Power transmission line design scheme management method based on unmanned aerial vehicle and digital twin technology

Abstract

The application discloses a power transmission line design scheme management method based on a unmanned aerial vehicle and a digital twin technology, and comprises the following steps: collecting multi-source heterogeneous data and inspection defect data of a target power transmission line corridor by using a multi-modal unmanned aerial vehicle; generating a three-dimensional real scene twin basement model containing a tower, a conductor, an insulator, vegetation and a topography based on the multi-source heterogeneous data; constructing a power transmission line digital twin body with a multi-physical field mapping relationship; virtually deploying at least two alternative power transmission line design schemes, and driving a multi-physical field simulation model to perform parallel calculation; automatically scoring and sorting the multi-dimensional results of the alternative power transmission line design schemes based on a pre-defined comprehensive evaluation index system; constructing a power transmission line design knowledge graph based on historical design cases, inspection defect data and multi-dimensional results; and outputting an intelligent recommended scheme to a designer based on semantic matching of a current design scene and the knowledge graph. Lossless information transmission and closed-loop management are realized.

Description

Technical Field

[0001] This invention relates to the field of power transmission line design scheme management technology, specifically a power transmission line design scheme management method based on unmanned aerial vehicle (UAV) and digital twin technology. Background Technology

[0002] Transmission lines are the backbone of the power grid, and the quality of their design directly affects the grid's construction costs, long-term operational safety, and maintenance efficiency. Traditional transmission line design management relies heavily on manual surveys, two-dimensional drawings, isolated design software, and static calculations. However, this approach depends heavily on preliminary manual surveys and satellite remote sensing data, making it difficult to obtain precise object coordinates, vegetation height, topography, and other details within the corridor. This results in weak design foundation data, easily leading to subsequent construction changes and safety hazards. Furthermore, existing design software (such as CAD and PLSCADD) primarily performs two-dimensional planar design and theoretical calculations based on standard conditions (such as sag and tension), which... The design scheme lacks in-depth integration and verification with the real three-dimensional geographical environment and the physical state of the line during operation (such as wind deflection, electric field, and icing). Design decisions are mostly based on experience and conservative estimates, making it difficult to achieve the optimal balance between economy and safety. Furthermore, the selection of existing design schemes relies heavily on expert review meetings, making it difficult to conduct objective and comprehensive quantitative assessments of full life-cycle indicators such as engineering workload, electromagnetic environment, and long-term operation and maintenance costs. A large amount of excellent design experience, historical cases, and design-related problems and defects exposed during operation and maintenance exist mostly in the form of unstructured documents, making it difficult to systematically retrieve, analyze, and reuse them. This makes it impossible to effectively learn from historical experience, leading to the recurrence of similar errors.

[0003] In recent years, drones (mainly used for inspection) and digital twin technology have each been applied in the power sector; however, there is currently no complete solution for building a digital twin based on multimodal drone data to serve the management of power transmission line design schemes and forming a complete closed-loop method from data collection to intelligent decision-making. Summary of the Invention

[0004] The purpose of this invention is to provide a method for managing power transmission line design schemes based on unmanned aerial vehicles (UAVs) and digital twin technology, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for managing power transmission line design schemes based on unmanned aerial vehicle (UAV) and digital twin technology, the method comprising:

[0006] Multimodal UAVs are used to conduct collaborative data collection on target power transmission line corridors, collecting multi-source heterogeneous data and inspection defect data;

[0007] A three-dimensional reality twin base model containing towers, conductors, insulators, vegetation, and topography is generated based on multi-source heterogeneous data.

[0008] Based on the obtained three-dimensional real-scene twin base model, a multi-physics simulation model is coupled and integrated with mechanical, electrical and environmental physical fields to construct a digital twin of the transmission line with multi-physics mapping relationship; the three-dimensional real-scene twin base model provides the geometric and spatial skeleton of the digital twin, and the multi-physics simulation model provides the dynamic simulation calculation capability of the digital twin.

[0009] In the digital twin of the transmission line, at least two alternative transmission line design schemes are virtually deployed, and a multiphysics simulation model is driven to perform parallel simulation calculations. Based on a predefined comprehensive evaluation index system, the multi-dimensional results output by the parallel simulation calculations of the alternative transmission line design schemes are automatically scored and ranked, and a comparison report of the alternative transmission line design schemes is output and fed back to the designers.

[0010] Based on historical design cases, inspection defect data, and multi-dimensional results, a knowledge graph for transmission line design is constructed; based on semantic matching between the current design scenario and the knowledge graph, intelligent recommended solutions are output to designers.

[0011] The beneficial effects of this invention are as follows:

[0012] (1) This invention utilizes a multiphysics simulation model driven by real environment data, which can simulate the behavior of transmission lines under various operating conditions in the future in a virtual environment, expose design defects in advance, and make the design scheme of transmission lines safer and more reliable.

[0013] (2) The present invention is carried out on a unified digital twin platform from initial survey, scheme design, comparison and optimization to operation and maintenance feedback, realizing lossless information transmission and closed-loop management, which greatly improves management efficiency and decision-making quality.

[0014] (3) By constructing a domain knowledge graph and an automated evaluation algorithm, this invention frees designers from repetitive calculations and data searches, while using artificial intelligence to recommend the optimal solution, thus significantly improving design efficiency and quality. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a flowchart illustrating the power transmission line design scheme management method disclosed in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0018] It should be noted that similar reference numerals or letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0019] like Figure 1 The power transmission line design scheme management method shown is based on drones and digital twin technology, including:

[0020] S1. Based on multimodal UAVs, conduct collaborative data collection on the target power transmission line corridor, collecting multi-source heterogeneous data and inspection defect data;

[0021] A 3D reality twin model is generated based on multi-source heterogeneous data, including towers, conductors, insulators, vegetation, and terrain. The multi-source heterogeneous data includes visible light image data and lidar point cloud data; the inspection defect data includes defect types and location coordinates output from the visible light image data processed by a defect identification algorithm, and heating defect types and location coordinates output from infrared thermal image data processed by temperature anomaly detection.

[0022] Each point in a lidar point cloud consists of the world coordinates of the corresponding point and the echo intensity monitored by the corresponding point.

[0023] Visible light image data also includes the acquisition location and attitude of the drone corresponding to each image.

[0024] Specifically, during the collaborative data collection of the target power transmission line corridor based on multimodal UAVs, the collaborative flight path of multiple UAVs equipped with different types of sensors is planned according to the terrain and length of the target power transmission line corridor. The types of sensors carried by the UAVs include lidar and infrared cameras. Each UAV flies according to the planned flight path and performs time synchronization and spatial positioning based on GNSS / PPS signals.

[0025] LiDAR point clouds are acquired using LiDAR, and infrared thermal images and visible light images are acquired using infrared cameras.

[0026] Optionally, multimodal UAVs are used for collaborative data collection on the target transmission line corridor, including the acquisition of multispectral data. The UAVs are equipped with multispectral sensors to acquire this data. This multispectral data is used to identify vegetation health and type, vegetation cover density, and can be used as input data for wind load calculations in subsequent mechanical simulation models, ecological impact assessments in comprehensive evaluation index systems, and environmental condition associations in knowledge graph construction.

[0027] The process of generating a 3D reality twin model based on multi-source heterogeneous data, including towers, conductors, insulators, vegetation, and topography, includes the following steps:

[0028] S11. Denoise, register, and remove outlier noise points from the lidar point cloud data, and segment the point cloud into ground points and non-ground points; and generate a high-precision digital elevation model based on the ground point cloud.

[0029] S12. Based on visible light image data, perform three-dimensional real scene reconstruction to generate a three-dimensional mesh model with realistic texture. The three-dimensional mesh model contains the geometric shape and surface texture information of all ground features in the target transmission line corridor.

[0030] S13. Perform coordinate system matching and data registration between the complete lidar point cloud data (including at least ground points and non-ground points, such as towers, traverse lines, and vegetation point clouds) in the high-precision digital elevation model and the 3D mesh model: using the geographic coordinates of the lidar point cloud as the geometric reference, map the real texture of the 3D mesh model to the point cloud space to generate the fused point cloud and image dataset.

[0031] Each spatial point in the point cloud and image dataset corresponds to an array consisting of geometric coordinates, echo intensity, and RGB color information, which serves as the input data for subsequent semantic segmentation.

[0032] S14. A deep learning-based semantic segmentation model is used to perform semantic segmentation on the fused point cloud and image data, and outputs the semantic category label of each point cloud and pixel; the transmission line components and corridor features are automatically identified and classified, and a semantic category label is added to each identified object; the semantic category label includes towers, conductors, insulators, vegetation, buildings and preset terrain features;

[0033] S15. Associate and match the identified objects with semantic category labels with the preset BIM standard component library or GIS spatial database to form a three-dimensional real-scene twin base model with structured information.

[0034] S2. Based on the obtained three-dimensional real-scene twin base model, a multi-physics simulation model is coupled and integrated with mechanical, electrical and environmental physical fields to construct a digital twin of the transmission line with multi-physics mapping relationship; the three-dimensional real-scene twin base model provides the geometric and spatial skeleton of the digital twin, and the multi-physics simulation model provides the dynamic simulation calculation capability of the digital twin.

[0035] Preferred coupled and integrated physics simulation models include at least:

[0036] A mechanical simulation model is used to calculate the conductor sag, tower stress, and wind-induced sway of the target transmission line.

[0037] An electrical simulation model is used to calculate the spatial electric and magnetic field distribution and line loss of the target transmission line.

[0038] An environmental simulation model is used to predict the icing growth, galloping, and lightning strike risk of target transmission lines.

[0039] The real terrain, landforms, and vegetation spatial data in the 3D reality twin base model serve as the input boundary conditions for the environmental simulation model.

[0040] For example, in this embodiment, the mechanical simulation model adopts a catenary model, which can be represented by a state equation jointly constructed by the target transmission line tension, span, linear density, gravitational acceleration, and wind load driven by wind speed and direction in the twin environment; the construction process of the state equation is as follows:

[0041] For the conductors of the target transmission line, the general formula for its catenary equation is: In the formula: y(x) represents the sag of the conductor of the target transmission line at the horizontal coordinate x (i.e., relative to the line connecting the two suspension points); Hz represents the horizontal tension of the conductor of the target transmission line, in Newtons; q represents the self-weight load per unit length of the conductor of the target transmission line; this state equation needs to be extended to an exact solution of a parabola or catenary considering the height difference h between the two suspension points; using the known coordinates of the two suspension points, solve for the horizontal tension Hz that satisfies the geometric boundary conditions;

[0042] Then, the wind load and icing load are integrated, and the load vector becomes qt, with its direction inclined.

[0043]

[0044] In the formula, qice represents the icing load, the value of which is provided by the environmental model output; qwind represents the wind pressure load, the value of which is equal to the sum of the square of the wind speed at the corresponding conductor height obtained based on the twin environmental wind speed profile model, the air density, the conductor drag coefficient in the preset form, and the corresponding conductor diameter including the icing thickness.

[0045] Alternatively, wind pressure loads can be calculated based on the surface roughness distribution determined by vegetation type and cover density identified through multispectral data, and dynamically generated using a twin environment wind speed profile model.

[0046] Then, the real hanging point coordinates extracted from the 3D real-scene twin base model, the real-time wind speed and ice thickness provided by the environmental model are used as inputs, and the catenary equation is substituted into iterative solutions to obtain Hz and y(x) under the current working conditions, and the distance of any point to the ground is calculated.

[0047] Using the intermediate value of horizontal tension, all other mechanical results can be calculated.

[0048] For example, when obtaining sag and wind deflection, the solved Hz and state equation y(x) are the sag curve. Under different wind loads, solving for different Hz and y(x) yields the spatial position of the conductor after wind deflection; the difference between y(x) at different positions is the amount of wind deflection sway.

[0049] The forces acting on the tower include: horizontal tension (Hz), vertical load (weight), longitudinal unbalanced tension, and at least one of the tower bending moment and torque.

[0050] Among them, the horizontal tension Hz is the horizontal load of the conductor on the crossarm of the tower.

[0051] The vertical load (weight) is obtained by calculating the product of the conductor's self-weight q and the span L;

[0052] Longitudinal unbalanced tension is an unbalanced force that points towards the side with greater tension when the Hz of adjacent gears are different (e.g., one side is covered with ice and the other side is not). This force is obtained by comparing the Hz of adjacent gears.

[0053] The bending moment and torque of the tower are calculated by combining the horizontal, vertical and longitudinal forces obtained above acting on different hanging points on the tower body, and are the bending moment and torque at the base of the tower.

[0054] For example, in the process of constructing the electrical simulation model in this embodiment, the cross-section of the target transmission line corridor is used as the computational domain.

[0055] Triangular meshing was performed on the surface 3D model of all charged conductors (including wires, equalizing rings, and tower body) in the 3D reality twin base model to generate boundary element mesh;

[0056] Assuming the surface of the charged conductor is an equipotential body, based on the electrostatic field integral equation, the constraint condition satisfied by the surface charge density σ on each discrete unit is:

[0057]

[0058] in: This represents the known potential at the center point of the k-th discrete unit; This represents the Green's function coefficient matrix determined by the geometry and position of the k-th and d-th discrete units;

[0059] Solving the above system of linear equations yields the charge density distribution across the entire conductor surface; subsequently, the electric field strength EQ at any point of interest Q can be calculated. ,in, Represents a kernel function related to geometry. Let Q be the surface charge density on the d-th discrete unit. By setting the point of interest Q to the actual terrain surface point cloud or building surface point cloud, the actual electric field intensity distribution on the ground or building surface can be directly calculated and automatically compared with environmental protection standards.

[0060] Similar to the calculation of the electric field strength in space, the magnetic field distribution in space is calculated based on the Biot-Savart law and the known operating current of the line. The method is as follows: after obtaining the current I on the conductor, the conductor is discretized into a series of current-carrying segments. The magnetic induction intensity BQ generated at any point Q in space can be obtained by vector summation. Then, combined with the conductor resistance model and the calculated maximum electric field strength, the resistance loss and corona loss of the line are estimated.

[0061] Optionally, an environmental simulation model is used to predict the icing growth, galloping, and lightning strike risk of the target transmission line; wherein, the real terrain, landform, and vegetation spatial data in the three-dimensional real-scene twin base model are used as the input boundary conditions of the environmental simulation model.

[0062] For example, in this embodiment, the process of constructing an environmental simulation model using icing prediction as an example includes:

[0063] A data channel was established between the twin and the micro-weather station to acquire real-time time series data on temperature, relative humidity, wind speed, wind direction, and precipitation type and intensity. Then, a simplified heat balance model was used for time-step simulation, satisfying the following constraints within each time step Δt: Where: △Mice represents the increase in icing mass per unit length of conductor; LWC represents the liquid water content in the air, and the value of LWC is related to relative humidity, precipitation type, and precipitation intensity; Indicates wind speed; The collision efficiency is a function of the wire diameter, wind speed, and water droplet size. Indicates the freezing coefficient; Indicates the loss rate caused by sublimation or melting;

[0064] The process of obtaining the equivalent ice thickness is as follows: Assume that the ice is a cylindrical layer uniformly wrapped around the conductor, and the original radius of the conductor is a known parameter; then, the total ice mass after time Δt is equal to the sum of the initial mass and the accumulated ΔMice; at this time, the accumulated ΔMice is equivalent to the newly added ice being a cylinder, and the product of the newly added cylinder volume and the ice density is ΔMice; thus, the equivalent ice thickness at the current time can be solved; the increased ice load is the product of the accumulated ΔMice after time Δt and the gravitational acceleration g.

[0065] Then, the calculated equivalent ice thickness and increased ice load are output to the mechanical simulation model in real time, updating the load and parameters of the conductor (the diameter of the conductor will increase), triggering a new round of sag and tension calculations.

[0066] Predicting galloping of a target transmission line involves inputting the ice mass increment, calculated equivalent ice thickness, and ice shape from the model into a mechanical simulation model. After obtaining the conductor parameters (mass, diameter, cross-sectional shape) after icing, the mechanical simulation model, combined with wind load, can perform stability analysis (e.g., by calculating the Den Hartog coefficient). If galloping conditions are met, a high-risk galloping warning is output.

[0067] When predicting the lightning risk of a target transmission line, the terrain elevation and line design parameters in the twin-based model are used as inputs, and the annual lightning tripping rate risk index calculated based on the existing electrical geometry model is output to determine the lightning risk level.

[0068] In the environmental simulation model, changes in the shape of ice accretion trigger updates to the electrical simulation model, allowing for the analysis of changes in the electric field distribution after icing.

[0069] S3. In the digital twin of the transmission line, at least two alternative transmission line design schemes are virtually deployed, and a multi-physics simulation model is driven to perform parallel simulation calculations. Based on a predefined comprehensive evaluation index system, the multi-dimensional results output by the parallel simulation calculations of the alternative transmission line design schemes are automatically scored and ranked, and a comparison report of the alternative transmission line design schemes is output and fed back to the designers.

[0070] Preferably, the virtual deployment includes: instantiating the tower coordinates, conductor parameters, and fitting models in the alternative transmission line design scheme in the transmission line digital twin, and automatically performing spatial conflict detection, including geometric interference with existing ground features;

[0071] During the spatial conflict detection process for alternative transmission line design schemes, each instantiated object in the digital twin of the transmission line is used as a candidate twin. The hierarchical bounding box tree (BVH) algorithm is used to check whether the minimum distance between the candidate twin and the vegetation and buildings in the existing ground features meets the preset safety regulations. If the preset safety regulations are met, the corresponding candidate twin is determined to be in normal condition; if the preset safety regulations are not met, the corresponding candidate twin is determined to have spatial conflict behavior with the existing ground features. Each candidate twin with spatial conflict behavior with the existing ground features is marked, and a multiphysics simulation model is driven to perform parallel simulation calculations on the marked candidate twins to evaluate their actual clearance distance and safety under dynamic conditions such as wind deflection, icing, and high temperature. Candidate schemes that still do not meet the safety regulations after simulation calculation are eliminated, and candidate schemes that meet the safety regulations enter the subsequent comparison and selection stage.

[0072] Preferably, the predefined comprehensive evaluation index system is a multi-level quantitative index set, including:

[0073] Economic indicators: one or more of the following: material cost, project volume, and estimated construction period;

[0074] Technical indicators: one or more of the following: mechanical safety margin and construction difficulty index;

[0075] Reliability metrics: Failure prediction probability based on simulation;

[0076] Environmental indicators: vegetation clearing volume, ecological impact assessment;

[0077] The automated scoring and ranking algorithm determines the index weights based on the entropy weight method and performs weighted calculations on the simulation results of each candidate transmission line design scheme.

[0078] Optionally, the ecological impact assessment is based on the quantification of vegetation economic value and ecological sensitivity identified by multispectral data.

[0079] In S3, the specific implementation steps for outputting the comparison report of alternative transmission line design schemes are as follows:

[0080] S31. Based on a predefined comprehensive evaluation index system and alternative transmission line design schemes, construct an evaluation matrix for transmission line design schemes, denoted as... ; , This indicates the number of alternative transmission line design schemes. This indicates the number of indicators in a predefined comprehensive evaluation index system. Indicates the first The alternative transmission line design schemes are ranked first in the predefined comprehensive evaluation index system. Quantitative values ​​for each indicator;

[0081] S32. The quantitative values ​​of each indicator in the transmission line design scheme evaluation matrix are normalized using a preset formula to obtain a normalized evaluation matrix for the transmission line design scheme. The values ​​of each element in the normalized evaluation matrix for the transmission line design scheme are all within the range of... Within the range;

[0082] S33. Calculate the first index in the predefined comprehensive evaluation index system using the entropy weight method. The entropy value of each indicator is calculated using the following formula:

[0083] ;

[0084] in, This indicates the first in the predefined comprehensive evaluation index system. The entropy value of each indicator; express It accounts for the first place among the various alternative transmission line design schemes in the predefined comprehensive evaluation index system. The ratio of the sum of the quantified values ​​of each indicator is calculated using the following formula;

[0085] ;

[0086] S34. Based on the predefined comprehensive evaluation index system, the first... The entropy value of the nth indicator is used to calculate the entropy value of the nth indicator in the predefined comprehensive evaluation index system. The weights of each indicator and the comprehensive score for each alternative transmission line design scheme are calculated using the following formulas:

[0087] ;

[0088] ;

[0089] in, This indicates the first in the predefined comprehensive evaluation index system. The weight of each indicator; Indicates the first The overall score of the alternative transmission line design schemes; Indicates the first The alternative transmission line design schemes are ranked first in the predefined comprehensive evaluation index system. The normalization result of the quantified values ​​on each indicator;

[0090] S35. Output a comparison report of alternative transmission line design schemes. The comparison report includes the ranking results of each alternative transmission line design scheme according to the comprehensive score, and the comprehensive score corresponding to each alternative transmission line design scheme.

[0091] S4. Based on historical design cases, inspection defect data, and multi-dimensional results, construct a knowledge graph for transmission line design; based on semantic matching between the current design scenario and the knowledge graph, output intelligent recommendation solutions to designers.

[0092] The specific implementation steps for constructing a knowledge graph for transmission line design include:

[0093] S41. Extract structured information from drawings and comparison reports of historical design cases to form a triple containing the first entity object, the second entity object, and the relationship between the first entity object and the second entity object.

[0094] S42. The defect type, defect location coordinates, design parameters in the historical design scheme corresponding to the corresponding defect location coordinates, and environmental conditions where the corresponding defect location coordinates are located are associated and stored through spatial coordinate matching. The environmental conditions where the corresponding defect location coordinates are located include terrain information extracted from the digital elevation model and micro-meteorological parameters obtained from the meteorological database.

[0095] Optionally, the environmental conditions for the corresponding defect location coordinates also include the vegetation type identified from multispectral data. S43. Correlate the performance data of the simulation results with the characteristics of the corresponding transmission line design scheme; the performance data here are the quantitative values ​​of each scheme for each indicator in the comprehensive evaluation index system.

[0096] S44. Integrate the results obtained from steps S41 to S43 to construct a knowledge graph for transmission line design with design scenarios, equipment components, fault modes, and performance parameters as core nodes.

[0097] In the process of providing intelligent recommendation solutions to designers, key features of the current design task are extracted to form a query vector; the cosine similarity between the query vector and the node vectors of each historical scenario is calculated, historical similar scenarios are matched, and the similarity between the current design task and the historical scenarios is output; successful design solutions, risk avoidance measures, and key component selections used in historical scenarios with similarity greater than a preset value are used as the basis for recommendation and output in a sorted manner.

[0098] Specifically, the query vector is used as the input for semantic similarity retrieval based on graph neural networks. The cosine similarity between the obtained query vector and the node vectors of each historical scene is calculated in the knowledge graph of transmission line design to match historical similar scenes.

[0099] Preferably, the method further includes:

[0100] S5. Regularly obtain power transmission line operation status data through drone inspections;

[0101] S6. Compare the obtained transmission line operation status data with the expected status of the transmission line digital twin to identify abnormal differences; if the difference exceeds the threshold, trigger an early warning and recommend maintenance or design correction schemes based on the knowledge graph, and update the transmission line digital twin and design scheme library.

[0102] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0103] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for managing power transmission line design schemes based on unmanned aerial vehicle (UAV) and digital twin technologies, characterized in that: include: Multimodal UAVs are used to conduct collaborative data collection on target power transmission line corridors, collecting multi-source heterogeneous data and inspection defect data; A three-dimensional reality twin base model containing towers, conductors, insulators, vegetation, and topography is generated based on multi-source heterogeneous data. Based on the obtained three-dimensional real-scene twin base model, a multi-physics simulation model is coupled and integrated with mechanical, electrical and environmental physical fields to construct a digital twin of the transmission line with multi-physics mapping relationship; the three-dimensional real-scene twin base model provides the geometric and spatial skeleton of the digital twin, and the multi-physics simulation model provides the dynamic simulation calculation capability of the digital twin. In the digital twin of the transmission line, at least two alternative transmission line design schemes are virtually deployed, and a multiphysics simulation model is driven to perform parallel simulation calculations. Based on a predefined comprehensive evaluation index system, the system automatically scores and ranks the multi-dimensional results of the parallel simulation calculations of the alternative transmission line design schemes, outputs a comparison report of the alternative transmission line design schemes, and provides feedback to the designers. Based on historical design cases, inspection defect data, and multi-dimensional results, a knowledge graph for transmission line design is constructed. Based on semantic matching between the current design scenario and the knowledge graph, intelligent recommendation solutions are output to designers.

2. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 1, characterized in that, Multi-source heterogeneous data includes visible light image data and lidar point cloud data; Each point in the lidar point cloud data includes the world coordinates of the corresponding point and the echo intensity monitored at the corresponding point; Visible light image data includes the acquisition location and attitude of the drone corresponding to each image; The inspection defect data includes the defect type and defect location coordinates output by the defect identification algorithm based on the visible light image data, and the heating defect type and heating defect location coordinates output by the temperature anomaly detection processing based on the infrared thermal image data.

3. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 2, characterized in that, During the collaborative data collection of target power transmission line corridors using multimodal drones, the collaborative flight paths of multiple drones equipped with different types of sensors are planned according to the terrain and length of the target power transmission line corridor. The types of sensors carried by the drones include lidar and infrared cameras. Each drone flies according to the planned flight path, and time synchronization and spatial positioning are performed based on GNSS / PPS signals. LiDAR point cloud data is acquired through LiDAR, and infrared thermal and visible light image data are acquired through infrared cameras.

4. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 2, characterized in that, Automatically identify and model to generate a 3D reality twin base model including towers, conductors, insulators, vegetation, and terrain features, including: The lidar point cloud data is denoised, registered, and outlier noise points are removed. The point cloud is then segmented into ground points and non-ground points. A high-precision digital elevation model is generated based on the ground point cloud. Three-dimensional real-scene reconstruction is performed based on visible light image data to generate a three-dimensional mesh model with realistic texture. The three-dimensional mesh model contains the geometric shape and surface texture information of all ground features within the target transmission line corridor. The complete lidar point cloud data in the high-precision digital elevation model is registered with the three-dimensional mesh model using coordinate system one: with the geographic coordinates of the lidar point cloud as the geometric reference, the real texture of the three-dimensional mesh model is mapped to the point cloud space to generate a fused point cloud and image dataset. A deep learning-based semantic segmentation model is used to perform semantic segmentation on the fused point cloud and image data, and outputs a semantic category label for each point cloud and pixel; power transmission line components and corridor features are automatically identified and classified, and a semantic category label is added to each identified object; the semantic category labels include towers, conductors, insulators, vegetation, buildings and preset terrain features; The identified objects with semantic category labels are associated and matched with a pre-set BIM standard component library or GIS spatial database to form a three-dimensional real-scene twin base model with structured information.

5. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 1, characterized in that, Coupled integrated physical field simulation models include at least: A mechanical simulation model is used to calculate the conductor sag, tower stress, and wind-induced sway of the target transmission line. An electrical simulation model is used to calculate the spatial electric and magnetic field distribution and line loss of the target transmission line. An environmental simulation model is used to predict the icing growth, galloping, and lightning strike risk of target transmission lines. The real terrain, landforms, and vegetation spatial data in the three-dimensional real-scene twin base model serve as the input boundary conditions for the environmental simulation model.

6. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 1, characterized in that, The virtual deployment includes: instantiating the tower coordinates, conductor parameters, and fitting models in the alternative transmission line design schemes in the digital twin of the transmission line, and automatically performing spatial conflict detection, including geometric interference with existing ground features; During the spatial conflict detection process for alternative transmission line design schemes, each instantiated object in the digital twin of the transmission line is used as a candidate twin. The hierarchical bounding box tree (BVH) algorithm is used to check whether the minimum distance between the candidate twin and the vegetation and buildings in the existing ground features meets the preset safety regulations. If the preset safety regulations are met, the corresponding candidate twin is determined to be in normal condition; if the preset safety regulations are not met, the corresponding candidate twin is determined to have spatial conflict behavior with the existing ground features. Each candidate twin with spatial conflict behavior with the existing ground features is marked, and a multiphysics simulation model is driven to perform parallel simulation calculations on the marked candidate twins to evaluate their actual clearance distance and safety under dynamic conditions such as wind deflection, icing, and high temperature. Candidate schemes that still do not meet the safety regulations after simulation calculation are eliminated, and candidate schemes that meet the safety regulations enter the subsequent comparison and selection stage.

7. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 1, characterized in that, The predefined comprehensive evaluation index system is a multi-level quantitative index set, including: Economic indicators: one or more of the following: material cost, project volume, and estimated construction period; Technical indicators: one or more of the following: mechanical safety margin and construction difficulty index; Reliability metrics: Failure prediction probability based on simulation; Environmental indicators: vegetation clearing volume, ecological impact assessment; The automated scoring and ranking algorithm determines the index weights based on the entropy weight method and performs weighted calculations on the simulation results of each candidate transmission line design scheme.

8. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 7, characterized in that, The specific implementation steps for generating a comparative report of alternative transmission line design schemes are as follows: Based on a predefined comprehensive evaluation index system and alternative transmission line design schemes, an evaluation matrix for transmission line design schemes is constructed, denoted as . ; , This indicates the number of alternative transmission line design schemes. This indicates the number of indicators in a predefined comprehensive evaluation index system. Indicates the first The alternative transmission line design schemes are ranked first in the predefined comprehensive evaluation index system. Quantitative values ​​for each indicator; The quantified values ​​of each indicator in the evaluation matrix of transmission line design schemes are normalized to obtain a normalized evaluation matrix of transmission line design schemes. The values ​​of each element in this normalized matrix are all within the range of... Within the range; The entropy weight method is used to calculate the first index in a predefined comprehensive evaluation index system. The entropy value of each indicator is calculated using the following formula: ； in, This indicates the first in the predefined comprehensive evaluation index system. The entropy value of each indicator; express It accounts for the first place among the various alternative transmission line design schemes in the predefined comprehensive evaluation index system. The ratio of the sum of the quantified values ​​of each indicator is calculated using the following formula; ； Based on the predefined comprehensive evaluation index system, the first The entropy value of the nth indicator is used to calculate the entropy value of the nth indicator in the predefined comprehensive evaluation index system. The weights of each indicator and the comprehensive score for each alternative transmission line design scheme are calculated using the following formulas: ； ； in, This indicates the first in the predefined comprehensive evaluation index system. The weight of each indicator; Indicates the first The overall score of the alternative transmission line design schemes; Indicates the first The alternative transmission line design schemes are ranked first in the predefined comprehensive evaluation index system. The normalization result of the quantified values ​​on each indicator; Output a comparison report of alternative transmission line design schemes. The comparison report includes the ranking results of each alternative transmission line design scheme according to the comprehensive score, as well as the comprehensive score corresponding to each alternative transmission line design scheme.

9. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 4, characterized in that, The specific implementation steps for constructing a knowledge graph for transmission line design include: The acquisition of historical design cases includes: extracting structured information from the drawings and selection reports of historical design cases to form a triple containing the first entity object, the second entity object, and the relationship between the first entity object and the second entity object; The acquisition of inspection defect data includes: associating and storing the defect type, defect location coordinates, design parameters in the historical design scheme corresponding to the corresponding defect location coordinates, and the environmental conditions where the corresponding defect location coordinates are located through spatial coordinate matching. The environmental conditions where the corresponding defect location coordinates are located include terrain information extracted from the digital elevation model and micro-meteorological parameters obtained from the meteorological database. Obtaining multi-dimensional results includes: associating the performance data of the simulation results with the characteristics of the corresponding transmission line design scheme; By integrating the results obtained from historical design cases, inspection defect data, and multi-dimensional results, a knowledge graph for transmission line design is constructed with design scenarios, equipment components, fault modes, and performance parameters as core nodes.

10. The method for managing transmission line design schemes based on UAV and digital twin technology according to claim 1, characterized in that, In the process of providing intelligent recommendation solutions to designers, key features of the current design task are extracted to form a query vector; Calculate the cosine similarity between the query vector and the node vectors of each historical scene, match similar historical scenes, and output the similarity between the scene corresponding to the current design task and the historical scenes; use the successful design schemes, risk avoidance measures, and key component selections adopted in the historical scenes with similarity greater than the preset value as the basis for recommendation and output them in a sorted manner.

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