A method and system for modeling a power transmission line digital twin based on geological disasters

Abstract

The application discloses a kind of power transmission line digital twin modeling method and system based on geological disasters, it is related to power line modeling technical field, including using unmanned aerial vehicle to carry out flight collection, obtains power line area image data;Image data collected is handled, generates three-dimensional scene, and three-dimensional scene correction is carried out in combination with geology and topographic map;Using unmanned aerial vehicle five tilting camera to obtain tilt photo, control point information generates three-dimensional model data and real orthophoto image data, and collect present high two-dimensional vector data on real orthophoto image data;Various data are integrated, and the fine power line digital twin model is built.The present application has important significance to the digitization upgrade of power line, will improve the intelligent level and operation and maintenance efficiency of power grid, reduce risk cost, and has broad application prospect.

Description

Technical Field

[0001] This invention relates to the field of power line modeling technology, and in particular to a method and system for digital twin modeling of transmission lines based on geological hazards. Background Technology

[0002] With the development of my country's power industry, power lines are increasingly showing a trend towards large-scale and intensive development, with line spans and capacities continuously increasing, placing higher demands on the safe operation of these lines. During long-term operation, power lines are susceptible to various faults and safety hazards due to the influence of the natural environment and external factors, such as tower tilting, broken conductor wires, and damaged insulators. If these faults are not detected and addressed in a timely manner, they can lead to serious consequences.

[0003] Traditional power line inspection and modeling mainly rely on regular manual patrols and the acquisition of line data using laser photoelectric and satellite remote sensing technologies. These traditional methods suffer from high costs, low efficiency, and poor safety. In recent years, with the development of drones and 3D reconstruction technology, using drones for aerial inspection and 3D modeling of power assets has become a new technological direction. Drones can fly at low altitudes to take multi-angle photos of power lines, offering far greater flexibility than satellites and fixed-position cameras.

[0004] Currently, the common method for 3D reconstruction of power lines in the industry is to use drones equipped with RGB cameras to comprehensively photograph the target area, acquire 2D image data, and use structured light motion algorithms to generate 3D point clouds to further construct a 3D model of the line facilities. This method has the following problems: low reconstruction accuracy, only able to generate a simplified overview of the line and unable to present detailed structures; poor adaptability, insufficient handling of occlusion, and easy to produce dead zones; low level of informatization, only modeling geometric shapes and not including power system parameters, status and other information; and poor closure, the reconstructed virtual model is detached from the physical system and cannot achieve deep collaboration of the virtual system. Summary of the Invention

[0005] In view of the problems existing in the above-mentioned three-dimensional reconstruction methods of power lines, the present invention is proposed.

[0006] Therefore, the problem to be solved by this invention is how to provide a method and system for achieving high-precision modeling by combining control point data and introducing UAV collaboration to avoid occlusion.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a method for modeling a digital twin of a power transmission line based on geological hazards. The method includes: using a drone to collect image data of the power line area; processing the collected image data to generate a three-dimensional scene, and correcting the three-dimensional scene by combining geological and topographic maps; using a drone's five-panel oblique camera to acquire oblique photos, generating three-dimensional model data and true orthophoto data from control point information, and collecting highly up-to-date two-dimensional vector data on the true orthophoto data; and integrating the various data to construct a refined digital twin model of the power line.

[0009] As a preferred embodiment of the digital twin modeling method for power transmission lines based on geological hazards described in this invention, the method of using a drone to collect image data of the power line area includes the following steps: using the drone's built-in visual positioning system to allow the drone to fly along a preset route and automatically record perspective information; using sonar detection technology to emit sound waves to detect obstructions within the drone's field of view and obtain the size and location information of the obstructions; using the drone's inertial navigation system and combining the sonar detection results to draw a three-dimensional obstruction distribution map; automatically planning supplementary flight routes according to artificial intelligence algorithms to ensure that the camera covers the obstructed area; and using network collaboration between drones, where images of severely obstructed areas are provided by other drones to collaboratively acquire data.

[0010] As a preferred embodiment of the digital twin modeling method for power transmission lines based on geological hazards described in this invention, the step of using the inertial navigation system of an unmanned aerial vehicle (UAV) and combining sonar detection results to draw a three-dimensional occlusion distribution map includes the following steps: Assume the position provided by the UAV's inertial navigation system includes: P(x,y,z) and velocity V(v). x ,v y ,v z ) and direction data θ (θ x ,θ y ,θ z The sonar system provides the distance d and orientation θ of the obstructing object relative to the sonar detector. son (θ sx ,θ sy ,θ sz Considering the influence of the time factor t on the UAV's position, Kalman filtering or particle filtering algorithms are used for dynamic data fusion and position prediction; the estimated position P of the UAV. est The formula is determined by both inertial navigation system data and dynamic adjustment algorithms, as follows:

[0011] P est (t)=F(P,V,θ,t,N)

[0012] The position P of the obstructing object relative to the drone obsPosition calculations based on sonar data and drone estimations:

[0013] P obs =P est +d.Transform(θ son ,θ)

[0014] By projecting the position coordinates of the obstruction onto the three-dimensional environment, a three-dimensional visualization map of the obstruction distribution within the drone's field of view is generated.

[0015] As a preferred embodiment of the digital twin modeling method for power transmission lines based on geological hazards described in this invention, the step of automatically planning supplementary flight routes according to artificial intelligence algorithms to cover the obscured areas includes the following steps: loading a spatial obscuration distribution graphic, reading the coordinate range information of the obscured areas, and storing it as an obscured area list; inputting UAV flight parameters, including the flight altitude range, flight speed range, and turning radius range, and storing them as a UAV parameter list; inputting camera equipment parameters, including the field of view angle, flip angle range, and angle with the UAV, and storing them as a camera parameter list; initializing the flight path map as a two-value image, where a value of 0 indicates an uncovered area and a value of 1 indicates a covered area; selecting an obscured area from the obscuration list and obtaining the coordinate range information of that obscured area; calculating the two-dimensional boundary range of the ground projection corresponding to the obscured area, marking the boundary range as an uncovered area in the flight path map, i.e., setting the value to 0; generating multiple candidate flight path lists, so that the flight paths in the flight path list pass through and overlook the obscured areas; for each flight path, simulating flight and calculating the camera equipment's shooting range.

[0016] As a preferred embodiment of the digital twin modeling method for power transmission lines based on geological hazards described in this invention, the calculation of the camera's shooting range includes: acquiring the three-dimensional spatial coordinate information of the flight path; determining the UAV attitude using the UAV parameter list; calculating the shooting range of each position using the camera parameter list; merging the shooting ranges to obtain the overall shooting range; for each shooting range, calculating the area of ​​the covered and obscured region; selecting the flight path with the largest coverage area as the current optimal flight path; updating the flight path map and setting the coverage area corresponding to the optimal flight path to 1; determining whether the 0-value area in the flight path map is empty: if it is empty, it means that all obscured areas have been covered, and returning to the optimal flight path; if it is not empty, there are still uncovered obscured areas, and a new obscured area is selected from the obscured list to obtain its coordinate range information; returning the final optimal flight path as a supplementary flight path.

[0017] As a preferred embodiment of the digital twin modeling method for power transmission lines based on geological hazards described in this invention, the following steps are included: processing the acquired image data to generate a three-dimensional scene, and correcting the three-dimensional scene by combining geological and topographic maps: preprocessing the acquired UAV image data; using the Structure from Motion (SFM) algorithm, with the preprocessed image data as input, calculating feature matching between images to restore the camera pose and the sparse three-dimensional point cloud of the scene; applying the Multi-View Stereo (MVS) method to the sparse point cloud to generate a detailed high-density three-dimensional point cloud; filtering and smoothing the high-density three-dimensional point cloud to remove outliers; using a three-dimensional reconstruction algorithm, with the smoothed point cloud and preprocessed image as input, generating a two-dimensional mesh and scene model; collecting relevant geological and topographic map data and performing feature matching with the scene model to mark the locations of key features.

[0018] As a preferred embodiment of the digital twin modeling method for transmission lines based on geological disasters described in this invention, the various data include a basic real-world 3D model, a high-precision model of the tower, and conductor data.

[0019] Secondly, to further address the problems existing in current 3D reconstruction methods for power lines, this invention provides a digital twin modeling system for power transmission lines based on geological hazards. This system includes: an image acquisition module for using a drone to acquire image data of the power line area; a 3D reconstruction module for processing the acquired image data, generating a 3D scene model, and combining it with geological and topographic data to achieve 3D scene correction and optimization; a thematic data acquisition module for using a drone's five-panel oblique camera to acquire oblique photographs, and combining them with control points to generate 3D model data and true orthophotos, and acquiring 2D vector data on the orthophotos; a data fusion module for integrating various data obtained from image acquisition, 3D reconstruction, and thematic data acquisition to ultimately construct a refined digital twin model of the power line; a model optimization module for adjusting parameters, optimizing details, and mining knowledge in the constructed digital twin model to improve its accuracy and usability; and a data management module for standardized management of images, 3D models, vector and attribute data, achieving standardized storage, indexing, and maintenance of the data.

[0020] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the digital twin modeling method for transmission lines based on geological hazards as described in the first aspect of the present invention.

[0021] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the digital twin modeling method for transmission lines based on geological hazards as described in the first aspect of the present invention.

[0022] The beneficial effects of this invention are as follows: It significantly improves the data acquisition efficiency of power line facilities. Through the automatic flight of drones, detailed image data can be collected quickly and efficiently, greatly enhancing the digitalization of power lines. Real-time monitoring and management of line conditions are achieved through digital twin technology, improving the refinement of power line models. Comprehensive digital modeling of parameters and states is realized through multi-source data fusion. It enhances the adaptive capability and flexibility of the power grid, adjusts the load configuration of substations through digital twins, improves the overall intelligence level of the power system, and accelerates the transformation of the power industry towards digitalization and informatization. This invention is of great significance for the digital upgrading of power lines, will improve the intelligence level and operation and maintenance efficiency of the power grid, reduce risk costs, and has broad application prospects. Attached Figure Description

[0023] 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 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. Wherein:

[0024] Figure 1 This is a flowchart of the technical route for 3D terrain modeling based on aerial survey images in Example 1.

[0025] Figure 2 This is a schematic diagram of aerial image data acquisition in Example 1.

[0026] Figure 3 Figure a shows the data collection results in Example 2.

[0027] Figure 4 Figure b shows the data collection results in Example 2.

[0028] Figure 5 Figure c shows the data collection results in Example 2.

[0029] Figure 6 The image shown is d, representing the data collection results from Example 2.

[0030] Figure 7 Figure e shows the data collection results in Example 2. Detailed Implementation

[0031] To make the above-mentioned objects, features and advantages of 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.

[0032] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1

[0035] Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for digital twin modeling of transmission lines based on geological hazards, including the following steps:

[0036] S1: Use drones to collect images of the power line area.

[0037] S1.1: Use the drone's built-in visual positioning system to allow the drone to fly along a preset route and automatically record perspective information.

[0038] Specifically, visual positioning sensors, including optical flow positioning sensors and visual-inertial navigation systems, are installed on drones to locate the drone's spatial position in real time through visual recognition of ground features. Using the drone's automatic flight control system, combined with the visual positioning results, the drone can automatically fly on a preset three-dimensional spatial route, which is planned and designed in advance and uploaded to the drone's flight control system.

[0039] In the drone flight control system, data such as the spatial position coordinates and attitude angles of the drone during flight are recorded to determine the drone's perspective information, that is, where the drone is in space and which direction the camera is pointing. The recorded perspective information is then correlated with the corresponding image data to obtain the shooting perspective information of each image, providing data support for subsequent applications.

[0040] S1.2: Using sonar detection technology, emit sound waves to detect obstructions within the drone's field of view and obtain information on the size and location of the obstructions.

[0041] Installing a sonar detection system on a drone can emit ultrasonic waves or other frequencies of sound waves to detect the environment within a certain range in front of the drone. The sound wave transmitter emits a detection signal, and the echo is reflected by the obstructed object. By analyzing the size and direction of the echo, information such as the position and shape of the obstructing object can be detected.

[0042] By combining sonar detection with the attitude information of the UAV, three-dimensional information such as the spatial coordinates and size of the obstruction relative to the UAV can be obtained. The acquired obstruction information can be plotted in the three-dimensional environment to mark the areas with obstruction within the UAV's field of view, providing a basis for subsequent flight route planning.

[0043] S1.3: Using the UAV's inertial navigation system and sonar detection results, a three-dimensional occlusion distribution map is drawn.

[0044] The inertial navigation system of a drone can measure changes in its position and velocity to determine its trajectory and attitude. The drone's motion information measured by the inertial navigation system is fused with the location information of obstructions detected by sonar. Based on the determined drone position, the precise coordinates of the obstructing object relative to the drone are calculated, as follows:

[0045] Assume the UAV's inertial navigation system provides position, P(x,y,z) and velocity, V(v). x ,v y ,v z ) and direction data θ (θ x ,θ y ,θ z The sonar system provides the distance d and orientation θ of the obstructing object relative to the sonar detector. son (θ sx ,θ sy ,θ sz Considering the impact of time factor t on the UAV's position, Kalman filtering or particle filtering algorithms are used for dynamic data fusion and position prediction; a noise correction parameter N and a correction algorithm are introduced to optimize the accuracy of the position.

[0046] Estimated position P of the drone est Determined jointly by inertial navigation system data and dynamic adjustment algorithms:

[0047] P est (t)=F(P,V,θ,t,N)

[0048] The position P of the obstructing object relative to the drone obs The location can be calculated using sonar data and drone-estimated positions:

[0049] P obs =Pest +d.Transform(θ son ,θ)

[0050] By projecting the location coordinates of obstructions onto a 3D environment, a 3D visualization map of the obstruction distribution within the UAV's field of view is generated. The 3D obstruction distribution map intuitively displays the obstruction situation in the UAV's field of view, providing a basis for subsequent flight planning.

[0051] S1.4: Automatically plan supplementary flight routes based on artificial intelligence algorithms to ensure that the camera covers the obstructed area.

[0052] Furthermore, the system loads a spatial occlusion distribution map, reads the coordinate range information of the occluded areas, and stores it as an occlusion area list; it inputs the UAV flight parameters: flight altitude range, flight speed range, and turning radius range, and stores them as a UAV parameter list; it inputs the camera equipment parameters: field of view angle, flip angle range, and angle with the UAV, and stores them as a camera parameter list; it initializes the flight path map as a two-value image, with a value of 0 indicating an uncovered area and a value of 1 indicating a covered area; it selects one occluded area from the occlusion list and obtains its coordinate range information; it calculates the two-dimensional boundary range of the ground projection corresponding to the occluded area, and marks the boundary range as an uncovered area in the flight path map, i.e., sets the value to 0; it generates multiple candidate flight path lists, ensuring that the flight paths in the list pass through and overlook the occluded areas; for each flight path, it simulates flight and calculates the camera equipment's shooting range.

[0053] The process of calculating the camera's shooting range includes: acquiring the three-dimensional spatial coordinates of the flight path; determining the drone's attitude using the drone parameter list; calculating the shooting range for each location using the camera parameter list; merging the shooting ranges to obtain the overall shooting range; for each shooting range, calculating the area of ​​the covered / obscured region; selecting the flight path with the largest coverage area as the current optimal flight path; updating the flight path map and setting the coverage area corresponding to the optimal flight path to 1; determining whether the 0-value area in the flight path map is empty: if empty, it means that all covered / obscured areas have been covered, and returning to the optimal flight path; if not empty, there are still uncovered / obscured areas, and one covered / obscured area from the cover list is selected again to obtain its coordinate range information; and returning the final optimal flight path as a supplementary flight path.

[0054] S1.5: By using network collaboration among drones, images of severely obscured areas can be provided by other drones, and data can be acquired collaboratively, improving efficiency.

[0055] It should be noted that designing flight paths and perspectives, and conducting occlusion analysis, can ensure the acquisition of comprehensive scene data.

[0056] S2: Process the acquired image data to generate a 3D scene, and then correct the 3D scene by combining it with geological and topographic maps.

[0057] Preferably, point cloud processing algorithms are used to denoise and register data in complex environments.

[0058] S2.1: Preprocess the acquired UAV image data, including distortion correction and image inpainting, to obtain preprocessed image data I. proc，i The specific calculation formula is as follows:

[0059] I proc，i =ImageRepair(Distortion Correction(I raw,i ))

[0060] S2.2: Using the Structure from Motion (SFM) algorithm, with I proc，i Using the input image, feature matching between images is calculated to reconstruct the camera pose and the sparse 3D point cloud of the scene. The calculation formula is as follows:

[0061] P sparse =SFM({I proc,i})

[0062] S2.3: Apply the Multi-View Stereo (MVS) method to the sparse point cloud to generate a detailed, high-density 3D point cloud P. dense The calculation formula is as follows:

[0063] P dense =MVS(P sparse )

[0064] S2.4: For P dense After filtering and smoothing to remove outliers, P is obtained. smooth The calculation is as follows:

[0065] P smooth =FilterAndSmooth(P dense )

[0066] S2.5: Use a 3D reconstruction algorithm to smooth the point cloud P smooth and preprocessed image I proc，i As input, generate a 2D mesh M and a scene model S:

[0067] S = 3DReconstruction(P) smooth , {I proc,i})

[0068] S2.6: Collect relevant geological and topographic map data G and perform feature matching with the scene model S, and label the locations of key features F:

[0069] F = FeatureMatching(S,G)

[0070] Preferably, when a match is successful, the location and shape of the corresponding land features in the scene model S are corrected based on the geological topographic map data G to generate a modified scene (Modified_scene); when a match fails, unlabeled land features from the geological topographic map data G are inserted into the scene model S to generate a modified scene, and the above correction process is iterated until the correction effect requirements are met, and the final modified scene is output as the corrected 3D scene.

[0071] S3: Use a drone's five-panel tilt camera to acquire tilted photos, control point information to generate 3D model data and true orthophoto data, and collect highly up-to-date 2D vector data on the true orthophoto data.

[0072] The main approach involves using drones equipped with five-panel oblique cameras to acquire high-resolution oblique photographs through oblique photography. These photographs are then combined with software such as SMART3D and control point information collected in the field to generate 3D model data and true orthophoto data. Modeling software such as 3DMAX is used to refine and categorize the original 3D model, ultimately obtaining usable, accurate, and highly up-to-date 3D data. Finally, professional GIS software such as ArcGIS is used to collect highly up-to-date 2D vector data from the true orthophoto data.

[0073] S4: Integrate the three types of data to construct a refined digital twin model of power lines.

[0074] The three models include a basic real-world 3D model, a high-precision model of the tower, and conductor data.

[0075] Furthermore, the basic real-scene 3D model is created by acquiring raw photo data from drone oblique photography, processing it into a real-scene 3D model using software, and then performing simple data editing using professional processing software. Its data format is the internationally recognized OSGB format, which can be displayed in most 3D software programs.

[0076] The high-precision model of the tower is generated by professional technicians through manual modeling and other methods using oblique photography data.

[0077] The tower model data was initially in OBJ format, which was then converted to OSGB format by software and merged with the real-world 3D model.

[0078] Due to the long span and small diameter of the traverse data, it is impossible to generate it through oblique photogrammetry. Manual modeling is also very difficult. This part of the data is used to generate general LAS point cloud data using professional software and saved separately. Because the data format is inconsistent with the actual 3D model data, it is saved separately.

[0079] S4.1: Establish data link interfaces with GIS and monitoring systems to update the model in real time.

[0080] Preferably, the data interface specifications of the GIS system used by the power company are collected, including data format, communication protocol, access authentication, etc., and interface adapters are developed to realize functions such as format conversion and decoding for data access with specific GIS systems.

[0081] Collect details of the power company's monitoring system interfaces to obtain access methods for real-time status data, supporting caching and decoding of different monitoring data; in the digital twin modeling system, design an asynchronous task scheduling mechanism to periodically poll the interfaces to obtain the latest GIS and monitoring data, perform filtering, mapping, and redundancy removal on the access data, convert it into the system's internal standard format, compare new input data with the existing digital twin model, determine the differences and update content, and automatically update the digital twin scene model using graphical algorithms according to the differences, establish a version control mechanism, save model iteration update logs, and ensure traceability.

[0082] S4.2: Add power parameters and state attributes to build an information-rich model.

[0083] S4.3: Enables linkage with field equipment and establishes a closed-loop digital twin platform.

[0084] The digital twin system integrates an industrial control computer and its communication interface to receive status data from field devices; it develops virtual models of sensors, imports them into the corresponding locations in the digital twin scene, and realizes bidirectional data mapping and control relationships between the virtual model and the physical devices; it simulates control commands for the devices in the virtual scene, sends them to the physical devices through the industrial control computer, and synchronously transmits the operating status of the physical devices back, driving the virtual model to update dynamically, thus constructing a simulation environment for dual closed-loop control of virtual and physical devices; it applies the predictive analysis function of digital twins to evaluate the effect of control commands and achieve closed-loop optimization; and through the chain connection of scene-virtual model-physical device, it connects the virtual physical system to achieve a realistic digital twin.

[0085] This embodiment also provides a digital twin modeling system for power transmission lines based on geological hazards, including: an image acquisition module for using a drone to fly and acquire image data of the power line area; a 3D reconstruction module for processing the acquired image data, generating a 3D scene model, and combining it with geological and topographic data to achieve 3D scene correction and optimization; a thematic data acquisition module for using a drone's five-panel oblique camera to acquire oblique photos, and combining them with control points to generate 3D model data and true orthophotos, and acquiring 2D vector data on the orthophotos; a data fusion module for integrating various types of data obtained from image acquisition, 3D reconstruction, and thematic data acquisition to finally construct a refined digital twin model of the power line; a model optimization module for adjusting parameters, optimizing details, and mining knowledge in the constructed digital twin model to improve the model's accuracy and usability; and a data management module for standardized management of images, 3D models, vector and attribute data, achieving standardized storage, indexing, and maintenance of data.

[0086] This embodiment also provides a computer device applicable to the digital twin modeling method for transmission lines based on geological hazards, including: 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 digital twin modeling method for transmission lines based on geological hazards proposed in the above embodiment.

[0087] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0088] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for digital twin modeling of transmission lines based on geological hazards as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0089] In summary, this invention significantly improves the data acquisition efficiency of power line facilities. Through automated drone flight, detailed image data can be collected quickly and efficiently, greatly enhancing the digitalization of power lines. Digital twin technology enables real-time monitoring and management of line conditions, improving the precision of power line models. Multi-source data fusion achieves comprehensive digital modeling of parameters and states. This enhances the adaptive capabilities and flexibility of the power grid. Digital twins allow for adjustments to substation load configurations, improving the overall intelligence level of the power system and accelerating the power industry's transformation towards digitalization and informatization. This invention is of great significance for the digital upgrading of power lines, improving the intelligence level and operation and maintenance efficiency of the power grid, reducing risk costs, and has broad application prospects.

[0090] Example 2

[0091] Reference Figures 3-7 The second embodiment of the present invention provides an experimental simulation diagram of the digital twin modeling method for power transmission lines based on geological disasters to further verify the advanced nature of the present invention.

[0092] The data for this model construction was collected from a section of each of the 220kV Shuangliu I and Shuangliu II lines in Puan County, Guizhou Province. The total area of ​​the test area is about 0.1km2, and the total length of the lines involved is about 400 meters.

[0093] The data collection for this model was completed in early April 2023, primarily using a DJI Phantom 4 RTK drone. A total of 7 flights were conducted, collecting approximately 2200 photos, including... Figures 3-7The data collection results are shown in the image. Photographic control points: 40 control points were set up in the target area, and the coordinates of the control points were measured using RTK technology. GIS data: 2D CAD layer data of the substation area was obtained from the substation database. Monitoring data: Real-time status data such as the temperature and current of the main transformer were obtained by accessing the substation's microcomputer monitoring system using the OPC protocol.

[0094] Pix4DMapper software was used to process drone images, generating point clouds, 3D models, and orthophotos. Control point data was used for georeferencing, achieving a point cloud accuracy of 0.1 meters. CAD layers were matched with the 3D model to complete scene optimization. A detailed 3D model of the main transformer was reconstructed using 3ds Max. An OPC server was developed to enable data exchange with the monitoring system. A digital twin system for the substation was built in Unity to achieve status updates driven by monitoring data.

[0095] 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 digital twin modeling of transmission lines based on geological hazards, characterized in that: include: Using drones to collect image data of power line areas; The collected image data is processed to generate a 3D scene, and the 3D scene is corrected by combining geological and topographic maps; The drone uses a five-panel tilt camera to acquire tilted photos, and uses control point information to generate 3D model data and true orthophoto data. It also collects highly up-to-date 2D vector data on the true orthophoto data. By integrating various types of data, a refined digital twin model of power lines can be constructed. The process of using a drone to collect image data of the power line area includes the following steps: Using the drone's built-in visual positioning system, the drone flies along a preset route and automatically records the viewpoint information; Using sonar detection technology, sound waves are emitted to detect obstructions within the drone's field of view, and information on the size and location of the obstructions is obtained; Using the inertial navigation system of the UAV and combined with sonar detection results, a three-dimensional occlusion distribution map was drawn. The system automatically plans supplementary flight routes based on artificial intelligence algorithms to ensure that the camera covers the obstructed areas. By using network collaboration among drones, images of severely obscured areas are provided by other drones, enabling collaborative data acquisition.

2. The method for digital twin modeling of transmission lines based on geological hazards as described in claim 1, characterized in that: The process of using the inertial navigation system of an unmanned aerial vehicle (UAV) and combining sonar detection results to create a three-dimensional occlusion distribution map includes the following steps: Assume the location provided by the UAV's inertial navigation system includes: and speed and directional data ; The sonar system provides the distance d and orientation of the obstructing object relative to the sonar detector. ; Considering the impact of the time factor t on the UAV's position, Kalman filtering or particle filtering algorithms are used for dynamic data fusion and position prediction. Estimated location of the drone The formula is determined by both inertial navigation system data and dynamic adjustment algorithms, as follows: The position of the obstructing object relative to the drone Position calculations based on sonar data and drone estimations: By projecting the position coordinates of the obstruction onto the three-dimensional environment, a three-dimensional visualization map of the obstruction distribution within the drone's field of view is generated.

3. The method for digital twin modeling of transmission lines based on geological hazards as described in claim 2, characterized in that: The step of automatically planning supplementary flight routes based on artificial intelligence algorithms to ensure camera coverage of obstructed areas includes the following steps: Load the spatial occlusion distribution map, read the coordinate range information of the occlusion area, and store it as an occlusion area list; Input the drone flight parameters, including the flight altitude range, flight speed range, and turning radius range, and store them as a drone parameter list; Input the camera equipment parameters, including field of view angle, flip angle range, and angle with the drone, and save them as a camera parameter list; Initialize the route map as a two-valued image, where a value of 0 represents an uncovered area and a value of 1 represents a covered area. Select a masked area from the masking list and obtain the coordinate range information of that masked area; Calculate the two-dimensional boundary range of the ground projection corresponding to the shaded area, and mark the area within the boundary range as the uncovered area in the flight path map, that is, set the value to 0; Generate a list of multiple candidate flight paths, so that the flight paths in the list pass over and overlook the area above the obscured area. For each flight path, simulate flight and calculate the shooting range of the camera equipment.

4. The method for digital twin modeling of transmission lines based on geological hazards as described in claim 3, characterized in that: The shooting range of the computational camera device includes, Obtain the three-dimensional spatial coordinate information of the flight path, and determine the UAV attitude using the UAV parameter list; The shooting range for each location is calculated using the camera parameter list, and the shooting ranges are combined to obtain the overall shooting range; For each shooting range, calculate the coverage area of ​​the obscured area and select the route with the largest coverage area as the current best route; Update the flight path map and set the coverage area corresponding to the best flight path to 1; Determine if the 0-value area in the route map is empty: if it is empty, it means that the entire obscured area has been covered, and return to the best route; If it is not empty, there are still uncovered areas, and a new area is selected from the occlusion list to obtain its coordinate range information; Return to the final optimal route as a supplementary flight route.

5. The method for digital twin modeling of transmission lines based on geological hazards as described in claim 4, characterized in that: The process of processing the acquired image data to generate a 3D scene, and then correcting the 3D scene by combining it with geological and topographic maps, includes the following steps: Preprocess the collected UAV image data; Using the structure from motion (SFM) algorithm, with preprocessed image data as input, feature matching between images is calculated to recover the camera pose and the sparse 3D point cloud of the scene; Apply the multi-view stereo method (MVS) to sparse point clouds to generate detailed, high-density 3D point clouds; High-density 3D point clouds are filtered and smoothed to remove outliers; Using a 3D reconstruction algorithm, a 2D mesh and scene model are generated from smooth point cloud and preprocessed image as input; Collect relevant geological and topographic map data and perform feature matching with the scene model to mark the locations of key features.

6. The method for digital twin modeling of transmission lines based on geological hazards as described in claim 5, characterized in that: The various data include basic real-world 3D models, high-precision tower models, and conductor data.

7. A digital twin modeling system for transmission lines based on geological hazards, characterized in that, The method of claim 1 is used, comprising: The image acquisition module is used to acquire image data of power line areas by using a drone to fly. The 3D reconstruction module is used to process the acquired image data, generate a 3D scene model, and combine it with geological and terrain data to achieve correction and optimization of the 3D scene. The thematic data acquisition module is used to acquire tilted photos using a UAV's five-panel tilted camera, and combine them with control points to generate 3D model data and true orthophotos, and then acquire 2D vector data on the orthophotos. The data fusion module is used to integrate various types of data obtained from image acquisition, 3D reconstruction, and thematic data acquisition, and finally construct a refined digital twin model of power lines. The model optimization module is used to adjust parameters, optimize details, and mine knowledge in the constructed digital twin model; The data management module is used to standardize the management of images, 3D models, vectors, and attribute data, enabling standardized storage, indexing, and maintenance of the data.

8. 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 digital twin modeling method for power transmission lines based on geological hazards as described in any one of claims 1 to 6.

9. 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 digital twin modeling method for power transmission lines based on geological hazards as described in any one of claims 1 to 6.

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