifications, design documents, or engineering verification requirements.

[0097] Furthermore, in step S4, a finite element model is established based on the benchmark model. The coupled load vector is applied to the finite element model step by step, and a nonlinear solution is performed to obtain the internal force distribution of the members, the stress of the members, and the nodal displacements, including the following steps D1-D4: D1. Based on the spatial location of the tower, component properties and component connection relationships in the benchmark model, establish a finite element model, configure the main material and crossarm as beam elements, and configure the diagonal material and auxiliary material as pole elements.

[0098] D2. Based on the connection method in the multi-level tower line information model, set the finite element constraint relationships of the connection points between the main material and the diagonal material, the connection points between the main materials and the main material, and the tower foot boundary.

[0099] D3. Apply the coupled load vector in the load envelope to the finite element model step by step according to the time step, and use a nonlinear iterative solution method that considers geometric nonlinearity and material nonlinearity to calculate the force.

[0100] D4. Based on the force residual and displacement increment, determine the convergence state of the load step. In response to the load step satisfying the convergence condition, extract the internal force distribution of the members, the stress of the members, and the nodal displacement.

[0101] Establish a finite element model and select and assign values ​​to elements: Beam elements (Beam188 / 4) are used for the main members and crossarms. They are assigned cross-sectional geometric parameters and a material elastoplastic constitutive model. The cross-sectional geometric parameters include area and moment of inertia. Torsional constant The material elastoplastic constitutive model includes ideal elastoplastic or bilinear kinematic hardening. Among them, Let x be the moment of inertia of the cross section about the x-axis. Let be the moment of inertia of the cross section about the y-axis.

[0102] The Link180 element is used for diagonal and auxiliary members. Only the cross-sectional area and axial stiffness are assigned; bending is not considered.

[0103] Connection and boundary conditions include main member-diagonal member connection points, main member-main member connection points, and tower foot boundaries.

[0104] The connection point between the main member and the diagonal member is set as a hinge to release the bending moment degree of freedom at the beam end. , These represent the rotational degrees of freedom about the x-axis, y-axis, and z-axis, respectively.

[0105] Based on semantic information, the connection points between main materials are set as rigid connections if they are welded, and semi-rigid connections if they are bolted. Rigid connections couple all degrees of freedom, while semi-rigid connections are simulated using nonlinear spring elements, defining the moment-rotation curve.

[0106] Based on the terrain model fused in step S2, the height of each tower leg is determined at the tower foot boundary. If there is foundation deformation, the tower foot nodes are set as displacement boundary conditions, and the settlement is directly input. and tilt angle ,in, These represent the displacements of the tower foot nodes in the x, y, and z directions, respectively. These are the tilt angles of the tower foot nodes around the x-axis, y-axis, and z-axis, respectively.

[0107] The solver employs the Newton-Raphson iterative method, considering both geometric and material nonlinearities. Geometric nonlinearities include large deformation effects, which are calculated by enabling a large deformation switch; material nonlinearities include updating the stiffness matrix according to a bilinear model when the stress exceeds the yield strength.

[0108] The coupled load time history generated in step S3 The load is applied in steps. For example, a 10-minute time history is divided into 6000 load steps, each lasting 0.1 seconds. The equilibrium equations are solved iteratively within each load step, as follows: in, Here is the tangent stiffness matrix. For the first The displacement of the next iteration. For the first The displacement increment vector obtained in the next iteration. For external loads, For the first The internal resistance vector formed by the internal forces of the structure under the next iteration displacement state.

[0109] In this embodiment, the convergence criterion employs both force and displacement convergence criteria. The 2-norm of the force residual is less than 1 × 10⁻⁶. -3 N or the displacement increment norm is less than 1×10 -6 The current load step is considered to converge. N is the unit of force, Newton, 1 × 10⁻⁶. -3 N and 1×10 -6 The calculation accuracy requirements of the finite element model, the load step size, and the nonlinear iterative stability are determined.

[0110] Extract the axial force of each element / node under each load step. Shear force Bending moment ,stress and displacement For beam elements, the von Mises stress is calculated according to the fourth strength theory, where, It is an axial force. These are the shear forces in the x and y directions, respectively. These are the bending moments about the x-axis, y-axis, and z-axis, respectively. For von Mises stress, These represent the displacements in the x, y, and z directions, respectively.

[0111] Furthermore, in step S5, based on the internal force distribution of the members, the stress of the members, and the nodal displacement, stress ratio classification early warning, displacement threshold early warning, and remaining life assessment are performed, and the tower health status assessment results are output, including the following steps E1-E4: E1. Based on the stress, axial force, material strength design value, and cross-sectional area of ​​the members under the most unfavorable load combination, determine the maximum stress ratio of each member, and generate stress ratio classification early warning results according to the interval where the maximum stress ratio is located.

[0112] E2. Extract the horizontal displacement of the tower top, the vertical displacement of the crossarm, and the inter-story lateral displacement angle from the node displacement. Compare the horizontal displacement of the tower top, the vertical displacement of the crossarm, and the inter-story lateral displacement angle with preset limits respectively, and generate displacement threshold warning results based on the proportion of exceeding the limits.

[0113] E3. Based on historical meteorological data and member stress, the stress time history of key members is formed. Rainflow count is performed on the stress time history of key members to obtain a fatigue load spectrum that includes stress amplitude and stress mean.

[0114] E4. Based on the fatigue load spectrum and material life curve, perform cumulative damage calculation, generate remaining life assessment results based on the cumulative damage results and annual average load spectrum, integrate stress ratio classification early warning results, displacement threshold early warning results and remaining life assessment results, and output tower health status assessment results.

[0115] Stress ratio calculation: For each member, calculate the maximum stress ratio under the most unfavorable load combination. : in, The maximum stress ratio, This is the design value for material strength. The gross cross-sectional area of ​​the rod, This refers to the axial force on the rod.

[0116] A first stress ratio threshold of 0.85 and a second stress ratio threshold of 1 are set. The second stress ratio threshold of 1 represents the utilization boundary where the stress ratio of the member reaches the material strength design value or axial bearing capacity; the first stress ratio threshold of 0.85 represents the advance warning amount set before the member reaches the utilization boundary. In this embodiment, the first and second stress ratio thresholds are determined based on the structural safety verification requirements of the transmission tower, the utilization degree of material strength, and operational monitoring requirements. In other embodiments, the first stress ratio threshold can be adjusted according to the line importance level, maintenance cycle, and safety margin requirements.

[0117] like If it is normal, it will display green, indicating a safe state; if A yellow alert has been issued, and it is recommended to increase the monitoring frequency; this is a warning status. An orange alert has been issued, requiring immediate and detailed manual inspection; this is a critical state. A red alarm is issued, indicating a risk and requiring load reduction or reinforcement; this is a failure state. To avoid overlapping warning zones, this embodiment can perform judgments according to the priority order of warning levels: first judge... Whether it is within the range of 1.0±0.02, if it is within this range, output the critical state; if it is not within this range, and Output failure status when not in this range and Output warning status in real time; Output the safe status at the time.

[0118] According to the "Operation Regulations for Overhead Transmission Lines," threshold values ​​are set for horizontal displacement at the tower top, vertical displacement at the crossarm, and lateral displacement angle between floors. The preset limits refer to these three threshold values: horizontal displacement at the tower top, vertical displacement at the crossarm, and lateral displacement angle between floors. The horizontal displacement threshold at the tower top... , The total tower height is in mm; the vertical displacement threshold for the crossarm is: , Crossarm length, in mm; inter-story drift angle threshold. .

[0119] Displacement values ​​of key nodes are extracted from the finite element analysis results and compared with thresholds. Any exceeding of the limit triggers a graded alarm based on the proportion of the exceedance: Level III alarm for exceeding the limit by less than 10%, Level II for 10%-30%, and Level I for exceeding 30%. The aforementioned exceedance proportions are the ratio of the actual displacement value exceeding the corresponding threshold to that threshold.

[0120] Meteorological data such as wind speed and direction for a historical period, such as one year, are extracted and converted into stress time history curves acting on key members, such as the diagonal members under the crossarm, through steps S3 and S4. All stress cycles are extracted from the stress time history using the Rainflow Counting Algorithm, with each stress cycle including stress amplitude. and mean .

[0121] Obtain the SN curve (stress-life curve) parameters of the material. For example, for Q345 steel, the common parameters are: in, This is the commonly used logarithm of material fatigue life. This is the commonly used logarithm of the stress amplitude.

[0122] According to Miner's linear cumulative damage rule, the total damage degree for: in, For the first The actual number of cycles for the stress amplitude level. For the corresponding SN curve The permissible number of cycles for each stress amplitude.

[0123] When cumulative damage Upon reaching version 1.0, its lifespan is exhausted. (Based on current...) The system uses the value and annual average load spectrum to predict the remaining lifespan. When the predicted remaining lifespan is less than 5 years, it proposes a maintenance recommendation of "replacement or hardening".

[0124] like Figure 3 As shown in the figure, the process of health assessment based on the internal force distribution of members, member stress and nodal displacement is illustrated. This includes the judgment of maximum stress ratio, the judgment of displacement exceeding the limit and the judgment of remaining life. The results of stress ratio classification warning, displacement threshold warning and remaining life assessment are integrated to finally output the health status assessment result of the tower.

[0125] Example 3 is an embodiment of the present invention, which provides a method for dynamic analysis of the load of power transmission towers. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0126] Step S1: Lightweight modification of the transmission tower GIM model for the 3D design of transmission lines under current specifications. Using the mesh generation and block processing techniques mentioned earlier, the transmission tower GIM model is layered. A LOD model of the point cloud data is constructed through random sampling, achieving efficient loading and rendering of the model.

[0127] Semantic information enhancement is performed on the GIM model of transmission towers. Based on the existing GIM model of transmission towers, engineering attributes such as tower material properties, connection methods, and component specifications are added. Tower material properties include steel grade and strength level, connection methods include welding and bolting, and component specifications include angle steel size and thickness, forming a multi-level tower-line transmission tower GIM model with complete semantic information.

[0128] Step S2: Obtain actual environmental data of the transmission line corridor using laser point cloud data and oblique photography technology. The specific process is as follows: Using lidar technology, UAVs were used to collect laser point cloud data of power transmission lines. GNSS positioning and IMU attitude information were used to calculate the point cloud coordinates and establish a laser point cloud model of the power transmission lines.

[0129] Using oblique photogrammetry, oblique image data of the power transmission line corridor were collected by drones, and a real-world 3D model of the corridor was reconstructed.

[0130] Based on spherical projection and feature point extraction algorithms, the GIM model of the modified transmission tower is fused with point cloud data and a real-world model. Specifically, the spherical SIFT feature extraction algorithm is used to perform spherical projection on the 3D model and extract local salient features as the basis for model fusion.

[0131] By fusing multi-source data, a high-precision benchmark model is formed to ensure the consistency between the model and the actual transmission lines.

[0132] Step S3: Calculate the dynamic wind load on the conductor based on pre-set wind field data and the power spectrum method. Wind field data includes wind speed, wind direction, wind field distribution, and turbulence intensity, which can be obtained through meteorological monitoring equipment or numerical weather prediction. For specific wind load calculation, refer to the method described in the reference section, and analyze and calculate the wind load on the conductor based on the GIM model of the tower-to-line transmission tower and the power spectrum method.

[0133] The temperature load is calculated based on the ambient temperature, and the icing load is calculated based on the icing thickness. The temperature load is calculated based on the coefficient of thermal expansion of the conductor and tower materials; the icing load is calculated based on the icing thickness, icing density, and conductor diameter.

[0134] A load model coupled with wind, ice, and temperature was established to analyze the stress state of the tower under different load combinations. The most unfavorable load combinations were considered, such as maximum wind load and normal temperature, and normal wind load and maximum icing and minimum temperature, to ensure the comprehensiveness of the analysis.

[0135] Step S4: Input the coupled load into the modified transmission tower GIM model and perform multi-dimensional stress analysis of the tower. The finite element method is used to establish the mechanical model of the tower: The main members of the tower and the main members of the crossarm are simulated using beam elements, taking into account bending and axial deformation; the diagonal members are simulated using rod elements, taking only axial deformation into account; the connection between the main members and the diagonal members is a hinge, which conforms to the actual structural characteristics of the tower.

[0136] The modeling method references the finite element model establishment method for iron towers, but adds the integration of GIM models of power transmission towers.

[0137] The effects of foundation deformation, including uneven settlement, tilting, and horizontal slippage, are considered. Constraints for different types of foundation deformation are determined, and the impact of foundation deformation on the stress on the tower is simulated.

[0138] Finite element analysis was used to calculate the internal forces, stresses, and displacements of the tower members under coupled loads, providing data support for safety assessment.

[0139] Step S5: Establish a tower health status assessment system, and conduct safety warnings and life assessments based on the stress ratio of the members and the displacement threshold.

[0140] Calculate the stress ratio of the members, which is the ratio of the actual stress of the member to its design bearing capacity. The evaluation criteria in the reference are: when the stress ratio is less than 1, the member is in a safe state; when it is equal to 1, it is in a critical state; and when it is greater than 1, it enters a failure state.

[0141] A displacement threshold is set, and a safety warning is issued when the displacement of critical nodes of the tower, such as the tower head and crossarms, exceeds the threshold. The displacement threshold is determined based on tower design specifications and operational experience.

[0142] Based on historical load data and stress variation trends in structural members, the remaining service life of the tower is assessed. By statistically analyzing the stress variation patterns in structural members, the fatigue life and remaining service life of the tower are predicted, providing a basis for operation and maintenance decisions.

[0143] Example 4 is an embodiment of the present invention. This embodiment provides a dynamic load analysis system for power transmission towers, including a model modification module, a data fusion module, a load analysis module, a structural analysis module, and an evaluation and early warning module.

[0144] The model modification module is used to perform lightweight modification and semantic information enhancement on the GIM model of transmission towers. It uses an octree to partition the model space, constructs a multi-level detailed hierarchical model of data blocks and an R-tree spatial index, and obtains a multi-level tower line information model based on the component information in the GIM model of transmission towers and the associated engineering attribute map library.

[0145] The data fusion module is used to extract feature points from a multi-level tower line information model based on laser point cloud data and oblique photogrammetry real scene model, establish initial pose relationships, and iteratively register the initial pose relationships to obtain the reference model of coordinate system one.

[0146] The load analysis module is used to generate coupled load vectors based on dynamic wind load, temperature load, and icing load, and form a load envelope according to the combination of working conditions.

[0147] The structural analysis module is used to establish a finite element model based on the benchmark model, apply the coupled load vector to the finite element model step by step, perform nonlinear solution, and obtain the internal force distribution of the members, the stress of the members, and the nodal displacement.

[0148] The assessment and early warning module is used to perform stress ratio classification early warning, displacement threshold early warning and remaining life assessment based on the internal force distribution of the members, the stress of the members and the nodal displacement, and output the health status assessment results of the tower.

[0149] This embodiment also provides an electronic device applicable to a dynamic load analysis method for transmission towers, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the dynamic load analysis method for transmission towers as proposed in the above embodiment.

[0150] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a dynamic load analysis method for power transmission towers as proposed in the above embodiments.

[0151] The storage medium proposed in this embodiment and the method for dynamic analysis of power transmission tower load proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0152] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic load analysis of transmission towers, characterized in that, include: The GIM model of the transmission tower is lightened and its semantic information is enhanced. The model space is partitioned by an octree, and a multi-level detail model and R-tree spatial index of data blocks are constructed. Based on the component information in the GIM model of the transmission tower, an engineering attribute map library is associated to obtain a multi-level tower line information model. Based on laser point cloud data and oblique photogrammetry real scene model, feature points are extracted from multi-level tower line information model, initial pose relationship is established, and the initial pose relationship is iteratively registered to obtain the reference model of coordinate system one. Coupled load vectors are generated based on dynamic wind load, temperature load and icing load, and load envelopes are formed according to the combination of working conditions. A finite element model is established based on the benchmark model. The coupled load vector is applied to the finite element model step by step, and nonlinear solution is performed to obtain the internal force distribution of the members, the stress of the members and the nodal displacements. Based on the internal force distribution of the members, the stress of the members, and the nodal displacement, stress ratio classification early warning, displacement threshold early warning, and remaining life assessment are performed, and the tower health status assessment results are output.

2. The method for dynamic load analysis of transmission towers as described in claim 1, characterized in that, The method involves lightweighting and semantic enhancement of the transmission tower GIM model. An octree is used to partition the model space, constructing a multi-level detail model of data blocks and an R-tree spatial index. Based on the component information in the transmission tower GIM model and its association with an engineering attribute map library, a multi-level tower-line information model is obtained, including: The GIM model of the power transmission tower is analyzed to obtain the geometric information and topological relationship of the components. Based on the local structural complexity and component density of the tower structure, the model space is divided into octree non-uniform recursive partitions to form data blocks based on the local structural complexity and component density of the tower. A multi-level model of detail is constructed for the data block. The original grid data is retained to form the first level of detail model, edge folding is used to form the second level of detail model, and sampling reconstruction is used to form the third level of detail model. An R-tree spatial index is built using the spatial bounding range of the data block as the index key, and the target data block and the target level of detail model are called according to the viewpoint distance and viewpoint range. The transmission tower GIM model is converted into a component node diagram structure. Based on the component information in the transmission tower GIM model, component attributes are injected from the engineering attribute map library to obtain a multi-level tower line information model.

3. The method for dynamic load analysis of transmission towers as described in claim 2, characterized in that, The method involves extracting feature points from a multi-layered tower line information model based on laser point cloud data and oblique photogrammetry real-world model, establishing initial pose relationships, and iteratively registering these initial pose relationships to obtain a reference model for coordinate system one, including: Acquire laser point cloud data of the power transmission line corridor and calculate the flight trajectory and attitude information during the point cloud data acquisition process; Based on flight trajectory and attitude information, the laser point cloud data is transformed from the sensor coordinate system to the geodetic coordinate system. The transformed laser point cloud data is then filtered, classified, and reconstructed to generate a laser point cloud model. Oblique photogrammetry images are acquired, and aerial triangulation, point cloud generation, irregular triangulation construction, and texture mapping are performed on the oblique photogrammetry images to generate oblique photogrammetry reality models. Shared structural feature points are extracted from the multi-level tower line information model, laser point cloud model and oblique photogrammetry real scene model. Initial pose relationship is established based on the shared structural feature points. The initial pose relationship is iteratively registered to obtain the reference model of coordinate system one.

4. The method for dynamic load analysis of transmission towers as described in claim 3, characterized in that, The process of generating coupled load vectors based on dynamic wind load, temperature load, and icing load, and forming load envelopes according to load case combinations, includes: The average wind speed along the height is established based on the wind field parameters, and the time history of the fluctuating wind speed is generated based on the fluctuating wind simulation method. The wind load on the conductor and the wind load on the tower are calculated based on the time history of average wind speed and fluctuating wind speed. The wind load on the conductor is determined by superimposing the calculated outer diameter of the conductor and the wind speed, and the wind load on the tower is applied to the centroid of the segment based on the segmented height of the tower. The temperature load is determined based on the difference between the ambient temperature and the reference temperature, and the ice load on the conductor and the ice load on the tower are determined based on the ice thickness, ice density and ice distribution. The conductor wind load, tower wind load, temperature load, conductor icing load, and tower icing load are integrated according to time steps and spatial points to generate a coupled load vector. The coupled load vectors are combined and calculated based on the conditions of strong wind, icing, low temperature and uneven icing wind to form a load envelope.

5. The method for dynamic load analysis of transmission towers as described in claim 4, characterized in that, The finite element model is established based on the benchmark model. The coupled load vector is applied to the finite element model step by step, and nonlinear solutions are performed to obtain the internal force distribution of the members, the stress of the members, and the nodal displacements, including: A finite element model is established based on the spatial location of the tower, the component properties, and the component connection relationships in the benchmark model. The main members and crossarms are configured as beam elements, and the diagonal members and auxiliary members are configured as pole elements. Based on the connection methods in the multi-level tower line information model, finite element constraint relationships are set for the connection points between main members and diagonal members, the connection points between main members and main members, and the tower foot boundary. The coupled load vector in the load envelope is applied to the finite element model step by step according to the time step, and the stress is calculated by a nonlinear iterative solution method that considers geometric nonlinearity and material nonlinearity. The convergence state of the load step is determined based on the force residual and displacement increment. In response to the convergence condition being met by the load step, the internal force distribution of the members, the stress of the members, and the nodal displacements are extracted.

6. The method for dynamic load analysis of transmission towers as described in claim 5, characterized in that, Based on the internal force distribution of the members, the stress of the members, and the nodal displacement, the system performs stress ratio classification early warning, displacement threshold early warning, and remaining life assessment, outputting the tower health status assessment results, including: Based on the stress, axial force, material strength design value, and cross-sectional area of ​​the members under the most unfavorable load combination, the maximum stress ratio of each member is determined, and stress ratio graded early warning results are generated according to the interval where the maximum stress ratio is located. The horizontal displacement at the top of the tower, the vertical displacement of the crossarm, and the lateral displacement angle between floors are extracted from the node displacements. The horizontal displacement at the top of the tower, the vertical displacement of the crossarm, and the lateral displacement angle between floors are compared with preset limits, and displacement threshold warning results are generated based on the proportion of exceeding the limits. Based on historical meteorological data and stress time history of key members, rainflow counting is performed on the stress time history of key members to obtain fatigue load spectrum including stress amplitude and stress mean. Cumulative damage is calculated based on fatigue load spectrum and material life curve. The remaining life assessment result is generated based on the cumulative damage result and annual average load spectrum. The stress ratio classification early warning result, displacement threshold early warning result and remaining life assessment result are integrated to output the tower health status assessment result.

7. The method for dynamic load analysis of transmission towers as described in claim 3, characterized in that, The process involves extracting shared structural feature points from a multi-level tower line information model, a laser point cloud model, and an oblique photogrammetry real-world model; establishing initial pose relationships based on these shared structural feature points; and iteratively registering these initial pose relationships to obtain a reference model for coordinate system one, including: Based on the matching relationship between shared structural feature points, the matching point pairs between the multi-level tower line information model and the laser point cloud model and the oblique photogrammetry real scene model are determined; The initial rotation matrix and translation vector between the multi-level tower line information model, the laser point cloud model, and the oblique photogrammetry real scene model are calculated based on the matching point pairs, and the initial pose relationship is formed by the initial rotation matrix and translation vector. Using the initial pose relationship as the initial value for registration, the multi-level tower line information model, laser point cloud model and oblique photogrammetry real scene model are iteratively registered. In response to the satisfaction of the iteration termination condition, the baseline model of coordinate system one is output.

8. A dynamic load analysis system for transmission towers, employing the dynamic load analysis method for transmission towers as described in any one of claims 1 to 7, characterized in that, include: Model modification module, data fusion module, load analysis module, structural analysis module, and assessment and early warning module; The model modification module is used to perform lightweight modification and semantic information enhancement on the GIM model of the transmission tower. It uses an octree to divide the model space, constructs a multi-level detail hierarchy model of data blocks and an R-tree space index, and obtains a multi-level tower line information model based on the component information in the GIM model of the transmission tower associating with the engineering attribute map library. The data fusion module is used to extract feature points from a multi-level tower line information model based on laser point cloud data and oblique photogrammetry real scene model, establish an initial pose relationship, and perform iterative registration on the initial pose relationship to obtain a reference model of coordinate system one. The load analysis module is used to generate coupled load vectors based on dynamic wind load, temperature load and icing load, and form load envelopes according to the combination of working conditions. The structural analysis module is used to establish a finite element model based on the benchmark model, apply the coupled load vector to the finite element model step by step, perform nonlinear solution, and obtain the internal force distribution of the members, the stress of the members and the nodal displacements. The assessment and early warning module is used to perform stress ratio graded early warning, displacement threshold early warning and remaining life assessment based on the internal force distribution of the members, the stress of the members and the nodal displacement, and output the health status assessment results of the tower.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for dynamic analysis of power transmission tower loads as described in any one of claims 1 to 7.

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

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