Three-dimensional power transmission channel reconstruction method and device combined with a one-line laser

Abstract

The present invention discloses a three-dimensional transmission line corridor reconstruction method and device combined with a one-dimensional line laser, which relates to the technical field of power engineering. The method includes: collecting environmental data of the target area, reading the transmission line design data to establish a layout, planning the scanning path of the mobile platform and configuring the moving strategy, synchronously activating the laser and the camera to capture the laser line projection to establish an image when the mobile platform moves, obtaining the baseline distance and the device angle, establishing two-dimensional coordinates based on the image and calculating the depth data, and performing three-dimensional reconstruction using the two-dimensional coordinates and the depth data and analyzing the similarity loss to update the reconstruction result. It solves the technical problem in the prior art that due to obstacles and light interference in the target area, the reliability and accuracy of the reconstruction result are insufficient. Through the highly accurate, highly reliable and high-precision reconstruction of the three-dimensional transmission line corridor, it achieves the technical effect of providing reliable support for the monitoring and maintenance of transmission lines.

Description

Technical Field

[0001] This application relates to the technical field of power engineering, and particularly to a three-dimensional transmission channel reconstruction method and device combined with a one-line laser. Background Art

[0002] In the scenario of power transmission channel management, the safety monitoring of transmission channels is crucial, and there are many deficiencies in the existing technologies. Traditional transmission channel monitoring is often relatively crude, with low accuracy, greatly affected by environmental factors, relying only on conventional monitoring means, lacking sufficient control over the overall condition of the transmission channel, lacking in-depth analysis and response to complex environmental factors, and being difficult to comprehensively and accurately reflect the actual situation of the transmission channel. In terms of resource allocation, the existing monitoring technologies are unreasonable, resulting in insufficient monitoring in some key areas and waste of resources in some areas. The monitoring scheme for transmission channels is relatively simple and fixed, and cannot well cope with the complex and changeable transmission environment. Therefore, there is an urgent need for a new three-dimensional transmission channel reconstruction method to improve the accuracy, reliability, and precision of reconstruction, and provide strong guarantee for the safe operation of transmission lines.

[0003] In the related technologies at the present stage, there are technical problems of insufficient reliability and precision of the reconstruction results due to obstacles and light interference in the target area. Summary of the Invention

[0004] This application provides a three-dimensional transmission channel reconstruction method and device combined with a one-line laser. By collecting environmental data of the target area (including obstacle and light characteristics), reading the design data of the transmission line to establish a layout, planning the scanning path of the mobile platform and configuring the movement strategy (the platform integrates a one-line laser and a monocular camera and is fixed), synchronously activating the laser and the camera when the mobile platform moves to capture the laser line projection to establish an image, obtaining the baseline distance and the device angle, establishing two-dimensional coordinates based on the image and calculating the depth data, and using the two-dimensional coordinates and the depth data for three-dimensional reconstruction and analyzing the similarity loss to update the reconstruction result, it realizes the high-accuracy, high-reliability, and high-precision reconstruction of the three-dimensional transmission channel, achieving the technical effect of providing reliable support for the monitoring and maintenance of the transmission line.

[0005] This application provides a three-dimensional transmission channel reconstruction method combined with a one-line laser, including:

[0006] Perform regional environment acquisition of the target area, establish a regional environment dataset, where the regional environment dataset includes obstacle feature data and light feature data; read the design data of the transmission line, use the design data to establish the line layout of the transmission line, plan the scanning path based on the line layout and the regional environment dataset, and configure the movement strategy of the mobile platform. Among them, the mobile platform is integrated with a one-line laser and a monocular camera, and the one-line laser and the monocular camera are fixed on the mobile platform through a preset relative position; when the mobile platform starts to move based on the movement strategy, synchronously activate the one-line laser and the monocular camera, use the monocular camera to capture the laser line projection projected by the one-line laser on the transmission line in the target area, and establish a projection image; obtain the baseline distance and device angle between the one-line laser and the monocular camera through the preset relative position, establish the two-dimensional coordinates of the pixels according to the projection image, and calculate the depth data through the baseline distance and device angle; perform three-dimensional reconstruction using the two-dimensional coordinates and depth data, and perform similarity loss analysis of the associated points on the three-dimensional reconstruction result, and update the three-dimensional reconstruction result with the similarity loss analysis result.

[0007] This application also provides a three-dimensional transmission channel reconstruction device combined with a one-line laser, including:

[0008] A regional environment dataset establishment module, which is used to perform regional environment acquisition of the target area and establish a regional environment dataset, where the regional environment dataset includes obstacle feature data and light feature data; a movement strategy configuration module, which is used to read the design data of the transmission line, use the design data to establish the line layout of the transmission line, plan the scanning path based on the line layout and the regional environment dataset, and configure the movement strategy of the mobile platform. Among them, the mobile platform is integrated with a one-line laser and a monocular camera, and the one-line laser and the monocular camera are fixed on the mobile platform through a preset relative position; a projection image establishment module, which is used to synchronously activate the one-line laser and the monocular camera when the mobile platform starts to move based on the movement strategy, use the monocular camera to capture the laser line projection projected by the one-line laser on the transmission line in the target area, and establish a projection image; a depth data calculation module, which is used to obtain the baseline distance and device angle between the one-line laser and the monocular camera through the preset relative position, establish the two-dimensional coordinates of the pixels according to the projection image, and calculate the depth data through the baseline distance and device angle; a three-dimensional reconstruction result update module, which is used to perform three-dimensional reconstruction using the two-dimensional coordinates and depth data, and perform similarity loss analysis of the associated points on the three-dimensional reconstruction result, and update the three-dimensional reconstruction result with the similarity loss analysis result.

[0009] The three-dimensional transmission line corridor reconstruction method and device combining a one-dimensional line laser proposed in this application first collect the environmental data of the target area (including obstacle and light characteristics), read the transmission line design data to establish a layout, plan the scanning path of the mobile platform and configure the moving strategy (the platform integrates a one-dimensional line laser and a monocular camera and is fixed). When the mobile platform moves, the laser and the camera are synchronously activated to capture the laser line projection to establish an image, obtain the baseline distance and the device angle, establish two-dimensional coordinates based on the image and calculate the depth data, use the two-dimensional coordinates and the depth data for three-dimensional reconstruction and analyze the similarity loss to update the reconstruction result. Through the highly accurate, highly reliable and high-precision reconstruction of the three-dimensional transmission line corridor, the technical effect of providing reliable support for the monitoring and maintenance of transmission lines is achieved. Brief Description of the Drawings

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the devices according to the embodiments of the application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to needs, they can be executed in reverse order or processed simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0011] Figure 1 It is a schematic flowchart of the three-dimensional transmission line corridor reconstruction method combining a one-dimensional line laser provided by an embodiment of this application;

[0012] Figure 2 It is a schematic structural diagram of the three-dimensional transmission line corridor reconstruction device combining a one-dimensional line laser provided by an embodiment of this application.

[0013] Explanation of the reference numerals: The regional environmental data set establishment module 10, the moving strategy configuration module 20, the projection image establishment module 30, the depth data calculation module 40, the three-dimensional reconstruction result update module 50. Detailed Embodiments

[0014] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically illustrates the detailed embodiments of this application.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limitations to this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0016] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or server comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0017] The embodiments of this application provide a three-dimensional transmission line corridor reconstruction method combined with a one-dimensional laser, as Figure 1 shown. The method includes:

[0018] Step S100, perform regional environment acquisition of the target area, and establish a regional environment data set. The regional environment data set includes obstacle feature data and light feature data. Specifically, first determine the range of the target area for three-dimensional transmission line corridor reconstruction, and then collect obstacle feature data, including information such as the position, shape, and height of obstacles, through on-site investigation, sensor detection, and image recognition. At the same time, use tools such as light sensors, solar position sensors, and reflectance meters to collect light feature data, such as light intensity, light direction, and reflection characteristics. Finally, organize and store the collected obstacle feature data and light feature data to establish a regional environment data set, which is managed in the form of a database for convenient subsequent query and analysis, providing basic information for three-dimensional transmission line corridor reconstruction.

[0019] Step S200: Read the design data of the transmission line, use the design data to establish the line layout of the transmission line, plan the scanning path based on the line layout and the regional environment dataset, and configure the movement strategy of the mobile platform. The mobile platform is integrated with a line laser and a monocular camera, and the line laser and the monocular camera are fixed on the mobile platform at a preset relative position. Specifically, read the design data of the transmission line to establish the line layout, and use the pole tower positions as key nodes to construct the three-dimensional position information of the transmission line in space. Then, combine the line layout with the obstacle features and light features in the regional environment dataset to plan the scanning path. Consider the mobility and stability of the mobile platform, try to make the scanning path parallel to the transmission line and avoid obstacles, and at the same time adjust the movement strategy according to the light intensity and direction. Then, configure the movement strategy of the mobile platform, determine the movement speed, set the pause positions, and consider the impact of environmental changes on the scanning process. For example, the mobile platform performs regional scanning in a top-down scanning mode, with a 180-degree scanning angle to ensure full coverage of the transmission line area. During this process, adjust the power of the laser according to the light intensity to cope with different lighting conditions. Finally, the mobile platform is integrated with a line laser and a monocular camera, and the two are fixed on the mobile platform at a preset relative position, ensuring firm installation and considering the equipment heat dissipation and protection issues, providing reliable support for the three-dimensional reconstruction of the transmission channel.

[0020] In a possible implementation, the design data of the transmission line is read, and the line layout of the transmission line is established by using the design data. The scanning path is planned based on the line layout and the regional environment dataset, and the movement strategy of the mobile platform is configured. Wherein, the mobile platform is integrated with a one-dimensional line laser and a monocular camera, and the one-dimensional line laser and the monocular camera are fixed on the mobile platform at a preset relative position. Step S200 further includes step S210, obtaining the platform size of the mobile platform, performing a spatial impact analysis based on the platform size and the obstacle feature data in the regional environment dataset, and establishing a passing angle adaptation value for each position point. Specifically, determining the platform size of the mobile platform includes parameters such as length, width, and height. The size information provides data support for analyzing the movement ability and spatial adaptability of the mobile platform in the target area. Combining the obstacle feature data in the regional environment dataset for spatial impact analysis, the obstacle feature data includes information such as the positions, shapes, and sizes of various obstacles in the target area. By analyzing the spatial relationship between the mobile platform and the obstacles, the passing angle of the mobile platform at different position points can be determined. For example, if there is a tall building near a certain position point of the mobile platform, the passing angle of the mobile platform at this position point may be restricted. According to the shape and position of the obstacle, the maximum angle range within which the mobile platform can pass safely at this position point can be calculated, thereby establishing a passing angle adaptation value for each position point. The adaptation value reflects the spatial adaptability of the mobile platform to the obstacles at this position point. The higher the value, the larger the passing angle, and the easier it is for the mobile platform to pass through this position point.

[0021] Step S220, calling the light feature data, performing a layout angle light adaptation analysis of the mobile platform based on the light feature data and the preset relative position, and establishing a light angle adaptation value for each position point. Specifically, calling the light feature data in the regional environment dataset, the data includes information such as light intensity and light direction. The light features affect the working effects of the one-dimensional line laser and the monocular camera on the mobile platform. Based on the light feature data and the preset relative position, a layout angle light adaptation analysis of the mobile platform is performed. The preset relative position refers to the fixed position relationship between the one-dimensional line laser and the monocular camera on the mobile platform. According to the incident angle of the light and the preset relative position, the laser line projection effect of the one-dimensional line laser and the image capture effect of the monocular camera at different position points can be determined. For example, if the light direction does not match the layout angle of the mobile platform at a certain position point, it may cause the laser line projection to be unclear or the monocular camera to be unable to capture images normally. By analyzing the light features and the preset relative position, the optimal layout angle range of the mobile platform at this position point can be calculated, thereby establishing a light angle adaptation value for each position point. This adaptation value reflects the adaptability of the mobile platform to the light at this position point. The higher the value, the more suitable the light angle, and the better the laser line projection and image capture effects.

[0022] Step S230: Optimize the scanning path of the mobile platform according to the passage angle adaptation value and the light angle adaptation value, and configure the movement strategy according to the scanning path optimization result. Specifically, use the previously obtained passage angle adaptation value and light angle adaptation value as important parameters to optimize the scanning path of the mobile platform. The purpose of scanning path optimization is to find a path that enables the mobile platform to move efficiently and safely within the target area while ensuring that the one-dimensional line laser and the monocular camera can achieve the best working effects. Optimization algorithms such as genetic algorithms and simulated annealing algorithms can be used to find the optimal scanning path. During the optimization process, the passage angle adaptation value and the light angle adaptation value are used as part of the objective function, and other factors such as path length and movement time are also considered. Through continuous iterative optimization, an optimal scanning path with the best comprehensive performance is found. Configure the movement strategy according to the scanning path optimization result. The movement strategy includes the movement speed, stop position, steering angle, etc. of the mobile platform. According to the characteristics and requirements of the scanning path, reasonably configuring the movement strategy can improve the reconstruction efficiency and accuracy. For example, if there are some narrow channels or complex obstacle areas on the scanning path, the movement speed can be appropriately reduced and the stop position can be increased to better control the movement of the mobile platform. If there are some areas with good light conditions on the scanning path, the layout angle of the mobile platform can be adjusted to obtain better laser line projection and image capture effects. For example, the mobile platform scans the area in a top-down scanning mode with a 180-degree scanning angle to ensure full coverage of the transmission line area. During this process, adjust the power of the laser according to the light intensity to cope with different light conditions. By obtaining the platform size of the mobile platform and combining the obstacle feature data for spatial impact analysis, calling the light feature data for layout angle light adaptation analysis, and then optimizing the scanning path and configuring the movement strategy according to the passage angle adaptation value and the light angle adaptation value, the efficiency and accuracy of the three-dimensional transmission channel reconstruction combined with the one-dimensional line laser can be improved.

[0023] In a possible implementation manner, to optimize the scanning path of the mobile platform according to the passage angle adaptation value and the light angle adaptation value, and configure the movement strategy according to the scanning path optimization result, step S230 further includes step S231: Establish a comprehensive cost function, and optimize the scanning path with the comprehensive cost function as follows: ; where is the comprehensive cost function, is the total number of fitting position points of the scanning path, represents any position point, is the th passage angle adaptation value of the position point, is the th light angle adaptation value of the position point, For the action coherence cost value between the th position point and the th position point. Specifically, the comprehensive cost function formula is , where represents that the comprehensive cost function is used to measure the quality of the scanning path, is the total number of position points fitted by the scanning path, represents any position point, is the th position point passing angle adaptation value obtained according to the mobile platform size and obstacle feature data, reflecting the spatial adaptability between the mobile platform and the obstacle at this position point; The th position point light angle adaptation value obtained by analyzing the light feature data and the preset relative position reflects the adaptability between the laser line projection and image capture effect at this position point and the light; is the action coherence cost value between the th and the th position points, , , are the weight coefficients of the passing angle, light angle and action coherence cost value respectively. When optimizing the scanning path with the comprehensive cost function, first determine the initial scanning path and calculate its value, and then use optimization algorithms such as genetic algorithms to continuously generate new path populations through operations such as crossover and mutation and calculate the value to find the optimal scanning path that minimizes the value, providing a better basis for the three-dimensional transmission line corridor reconstruction.

[0024] Step S300, when the mobile platform starts to move based on the mobile strategy, synchronously activate the one-line laser and the monocular camera, and use the monocular camera to capture the laser line projection of the one-line laser projected on the transmission line in the target area to establish a projection image. Specifically, when the mobile platform starts according to the mobile strategy, synchronously activate the one-line laser and the monocular camera. The one-line laser projects a laser line onto the transmission line in the target area, and the monocular camera captures the laser line projection. A projection image is established through the image capture of the monocular camera. During this process, the camera parameters and position need to be adjusted, and the image is preliminarily processed and analyzed to provide basic data for subsequent depth data calculation and three-dimensional reconstruction.

[0025] Step S400: Obtain the baseline distance and device angle between the one-line laser and the monocular camera through a preset relative position. Establish the two-dimensional coordinates of pixels based on the projection image, and calculate the depth data through the baseline distance and device angle. Specifically, in the three-dimensional power transmission channel reconstruction method combined with the one-line laser, first, since the one-line laser and the monocular camera are fixed on the mobile platform through a preset relative position, the baseline distance (horizontal distance) and device angle (the angle between the laser and the optical axis of the camera) between the two can be obtained. Then, when the monocular camera captures the laser line projection, a projection image is established, and each pixel in the image can be represented by two-dimensional coordinates where is the abscissa, is the ordinate. The pixel position is determined through the image coordinate system to obtain the two-dimensional coordinates. Finally, using the triangulation method, the depth data is calculated based on the baseline distance, device angle, and the two-dimensional coordinates of the pixels in the projection image. The specific formula is where, is the depth data, is the baseline distance, is the device angle, is the horizontal coordinate of the pixel point, is the horizontal coordinate of the principal point of the image, is the focal length of the monocular camera. During the calculation process, it is necessary to ensure the accuracy of the parameters, consider the camera distortion error, and filter and optimize the depth data to provide key depth information for three-dimensional reconstruction.

[0026] In a possible implementation, obtain the baseline distance and device angle between the one-line laser and the monocular camera through a preset relative position. Establish the two-dimensional coordinates of pixels based on the projection image, and calculate the depth data through the baseline distance and device angle. Step S400 further includes step S410: perform image analysis on the projection image to determine the principal point of the image. Specifically, first, conduct in-depth image analysis on the projection image captured by the monocular camera. The projection image is formed by the laser line projected by the one-line laser on the power transmission line in the target area being captured by the monocular camera. The purpose of image analysis is to determine the principal point of the image. The principal point of the image is a special point in the image, usually located at the center of the image or corresponding to the optical axis of the camera. In the calculation of depth data, the position of the principal point of the image is very important because it affects the calculation of the deflection angle introduced by the pixel coordinates of the laser line in the projection image. Adopt various image analysis methods to determine the principal point of the image, such as feature point detection, edge detection, Hough transform, etc., to help identify the key features and structures in the image, thereby determining the position of the principal point of the image.

[0027] Step S420: Calculate the depth data using the formula as follows: ; where represents the depth data of the pixel point, is the baseline distance, is the device included angle, characterizes the deflection angle introduced by the pixel coordinates of the laser line in the projection image, is the horizontal coordinate of the pixel point, is the horizontal coordinate of the principal point of the image, is the focal length of the monocular camera. Specifically, , the formula is used to calculate the depth data of the pixel point , where, is the baseline distance, that is, the horizontal distance between the cross-line laser and the monocular camera; is the device included angle, that is, the included angle between the laser and the optical axis of the camera; characterizes the deflection angle introduced by the pixel coordinates of the laser line in the projection image, is the horizontal coordinate of the pixel point, is the horizontal coordinate of the principal point of the image, is the focal length of the monocular camera. The principle of the formula is based on the triangulation method. By the known baseline distance, device included angle, and the position of the pixel in the projection image (determined by the horizontal coordinate and the horizontal coordinate of the principal point of the image ), and the camera focal length , the distance from the object to the camera, that is, the depth data, can be calculated , determine the values of each parameter in the formula. The baseline distance and the device included angle are determined in the system design stage and can be obtained through measurement or known parameters. The camera focal length can be obtained from the camera's specification manual or camera calibration. The horizontal coordinate of the principal point of the image is determined in the image analysis stage. The horizontal coordinate of the pixel point can be obtained through pixel analysis of the projection image. Calculate the deflection angle , the angle is determined by the horizontal coordinate of the pixel point and the horizontal coordinate of the principal point of the image and the camera focal length , which represents the deflection degree of the pixel coordinates of the laser line in the projection image relative to the principal point of the image. Calculate to represent the sum of the device included angle and the deflection angle. Calculate and divide the baseline distance by this value to obtain the depth data of the pixel point , determine the principal point of the image through image analysis of the projection image, and then use a specific formula for depth data calculation to obtain the depth information of the pixel point, providing key data support for the three-dimensional transmission line corridor reconstruction combined with the cross-line laser.

[0028] Step S500: Perform 3D reconstruction using 2D coordinates and depth data, and conduct similarity loss analysis on associated points of the 3D reconstruction results, and update the 3D reconstruction results based on the similarity loss analysis results. Specifically, using the 2D coordinates and depth data of the projected image pixels obtained, convert the 2D coordinates into 3D space coordinates through calculation, thereby constructing a preliminary 3D model of the transmission line in the target area. Determine the associated points in the 3D reconstruction results, calculate the similarity loss of each pair of associated points in different reconstruction results, understand the stability and reliability of the reconstruction results by analyzing the similarity loss, determine the update strategy according to the similarity loss analysis results. If the similarity loss is large, adjust the 3D model, such as slightly adjusting some point coordinates or recalculating the depth data, etc. After adjustment, perform 3D reconstruction and similarity loss analysis again, and repeat this process until the similarity loss reaches an acceptable range or a preset number of iterations, so as to continuously improve the accuracy and reliability of the 3D transmission channel reconstruction.

[0029] In a possible implementation, when performing 3D reconstruction using 2D coordinates and depth data, and conducting similarity loss analysis on associated points of the 3D reconstruction results, and updating the 3D reconstruction results based on the similarity loss analysis results, step S500 further includes step S510: Obtain the acquisition time of the same reconstructed pixel point in the 3D reconstruction results, and establish a temporal reconstruction result using the acquisition time. Specifically, for the obtained 3D reconstruction results, it is necessary to obtain the acquisition time of the same reconstructed pixel point therein. During the process of 3D transmission channel reconstruction, as the mobile platform moves and data is acquired, the acquisition times of different pixel points are different. By recording the acquisition time of each pixel point, it can provide a basis for subsequent temporal analysis. Using the acquisition time, a temporal reconstruction result can be established, that is, arranging the 3D reconstruction results in chronological order to form a reconstruction sequence that changes over time, and the state changes of the 3D transmission channel at different time points can be observed, providing more comprehensive information for further analysis.

[0030] Step S520: Conduct temporal similarity loss analysis on the temporal reconstruction results to establish a temporal similarity loss analysis result. Specifically, for the established temporal reconstruction results, conduct temporal similarity loss analysis. The purpose is to evaluate the similarity degree between the 3D reconstruction results at different time points. If the reconstruction results at two time points are very similar, it indicates that there is no obvious change in the state of the transmission channel during this period; if the similarity degree is low, it indicates that some changes may have occurred. The specific analysis method can be to calculate the differences of the corresponding pixel points in the reconstruction results at different time points. For example, the coordinate differences and color differences of the pixel points can be calculated. Based on these differences, a temporal similarity loss value can be determined, which reflects the similarity degree between the reconstruction results at two time points, and establish a temporal similarity loss analysis result to provide a basis for subsequent updates.

[0031] Step S530: Establish position adjacent associations for pixel points. The position adjacent associations include direct adjacent associations and indirect adjacent associations. Specifically, establish position adjacent associations for pixel points in the 3D reconstruction result. A direct adjacent association refers to the association between pixel points that are directly adjacent in the physical sense. For example, in an image, the eight pixel points surrounding a pixel point are its directly adjacent pixel points. An indirect adjacent association is determined by taking a predetermined range area. If a pixel point is within this predetermined range area, it is considered to have an indirect adjacent association with other pixel points within the area. The indirect adjacent association can consider the degree of clustering of pixel points in a larger range, not limited to directly adjacent pixel points.

[0032] Step S540: Use the position adjacent associations to perform position similarity loss analysis for pixel points and establish the position similarity loss analysis result. Specifically, use the established position adjacent associations to perform position similarity loss analysis for pixel points. For pixel points with adjacent associations, calculate the similarity degree between them. If the difference between adjacent pixel points is small, it indicates that they have a high similarity in position; if the difference is large, it indicates a low position similarity. The specific analysis method can be to calculate the coordinate difference, color difference, etc. between adjacent pixel points. Based on the difference, a position similarity loss value can be determined. This value reflects the position similarity degree between adjacent pixel points. If the difference is small, it indicates that they have a high similarity in position; if the difference is large, it indicates a low position similarity. The specific analysis method can be to calculate the coordinate difference, color difference, etc. between adjacent pixel points. Based on the difference, a position similarity loss value can be determined. This value reflects the position similarity degree between adjacent pixel points and establish the position similarity loss analysis result to provide a basis for subsequent updates.

[0033] Step S550: Use the position similarity loss analysis result and the time similarity loss analysis result as the similarity loss analysis result to update the 3D reconstruction result. Specifically, use the position similarity loss analysis result and the time similarity loss analysis result as the similarity loss analysis result. The two results respectively reflect the similarity degree of the 3D reconstruction result from the aspects of position and time. Using the similarity loss analysis result, the 3D reconstruction result can be updated. If the similarity loss is large, it indicates that there may be errors or inaccuracies in the reconstruction result and adjustments and optimizations are needed. The pixel points in the 3D reconstruction result can be adjusted according to the size and distribution of the similarity loss, such as adjusting attributes such as coordinates and colors, to improve the accuracy and reliability of the reconstruction result. By obtaining the acquisition time to establish the time-series reconstruction result, performing time similarity loss analysis and position similarity loss analysis, and using the results to update the 3D reconstruction result, the quality and accuracy of the 3D transmission line corridor reconstruction can be continuously improved.

[0034] In a possible implementation, the position similarity loss analysis result and the time similarity loss analysis result are used as the similarity loss analysis result to update the 3D reconstruction result. Step S550 further includes step S551 of performing independent threshold verification of the time deviation threshold on the time similarity loss analysis result to establish a first reconstruction influence result. Specifically, first, the obtained time similarity loss analysis result is further processed. The time similarity loss analysis result reflects the similarity degree between the 3D reconstruction results at different time points. An independent threshold verification is introduced by using the time deviation threshold, which is a standard value preset according to the actual situation and is used to determine whether the time similarity loss is within an acceptable range. If the time similarity loss is less than or equal to the time deviation threshold, it indicates that the change in the reconstruction results at different time points is small and within a reasonable range. If the time similarity loss is greater than the time deviation threshold, it indicates that the change in time is large and there may be abnormal situations. By comparing the time similarity loss analysis result with the time deviation threshold, a first reconstruction influence result is established. If the time similarity loss is within the threshold range, the first reconstruction influence result can consider that the influence of time factors on the 3D reconstruction result is small and a relatively positive evaluation can be given. If the time similarity loss exceeds the threshold range, the first reconstruction influence result indicates that time factors have a greater impact on the reconstruction result and further attention and processing are required.

[0035] Step S552 is to perform joint anomaly verification on the time similarity loss analysis result and the position similarity loss analysis result to establish a second reconstruction influence result. Specifically, then, the time similarity loss analysis result and the position similarity loss analysis result are combined for joint anomaly verification. The position similarity loss analysis result reflects the similarity degree of pixel points in spatial positions. The purpose of joint anomaly verification is to comprehensively consider factors in both time and space to more comprehensively evaluate the accuracy and reliability of the 3D reconstruction result. If both the time similarity loss and the position similarity loss are small, it indicates that the changes in both time and space are small and the reconstruction result is relatively stable. If one or both of the similarity losses are large, it indicates that there may be abnormal situations. By jointly analyzing the time similarity loss and the position similarity loss, a second reconstruction influence result is established. This result synthesizes the influence of time and space factors on the reconstruction result and can more accurately reflect the quality of the reconstruction result and the direction for improvement.

[0036] Step S553: Update the 3D reconstruction result according to the first reconstruction impact result and the second reconstruction impact result. Specifically, update the 3D reconstruction result based on the first reconstruction impact result and the second reconstruction impact result. If both the first reconstruction impact result and the second reconstruction impact result indicate that the reconstruction result is relatively stable and accurate, major updates may not be necessary, or some fine-tuning can be performed to further improve the accuracy of the result. If the first reconstruction impact result or the second reconstruction impact result shows significant problems, major adjustments and optimizations need to be made to the 3D reconstruction result. The attributes such as the coordinates and colors of the pixel points can be adjusted according to the specific problem situation, or certain steps of the data acquisition and reconstruction process can be repeated to improve the quality of the reconstruction result. By independently validating the time similarity loss analysis result with a threshold, jointly validating the time and position similarity loss analysis results for anomalies, and updating the 3D reconstruction result according to the validation results, the accuracy and reliability of the 3D transmission line corridor reconstruction can be continuously improved.

[0037] In a possible implementation, 3D reconstruction is performed using 2D coordinates and depth data, and similarity loss analysis of the associated points of the 3D reconstruction result is carried out to update the 3D reconstruction result according to the similarity loss analysis result. Step S500 further includes Step S560: Establish a reconstruction time window according to the similarity loss analysis result. Specifically, first, the similarity loss analysis result is obtained through multi-faceted analysis of the 3D reconstruction result, including the time similarity loss analysis result and the position similarity loss analysis result, etc. The result reflects the accuracy and stability of the 3D reconstruction result, as well as the changes in time and space. Establish a reconstruction time window according to the similarity loss analysis result. If the similarity loss is small, it indicates that the reconstruction result is relatively stable, and at this time, a relatively short time window can be selected for subsequent operations. If the similarity loss is large, it indicates that there may be significant errors or uncertainties in the reconstruction result, and a longer time window needs to be selected to obtain more data for analysis and correction. The determination of the reconstruction time window can be adjusted according to specific application requirements and actual situations. For example, the appropriate length of the time window can be determined according to factors such as the importance of the transmission line corridor, the cost and efficiency of data acquisition.

[0038] Step S570: Perform additional data acquisition within the reconstruction time window and iterate the 3D reconstruction result with the additional data acquisition result. Specifically, after determining the reconstruction time window, perform additional data acquisition within this time window. The purpose of additional data acquisition is to obtain more information to further improve the accuracy and reliability of the 3D reconstruction result. Additional data acquisition can be carried out in various ways, such as increasing the acquisition times of the mobile platform, adjusting the parameters of the one-dimensional line laser and the monocular camera, changing the acquisition position and angle, etc. During the acquisition process, it is necessary to ensure that the acquired data is consistent and comparable with the previous data for effective iteration. Iterate the 3D reconstruction result with the additional data acquisition result, integrate the newly acquired data with the previous 3D reconstruction result, and re-perform the calculation and analysis of 3D reconstruction. Through continuous iteration, the quality of the reconstruction result can be gradually improved to make it closer to the actual power transmission channel situation. During the iteration process, some optimization algorithms and techniques, such as weighted average and least squares method, can be adopted to improve the efficiency and accuracy of iteration, evaluate and verify the iteration result to ensure that the reconstructed result after iteration meets the expected requirements. Establish the reconstruction time window according to the similarity loss analysis result, perform additional data acquisition within the time window, and iterate the 3D reconstruction result with the additional data acquisition result, which can continuously improve the quality and accuracy of the 3D power transmission channel reconstruction.

[0039] The embodiment of the present application adopts collecting the environmental data of the target area (including obstacle and light characteristics), reading the power transmission line design data to establish the layout, planning the scanning path of the mobile platform and configuring the moving strategy (the platform integrates a one-dimensional line laser and a monocular camera and is fixed), synchronously activating the laser and the camera when the mobile platform moves to capture the laser line projection to establish an image, obtaining the baseline distance and the device angle, establishing two-dimensional coordinates based on the image and calculating the depth data, using the two-dimensional coordinates and the depth data for 3D reconstruction and analyzing the similarity loss to update the reconstruction result, achieving the technical effect of providing reliable support for the monitoring and maintenance of the power transmission line through the highly accurate, highly reliable and high-precision reconstruction of the 3D power transmission channel.

[0040] In the above text, reference is made to Figure 1 which describes in detail the 3D power transmission channel reconstruction method combining a one-dimensional line laser according to the embodiment of the present invention. Next, reference will be made to Figure 2 to describe the 3D power transmission channel reconstruction device combining a one-dimensional line laser according to the embodiment of the present invention.

[0041] The three-dimensional power transmission channel reconstruction device combined with a one-dimensional line laser according to an embodiment of the present invention solves the technical problem in the prior art that due to obstacles and light interference in the target area, the reliability and accuracy of the reconstruction result are insufficient. Through the highly accurate, highly reliable and high-precision reconstruction of the three-dimensional power transmission channel, the technical effect of providing reliable support for the monitoring and maintenance of the power transmission line is achieved. The three-dimensional power transmission channel reconstruction device combined with a one-dimensional line laser includes: a regional environment data set establishment module 10, a movement strategy configuration module 20, a projection image establishment module 30, a depth data calculation module 40, and a three-dimensional reconstruction result update module 50.

[0042] The regional environment data set establishment module 10 is used to perform the acquisition of the regional environment of the target area and establish a regional environment data set, and the regional environment data set includes obstacle feature data and light feature data.

[0043] The movement strategy configuration module 20 is used to read the design data of the power transmission line, establish the line layout of the power transmission line by using the design data, plan the scanning path based on the line layout and the regional environment data set, and configure the movement strategy of the mobile platform. Among them, the mobile platform is integrated with a one-dimensional line laser and a monocular camera, and the one-dimensional line laser and the monocular camera are fixed on the mobile platform through a preset relative position.

[0044] The projection image establishment module 30 is used to synchronously activate the one-dimensional line laser and the monocular camera when the mobile platform starts to move based on the movement strategy, and use the monocular camera to capture the laser line projection projected by the one-dimensional line laser on the power transmission line in the target area to establish a projection image.

[0045] The depth data calculation module 40 is used to obtain the baseline distance and the device angle between the one-dimensional line laser and the monocular camera through the preset relative position, establish the two-dimensional coordinates of the pixels according to the projection image, and calculate the depth data through the baseline distance and the device angle.

[0046] The three-dimensional reconstruction result update module 50 is used to perform three-dimensional reconstruction by using the two-dimensional coordinates and the depth data, and perform a similarity loss analysis on the associated points of the three-dimensional reconstruction result, and update the three-dimensional reconstruction result with the similarity loss analysis result.

[0047] Next, the specific configuration of the mobile strategy configuration module 20 will be described in detail. As described above, the design data of the transmission line is read, and the line layout of the transmission line is established by using the design data. The scanning path is planned based on the line layout and the regional environment dataset, and the mobile strategy of the mobile platform is configured. Among them, the mobile platform is integrated with a one-dimensional line laser and a monocular camera, and the one-dimensional line laser and the monocular camera are fixed on the mobile platform at a preset relative position. The mobile strategy configuration module 20 further includes: a passing angle adaptation value construction unit, which is used to obtain the platform size of the mobile platform, perform spatial impact analysis according to the platform size and the obstacle feature data in the regional environment dataset, and establish the passing angle adaptation value of each position point; a light adaptation analysis unit, which is used to call the light feature data, perform light adaptation analysis of the layout angle of the mobile platform based on the light feature data and the preset relative position, and establish the light angle adaptation value of each position point; a scanning path optimization unit, which is used to optimize the scanning path of the mobile platform according to the passing angle adaptation value and the light angle adaptation value, and configure the mobile strategy according to the scanning path optimization result.

[0048] Among them, the scanning path of the mobile platform is optimized according to the passing angle adaptation value and the light angle adaptation value, and the mobile strategy is configured according to the scanning path optimization result. The scanning path optimization unit further includes: a comprehensive cost function construction subunit, which is used to establish a comprehensive cost function and optimize the scanning path with the comprehensive cost function as follows: ; where is the comprehensive cost function, is the total number of fitting position points of the scanning path, represents any position point, is the th passing angle adaptation value of the position point, is the th light angle adaptation value of the position point, is the rd position point and the th position point's cost value for smooth movement.

[0049] Next, the specific configuration of the depth data calculation module 40 will be described in detail. As described above, the baseline distance and the device angle between the cross-line laser and the monocular camera are obtained through the preset relative position, the two-dimensional coordinates of the pixels are established based on the projection image, and the depth data is calculated through the baseline distance and the device angle. The depth data calculation module 40 further includes: an image principal point determination unit, which is used to perform image analysis on the projection image to determine the image principal point; a depth data calculation unit, which is used to calculate the depth data using the formula as follows: ; where represents the depth data of the pixel point, is the baseline distance, is the device angle, represents the deflection angle introduced by the pixel coordinates of the laser line in the projection image, is the horizontal coordinate of the pixel point, is the horizontal coordinate of the image principal point, is the focal length of the monocular camera.

[0050] Next, the specific configuration of the 3D reconstruction result update module 50 will be described in detail. As described above, 3D reconstruction is performed using the two-dimensional coordinates and the depth data, and the similarity loss analysis of the associated points of the 3D reconstruction result is performed, and the 3D reconstruction result is updated with the similarity loss analysis result. The 3D reconstruction result update module 50 further includes: a temporal reconstruction result construction unit, which is used to obtain the acquisition time of the same reconstructed pixel point in the 3D reconstruction result and establish a temporal reconstruction result using the acquisition time; a temporal similarity loss analysis result establishment unit, which is used to perform temporal similarity loss analysis on the temporal reconstruction result and establish a temporal similarity loss analysis result; a position adjacent association unit, which is used to establish a position adjacent association for the pixel points, and the position adjacent association includes direct adjacent association and indirect adjacent association; a position similarity loss analysis result establishment unit, which is used to perform position similarity loss analysis on the pixel points using the position adjacent association and establish a position similarity loss analysis result; a 3D reconstruction result update unit, which is used to use the position similarity loss analysis result and the temporal similarity loss analysis result as the similarity loss analysis result to update the 3D reconstruction result.

[0051] Among them, the position similarity loss analysis result and the time similarity loss analysis result are used as the similarity loss analysis result to update the three-dimensional reconstruction result. The three-dimensional reconstruction result update unit further includes: a first reconstruction impact result construction subunit, which is used to perform independent threshold verification of the time deviation threshold on the time similarity loss analysis result to establish a first reconstruction impact result; a joint anomaly verification subunit, which is used to perform joint anomaly verification on the time similarity loss analysis result and the position similarity loss analysis result to establish a second reconstruction impact result; an impact result update subunit, which is used to update the three-dimensional reconstruction result according to the first reconstruction impact result and the second reconstruction impact result.

[0052] Among them, the three-dimensional reconstruction result update module 50 can further include: a reconstruction time window construction unit, which is used to establish a reconstruction time window according to the similarity loss analysis result; an additional data acquisition unit, which is used to perform additional data acquisition under the reconstruction time window to iterate the three-dimensional reconstruction result with the additional data acquisition result.

[0053] The three-dimensional transmission line channel reconstruction device combined with a one-line laser provided by the embodiment of the present invention can execute the three-dimensional transmission line channel reconstruction method combined with a one-line laser provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0054] Although various references are made to certain modules in the device according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0055] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A three-dimensional power transmission channel reconstruction method combined with a line laser, characterized in that: The method comprises: Execute regional environment collection of the target area and establish a regional environment data set, wherein the regional environment data set includes obstacle feature data and light feature data; Reading the design data of the transmission line, establishing the line layout of the transmission line using the design data, planning the scanning path based on the line layout and the regional environment data set, and configuring the mobile strategy of the mobile platform, wherein the mobile platform is integrated with a line laser and a monocular camera, and the line laser and the monocular camera are fixed on the mobile platform by preset relative positions; When the mobile platform starts to move based on the mobile strategy, the straight line laser and the monocular camera are synchronously activated, and the monocular camera is used to capture the laser line projection projected by the straight line laser on the power transmission line in the target area to establish a projection image; Obtain the baseline distance and device angle of the line laser and the monocular camera by presetting the relative position, establish the two-dimensional coordinates of the pixel according to the projected image, and calculate the depth data by the baseline distance and the device angle; Perform three-dimensional reconstruction using two-dimensional coordinates and depth data, and perform similarity loss analysis on the associated points of the three-dimensional reconstruction results, and update the three-dimensional reconstruction results with the similarity loss analysis results; The step of performing similarity loss analysis on the associated points of the three-dimensional reconstruction result and updating the three-dimensional reconstruction result with the similarity loss analysis result further includes: Obtaining the acquisition time of the same reconstruction pixel point in the three-dimensional reconstruction result, and using the acquisition time to establish a time-series reconstruction result; Performing a temporal similarity loss analysis on the time series reconstruction result to establish a temporal similarity loss analysis result, wherein the temporal similarity loss analysis is intended to evaluate the similarity between the three-dimensional reconstruction results at different time points, specifically, calculating the coordinate difference and color difference of the pixel points, and determining a temporal similarity loss value based on these differences, which reflects the similarity between the reconstruction results at two time points; Establishing a positional neighbor association for the pixel points, wherein the positional neighbor association includes a direct neighbor association and an indirect neighbor association, wherein the indirect neighbor association is determined by taking a predetermined range area; Using the positional adjacent association to perform positional similarity loss analysis of pixel points, and establishing the positional similarity loss analysis result, the positional similarity loss analysis aims to use the established positional adjacent association to perform positional similarity loss analysis of pixel points, and for pixel points with adjacent associations, calculate the similarity between them, specifically, calculate the coordinate difference and color difference of adjacent pixel points, and determine a positional similarity loss value based on the difference, which reflects the positional similarity between adjacent pixel points; The position similarity loss analysis results and the time similarity loss analysis results are used as similarity loss analysis results to update the three-dimensional reconstruction results.

2. The three-dimensional power transmission channel reconstruction method combined with a line laser as claimed in claim 1, characterized in that: The depth data is calculated by using the baseline distance and the device angle, and further includes: Performing image analysis on the projection image to determine the image principal point; The depth data is calculated using the formula as follows: ； in, Represents pixel depth data, is the baseline distance, is the device angle, Characterizes the deflection angle introduced by the pixel coordinates of the laser line in the projected image, is the horizontal coordinate of the pixel, is the horizontal coordinate of the principal point of the image, is the focal length of the monocular camera.

3. The three-dimensional power transmission channel reconstruction method combined with a line laser as claimed in claim 1, characterized in that: The method of using the position similarity loss analysis result and the time similarity loss analysis result as the similarity loss analysis result to update the three-dimensional reconstruction result further includes: Performing independent threshold verification of a time deviation threshold on the time similarity loss analysis result to establish a first reconstruction impact result, wherein the time deviation threshold is a standard value pre-set according to actual conditions and used to determine whether the time similarity loss is within an acceptable range; Performing joint anomaly verification on the time-similar loss analysis result and the location-similar loss analysis result to establish a second reconstruction impact result; The three-dimensional reconstruction result is updated according to the first reconstruction influence result and the second reconstruction influence result.

4. The three-dimensional power transmission channel reconstruction method combined with a line laser according to claim 1, characterized in that: The scanning path planning based on the line layout and the regional environment data set and configuring the mobile strategy of the mobile platform also includes: Obtain the platform size of the mobile platform, perform spatial impact analysis based on the platform size and obstacle feature data in the regional environment data set, and establish a passage angle adaptation value for each location point. The passage angle adaptation value reflects the spatial adaptability of the mobile platform to the obstacle at the location point. The higher the value, the larger the passage angle, and the easier it is for the mobile platform to pass through the location point; Calling light feature data, performing light adaptation analysis of the mobile platform's layout angle based on the light feature data and the preset relative position, and establishing a light angle adaptation value for each position point, wherein the light angle adaptation value reflects the adaptability of the mobile platform to the light at the position point, and a higher value indicates a more suitable light angle, and a better laser line projection and image capture effect; The scanning path of the mobile platform is optimized according to the passage angle adaptation value and the light angle adaptation value, and the mobile strategy is configured according to the scanning path optimization result.

5. The three-dimensional power transmission channel reconstruction method combined with a line laser as claimed in claim 4, characterized in that: The step of optimizing the scanning path of the mobile platform according to the passage angle adaptation value and the light angle adaptation value further includes: A comprehensive cost function is established to optimize the scanning path as follows: ； in, is the comprehensive cost function, is the total number of fitting position points of the scanning path, Representing any location point, For the The passing angle adaptation value of each position point, For the The light angle adaptation value of each position point, For the The location point and The cost of the action continuity of the position point, , , They are the weight coefficients of the passage angle, light angle and action continuity cost.

6. The three-dimensional power transmission channel reconstruction method combined with a line laser as claimed in claim 1, characterized in that: The method further comprises: Establish a reconstruction time window based on similar loss analysis results; Additional data acquisition is performed within the reconstruction time window, and the three-dimensional reconstruction result is iterated based on the additional data acquisition result.

7. A three-dimensional power transmission channel reconstruction device combined with a line laser, characterized in that: The device is used to implement the three-dimensional power transmission channel reconstruction method combined with a line laser according to any one of claims 1 to 6, and the device comprises: A regional environment data set establishment module, the regional environment data set establishment module is used to perform regional environment acquisition of the target area and establish a regional environment data set, the regional environment data set includes obstacle feature data and light feature data; A mobile strategy configuration module, the mobile strategy configuration module is used to read the design data of the transmission line, use the design data to establish the line layout of the transmission line, perform scanning path planning based on the line layout and the regional environment data set, and configure the mobile strategy of the mobile platform, wherein the mobile platform is integrated with a line laser and a monocular camera, and the line laser and the monocular camera are fixed on the mobile platform by preset relative positions; A projection image establishment module, wherein the projection image establishment module is used to synchronously activate the straight line laser and the monocular camera when the mobile platform starts to move based on the mobile strategy, and use the monocular camera to capture the laser line projection projected by the straight line laser on the power transmission line in the target area to establish a projection image; A depth data calculation module, the depth data calculation module is used to obtain the baseline distance and device angle of the line laser and the monocular camera through a preset relative position, establish the two-dimensional coordinates of the pixel according to the projected image, and calculate the depth data through the baseline distance and the device angle; The three-dimensional reconstruction result updating module is used to perform three-dimensional reconstruction using two-dimensional coordinates and depth data, and to perform similarity loss analysis of associated points on the three-dimensional reconstruction result, and to update the three-dimensional reconstruction result with the similarity loss analysis result.

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