A method for simulating and evaluating power transmission and distribution lines under disaster conditions

Abstract

The application relates to the technical field of point cloud model construction, in particular to a power transmission and distribution line simulation evaluation method under disaster conditions, which comprises the following steps: scanning the environment where the power transmission and distribution line is located to generate point clouds containing reflectivity and color information; classifying the point clouds and marking each object; calculating the first characteristic value, the second characteristic value and the tower evaluation value of each object, and marking the object corresponding to the power transmission tower; calculating the conductor evaluation value of each object, marking the object corresponding to the power transmission and distribution line; determining the elimination coefficient of each point cloud, identifying and eliminating abnormal point clouds on the object corresponding to the power transmission and distribution line, performing three-dimensional reconstruction on the object corresponding to the power transmission and distribution line after the point clouds are refilled, and performing simulation on the reconstructed three-dimensional point cloud model under disaster conditions to evaluate the operation state of the power transmission and distribution line. The application can improve the three-dimensional reconstruction accuracy of the power transmission and distribution line, and improve the evaluation accuracy and reliability of the operation state of the power transmission and distribution line under disaster conditions.

Description

Technical Field

[0001] This application relates to the field of point cloud model construction technology, specifically to a simulation evaluation method for power transmission and distribution lines under disaster conditions. Background Technology

[0002] With the increasing frequency of extreme weather events globally, disasters such as typhoons, ice storms, and wildfires pose a serious threat to the safe and stable operation of power transmission and distribution lines. These disasters not only cause physical damage to transmission lines, such as tower collapses and conductor breaks, but may also trigger secondary disasters, such as fires and large-scale power outages. To better assess and manage the risks of power transmission and distribution lines, a realistic 3D point cloud model of the lines is constructed to conduct multiphysics simulations under disaster conditions. This allows for the early identification of potential weaknesses and the development of effective response strategies, thereby improving the disaster resilience and emergency response capabilities of the power system.

[0003] When collecting point clouds on power transmission and distribution lines, the point clouds on the power transmission and distribution lines shift due to swaying at high altitudes and various factors such as obstruction and interference. This causes distortion in the geometric shape of the power transmission and distribution lines, which directly affects the accuracy of the construction of the 3D point cloud model and, consequently, the accuracy of the subsequent simulation and operational status assessment of the power transmission and distribution lines under disaster conditions. Summary of the Invention

[0004] To address the aforementioned technical problems, a simulation evaluation method for power transmission and distribution lines under disaster conditions is provided to resolve existing issues.

[0005] The solution to the technical problem presented in this application is to provide a simulation evaluation method for power transmission and distribution lines under disaster conditions, comprising the following steps: The environment in which the power transmission and distribution lines are located is scanned to generate point clouds containing reflectivity and color information, and a three-dimensional point cloud model is constructed. Classify all point clouds on the 3D point cloud model and mark each object; analyze the geometric morphological characteristics of each object and calculate the first characterization value of each object; determine the second characterization value of each object by the average level of reflectivity of the point cloud contained in each object and the size of the object; combine the first characterization value to obtain the tower evaluation value of each object; filter all objects and mark the objects corresponding to the transmission towers. Based on the spatial distribution of objects corresponding to transmission towers, the 3D point cloud model is divided into multiple spatial regions. Based on the geometric shape of each object in the spatial region and the fluctuation of the reflectivity of the point cloud, the conductor evaluation value of each object is calculated, and all objects in the spatial region are screened to mark the objects corresponding to the transmission and distribution lines. By analyzing the degree of color information deviation and reflectivity fluctuation of point clouds on objects corresponding to power transmission and distribution lines within a local area, the elimination coefficient of each point cloud is determined. Abnormal point clouds on objects corresponding to power transmission and distribution lines are identified and eliminated. After refilling the point cloud, the objects corresponding to power transmission and distribution lines are reconstructed in three dimensions. The reconstructed three-dimensional point cloud model is simulated under disaster conditions to evaluate the operating status of power transmission and distribution lines.

[0006] Preferably, the calculation of the first characterization value of each object includes: Construct the bounding box for each object; calculate the ratio of the length, width and height of the bounding box, and record them as the first ratio and the second ratio, respectively; The first representation value is the result of positive fusion of the first ratio and the second ratio.

[0007] Preferably, determining the second characterization value of each object includes: Calculate the mean reflectance of all point clouds contained in each object, denoted as the average reflectance; calculate the volume of the bounding box corresponding to each object. The second characterization value is the result of positively fusing the average reflectance and volume.

[0008] Preferably, the tower evaluation value is negatively correlated with the first characterization value and positively correlated with the second characterization value.

[0009] Preferably, the process of obtaining the object corresponding to the transmission tower is as follows: obtain the segmentation threshold of the tower evaluation value of all objects, denoted as the first threshold, and mark the objects whose tower evaluation value is greater than the first threshold as the objects corresponding to the transmission tower.

[0010] Preferably, the step of dividing the three-dimensional point cloud model into multiple spatial regions includes: within the bounding box of the object corresponding to each transmission tower, a plane passing through the centroid and perpendicular to the centroid line segment is denoted as a dividing surface; and the spatial region between the dividing surfaces corresponding to the objects of two adjacent transmission towers is obtained.

[0011] Preferably, the calculation of the wire evaluation value for each object includes: The difference in length and width of the bounding box corresponding to each object within the spatial region is denoted as the relative difference. The degree of dispersion of the reflectance of all point clouds on each object within a spatial region is denoted as reflectance variability. The conductor evaluation value is positively correlated with the relative difference, but negatively correlated with the reflection fluctuation.

[0012] Preferably, the process of obtaining the object corresponding to the power transmission and distribution line is as follows: obtain the segmentation threshold of the conductor evaluation value of all objects in each spatial area, denoted as the second threshold, and mark the objects whose conductor evaluation value is greater than the second threshold as objects corresponding to the power transmission and distribution line.

[0013] Preferably, determining the elimination coefficient for each point cloud includes: The average color value is the average value of the color values ​​of all point clouds on each object corresponding to the power transmission and distribution line in each color channel. For any point cloud on each object corresponding to the power transmission and distribution line, calculate the average color value of the point cloud and all point clouds in its neighborhood under each color channel, and record it as the local color value; perform positive fusion on the difference between the local color value and the average color value of the point cloud under all color channels, and use it as the color deviation of the point cloud. The degree of dispersion of reflectance of any point cloud and all point clouds in its neighborhood is calculated and denoted as local dispersion; the difference between the local dispersion of any point cloud and the reflectance fluctuation of the object to which it belongs is taken as the reflectance deviation of the point cloud. The rejection factor is the result of positive fusion of color deviation and reflection deviation.

[0014] Preferably, the step of identifying and removing abnormal point clouds on objects corresponding to power transmission and distribution lines includes: performing anomaly detection on the removal coefficients of all point clouds on each object corresponding to the power transmission and distribution line, obtaining abnormal point clouds, and removing them.

[0015] This application has at least the following beneficial effects: This application classifies point clouds and labels different objects. The advantage of this method is that it identifies different objects through point cloud classification, facilitating subsequent differentiation of objects such as power transmission towers and power lines. It calculates the first characteristic value for each object, which takes into account geometric features, reflecting that the object conforms to a tall and slender structural characteristic, thus assessing the likelihood that the object belongs to a power transmission tower. It determines the second characteristic value for each object, which takes into account the object's reflectivity level and size, reflecting the object's shape and reflectivity characteristics, further assessing the likelihood that the object belongs to a power transmission tower. Finally, it obtains the tower assessment value for each object, filters all objects, and labels the power transmission towers. The beneficial effect of this approach is that it comprehensively evaluates whether an object conforms to the characteristics of a transmission tower from multiple dimensions, such as geometric shape, material reflection properties, and structural volume, to accurately screen out objects corresponding to transmission towers, facilitating subsequent accurate segmentation of transmission and distribution lines. Dividing the 3D point cloud model into multiple spatial regions is beneficial because it segments the 3D point cloud model based on the spatial distribution of transmission towers, dividing long-distance transmission and distribution lines into multiple spatial regions, enabling subsequent identification of the corresponding section of transmission and distribution line within each spatial region for local reconstruction. Finally, it calculates the conductor evaluation value of each object, filters all objects within the spatial region, and marks the transmission and distribution lines. The benefits of identifying objects corresponding to power lines lie in considering their slender geometric shape and reflectivity uniformity within a spatial region. This reflects the simultaneous presence of both extremely slender shapes and highly uniform materials, assessing the likelihood that objects match the characteristics of power transmission lines and accurately identifying objects corresponding to power transmission lines. Determining the rejection coefficient for each point cloud is beneficial because it allows for the capture of point clouds with local color and reflectivity anomalies by analyzing reflectivity fluctuations and color characteristic deviations along power transmission lines. Identifying and removing anomalous point clouds on objects corresponding to power transmission lines is beneficial because it effectively identifies and removes birds, trees, and other objects that do not conform to the main characteristics of the power line. Point clouds containing obstructions such as leaves improve the accuracy of subsequent 3D reconstruction of power transmission and distribution lines. After refilling the point cloud, 3D reconstruction of the corresponding objects on the power transmission and distribution lines is performed. The reconstructed 3D point cloud model is then simulated under disaster conditions to assess the operating status of the power transmission and distribution lines. The beneficial effects are that by filling in the removed point cloud, the continuity of the power transmission and distribution lines is ensured, the accuracy of 3D reconstruction is improved, and the high-precision 3D point cloud model is used for simulation, which enhances the accuracy and reliability of assessing the operating status of power transmission and distribution lines under disaster conditions. This allows for the timely detection of potential risks, the development of precise prevention and control strategies and emergency response plans, and the safe operation of power transmission and distribution lines. Attached Figure Description

[0016] The following section provides a more detailed description of a simulation evaluation method for power transmission and distribution lines under disaster conditions, in conjunction with the accompanying drawings.

[0017] Figure 1A flowchart illustrating the steps of a simulation evaluation method for power transmission and distribution lines under disaster conditions, provided in this application embodiment; Figure 2 A flowchart illustrating the steps of a method for obtaining objects corresponding to power transmission towers provided in this application embodiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description, in conjunction with the accompanying drawings and implementation examples, provides a method for simulating and evaluating power transmission and distribution lines under disaster conditions. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a simulation evaluation method for power transmission and distribution lines under disaster conditions, provided in one embodiment of this application. The method includes the following steps: Step 1: Scan the environment where the power transmission and distribution lines are located to generate point clouds containing reflectivity and color information, and construct a three-dimensional point cloud model.

[0021] Extreme weather events such as typhoons, torrential rains, lightning, blizzards, and freezing rain occur frequently, posing a serious threat to critical facilities in the power system, such as transmission and distribution lines. These meteorological disasters can not only cause direct physical damage such as the collapse of transmission and distribution line towers and the breakage of conductors, but also trigger secondary disasters such as floods and mudslides, further increasing the operational risks of the power system. Therefore, in order to ensure the safe and stable operation of the power system, a high-precision three-dimensional simulation scenario of transmission and distribution lines is constructed to simulate and evaluate the mechanical behavior and operating status of the lines under disaster conditions, identify potential weaknesses in advance, and formulate effective response strategies to improve the disaster resistance and emergency response capabilities of the power system.

[0022] Based on the above analysis, under good weather conditions, a drone equipped with lidar can be used to scan the surrounding environment of the power transmission and distribution lines according to a pre-defined flight route, generate point clouds, and record the reflectivity of the point clouds. The high-definition imaging capabilities of lidar are used to record the color information of point clouds; Since scanning with LiDAR will capture too many other objects, such as the ground, trees and vegetation, a cloth simulation filtering algorithm is used to filter all generated point clouds and remove point clouds located on the ground. It should be noted that the fabric simulation filtering algorithm is a well-known technology and will not be described in detail here.

[0023] A 3D point cloud model of the power transmission and distribution line is constructed from all the filtered point clouds. It should be noted that the construction of 3D point cloud models is a well-known technology and will not be elaborated upon here.

[0024] At this point, all point clouds on the three-dimensional point cloud model of the power transmission and distribution line, as well as the reflectivity and color information of the point clouds, are obtained.

[0025] Step 2: Classify all point clouds on the 3D point cloud model and mark each object; analyze the geometric morphological characteristics of each object and calculate the first characterization value of each object; determine the second characterization value of each object by the average level of reflectivity of the point cloud contained in each object and the size of the object; combine the first characterization value to obtain the tower evaluation value of each object; filter all objects and mark the objects corresponding to the transmission towers.

[0026] Because power transmission and distribution lines are suspended high in the air, they are easily affected by wind and sway, causing random spatial shifts in the point cloud collected by lidar during long-distance scanning, resulting in distortion of the geometric shape of the lines in the model. Therefore, to improve the accuracy of multiphysics simulation of power transmission and distribution lines under subsequent disaster conditions, it is necessary to reconstruct the point cloud of the lines in three dimensions.

[0027] Secondly, the point cloud volume of the entire transmission and distribution line is large, and the terrain across the transmission and distribution line varies significantly. As a result, the height of the transmission towers and the tower body that support the line often need to be adjusted accordingly. This may result in inconsistent line heights on different transmission towers. If the entire line is reconstructed as a single catenary, deviations will be introduced because the local height differences will be ignored. Therefore, it is necessary to divide the entire line into multiple regions and reconstruct each region independently to improve the three-dimensional reconstruction effect of the transmission and distribution line and to accurately assess the line status under subsequent disaster conditions.

[0028] Based on the above analysis, power transmission and distribution lines are connected to transmission towers at regular intervals, and these towers are fixed and do not move. Therefore, the power transmission and distribution lines are divided into multiple regions using these towers. By analyzing the distribution of point clouds, the transmission towers are identified. The flowchart of the method for obtaining objects corresponding to transmission towers provided in this embodiment is as follows: Figure 2 As shown.

[0029] First, the point cloud is clustered, the shape features of the objects represented by the clusters are analyzed, and the first representation value is calculated, specifically: Classify all point clouds on the 3D point cloud model and mark each object; In this embodiment, the DBSCAN clustering algorithm is used for classification, where one cluster corresponds to one object. The DBSCAN algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers may use other methods of existing technology, such as the OPTICS clustering algorithm, the K-means clustering algorithm, etc. This embodiment does not impose any special restrictions on this.

[0030] Construct the bounding boxes for each object; In this embodiment, the type of bounding box is an axis-aligned bounding box (AABB). The construction of axis-aligned bounding boxes is a well-known technique and will not be described in detail here. The bounding box is constructed by obtaining the maximum and minimum values ​​of all point clouds on each object along the X, Y, and Z axes.

[0031] Calculate the ratio of the length to the height of the bounding box, and denote it as the first ratio. Calculate the ratio of the width to the height of the bounding box, and denote it as the second ratio; The first ratio and the second ratio are positively fused together to serve as the first characterization value for each object. In this embodiment, the specific process of forward fusion is as follows: the average of the first ratio and the second ratio is used as the first characterization value of each object; in other implementations, the implementer can calculate the sum of the first ratio and the second ratio as the first characterization value of each object.

[0032] It should be noted that the bounding box is constructed in a geographic coordinate system, where the length of the bounding box is defined as its span in the X-axis direction, the width of the bounding box is defined as its span in the Y-axis direction, and the height of the bounding box is defined as its span in the Z-axis direction.

[0033] It should be noted that the smaller the first characteristic value, the greater the height of the object is than its width and length, reflecting that the object is more in line with the characteristics of a tall and slender structure, and the higher the probability that it is a power transmission tower.

[0034] Secondly, transmission towers are typically constructed of galvanized steel, which has a high surface reflectivity. The reflection intensity of their point clouds is usually far higher than that of natural features such as vegetation and birds, making them among the most reflective objects in power transmission lines. Furthermore, the massive structure of transmission towers means their bounding boxes are significantly larger than those of vegetation and birds. Therefore, analyzing the average reflectivity of point clouds on each object and the size of their bounding boxes is crucial for calculating the second characteristic value: Calculate the mean reflectance of all point clouds contained in each object, and denot it as the average reflectance; Calculate the volume of the bounding box corresponding to each object; The average reflectance and the volume are positively fused together to serve as the second characterization value for each object; In this embodiment, the specific process of forward fusion is as follows: the average reflectance and volume are normalized respectively, and the sum of the normalized volume and the normalized average reflectance is used as the second characterization value of each object. In other implementations, the implementer can use the product of the normalized volume and the normalized average reflectance as the second characterization value of each object. Secondly, the specific normalization process is as follows: the ratio of the volume of the bounding box corresponding to each object to the maximum volume corresponding to all objects is used as the normalized volume, and the ratio of the average reflectance corresponding to each object to the maximum average reflectance corresponding to all objects is used as the normalized average reflectance.

[0035] It should be noted that the greater the average reflectivity, the stronger the ability of the material on the surface of the object to reflect laser light, reflecting that the material of the object is smoother or metallic. The larger the volume, the larger the scale that the object occupies in three-dimensional space, reflecting that the object's structure is larger. The larger the obtained second characterization value, the larger the object's size and the stronger its reflectivity, and the greater the likelihood that it belongs to a power transmission tower in the power transmission and distribution line scenario.

[0036] Furthermore, based on the first and second characterization values, the tower evaluation value is determined, specifically as follows: The evaluation values ​​of each object's tower are negatively correlated with the first characterization value, but positively correlated with the second characterization value; It should be noted that a positive correlation means that the dependent variable increases as the independent variable increases and decreases as the independent variable decreases; a negative correlation means that the dependent variable decreases as the independent variable increases and increases as the independent variable decreases.

[0037] In this embodiment, the ratio of the second characterization value to the first characterization value is used as the pole evaluation value of each object. In other implementations, the implementer can also calculate the difference between the second characterization value and the first characterization value. In order to avoid the difference being less than 0, the difference is positively mapped as the pole evaluation value of each object. The positive mapping process is as follows: the result of an exponential function with the natural constant as the exponent and the difference as the exponent is used as the pole evaluation value of each object.

[0038] It should be noted that the larger the tower evaluation value, the more the object conforms to the characteristics of a power transmission tower in multiple dimensions such as geometric shape, material reflection characteristics, and structural volume, and the higher the probability that the object is a power transmission tower.

[0039] Therefore, based on the tower evaluation values, the transmission towers were identified as follows: Obtain the segmentation threshold of the tower evaluation value of all objects, and denote it as the first threshold. Mark the objects whose tower evaluation value is greater than the first threshold as objects corresponding to transmission towers. In this embodiment, the Otsu's method is used to obtain the segmentation threshold. Otsu's method is a well-known technique and will not be described in detail here. As other implementation methods, implementers may use other methods of the prior art, such as cross-validation. This embodiment does not impose any special restrictions on this.

[0040] At this point, the object corresponding to the power transmission tower on the 3D point cloud model is obtained.

[0041] Step 3: Based on the spatial distribution of objects corresponding to the transmission towers, the 3D point cloud model is divided into multiple spatial regions; based on the geometric shape of each object in the spatial region and the fluctuation of the reflectivity of the point cloud, the conductor evaluation value of each object is calculated, all objects in the spatial region are screened, and the objects corresponding to the transmission and distribution lines are marked.

[0042] Furthermore, based on the objects corresponding to the transmission towers, the 3D point cloud model of the transmission and distribution lines is divided into multiple regions, specifically: Obtain the centroid of the bounding box corresponding to each transmission tower, and connect the centroids of the objects corresponding to two adjacent transmission towers to form a centroid line segment. Within the bounding box of the object corresponding to each transmission tower, the plane passing through the centroid and perpendicular to the centroid line segment is denoted as the dividing plane; Obtain the spatial region between the corresponding segmented surfaces of two adjacent power transmission towers; It should be noted that the acquisition of the centroid is a well-known technique and will not be elaborated upon here.

[0043] Secondly, within a spatial region, power transmission and distribution lines extend from one side to the other, resulting in a relatively long extension length and a small diameter and cross-section. Consequently, the length and width of these lines vary considerably. Furthermore, as the route of the lines changes with the terrain, the dominant extension direction of the power transmission and distribution lines within different spatial regions may vary in the geographic coordinate system. In other words, some regions may primarily extend along the X-axis, while others may extend along the Y-axis. Consequently, the coordinate axes corresponding to the length and width of the bounding box representing the objects in different spatial regions may vary randomly and cannot be measured by ratios. Therefore, the geometric shape of the lines is measured by their length and width differences.

[0044] Secondly, the materials used on the surface of power transmission and distribution lines are uniform and the processing technology is consistent, so the reflection intensity of lidar will also be consistent. After excluding ground points and objects corresponding to power transmission towers, other ground features in the spatial area, such as vegetation, birds, and floating debris, are prone to scattering or local enhancement of reflected signals due to their rough surfaces, loose structures, or fluctuating moisture content, which in turn leads to large fluctuations in the reflectivity of the internal point cloud.

[0045] Therefore, by analyzing the differences in length and width of the bounding boxes of objects within a spatial region, as well as the fluctuations in the reflectivity of the point cloud, the traverse evaluation value is calculated, specifically as follows: The difference in length and width of the bounding box corresponding to each object within the spatial region is denoted as the relative difference. In this embodiment, the absolute value of the difference between the length and width of the bounding box corresponding to each object within the spatial region is calculated and denoted as the relative difference; where length represents the span in the X-axis direction of the geographic coordinate system, and width represents the span in the Y-axis direction of the geographic coordinate system.

[0046] The degree of dispersion of the reflectance of all point clouds on each object within a spatial region is denoted as reflectance variability. In this embodiment, the degree of dispersion is measured by calculating the standard deviation of the reflectance of all point clouds on each object in the spatial region. As other implementation methods, implementers may use other methods of the prior art, such as the coefficient of variation, etc. This embodiment does not impose any special restrictions on this. The wire evaluation values ​​of each object within the spatial region are positively correlated with the relative differences, but negatively correlated with the reflection fluctuation. In this embodiment, the relative difference is normalized, and the ratio of the normalized relative difference to the reflection fluctuation is used as the guide line evaluation value of each object in the spatial region. The normalization process is as follows: the ratio of the relative difference to the maximum relative difference of all objects is used as the normalized relative difference.

[0047] It should be noted that, in order to avoid the denominator being 0 when calculating the ratio, a preset value greater than 0 is added to the denominator. In this embodiment, the preset value greater than 0 is 1. In other implementation methods, the implementer can set it according to the actual situation.

[0048] It should be noted that the greater the relative difference, the larger the difference in the dimensions of the object's bounding box in both length and width, reflecting that the object exhibits an extremely slender shape, which is more consistent with the geometric characteristics of power transmission and distribution lines. The smaller the reflection fluctuation, the more uniform and stable the reflection intensity of the point cloud on the object's surface is, reflecting that its surface material is uniform and its structure is continuous. The larger the conductor evaluation value, the more the object possesses both the characteristics of an extremely slender shape and highly uniform material, reflecting that the object is more consistent with the characteristics of power transmission lines.

[0049] Furthermore, based on the conductor evaluation values, the transmission and distribution lines contained within the spatial area are identified, specifically as follows: Obtain the segmentation threshold of the conductor evaluation value of all objects in each spatial region, and denote it as the second threshold. Mark the objects whose conductor evaluation value is greater than the second threshold as objects corresponding to power transmission and distribution lines. In this embodiment, the Otsu's method is used to obtain the segmentation threshold. Otsu's method is a well-known technique and will not be described in detail here. As other implementation methods, implementers may use other methods of the prior art, such as cross-validation. This embodiment does not impose any special restrictions on this.

[0050] At this point, the objects corresponding to the power transmission and distribution lines on the 3D point cloud model are obtained.

[0051] Step 4: By analyzing the degree of deviation in color information and the fluctuation of reflectivity of the point cloud on the object corresponding to the power transmission and distribution line in a local area, determine the elimination coefficient of each point cloud, identify and eliminate abnormal point clouds on the object corresponding to the power transmission and distribution line, and perform three-dimensional reconstruction of the object corresponding to the power transmission and distribution line after refilling the point cloud. Simulate the reconstructed three-dimensional point cloud model under disaster conditions to evaluate the operating status of the power transmission and distribution line.

[0052] Furthermore, since birds, leaves, and other obstructions can interfere with the 3D reconstruction of power transmission and distribution lines, if these obstructions are not removed, local anomalies may occur in the 3D point cloud model of the power transmission and distribution lines, affecting the simulation evaluation results of the power transmission and distribution lines under disaster conditions.

[0053] Based on the above analysis, since the color information of point clouds on power transmission and distribution lines differs significantly from the color information of obstructions such as birds and leaves, and the corresponding reflectance also differs significantly, a rejection factor is calculated by analyzing the deviations in color information and reflectance of point clouds on objects corresponding to power transmission and distribution lines. Specifically: The average color value is the average value of the color values ​​of all point clouds on each object corresponding to the power transmission and distribution line in each color channel. It should be noted that the color information of the point cloud represents RGB values, and the average color values ​​of all point clouds on each object corresponding to the power transmission and distribution line are calculated in the R, G, and B color channels respectively.

[0054] For any point cloud on each object corresponding to the power transmission and distribution line, calculate the average color value of the point cloud and all point clouds in its neighborhood under each color channel, and record it as the local color value. In this embodiment, the neighborhood range is the area containing a sphere with a neighborhood radius of 2cm, centered on each point cloud. In other implementation methods, the implementer can set the range according to the actual situation.

[0055] The difference between the local color value and the average color value of any point cloud under all color channels is positively fused and used as the color deviation of the any point cloud. In this embodiment, the specific process of forward fusion is as follows: calculate the mean of the absolute values ​​of the differences between the local color values ​​and the average color values ​​of any point cloud in all color channels, and use this as the color deviation of the point cloud. In other implementations, the implementer can calculate the sum of the absolute values ​​of the differences between the local color values ​​and the average color values ​​of any point cloud in all color channels, and use this as the color deviation of the point cloud.

[0056] The degree of dispersion of the reflectance of any point cloud and all point clouds in its neighborhood is calculated and denoted as the local dispersion. In this embodiment, the degree of dispersion is determined by calculating the standard deviation of the reflectance of any given point cloud and all point clouds in its neighborhood.

[0057] The difference between the local dispersion of any point cloud and the reflection fluctuation of its corresponding object is taken as the reflection deviation of the point cloud. In this embodiment, the absolute value of the difference between the local dispersion of any point cloud and the reflection fluctuation of its corresponding object is taken as the reflection deviation of the point cloud.

[0058] The color deviation and reflection deviation are positively fused and used as the elimination coefficient for any point cloud. In this embodiment, the specific process of forward fusion is as follows: the sum of color deviation and reflection deviation is used as the elimination coefficient of any point cloud.

[0059] It should be noted that the average color value reflects the overall color characteristics of the object corresponding to the power transmission and distribution line, while the local color value reflects the color characteristics of each point cloud and its neighborhood. The larger the color deviation, the greater the difference between the color of the point cloud in the local area and the overall color of the object corresponding to the power transmission and distribution line, indicating that it may be a noise point or an obstruction. The larger the local dispersion, the greater the fluctuation of the reflectivity of the point cloud in the local area, indicating that it may be a noise point or an obstruction. The larger the reflection deviation, the greater the difference between the reflectivity fluctuation of the point cloud in the local area and the overall reflectivity fluctuation of the object corresponding to the power transmission and distribution line, and the more likely it is a noise point or an obstruction. The larger the elimination coefficient, the greater the difference between the color and reflectivity characteristics of the point cloud on the object corresponding to the power transmission and distribution line and the overall characteristics of the conductor, and the more likely it is a noise point or an obstruction, which needs to be eliminated to improve the accuracy of the three-dimensional reconstruction of the power transmission and distribution line.

[0060] Anomaly detection is performed on the removal coefficients of all point clouds on each object corresponding to the power transmission and distribution line, and abnormal point clouds are obtained and removed. In this embodiment, the Local Outlier Factor (LOF) algorithm is used for anomaly detection. The LOF algorithm is a well-known technology and will not be described in detail here. As other implementation methods, implementers may use other methods of the prior art, such as the Laida method, for anomaly detection. This embodiment does not impose any special restrictions on this.

[0061] Because removing anomalous point clouds from power transmission and distribution lines can result in voids appearing in the objects corresponding to these lines, point cloud filling is performed on these objects. Specifically: Fill the point cloud after removing point clouds from each object corresponding to the power transmission and distribution line. It should be noted that the point cloud filling process is a well-known technique and will not be described in detail here.

[0062] Three-dimensional reconstruction is performed on all point clouds of objects corresponding to power transmission and distribution lines after filling. The reconstructed three-dimensional point cloud model is imported into finite element software to construct a finite element model of the power transmission and distribution line. Under disaster conditions, the finite element model is simulated to evaluate the operating status of the power transmission and distribution line.

[0063] In this embodiment, the finite element software used is ABAQUS. The construction of the finite element model is a well-known technique and will not be described in detail here.

[0064] It should be noted that since the cross-section of a power transmission and distribution line is a circle, the 3D reconstruction process is as follows: the least squares method is used to perform curve fitting on all the point clouds of each object corresponding to the power transmission and distribution line after filling, and the fitted curve is obtained. The curve is used as the central axis of the cylinder, and point clouds on the surface of the cylinder with equal diameter are generated in space according to the actual diameter of the power transmission and distribution line, so that the diameter of the 3D reconstructed power transmission and distribution line is consistent with the actual diameter.

[0065] It should be noted that during the actual operation of power transmission and distribution lines, a multi-source sensing network is deployed. Fiber optic temperature measurement nodes are installed at key joints of the power transmission and distribution lines, and vibration sensors are installed on the transmission towers to measure the temperature data of the power transmission and distribution lines and the vibration data of the transmission towers. At the same time, wind speed, wind force, and temperature and humidity data of the environment are collected through wind speed, wind force, and temperature and humidity sensors. All data are imported into finite element software for disaster simulation. By analyzing the stress changes of the power transmission and distribution lines and transmission towers under disaster scenarios, and by using a hierarchical structure model and dynamic current carrying capacity, the operating status of the power transmission and distribution lines is evaluated. The hierarchical structure model and dynamic current carrying capacity are well-known technologies and will not be elaborated here.

[0066] It should be understood that, although Figure 1The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, shall fall within the protection scope of the technical solution of this application.

Claims

1. A simulation evaluation method for power transmission and distribution lines under disaster conditions, characterized in that, The method includes the following steps: The environment in which the power transmission and distribution lines are located is scanned to generate point clouds containing reflectivity and color information, and a three-dimensional point cloud model is constructed. Classify all point clouds on the 3D point cloud model and mark each object; analyze the geometric morphological characteristics of each object and calculate the first characterization value of each object; determine the second characterization value of each object by the average level of reflectivity of the point cloud contained in each object and the size of the object; combine the first characterization value to obtain the tower evaluation value of each object; filter all objects and mark the objects corresponding to the transmission towers. Based on the spatial distribution of objects corresponding to transmission towers, the 3D point cloud model is divided into multiple spatial regions. Based on the geometric shape of each object in the spatial region and the fluctuation of the reflectivity of the point cloud, the conductor evaluation value of each object is calculated, and all objects in the spatial region are screened to mark the objects corresponding to the transmission and distribution lines. By analyzing the degree of deviation in color information and the fluctuation of reflectivity of point clouds on objects corresponding to power transmission and distribution lines within a local area, the elimination coefficient of each point cloud is determined. Abnormal point clouds on objects corresponding to power transmission and distribution lines are identified and eliminated. After refilling the point cloud, the objects corresponding to power transmission and distribution lines are reconstructed in three dimensions. The reconstructed three-dimensional point cloud model is simulated under disaster conditions to evaluate the operating status of power transmission and distribution lines. Among them, the least squares method is used to perform curve fitting on all the point clouds of each object corresponding to the power transmission and distribution line after filling, and obtain the fitting curve. The curve is used as the central axis of the cylinder, and point clouds on the surface of the cylinder with equal diameter are generated in space according to the actual diameter of the power transmission and distribution line, so that the diameter of the power transmission and distribution line after 3D reconstruction is consistent with the actual diameter. The calculation of the first characterization value of each object includes: constructing the bounding box of each object; calculating the ratio of the length, width and height of the bounding box, and recording them as the first ratio and the second ratio, respectively; the first characterization value is the result of positively fusing the first ratio and the second ratio. The step of dividing the 3D point cloud model into multiple spatial regions includes: within the bounding box of the object corresponding to each transmission tower, a plane passing through the centroid and perpendicular to the centroid line segment is denoted as a dividing surface; and the spatial region between the dividing surfaces corresponding to the objects of two adjacent transmission towers is obtained.

2. The method for simulating and evaluating power transmission and distribution lines under disaster conditions as described in claim 1, characterized in that, Determining the second characterization value for each object includes: Calculate the mean reflectance of all point clouds contained in each object, denoted as the average reflectance; calculate the volume of the bounding box corresponding to each object. The second characterization value is the result of positively fusing the average reflectance and volume.

3. The method for simulating and evaluating power transmission and distribution lines under disaster conditions as described in claim 1, characterized in that, The tower evaluation value is negatively correlated with the first characterization value, but positively correlated with the second characterization value.

4. The method for simulation evaluation of power transmission and distribution lines under disaster conditions as described in claim 1, characterized in that, The process of obtaining the object corresponding to the transmission tower is as follows: obtain the segmentation threshold of the tower evaluation value of all objects, denoted as the first threshold, and mark the objects whose tower evaluation value is greater than the first threshold as the objects corresponding to the transmission tower.

5. The method for simulation evaluation of power transmission and distribution lines under disaster conditions as described in claim 1, characterized in that, The calculation of the wire evaluation value for each object includes: The difference in length and width of the bounding box corresponding to each object within the spatial region is denoted as the relative difference. The degree of dispersion of the reflectance of all point clouds on each object within a spatial region is denoted as reflectance variability. The conductor evaluation value is positively correlated with the relative difference, but negatively correlated with the reflection fluctuation.

6. The method for simulation evaluation of power transmission and distribution lines under disaster conditions as described in claim 1, characterized in that, The process of obtaining the objects corresponding to the power transmission and distribution lines is as follows: obtain the segmentation threshold of the conductor evaluation value of all objects in each spatial area, which is denoted as the second threshold. Objects with conductor evaluation values ​​greater than the second threshold are marked as objects corresponding to the power transmission and distribution lines.

7. The method for simulating and evaluating power transmission and distribution lines under disaster conditions as described in claim 5, characterized in that, The determination of the removal coefficient for each point cloud includes: The average color value is the average value of the color values ​​of all point clouds on each object corresponding to the power transmission and distribution line in each color channel. For any point cloud on each object corresponding to the power transmission and distribution line, calculate the average color value of the point cloud and all point clouds in its neighborhood under each color channel, and record it as the local color value; perform positive fusion on the difference between the local color value and the average color value of the point cloud under all color channels, and use it as the color deviation of the point cloud. The degree of dispersion of reflectance of any point cloud and all point clouds in its neighborhood is calculated and denoted as local dispersion; the difference between the local dispersion of any point cloud and the reflectance fluctuation of the object to which it belongs is taken as the reflectance deviation of the point cloud. The rejection factor is the result of positive fusion of color deviation and reflection deviation.

8. The method for simulating and evaluating power transmission and distribution lines under disaster conditions as described in claim 1, characterized in that, The process of identifying and removing abnormal point clouds on objects corresponding to power transmission and distribution lines includes: performing anomaly detection on the removal coefficients of all point clouds on each object corresponding to the power transmission and distribution line, obtaining abnormal point clouds, and removing them.

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