Power distribution line non-stop operation condition evaluation method, device, equipment and medium

Abstract

The present application relates to the technical field of live-line work, and particularly relates to a power distribution line live-line work condition evaluation method, device, equipment and medium, which is used to solve the technical problem that the existing live-line work decision method has single data source and cannot support panoramic decision. The present application comprises: collecting the power distribution line through a preset device to obtain multi-source data of the power distribution line; constructing a three-dimensional model of the power distribution line using the multi-source data; obtaining equipment parameters, environmental parameters and work parameters of the power distribution line according to the multi-source data and the three-dimensional model; calculating a work condition evaluation score using the equipment parameters, the environmental parameters, the work parameters and a preset evaluation index; and determining that the power distribution line meets the live-line work requirement when the work condition evaluation score is greater than a preset threshold.

Description

Technical Field

[0001] This invention relates to the field of live-line working technology, and in particular to a method, apparatus, equipment and medium for assessing live-line working conditions for power distribution lines. Background Technology

[0002] The operation and maintenance of the power grid in the core area faces a series of challenges. Firstly, the limited and restricted working space, coupled with the complex grid structure, renders the traditional "survey first, then plan" strategy unsuitable or suboptimal in practice. This not only increases the workload of maintenance personnel but may also affect the grid's operational efficiency and reliability. On-site surveying and measurement play a crucial role in live-line work, directly impacting the safety of maintenance, installation, and replacement work. The main task of this stage is to determine whether live-line work is feasible, and to identify specific methods, tools, and necessary safety measures, providing a foundation for subsequent live-line work plans. Traditional manual surveying of lines, measuring line spacing, and terrain parameters suffers from large errors, is time-consuming, and carries risks associated with working at heights, resulting in low efficiency and high risk. Furthermore, there is a lack of systematic evaluation of the impact of tower structure, equipment layout, and environmental factors on different work methods such as insulated poles and robots, and a lack of quantitative standards for assessing work conditions.

[0003] To address the aforementioned issues, existing technologies propose a decision-making system and method for live-line work in power distribution networks based on point cloud data. This system solves the problems of traditional decision-making methods being time-consuming, labor-intensive, and difficult to accurately assess operational risks, achieving efficient and accurate operational decisions and improving operational quality and safety. Through point cloud data acquisition and 3D scene reconstruction modules, point cloud data images of the power distribution work site can be accurately and quickly acquired, and a detailed 3D scene model can be constructed based on these images. The key parameter calculation module can accurately calculate equipment parameters at the power distribution work site, such as those of poles, conductors, roads, and obstacles, based on the point cloud data images. This provides a solid foundation for subsequent simulations and analysis, ensuring the accuracy and reliability of operational decisions. The 3D simulation module can simulate live-line work schemes within the constructed 3D scene model, thereby determining the optimal operational decision-making scheme, improving the accuracy of operational decisions, and identifying and resolving potential safety hazards during the simulation process, further ensuring the safety and reliability of the operation.

[0004] However, the decision-making system and methods for live-line work in distribution networks based on point cloud data have a relatively singular data source. Point cloud data is acquired from point cloud images of the distribution work site, which cannot accurately recreate the working conditions. Furthermore, relying solely on point cloud data cannot support panoramic decision-making. Key decisions such as live conductor distance warning and bucket truck parking feasibility lack intelligent spatial algorithm support, and safety risk prediction still depends on experience. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for evaluating the conditions for live-line operation of power distribution lines, which solves the technical problem that existing live-line operation decision-making methods have limited data sources and cannot support comprehensive decision-making.

[0006] This invention provides a method for assessing the conditions for live-line operation of power distribution lines, comprising:

[0007] The power distribution line is subjected to multi-source data acquisition using a preset device to obtain multi-source data of the power distribution line;

[0008] A three-dimensional model of the power distribution line is constructed using the multi-source data.

[0009] Based on the multi-source data and the three-dimensional model, the equipment parameters, environmental parameters, and operating parameters of the power distribution line are obtained.

[0010] The work condition evaluation score is calculated using the equipment parameters, environmental parameters, work parameters, and preset evaluation indicators.

[0011] When the work condition evaluation score is greater than a preset threshold, the power distribution line is determined to meet the requirements for uninterrupted power supply operation.

[0012] Optionally, after the step of determining that the power distribution line meets the requirements for live-line operation when the work condition evaluation score is greater than a preset threshold, the method further includes:

[0013] Calculate the minimum safe distance between each live conductor of the power distribution line;

[0014] Calculate the minimum distance between each working tool and the charged body;

[0015] The target working tool is determined from each of the working tools based on the minimum distance and the minimum safe distance.

[0016] Optionally, it also includes:

[0017] The minimum safe distance calculation module is used to calculate the minimum safe distance between each live part of the power distribution line;

[0018] The minimum distance calculation module is used to calculate the minimum distance between each working tool and the charged body;

[0019] The target work tool determination module is used to determine the target work tool from the various work tools based on the minimum distance and the minimum safe distance.

[0020] Optionally, the multi-source data includes global data, denoised point cloud data, device texture data, and environmental data; the step of acquiring multi-source data of the power distribution line by using a preset device includes:

[0021] Global data of the power distribution lines were collected using drones;

[0022] Point cloud data of the power distribution line is collected using lidar;

[0023] Dynamic noise reduction is performed on the point cloud data to obtain denoised point cloud data;

[0024] The equipment texture data of each device on the power distribution line and the environmental data around the power distribution line are collected by ground imaging equipment.

[0025] Optionally, the step of constructing a three-dimensional model of the power distribution line using the multi-source data includes:

[0026] Spatial alignment and temporal synchronization are performed on the global data, the denoised point cloud data, the device texture data, and the environmental data to obtain a fused image;

[0027] The fused image is input into a pre-trained transfer learning model to identify device information in the fused image;

[0028] Based on the denoised point cloud data, the equipment information, and the fused image, geometric modeling is performed to obtain a three-dimensional model of the power distribution line.

[0029] Optionally, the equipment parameters include phase-to-phase distance and insulation class; the environmental parameters include wind speed and terrain undulation; the operational parameters include the boom truck's working radius and the robot's degrees of freedom; the step of calculating the operational condition evaluation score using the equipment parameters, the environmental parameters, the operational parameters, and preset evaluation indicators includes:

[0030] Based on preset evaluation indicators, determine whether there are any rejection items among the equipment parameters, environmental parameters, and operational parameters that meet the rejection conditions;

[0031] If not, calculate the phase-to-phase distance fraction, the insulation class fraction, the wind speed fraction, the terrain undulation fraction, the boom truck working radius fraction, and the robot degree of freedom fraction.

[0032] The phase-to-phase distance score and the insulation class score are weighted and summed to obtain the equipment parameter score;

[0033] The environmental parameter score is obtained by weighted summation of the wind speed score and the terrain relief score.

[0034] The working radius score of the boom truck and the degree-of-freedom score of the robot are weighted and summed to obtain the operation parameter score.

[0035] The operating condition evaluation score is obtained by summing the scores of the equipment parameters, the environmental parameters, and the operating parameters.

[0036] Optionally, the step of calculating the minimum safe distance between each live conductor of the power distribution line includes:

[0037] Obtain the safety factor, system nominal voltage, preset breakdown field strength, and human activity margin of each live part of the power distribution line;

[0038] The minimum safe distance between each live part of the power distribution line is calculated based on the safety factor, the nominal voltage of the system, the preset breakdown field strength, and the human activity margin.

[0039] The present invention also provides a device for evaluating the conditions for live-line operation, comprising:

[0040] A multi-source data acquisition module is used to acquire multi-source data of the power distribution line through a preset device to obtain multi-source data of the power distribution line.

[0041] A 3D model construction module for power distribution lines is used to construct a 3D model of power distribution lines using the multi-source data.

[0042] The equipment parameter, environmental parameter, and operational parameter acquisition module is used to acquire the equipment parameters, environmental parameters, and operational parameters of the power distribution line based on the multi-source data and the three-dimensional model.

[0043] The work condition assessment score calculation module is used to calculate the work condition assessment score using the equipment parameters, the environmental parameters, the work parameters, and preset assessment indicators;

[0044] The live-line operation determination module is used to determine that the power distribution line meets the live-line operation requirements when the operation condition evaluation score is greater than a preset threshold.

[0045] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0046] The memory is used to store program code and transmit the program code to the processor;

[0047] The processor is used to execute the power distribution line live-line working condition assessment method as described above, according to the instructions in the program code.

[0048] The present invention also provides a computer-readable storage medium for storing program code for executing the live-line working condition assessment method as described in any of the preceding claims.

[0049] As can be seen from the above technical solutions, the present invention has the following advantages: The present invention discloses a method for evaluating the conditions for live-line operation, and specifically discloses: multi-source data acquisition of the power distribution line is performed through preset equipment to obtain multi-source data of the power distribution line; a three-dimensional model of the power distribution line is constructed using the multi-source data; equipment parameters, environmental parameters, and operation parameters of the power distribution line are obtained based on the multi-source data and the three-dimensional model; an operation condition evaluation score is calculated using the equipment parameters, environmental parameters, operation parameters, and preset evaluation indicators; when the operation condition evaluation score is greater than a preset threshold, the power distribution line is determined to meet the requirements for live-line operation.

[0050] This invention constructs a three-dimensional model of power distribution lines by collecting multi-source data, thereby obtaining more accurate power distribution line data; then, it calculates the evaluation score of the working conditions by using preset evaluation indicators to quantify the working conditions, thereby improving the accuracy of live-line operation analysis based on power distribution line data. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating the steps of a method for evaluating the conditions for live-line operation in accordance with an embodiment of the present invention;

[0053] Figure 2 A flowchart illustrating the steps of a method for evaluating the conditions for live-line operation, as provided in another embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of multi-source data acquisition provided in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of multi-source data fusion provided in an embodiment of the present invention;

[0056] Figure 5 A schematic diagram of three-dimensional modeling provided for an embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram illustrating the assessment of uninterrupted power supply working conditions provided in an embodiment of the present invention;

[0058] Figure 7 This is a structural block diagram of a power distribution line uninterrupted operation condition assessment device provided in an embodiment of the present invention. Detailed Implementation

[0059] The decision-making system and method for live-line work in power distribution networks based on point cloud data suffers from a limited data source. Point cloud data is acquired from point cloud images of the power distribution work site, failing to accurately recreate the actual working conditions. Furthermore, relying solely on point cloud data cannot support comprehensive decision-making. Key decisions such as live conductor distance warnings and the feasibility of bucket truck parking lack intelligent spatial algorithms, and safety risk prediction still depends on experience.

[0060] In view of this, embodiments of the present invention provide a method, apparatus, equipment and medium for evaluating the conditions for live-line operation, which is used to solve the technical problem that existing live-line operation decision-making methods have a single data source and cannot support panoramic decision-making.

[0061] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0062] One of the core concepts of this invention is to construct a three-dimensional model of the power distribution line by collecting multi-source data, thereby obtaining more accurate power distribution line data; then, to calculate the work condition evaluation score by using preset evaluation indicators, and to quantify the work conditions, so as to improve the accuracy of power outage work analysis using power distribution line data.

[0063] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for evaluating the conditions for live-line operation, as provided in an embodiment of the present invention.

[0064] The present invention provides a method for assessing the conditions for live-line operation of power distribution lines, which may specifically include the following steps:

[0065] Step 101: Multi-source data acquisition of the power distribution line is performed using preset equipment to obtain multi-source data of the power distribution line;

[0066] In this embodiment of the invention, a power distribution line refers to a line system that transmits power from a step-down substation to a distribution transformer or a power-consuming unit. It consists of a main step-down substation, high-voltage distribution lines, branch substations, low-voltage distribution lines, and electrical equipment. According to voltage, it is divided into two categories: high voltage (3.6kV~40.5kV) and low voltage (not exceeding 1kV AC or 1500V DC). The conductor cross-section selection must meet the requirements of mechanical strength, allowable current carrying capacity, and voltage drop. The structure includes two forms: overhead lines and cable lines.

[0067] The preset equipment covers a variety of types, including drones, handheld LiDAR, and ground image acquisition equipment.

[0068] Unmanned aerial vehicles (UAVs) are advanced devices that are precisely controlled via radio remote control and built-in program control. They can also rely on onboard computers to achieve fully autonomous or intermittent intelligent operation. These devices integrate modern electronic technology, automatic control, and artificial intelligence, and are widely used in various fields such as military reconnaissance, civilian aerial photography, agricultural spraying, and logistics delivery. Their operation is flexible and diverse; they can be remotely controlled in real time by ground personnel, or they can independently execute complex flight missions according to preset routes and mission parameters, demonstrating the innovative applications and broad prospects of high technology in the aviation field.

[0069] LiDAR, short for Laser Radar, is a system that detects and ranges laser signals. Its core principle involves actively emitting specific laser pulses towards a target and accurately capturing the echoes reflected back from the target. By precisely calculating the time difference between the emission and reception of the laser pulse, the system can accurately estimate the distance between the target and the radar, and further analyze and obtain key information such as the target's speed, azimuth, and even shape.

[0070] Ground-based image acquisition equipment can specifically use a 4K camera, in conjunction with an RTK-GPS module (positioning accuracy ±3cm), a shooting frame rate of 30fps, and a lens focal length of 16-35mm, to capture equipment textures (such as wire wear, equipment nameplates) and close-up environment (such as tower foundations, surrounding obstacles).

[0071] In the specific implementation of power distribution line data acquisition, a variety of modern technologies can be comprehensively utilized: drones are used for aerial line inspection to quickly acquire overall three-dimensional information and macroscopic status of the line corridor; handheld mobile LiDAR devices are used for close-range, high-precision scanning of towers, conductors, and key connection points to capture fine geometric structures and spatial relationships; simultaneously, high-resolution ground-based imaging equipment is used to capture the appearance details and texture information of line equipment from fixed or moving perspectives. Through this collaborative operation mode, observational data from multiple dimensions—aerial, near-ground, and ground—can be systematically acquired, thereby constructing a rich, three-dimensional, multi-source dataset about power distribution lines. This multi-dimensional acquisition strategy not only greatly expands and enriches the sources and information levels of traditional power distribution line data, but also enables the final aggregated data to more comprehensively and accurately depict and reflect the real situation of power distribution lines in terms of spatial morphology, equipment status, and operating environment from different scales and angles. Therefore, this method effectively overcomes the limitations of a single data source, and through the complementarity and fusion of multi-source data, significantly improves the accuracy, completeness, and reliability of the overall data acquisition.

[0072] Step 102: Construct a three-dimensional model of the power distribution line using multi-source data;

[0073] A 3D model is a three-dimensional virtual model constructed digitally using specialized 3D modeling software. Its construction methods are diverse, primarily including manual software modeling, 3D scanner reconstruction, and modeling based on multi-view images. Internally, it is mainly distinguished into solid models that describe the complete internal properties of an object, and surface models that only describe the surface geometry. In practical applications, different modeling techniques, such as polygonal mesh modeling and spline surface modeling, are flexibly selected to create and optimize the model, depending on the specific needs of the scenario.

[0074] In this embodiment of the invention, a three-dimensional model of the power distribution line is constructed by using multi-source data, which enables spatial display and analysis of the power distribution line, thereby improving the accuracy of analysis of data such as distance.

[0075] Specifically, the process begins with registering and aligning multi-source data from drones, handheld LiDAR, and ground-based imaging equipment to create a unified spatial data foundation. Next, LiDAR point cloud data is used to precisely characterize the terrain of the power line corridor, as well as the spatial geometry and location of towers and conductors. Simultaneously, high-definition textures and details from drones and ground-based imaging are integrated to create detailed models of equipment appearance, insulators, hardware, and other components. Finally, by integrating these complementary multi-source data, a digital 3D model is generated that possesses both accurate spatial coordinates and realistic visual textures, thus comprehensively and realistically reproducing the actual on-site conditions of the power distribution line.

[0076] Step 103: Obtain equipment parameters, environmental parameters, and operational parameters based on multi-source data and the 3D model;

[0077] In the specific implementation of this invention, after the system successfully acquires data from multiple sources and completes the construction of a three-dimensional model, it can accurately extract key equipment parameters, detailed environmental parameters, and specific operational parameters. These extracted and organized multi-dimensional parameters will provide a solid and rich data foundation and support for subsequent in-depth comprehensive analysis and evaluation of uninterrupted power supply operations.

[0078] Step 104: Calculate the work condition evaluation score using equipment parameters, environmental parameters, work parameters, and preset evaluation indicators;

[0079] In this embodiment of the invention, a quantitative evaluation system for working conditions can be established, which combines equipment parameters, environmental parameters, and working parameters to calculate the evaluation score of working conditions, thereby quantifying the working conditions.

[0080] Step 105: When the work condition evaluation score is greater than the preset threshold, it is determined that the power distribution line meets the requirements for uninterrupted power supply work.

[0081] In this embodiment of the invention, when the work condition evaluation score is greater than a preset threshold, it can be determined that the power distribution line meets the requirements for uninterrupted power supply operation.

[0082] Optionally, when the work condition assessment score is less than or equal to the preset threshold, it can be determined that the power distribution line does not meet the requirements for live-line work, and it is not recommended to carry out live-line work. The work conditions need to be further adjusted or corresponding safety measures need to be taken.

[0083] This invention constructs a three-dimensional model of power distribution lines by collecting multi-source data, thereby obtaining more accurate power distribution line data; then, it calculates the evaluation score of the working conditions by using preset evaluation indicators to quantify the working conditions, thereby improving the accuracy of live-line operation analysis based on power distribution line data.

[0084] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a method for assessing live-line working conditions, provided in another embodiment of the present invention. Based on the above embodiments, this embodiment provides a more preferred implementation. Specifically, the method may include the following steps:

[0085] Step 201: Multi-source data acquisition of the power distribution line is performed using preset equipment to obtain multi-source data of the power distribution line;

[0086] In one example, the multi-source data includes global data, denoised point cloud data, device texture data, and environmental data. The steps to obtain the multi-source data of the power distribution line by collecting data from multiple sources using preset devices can specifically include the following sub-steps:

[0087] S11 uses drones to collect global data on power distribution lines;

[0088] S12 collects point cloud data of power distribution lines using lidar;

[0089] S13, dynamically denoise the point cloud data to obtain denoised point cloud data;

[0090] S14: Collect equipment texture data of each device on the power distribution line and environmental data around the power distribution line through ground imaging equipment.

[0091] Point cloud data is a three-dimensional data set composed of massive discrete points, each point carrying spatial coordinates (x, y, z) and optional attributes (intensity). Point clouds are collections of points used for dynamic storage, typically from LiDAR systems. Point cloud data is characterized by its unordered nature (the order of points does not affect the overall structure), sparsity (few points are needed for distant objects), and unstructured nature (no grid or pixel structure).

[0092] In practical implementation, multi-source data acquisition is the foundation of 3D reconstruction. Full scene coverage can be achieved through a three-level acquisition system: air-ground-nearby. Figure 3 The specific equipment and parameters are as follows:

[0093] 1) Unmanned Aerial Vehicle (UAV) Oblique Photogrammetry: Primarily used for comprehensive geospatial data collection. In practice, a quadcopter UAV can be selected as the flight platform, offering excellent stability and maneuverability. The UAV is equipped with a five-lens oblique photogrammetry camera system, each lens with a focal length of 24mm and a single lens resolution of 20 megapixels, capable of acquiring high-resolution images simultaneously from vertical and multiple oblique angles. Furthermore, the system integrates a real-time dynamic differential (RTK) positioning module, which provides high-precision positioning information, with a horizontal positioning accuracy of ±1 cm. Combined with a distance-related accuracy correction of one part per million (ppm), this effectively ensures the spatial accuracy of the collected data.

[0094] Data acquisition parameters: flight altitude 100m, lateral overlap 80%, lateral overlap 70%, ground resolution 0.5cm, single flight duration 30 minutes, suitable for large-scale line corridors (such as power distribution lines crossing farmland and mountains).

[0095] 2) LiDAR (Light Detection and Ranging) Data Acquisition: Used for acquiring point cloud data. Specifically, a handheld portable LiDAR can be used, with a selectable laser wavelength of 1550nm, a point cloud density of 500 points / ㎡, a ranging accuracy of ±2mm, a scanning rate of 2 million points / second, and equipped with a built-in IMU (attitude accuracy 0.01°) and GNSS module. It is suitable for fine acquisition of tower details (such as insulators and crossarms) and complex terrain (such as areas obscured by trees).

[0096] 3) Ground-based image acquisition: This is used to collect equipment texture data of various devices on the power distribution line and environmental data of the surrounding area. Specifically, a 4K camera can be used with an RTK-GPS module (positioning accuracy ±3cm), a shooting frame rate of 30fps, and a lens focal length of 16-35mm to capture equipment texture (such as wire wear, equipment nameplates) and close-up environment (such as tower foundations, surrounding obstacles).

[0097] The specific multi-source data acquisition process is as follows: first, global data of the power distribution line corridor is acquired using a drone; then, details of the towers and equipment are measured using a handheld LiDAR; and finally, texture and close-up environment are acquired using ground imagery, forming a three-level data chain of "global-local-detail". This three-level data chain ensures the integrity of spatial information and the authenticity of texture information. Data from each stage are registered and fused using a unified spatiotemporal reference (WGS84 coordinate system + UTC timestamp), supporting subsequent 3D modeling, defect identification, and digital twin construction.

[0098] Step 202: Construct a three-dimensional model of the power distribution line using multi-source data;

[0099] In this embodiment of the invention, a three-dimensional model of the power distribution line is constructed by using multi-source data, which enables spatial display and analysis of the power distribution line, thereby improving the accuracy of analysis of data such as distance.

[0100] In one example, the steps for constructing a 3D model of a power distribution line using multi-source data may specifically include the following sub-steps:

[0101] S21, spatial alignment and temporal synchronization are performed on global data, denoised point cloud data, device texture data and environmental data to obtain a fused image;

[0102] After successfully acquiring various types of information, including global data, denoised point cloud data, device texture data, and environmental data, these different data types can be further processed comprehensively. Specifically, spatial alignment and temporal synchronization operations need to be performed on the global data, denoised point cloud data, device texture data, and environmental data respectively. Spatial alignment ensures that the coordinate positions of various data types in three-dimensional space remain consistent; temporal synchronization ensures that all data are matched under the same time reference. Finally, through these precise processing and integration steps, a highly fused and information-complete integrated image can be generated.

[0103] In the specific implementation, spatial alignment and temporal synchronization are performed on global data, denoised point cloud data, device texture data, and environmental data to obtain the fused image. The specific implementation process is as follows:

[0104] First, to ensure that the data can be accurately matched and coordinated in time sequence, strict time synchronization processing is required for global data, point cloud data, device texture data and environmental data, so as to build a unified and reliable time reference system.

[0105] Secondly, the global data and point cloud data that have completed time synchronization are placed in the same spatial coordinate system. Through precise registration and alignment operations, the two are ensured to achieve a high degree of consistency in spatial position and geometric structure, thereby constructing an initial point cloud dataset that integrates multi-source information.

[0106] Next, the initial point cloud dataset is purified to obtain purified point cloud data.

[0107] Specifically, this may include: using temporal filtering algorithms to remove dynamic point clouds generated by dynamic environmental changes; and using semantic segmentation technology to identify and remove temporary obstacle point clouds in the scene.

[0108] Time-series filtering algorithms are techniques used to process noise and outliers in time-series data. Due to noise interference and outliers during data collection and recording, the raw data may contain a large amount of invalid or highly erroneous information. Therefore, time-series filtering algorithms are needed to preprocess time-series data to improve data quality and reliability.

[0109] Subsequently, surface reconstruction and gap repair are performed on the purified point cloud data: For data gaps or voids that may remain after registration of data from different sources, a surface reconstruction algorithm based on the Poisson equation can be used to generate a continuous and complete refined point cloud surface model. This process first uses the Poisson equation to mathematically model the spatial distribution of the point cloud data, implicitly representing the surface geometry by solving the equation. The algorithm effectively integrates geometric information from different data sources, intelligently filling in local missing or gap-filling defects caused by imperfect registration, ensuring that the reconstructed surface is not only continuous and smooth but also maintains the detailed features and overall morphology of the original data. Ultimately, a high-quality refined point cloud surface is obtained, suitable for further analysis and application.

[0110] Then, using the refined point cloud surface as a three-dimensional spatial reference, the device texture data and environmental data are spatially mapped to accurately associate them with the geometric model.

[0111] Finally, the spatially mapped device texture data, environmental data, and refined point cloud surfaces are fused to generate a fused image for constructing a high-precision 3D model of the power distribution line.

[0112] like Figure 4 As shown, before performing 3D modeling of power distribution lines, data fusion of multiple sources can be performed first. The process includes:

[0113] 1) Multi-source registration: A unified coordinate system is established through ground control points (GCPs), and the UAV RTK positioning data and the handheld LiDAR GNSS data are spatially aligned using the weighted least squares method;

[0114] 2) Dynamic noise reduction: To address the issue of tree swaying, a time-series filtering algorithm is used for dynamic noise reduction;

[0115] 3) Accuracy Guarantee: Model resolution ≥ 5mm, power equipment recognition accuracy > 95% (based on ResNet50 transfer learning).

[0116] In a specific scenario, the multi-source data fusion process is as follows:

[0117] 1) Spatiotemporal registration:

[0118] Establish a unified coordinate system: Select more than 3 ground control points (GCPs, measured with a total station, with an accuracy of ±1mm), and the coordinate system should be the 2000 National Geodetic Coordinate System.

[0119] Spatial alignment: Drone RTK data (WGS84 coordinate system) and handheld LiDAR data (local coordinate system) are transformed using weighted least squares method, with the following formula:

[0120] ;

[0121] in, These are the coordinates of the target point after spatial alignment. The coordinates collected by the drone. The coordinates are those collected by the lidar. , Weights are set based on data precision, such as the weight of drones. =0.6, LiDAR weights =0.4.

[0122] Time synchronization: Alignment is achieved through GPS timestamps (error ≤ 5ms). In areas without GNSS (such as near tunnels), feature point matching is used (SIFT algorithm is used to extract tower vertices as time references).

[0123] 2) Dynamic noise reduction:

[0124] For tree swaying: a time-series filtering algorithm is used to calculate the variance of 5 consecutive frames of point cloud data (frame rate 10Hz) and remove dynamic points with variance > 0.1m (tree swaying amplitude is usually > 0.1m, and the variance of static points of equipment is < 0.02m).

[0125] For temporary obstacles (such as construction vehicles): non-power facility point clouds are identified through semantic segmentation (based on the YOLOv8 model) and directly removed.

[0126] 3) Gap Repair:

[0127] For seams between drone and handheld LiDAR data (such as the junction between the top of a tower and drone imagery), a Poisson surface reconstruction method is used for repair. This method is achieved by solving the following Poisson equation:

[0128] ;

[0129] Where V is a vector field composed of the normal vectors of the point cloud surrounding the gap. For gradient operators, Let V be the divergence of the vector field V, which serves as the source term of the Poisson equation. Let V be the indicator function (whose gradient approximates the vector field V), and Δ be the Laplace operator. The indicator function is obtained by solving this equation. And then according to the indicator function The isosurface is extracted as the reconstructed surface to fill the point cloud voids in the gap region (the point cloud density after filling is ≥300 points / ㎡).

[0130] S22, input the fused image into the pre-trained transfer learning model to identify device information in the fused image;

[0131] Specifically, the fused image can be input into a semantic recognition model (usually called a transfer learning model) pre-trained through transfer learning for detailed analysis and processing. This model is built on a deep residual network architecture and has been fully trained using a large dataset of labeled images containing various power equipment such as towers, conductors, and insulators, demonstrating powerful feature extraction and pattern recognition capabilities. In practical applications, this model can automatically identify various types of power equipment in the fused image, accurately detect and locate their specific positions, and assign structured semantic labels to the identified equipment, thereby achieving a deep understanding and efficient parsing of the image content.

[0132] S23. Based on the denoised point cloud data, equipment information and fused images, geometric modeling is performed to obtain a three-dimensional model of the power distribution line.

[0133] In this embodiment of the invention, after completing the identification of device information, geometric modeling can be performed based on noise-reduced point cloud data, device information, and fused images.

[0134] For example, firstly, a 3D mesh model of the scene is constructed based on the purified and denoised point cloud data. Next, by combining equipment information with textures and details from the fused image, detailed modeling of specific equipment is performed: for conductors, a cylinder is used for fitting based on the preset physical parameters of their model (e.g., LGJ-120); for tower structures, a polyhedral splicing method is used for construction; for complex equipment such as transformers, their geometry is reconstructed by matching and fusing with a preset CAD standard model library. Finally, the geometric models and semantic information of all components are integrated to generate a complete 3D model of the power distribution line with high-precision geometry and rich attributes.

[0135] To verify the accuracy of the model, an accuracy verification can be performed after modeling is completed: randomly select no less than a preset number of control points in the 3D model, compare their 3D coordinates with the actual measured values ​​one by one, and evaluate and confirm the geometric accuracy of the model based on the comparison results.

[0136] In specific implementations, such as Figure 5 As shown, the reconstruction process consists of three steps:

[0137] 1) Semantic modeling: The ResNet50 transfer learning model is used as the backbone network of the Mask-CNN model. The training set contains 100,000 images of power equipment (towers, conductors, insulators, etc.). The equipment is located by bounding boxes to achieve automatic recognition (accuracy > 95%) and is assigned semantic labels (such as "10kV straight tower" and "suspension insulator").

[0138] 2) Measurable Model Construction: A mesh model is constructed based on point cloud data. Conductors are fitted with cylinders (diameter preset according to the model, e.g., 14mm diameter for LGJ-120 conductors). Towers are constructed using polyhedral splicing. Equipment (such as transformers) is matched and fused using a CAD model library. Accuracy verification is then performed by randomly selecting 20 control points. The error between the measured value and the model value is ≤5mm, ensuring that the constructed 3D model is measurable and possesses high-precision spatial measurement capabilities.

[0139] 3) Incremental modeling technology: The system supports local updates and optimizations of the model based on newly collected data (such as local environmental changes, new equipment, etc.) on the basis of the existing model, avoiding repeated modeling and improving modeling efficiency and timeliness.

[0140] Step 203: Obtain the equipment parameters, environmental parameters, and operational parameters of the power distribution line based on multi-source data and the 3D model;

[0141] In this embodiment of the invention, equipment parameters may include phase-to-phase distance and insulation class; environmental parameters may include wind speed and terrain undulation; and operational parameters may include the working radius of the boom truck and the robot's degrees of freedom.

[0142] In practical implementation, based on the geometric and attribute information contained in the high-precision three-dimensional model of the power distribution line constructed in step S23, and combined with multi-source data, the above three types of parameters that are critical to live-line work can be automatically extracted:

[0143] Equipment parameters: These can be obtained directly from the 3D model through analysis or calculation. They mainly include parameters used to assess electrical safety and insulation requirements, such as the phase-to-phase distance of the lines and the insulation class of the equipment and components.

[0144] Environmental parameters: obtained by analyzing the 3D model and associated multi-source data, mainly including parameters used to assess the safety and stability of the working environment, such as the real-time wind speed of the working area obtained from meteorological data interfaces or sensor data, and the terrain undulation. Specifically, it refers to the difference between the highest and lowest elevation points determined based on the surface grid of the 3D model within a preset radius (e.g., 50 meters) centered on the preset working point (e.g., the predetermined parking position of the insulated bucket truck).

[0145] Operational parameters: These are derived from the operational scenarios and constraints defined by equipment and environmental parameters, combined with the performance specifications of pre-set operational equipment (such as insulated bucket trucks and operational robots). They mainly include parameters used to plan specific operational schemes, such as the minimum effective working radius of the bucket truck required to avoid equipment and the minimum degrees of freedom required for the operational robot to complete the task.

[0146] Using the above methods, quantitative and reliable decision parameters are extracted in a structured manner from the 3D model and associated multi-source data, providing direct input for the quantitative assessment and safety determination of subsequent operating conditions.

[0147] Step 204: Calculate the work condition evaluation score using equipment parameters, environmental parameters, work parameters, and preset evaluation indicators;

[0148] In this embodiment of the invention, after collecting equipment parameters, environmental parameters, and operational parameters, an operational condition evaluation score can be calculated by combining preset evaluation indicators.

[0149] Specifically, based on preset evaluation indicators, it can first be determined whether there are any rejection items among the equipment parameters, the environmental parameters, and the operating parameters that meet the rejection conditions, such as whether the wind speed is greater than level 5. If so, the operating conditions are determined to be infeasible.

[0150] If there are no veto items that meet the veto conditions, then calculate the phase-to-phase distance score, insulation class score, wind speed score, terrain undulation score, boom truck working radius score, and robot degrees of freedom score.

[0151] Next, the phase-to-phase distance score and the insulation class score are weighted and summed to obtain the equipment parameter score;

[0152] The environmental parameter score is obtained by weighted summation of the wind speed score and the terrain relief score.

[0153] The working radius score of the boom truck and the degree of freedom score of the robot are weighted and summed to obtain the operation parameter score.

[0154] The operating condition assessment score is obtained by summing the scores of equipment parameters, environmental parameters, and operational parameters.

[0155] In a specific example of an embodiment of the present invention, the evaluation indicators and quantitative standards are shown in Table 1:

[0156] Table 1

[0157]

[0158] 1) Analytic Hierarchy Process (AHP): Also known as the Hierarchical Analysis Method, it is a structured technique combining mathematics and psychology for organizing and analyzing complex decision-making problems. The main idea is to decompose complex problems into several levels and factors, compare the importance of each pair of indicators, establish a judgment matrix, and calculate the weights of different options' importance by calculating the largest eigenvalue and corresponding eigenvector of the judgment matrix, thus providing a basis for selecting the optimal solution. As a multi-criteria decision analysis method, the key feature of AHP is its ability to seamlessly integrate subjective judgments and objective data within the same analytical framework. By processing discrete or continuous pairwise comparisons, it derives a mathematically rigorous ratio scale, thereby achieving unified quantification and comparison of various tangible and intangible factors.

[0159] In this embodiment of the invention, based on the AHP (Analytic Hierarchy Process), a three-level indicator hierarchy structure can be constructed (target layer: operational feasibility; criteria layer: equipment, environment, and operational parameters; indicator layer: specific parameters). A judgment matrix is ​​constructed by expert scoring (10 live-line working engineers), and weights are calculated (e.g., equipment parameter weight 0.4, environmental parameter weight 0.3, operational parameter weight 0.3).

[0160] 2) Bayesian dynamic update: This is a method that uses new information to gradually revise beliefs about a certain event or parameter. Its core idea is: not to treat beliefs as fixed and unchanging, but to continuously adjust them as data arrives—first there is a "prior belief", then after seeing new data, combined with the probabilities of the data, a "posterior belief" is obtained, and then the posterior is used as the prior for the next round, and so on.

[0161] In this embodiment of the invention, Bayesian dynamic updating is adopted, which can establish a conditional probability table based on historical data (1,000 operation records in the past 5 years), such as "the operation accident rate is 5% when the wind speed is greater than level 5", and update the index weights in real time (such as increasing the environmental parameter weight from 0.3 to 0.45 when it rains).

[0162] 3) Scoring Calculation: First, determine if there are any veto items that meet the rejection criteria. For example, if the wind speed is greater than level 5, it is directly judged as "infeasible" and no further scoring is conducted. If there are no veto items, a percentage system is used for scoring. During scoring, for benefit-related indicators such as phase-to-phase distance, insulation class, boom truck working radius, and robot degrees of freedom, the indicator score = (measured value / standard value) × 100; for cost-related indicators such as wind speed and terrain undulation, the indicator score = (standard value / measured value) × 100. The final comprehensive score = Σ(indicator score × weight). A score ≥ 80 is judged as "feasible," 60-79 is "feasible under constraints," and < 60 is "infeasible."

[0163] For easier understanding, please refer to Figure 6, Figure 6 This is a schematic diagram illustrating an assessment of uninterrupted power supply working conditions, provided as an embodiment of the present invention.

[0164] First, a constraint analysis is conducted, which includes equipment parameters, environmental parameters, and operational parameters. Equipment parameters include phase-to-phase distance and insulation class; environmental parameters include wind speed and terrain undulation; and operational parameters include the boom truck's working radius and the robot's degrees of freedom.

[0165] Next, a hierarchical analysis (AHP) model is used to perform hierarchical analysis on the collected data. This AHP model is connected not only to equipment parameters (phase-to-phase distance, insulation class) and environmental parameters (wind speed, terrain undulation), but also to operational parameters (bumper truck working radius, robot degrees of freedom), forming a unified evaluation system. The AHP model outputs a weight matrix, which is logically connected to the safety scoring matrix, working together in the scoring calculation module to ultimately derive the operational condition evaluation score and output an operational feasibility decision, ensuring the systematic and comprehensive nature of the evaluation. The weight matrix can be obtained by dynamically updating the weights of each constraint using Bayesian methods.

[0166] Finally, the work condition assessment score is determined through scoring, and the work feasibility decision is made based on the work condition assessment score.

[0167] In this embodiment of the invention, electrical parameters (voltage level), mechanical parameters (bucket truck load), and environmental parameters (temperature and humidity) can also be included in a unified evaluation dimension; and the weight of evaluation indicators can be dynamically optimized by a machine learning model based on historical accident data (such as automatically increasing the weight of grounding requirements during thunderstorms).

[0168] Step 205: When the work condition evaluation score is greater than the preset threshold, it is determined that the power distribution line meets the requirements for live-line operation.

[0169] In this embodiment of the invention, when the work condition evaluation score is greater than a preset threshold, it can be determined that the power distribution line meets the requirements for uninterrupted power supply operation.

[0170] Step 206: Calculate the minimum safe distance between each live part of the power distribution line;

[0171] Step 207: Calculate the minimum distance between each working tool and the live conductor;

[0172] Step 208: Determine the target working tool from among the working tools based on the minimum distance and the minimum safe distance.

[0173] In this embodiment of the invention, the formula for calculating the minimum safe distance between each live component of the power distribution line is as follows:

[0174] ;

[0175] Among them, D min U is the minimum safe distance (unit: meters), K is the safety factor (dimensionless), which can be 1.2, and U n The nominal voltage of the system is (in kilovolts); E is the preset breakdown field strength (in kilovolts / meter), which can be taken as an empirical value of 500 kilovolts / meter for air medium; D0 is the human activity margin (in meters), which can be taken as 0.5 meters.

[0176] For example:

[0177] For 10kV lines (U n =10kV): Based on actual safety regulations, the minimum safe distance can be taken as 0.5 meters.

[0178] For 35kV lines (U n =35kV). Based on actual safety regulations, the minimum safe distance can be taken as 0.6 meters.

[0179] After calculating the minimum safe distance from each charged body, the minimum distance between each working tool (such as an insulating rod) and the charged body can be calculated using the GJK (Gilbert-Johnson-Keerthi) algorithm. By determining whether the minimum distance is less than the minimum safe distance, the appropriate working tool can be selected.

[0180] The GJK algorithm is an efficient algorithm for detecting collisions between two convex shapes. Its basic idea is to construct a support function to progressively approximate the shortest distance between the two shapes and determine if they intersect. The support function is defined as: for a given direction, it returns the farthest point of the shape in that direction. Through iterative processing, the GJK algorithm can determine whether two shapes intersect.

[0181] The steps of the GJK algorithm:

[0182] 1. Initialization:

[0183] Choose an initial direction (usually any direction) and calculate the support points of each working tool (such as an insulating rod) and the live conductor in that direction.

[0184] 2. Construct simple shapes:

[0185] Construct a simple shape (such as a point, line segment, triangle, etc.) using support points, and check if the simple shape contains the origin.

[0186] 3. Iteration:

[0187] If the simple shape contains the origin, it means that the two shapes intersect; if not, the orientation is updated, new support points are calculated, and the process of constructing the simple shape is repeated.

[0188] 4. Termination conditions:

[0189] The algorithm constructs a simplex iteratively. In each iteration, the algorithm searches for new support points in the current search direction. If the new support points cannot bring the simplex closer to the origin, or if the simplex already contains the origin, the algorithm terminates. If the simplex contains the origin, the two shapes (the tool and the charged body) are considered to intersect (i.e., the minimum distance is 0); otherwise, the two shapes are considered not to intersect, and the minimum distance between the two shapes can be calculated based on the final simplex.

[0190] Furthermore, in this embodiment of the invention, an octree index can be used for path risk prediction:

[0191] An octree is a tree-like data structure used to describe and partition three-dimensional space. Each of its internal nodes has exactly eight child nodes. This structure organizes data by recursively dividing a cubic space into eight smaller sub-cubes, thus achieving efficient spatial management. Octrees are widely used in fields requiring spatial partitioning and fast querying, such as collision detection and ray tracing acceleration in computer graphics, and nearest neighbor search in point cloud data processing. It is a direct extension of the quadtree in three-dimensional space, providing an effective hierarchical method for managing and retrieving three-dimensional spatial information.

[0192] Octree indexing: Divides the 3D model into 1m×1m×1m cube units, enabling rapid location of live conductors (wires, equipment) and work paths (such as the movement trajectory of a bucket truck boom). This invention overcomes the limitations of existing technologies with single data sources through "3D reconstruction based on multi-source data fusion," solves the problem of subjective assessment through a "multi-dimensional quantitative evaluation system," and automates safety decision-making through "intelligent spatial algorithms." Ultimately, it achieves more efficient (significantly shortens the assessment time), more accurate (greatly improves the accuracy of risk prediction), and safer (avoids accidents caused by experience-based misjudgments) assessment of live-line operation conditions, providing a systematic technical solution for power grid live-line operation and maintenance.

[0193] To facilitate understanding, the embodiments of the present invention will be described below through specific examples:

[0194] Scenario: Assessment of uninterrupted power supply maintenance work on a 10kV suburban power distribution line:

[0195] Step 1: Data Collection

[0196] Drone: Fly along the line for 2km, acquire 500 oblique images, covering 10 towers and 3km of conductor.

[0197] Handheld LiDAR: Scanning tower #5 (straight-line tower) to acquire 8 million point cloud data points, focusing on insulator strings and conductor spacing.

[0198] Ground imagery: The foundation of tower #5 and its surrounding environment (including one house and two poplar trees) were photographed, and the real-time wind speed was recorded as level 3 (5.5 m / s).

[0199] Step 2: 3D Reconstruction

[0200] Spatiotemporal registration: Multi-source data are unified into a coordinate system using three GCPs (coordinates: X=35210.2m, Y=12560.8m, Z=56.3m, etc.), with an error ≤3mm.

[0201] Dynamic noise reduction: Remove poplar swaying point clouds (variance 0.12m) and retain static point clouds.

[0202] Model reconstruction: A 3D model of tower #5 was generated. The measured phase-to-phase distance was 0.35m (meeting the ≥0.3m standard), and the insulator leakage distance was 22mm / kV (Class I standard).

[0203] Step 3: Quantify the evaluation indicators:

[0204] Equipment parameters: Phase-to-phase distance score = (0.35 / 0.3) × 100 ≈ 117 points; Insulation class score = (22 / 20) × 100 = 110 points; Weighted score = (117 × 0.5 + 110 × 0.5) × 0.4 = 45.4 points.

[0205] Environmental parameters: wind speed level 3 (score 100 points), terrain undulation 2.5m (≤3m, score 100 points), weighted score = (100×0.6+100×0.4)×0.3=30 points.

[0206] Operation parameters: The working radius of the boom truck (XZJ5160JQJ5 type) is 15m (the distance between the work points is 12m, and the score is 15 / 12×100=125 points), the robot has 6 degrees of freedom (score 100 points), and the weighted score is (125×0.6+100×0.4)×0.3=31.5 points.

[0207] Overall score = 45.4 + 30 + 31.5 = 106.9 points (≥80 points, judged as "feasible").

[0208] Step 4: Space Security Analysis:

[0209] Calculate line D of 10kV line min=0.5m, the minimum distance between the insulating rod and the conductor detected by the GJK algorithm is 8.2m (>0.5m, safe).

[0210] The terrain undulation of the bucket truck parking area is 2.5m, which meets the parking requirements and eliminates the risk of collision.

[0211] Assessment conclusion: The working conditions meet the requirements for uninterrupted power supply. It is recommended to use insulated poles for the operation and monitor the wind speed in real time during the operation (stop immediately if the wind speed is greater than level 5).

[0212] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0213] Please see Figure 7 , Figure 7 This is a structural block diagram of a power distribution line uninterrupted operation condition assessment device provided in an embodiment of the present invention.

[0214] This invention provides a device for assessing the conditions for live-line operation, comprising:

[0215] The multi-source data acquisition module 701 is used to acquire multi-source data of the power distribution line through preset equipment to obtain multi-source data of the power distribution line.

[0216] The 3D model building module 702 for power distribution lines is used to build 3D models of power distribution lines using multi-source data.

[0217] The equipment parameter, environmental parameter, and operational parameter acquisition module 703 is used to acquire the equipment parameters, environmental parameters, and operational parameters of the power distribution line based on multi-source data and a three-dimensional model.

[0218] The work condition assessment score calculation module 704 is used to calculate the work condition assessment score using equipment parameters, environmental parameters, work parameters, and preset assessment indicators.

[0219] The live-line work determination module 705 is used to determine that the power distribution line meets the requirements for live-line work when the work condition evaluation score is greater than a preset threshold.

[0220] In this embodiment of the invention, it further includes:

[0221] The minimum safe distance calculation module is used to calculate the minimum safe distance between each live part of a power distribution line;

[0222] The minimum distance calculation module is used to calculate the minimum distance between each working tool and a live conductor;

[0223] The target work tool determination module is used to determine the target work tool from among the various work tools based on the minimum distance and the minimum safe distance.

[0224] In this embodiment of the invention, the minimum safe distance calculation module includes:

[0225] The submodule for acquiring safety factor, system nominal voltage, preset breakdown field strength and human activity margin is used to acquire the safety factor, system nominal voltage, preset breakdown field strength and human activity margin of each live part of the power distribution line.

[0226] The minimum safe distance calculation submodule is used to calculate the minimum safe distance between each live part of the power distribution line based on the safety factor, the system nominal voltage, the preset breakdown field strength, and the human activity margin.

[0227] In this embodiment of the invention, the multi-source data includes global data, denoised point cloud data, device texture data, and environmental data; the multi-source data acquisition module 701 includes:

[0228] The global data acquisition submodule is used to collect global data of power distribution lines via drones;

[0229] The point cloud data acquisition submodule is used to acquire point cloud data of power distribution lines using lidar.

[0230] The noise reduction submodule is used to dynamically reduce noise in point cloud data to obtain noise-reduced point cloud data.

[0231] The equipment texture data and environmental data acquisition submodule is used to acquire equipment texture data of each device on the power distribution line and environmental data around the power distribution line through ground imaging equipment.

[0232] In this embodiment of the invention, the three-dimensional model construction module 702 for power distribution lines includes:

[0233] The data fusion submodule is used to spatially align and temporally synchronize global data, denoised point cloud data, device texture data, and environmental data to obtain a fused image.

[0234] The device information recognition submodule is used to input the fused image into a pre-trained transfer learning model to identify device information in the fused image;

[0235] The power distribution line 3D model generation submodule is used to perform geometric modeling based on noise-reduced point cloud data, equipment information and fused images to obtain a 3D model of the power distribution line.

[0236] In this embodiment of the invention, the equipment parameters include phase-to-phase distance and insulation class; the environmental parameters include wind speed and terrain undulation; the operational parameters include the working radius of the boom truck and the robot's degrees of freedom; and the operational condition evaluation score calculation module 704 includes:

[0237] The rejection judgment submodule is used to determine, based on preset evaluation indicators, whether there are any rejection items among the equipment parameters, the environmental parameters and the operating parameters that meet the rejection conditions.

[0238] The parameter score calculation submodule is used to calculate the phase distance score, insulation score, wind speed score, terrain undulation score, boom truck working radius score, and robot degree of freedom score, if not.

[0239] The equipment parameter score calculation submodule is used to perform a weighted summation of the phase-to-phase distance score and the insulation class score to obtain the equipment parameter score;

[0240] The environmental parameter score calculation submodule is used to perform a weighted summation of the wind speed score and the terrain relief score to obtain the environmental parameter score.

[0241] The task parameter score calculation submodule is used to perform a weighted summation of the work radius score of the boom truck and the degree of freedom score of the robot to obtain the task parameter score;

[0242] The job condition assessment score calculation submodule is used to sum the equipment parameter scores, environmental parameter scores, and job parameter scores to obtain the job condition assessment score.

[0243] This invention also provides an electronic device, which includes a processor and a memory:

[0244] The memory is used to store program code and transfer the program code to the processor;

[0245] The processor is used to execute the power distribution line live-line working condition assessment method according to the instructions in the program code of this invention.

[0246] This invention also provides a computer-readable storage medium for storing program code, which is used to execute the power distribution line live-line working condition assessment method of this invention.

[0247] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0248] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0249] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0251] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0252] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0253] Although preferred embodiments of the present 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 embodiments of the present invention.

[0254] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0255] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0256] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the conditions for live-line operation of power distribution lines, characterized in that, include: The power distribution line is subjected to multi-source data acquisition using a preset device to obtain multi-source data of the power distribution line; A three-dimensional model of the power distribution line is constructed using the multi-source data. Based on the multi-source data and the three-dimensional model, the equipment parameters, environmental parameters, and operating parameters of the power distribution line are obtained. The work condition evaluation score is calculated using the equipment parameters, environmental parameters, work parameters, and preset evaluation indicators. When the work condition evaluation score is greater than a preset threshold, the power distribution line is determined to meet the requirements for uninterrupted power supply operation.

2. The method according to claim 1, characterized in that, After the step of determining that the power distribution line meets the requirements for uninterrupted power supply work when the work condition evaluation score is greater than a preset threshold, the method further includes: Calculate the minimum safe distance between each live conductor of the power distribution line; Calculate the minimum distance between each working tool and the charged body; The target working tool is determined from each of the working tools based on the minimum distance and the minimum safe distance.

3. The method according to claim 2, characterized in that, The step of calculating the minimum safe distance between each live conductor of the power distribution line includes: Obtain the safety factor, system nominal voltage, preset breakdown field strength, and human activity margin of each live part of the power distribution line; The minimum safe distance between each live part of the power distribution line is calculated based on the safety factor, the nominal voltage of the system, the preset breakdown field strength, and the human activity margin.

4. The method according to claim 1, characterized in that, The multi-source data includes global data, noise-reduced point cloud data, device texture data, and environmental data; The step of acquiring multi-source data of the power distribution line by using a preset device includes: Global data of the power distribution lines were collected using drones; Point cloud data of the power distribution line is collected by lidar; the point cloud data is dynamically denoised to obtain denoised point cloud data. The equipment texture data of each device on the power distribution line and the environmental data around the power distribution line are collected by ground imaging equipment.

5. The method according to claim 4, characterized in that, The step of constructing a three-dimensional model of a power distribution line using the multi-source data includes: Spatial alignment and temporal synchronization are performed on the global data, the denoised point cloud data, the device texture data, and the environmental data to obtain a fused image; The fused image is input into a pre-trained transfer learning model to identify device information in the fused image; Based on the denoised point cloud data, the equipment information, and the fused image, geometric modeling is performed to obtain a three-dimensional model of the power distribution line.

6. The method according to claim 1, characterized in that, The equipment parameters include phase-to-phase distance and insulation class; the environmental parameters include wind speed and terrain undulation; the operational parameters include the boom truck's working radius and the robot's degrees of freedom; the step of calculating the operational condition evaluation score using the equipment parameters, environmental parameters, operational parameters, and preset evaluation indicators includes: Based on preset evaluation indicators, determine whether there are any rejection items among the equipment parameters, environmental parameters, and operational parameters that meet the rejection conditions; If not, calculate the phase-to-phase distance fraction, the insulation class fraction, the wind speed fraction, the terrain undulation fraction, the boom truck working radius fraction, and the robot degree of freedom fraction. The phase-to-phase distance score and the insulation class score are weighted and summed to obtain the equipment parameter score; The environmental parameter score is obtained by weighted summation of the wind speed score and the terrain relief score. The working radius score of the boom truck and the degree-of-freedom score of the robot are weighted and summed to obtain the operation parameter score. The operating condition evaluation score is obtained by summing the scores of the equipment parameters, the environmental parameters, and the operating parameters.

7. A device for evaluating the conditions for live-line operation of power distribution lines, characterized in that, include: A multi-source data acquisition module is used to acquire multi-source data of the power distribution line through a preset device to obtain multi-source data of the power distribution line. A 3D model construction module for power distribution lines is used to construct a 3D model of power distribution lines using the multi-source data. The equipment parameter, environmental parameter, and operational parameter acquisition module is used to acquire the equipment parameters, environmental parameters, and operational parameters of the power distribution line based on the multi-source data and the three-dimensional model. The work condition assessment score calculation module is used to calculate the work condition assessment score using the equipment parameters, the environmental parameters, the work parameters, and preset assessment indicators; The live-line operation determination module is used to determine that the power distribution line meets the live-line operation requirements when the operation condition evaluation score is greater than a preset threshold.

8. The apparatus according to claim 7, characterized in that, Also includes: The minimum safe distance calculation module is used to calculate the minimum safe distance between each live part of the power distribution line; The minimum distance calculation module is used to calculate the minimum distance between each working tool and the charged body; The target work tool determination module is used to determine the target work tool from the various work tools based on the minimum distance and the minimum safe distance.

9. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the power distribution line live-line working condition assessment method according to any one of the claims 1-6 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the method for evaluating the conditions for live-line operation as described in any one of claims 1-6.

Images