Power transmission line wind-induced deviation and galloping monitoring method based on crosshair laser and binocular vision

Abstract

The present application provides a method for monitoring the wind-induced galloping of transmission lines based on crosshair laser and binocular vision, which relates to the technical field of wind-induced galloping monitoring, and includes: generating a preset propagation path of the crosshair laser beam; continuously and dynamically monitoring the laser profile based on binocular vision devices to generate a set of wind-induced galloping images; calculating the probability of wind deviation anomaly to generate a first probability of wind deviation anomaly; analyzing the wind resistance of transmission conductors and compensating the first probability of wind deviation anomaly; when the second probability of wind deviation anomaly is greater than or equal to the preset anomaly probability, analyzing the set of wind-induced galloping images; and giving an early warning according to the wind-induced galloping data. Through the present application, the technical problem in the prior art that the accuracy of monitoring the wind-induced galloping of transmission lines is affected due to the complex installation of physical sensors and their susceptibility to the environment can be solved, the technical goal of accurately monitoring the wind-induced galloping of transmission lines can be achieved, and the technical effect of improving the monitoring accuracy can be achieved.

Description

Technical Field

[0001] This application relates to the technical field of wind-induced galloping monitoring, and particularly to a method for monitoring wind-induced galloping of transmission lines based on cross-line laser and binocular vision. Background Art

[0002] With the continuous increase in power demand in modern society, the stability and safety of transmission lines are particularly important. Wind-induced galloping is one of the common external factors affecting the stable operation of transmission lines. Especially in areas with high wind speeds, transmission lines will generate violent galloping under the action of wind, which will not only cause physical damage to the lines but also may lead to large-scale power outages.

[0003] Currently, traditional wind-induced galloping monitoring methods mainly rely on the layout of physical sensors, such as strain gauges and acceleration sensors. These sensors are directly installed on transmission towers or wires to monitor the deformation and stress conditions of the lines. However, physical sensors are not only complex to install but also easily damaged under harsh weather conditions, resulting in inaccurate or interrupted data collection, and relatively high maintenance costs. In addition, the monitoring range of physical sensors is limited, and it is impossible to achieve all-round real-time monitoring of long-distance transmission lines, with a large monitoring blind area. Therefore, traditional monitoring methods have obvious deficiencies in terms of coverage and real-time performance. In addition, although the method based on manual inspection can make up for the problem of insufficient coverage of physical sensors, its efficiency is low, relying on manual operation, and it is difficult to respond promptly to emergencies such as wind-induced galloping.

[0004] In summary, there is a technical problem in the prior art that due to the complex installation and environmental susceptibility of physical sensors, data collection is inaccurate or interrupted, further affecting the real-time performance and accuracy of wind-induced galloping monitoring of transmission lines, as well as the timely response to emergencies. Summary of the Invention

[0005] The purpose of this application is to provide a method for monitoring wind-induced galloping of transmission lines based on cross-line laser and binocular vision, so as to solve the technical problem in the prior art that due to the complex installation and environmental susceptibility of physical sensors, data collection is inaccurate or interrupted, further affecting the real-time performance and accuracy of wind-induced galloping monitoring of transmission lines, as well as the timely response to emergencies.

[0006] In view of the above problems, the present application provides a monitoring method for wind-induced galloping of transmission lines based on crosshair laser and binocular vision, including: setting the scanning parameters of the crosshair laser for the transmission conductors within a preset area in the non-wind-induced state to generate a preset cross-laser beam propagation path; using the preset cross-laser beam propagation path as the target, controlling the crosshair laser to perform laser profile scanning on the transmission conductors, and at the same time, continuously and dynamically monitoring the laser profile based on the binocular vision device to generate a set of wind-induced galloping images; calculating the probability of wind-induced anomaly based on the set of wind-induced galloping images to generate a first probability of wind-induced anomaly; analyzing the wind resistance of the transmission conductors within the preset area, and compensating the first probability of wind-induced anomaly with the result of the wind resistance analysis to generate a second probability of wind-induced anomaly; when the second probability of wind-induced anomaly is greater than or equal to the preset anomaly probability, performing wind-induced galloping analysis on the transmission conductors based on the crosshair laser and the binocular vision device to generate wind-induced galloping data; and giving an alarm according to the wind-induced galloping data.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] By setting the scanning parameters of the crosshair laser for the transmission conductors within a preset area in the non-wind-induced state to generate a preset cross-laser beam propagation path; using the preset cross-laser beam propagation path as the target, controlling the crosshair laser to perform laser profile scanning on the transmission conductors, and at the same time, continuously and dynamically monitoring the laser profile based on the binocular vision device to generate a set of wind-induced galloping images; calculating the probability of wind-induced anomaly based on the set of wind-induced galloping images to generate a first probability of wind-induced anomaly; analyzing the wind resistance of the transmission conductors within the preset area, and compensating the first probability of wind-induced anomaly with the result of the wind resistance analysis to generate a second probability of wind-induced anomaly; when the second probability of wind-induced anomaly is greater than or equal to the preset anomaly probability, performing wind-induced galloping analysis on the transmission conductors based on the crosshair laser and the binocular vision device to generate wind-induced galloping data; and giving an alarm according to the wind-induced galloping data, the technical goal of real-time and accurate monitoring of the wind-induced galloping of transmission lines is achieved, and the technical effects of improving the monitoring coverage, reducing the maintenance cost, and enhancing the alarm response speed and accuracy are achieved.

[0009] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present 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 the present application more obvious and understandable, the following specifically illustrates the embodiments of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0010] To more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are merely exemplary. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0011] Figure 1 It is a schematic flow chart of the monitoring method for wind-induced deflection and galloping of transmission lines based on crosshair laser and binocular vision in the present application;

[0012] Figure 2 It is a schematic flow chart of generating the first probability of wind-induced deflection anomaly in the monitoring method for wind-induced deflection and galloping of transmission lines based on crosshair laser and binocular vision in the present application. Detailed Embodiments

[0013] By providing a monitoring method for wind-induced deflection and galloping of transmission lines based on crosshair laser and binocular vision, the present application solves the technical problems in the prior art that due to the complex installation of physical sensors and their susceptibility to environmental influences, data acquisition is inaccurate or interrupted, further affecting the real-time performance and accuracy of the monitoring of wind-induced deflection and galloping of transmission lines, as well as the timely response to emergencies. The technical objective of realizing real-time and accurate monitoring of wind-induced deflection and galloping of transmission lines is achieved, and the technical effects of improving the monitoring coverage, reducing the maintenance cost, enhancing the early warning response speed and accuracy are obtained.

[0014] Next, the technical solutions in the present application will be clearly and completely described with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application. Additionally, it should be noted that for the sake of description, only the parts related to the present application are shown in the drawings rather than all of them.

[0015] Please refer to the attached Figure 1 , the present application provides a monitoring method for wind-induced deflection and galloping of transmission lines based on crosshair laser and binocular vision, which specifically includes the following steps:

[0016] Step 1: Set the scanning parameters of the crosshair laser for the transmission conductors in the preset area in the non-wind-induced deflection state to generate a preset cross laser beam propagation path.

[0017] Specifically, the preset area refers to the area preset by the workers in the field. In the case of no wind deflection, the working parameters of the cross-line laser for scanning the transmission wires within the preset area are determined to generate a preset cross-laser beam propagation path. Among them, the cross-line laser refers to the type of laser that forms the intersection of two laser lines, horizontal and vertical, in space. The cross-line laser captures images simultaneously through two cameras and uses the triangulation method to calculate the three-dimensional position of the object. In the case of no wind deflection of the transmission wires, the center point of the cross-laser beam emitted by the laser will completely coincide with the position of the transmission wires. By scanning multiple times, an accurate preset cross-laser beam propagation path is generated as the reference path of the transmission wires in the static state, that is, the marking network. The marking network is a reference coordinate system used to detect whether the transmission wires dance due to the action of wind later. For example, if it is found in the scan that the line deviates from the reference path by more than 1 mm, the dancing phenomenon is identified and an alarm is issued. The change of the line under the action of wind is effectively monitored by the method of the marking network.

[0018] Step 2: Taking the preset cross-laser beam propagation path as the target, control the cross-line laser to perform laser profile scanning on the transmission wires, and at the same time, based on the binocular vision device, conduct continuous dynamic monitoring of the laser profile to generate a set of wind deflection and dancing images.

[0019] Specifically, taking the preset cross-laser beam propagation path as the reference, control the cross-line laser to perform laser profile scanning on the transmission wires. The laser beam emitted by the cross-line laser forms a profile line on the transmission wires, and the binocular vision device captures the dynamic changes of the laser profile in real time and continuously monitors the position of the laser line. By measuring the relative position between the laser profile and the transmission wires through the binocular vision device, a set of wind deflection and dancing images is generated for subsequent analysis.

[0020] Step 3: Calculate the probability of wind deflection anomaly based on the set of wind deflection and dancing images to generate the first wind deflection anomaly probability.

[0021] Specifically, calculate the probability of wind deflection anomaly based on the set of wind deflection and dancing images. Use multiple groups of image data collected by the binocular vision device to analyze the dancing situation of the transmission wires, identify the position offset of the transmission wires, and compare it with the preset reference position. Then, generate the first wind deflection anomaly probability according to the degree of offset. The greater the offset, the higher the anomaly probability, and vice versa. For example, when the offset is 2 mm, a 10% anomaly probability is generated, and if the offset reaches 5 mm, the anomaly probability rises to 100%. By comparing the image data with the offset situation, the abnormal degree of wind deflection is quantified, and a clear probability value is generated for each dancing situation to help predict and warn the wind deflection risk of the transmission wires.

[0022] Step 4: Conduct a wind resistance analysis on the power transmission conductors within the preset area, and compensate the first wind deviation anomaly probability with the result of the resistance analysis to generate a second wind deviation anomaly probability.

[0023] Specifically, conduct a wind resistance analysis on the power transmission conductors within the preset area. By calculating the wind resistance capacity of the line under the action of wind, evaluate the degree to which it is affected by the wind. Use the result of the resistance analysis to compensate the first wind deviation anomaly probability, and adjust the wind deviation anomaly probability according to the magnitude of the line's wind resistance capacity. For example, the first wind deviation anomaly probability is 20%, but if the wind resistance capacity is weak, increase the anomaly probability to generate the second wind deviation anomaly probability, which represents a more accurate wind deviation risk probability after considering the wind resistance capacity. Suppose the wind resistance capacity is 80%, then after compensation, the second wind deviation anomaly probability may rise to 25%, more realistically reflecting the risk of the power transmission conductors in the wind.

[0024] Step 5: When the second wind deviation anomaly probability is greater than or equal to the preset anomaly probability, conduct a wind deviation galloping analysis on the power transmission conductors based on the crosshair laser and the binocular vision device to generate wind deviation galloping data.

[0025] Specifically, when the second wind deviation anomaly probability is greater than or equal to the preset anomaly probability, use the crosshair laser and the binocular vision device to conduct a detailed wind deviation galloping analysis on the power transmission conductors, and then generate detailed wind deviation galloping data. The crosshair laser can precisely project a crosshair to help locate the position of the conductor; while the binocular vision device obtains the three-dimensional motion information of the conductor by simulating the observation of the human eye. The second wind deviation anomaly probability is a value obtained through previous calculations, indicating the possibility of the line having a wind deviation anomaly, and the preset anomaly probability is a threshold set in advance. For example, if the preset anomaly probability is 20%, when the second wind deviation anomaly probability reaches or exceeds this value, it is considered that the power transmission conductors may have an anomaly, and at this time, a more in-depth analysis is required.

[0026] Step 6: Issue a warning based on the wind deviation galloping data.

[0027] Specifically, by analyzing the wind deviation galloping data of the line, determine whether a warning needs to be issued. The wind deviation galloping data contains various dynamic information of the power transmission conductors under the action of wind, such as the wind deviation angle, the galloping amplitude, the vertical and horizontal displacements, etc. Evaluate the safety status of the line based on this data. For example, if the wind deviation angle reaches 5 degrees and the galloping amplitude is 10 cm, this may indicate that the line is in an unstable state. By setting a safety threshold, when the galloping data exceeds this threshold, a warning will be triggered, reminding the maintenance personnel that the line is affected by the wind and may have a fault, and then timely identify potential risks to ensure the safe operation of the power transmission conductors.

[0028] The transmission line wind yaw and sway monitoring method based on cross-line laser and binocular vision can achieve the technical goal of real-time and accurate monitoring of the wind yaw and sway of the transmission line, and achieve the technical effects of increasing the monitoring coverage, reducing maintenance costs, and enhancing the early warning response speed and accuracy.

[0029] Furthermore, the present application also includes:

[0030] The windage dancing data includes windage angle, dancing amplitude, vertical dancing amplitude, horizontal dancing amplitude and elliptical tilt angle data.

[0031] Specifically, the wind deflection angle refers to the deviation angle of the transmission line under the action of wind, which is used to indicate the angle between the line and its original position. The dancing amplitude refers to the overall amplitude of the transmission line under the action of wind, indicating the maximum distance that the line deviates from the original position during the dancing process. The vertical dancing amplitude is the vertical displacement amplitude during the dancing process, which refers to the amplitude of the dancing of the transmission line in the vertical direction. The horizontal dancing amplitude indicates the dancing amplitude of the transmission line in the horizontal direction. The ellipse inclination angle data is used to describe the inclination degree of the dancing trajectory of the transmission line under the action of wind. When the dancing trajectory is elliptical, the inclination angle of the ellipse indicates the angle between the major axis of the ellipse and the horizontal line.

[0032] The wind deviation and dancing data integrates parameters of multiple dimensions, including the wind deviation angle of the line, the overall dancing amplitude, as well as the amplitudes in the vertical and horizontal directions and the inclination angle of the elliptical trajectory, to fully understand the dynamic behavior of the transmission line under the action of wind.

[0033] Furthermore, the present application also includes:

[0034] Acquire multiple adjacent lines of the transmission line in the preset area; perform abnormal impact analysis based on the connection structure and distance between the multiple adjacent lines and the transmission line to generate multiple abnormal impact coefficients; establish an adjacent line topology based on the multiple abnormal impact coefficients with the transmission line in the preset area as the center; and perform linkage warning based on the adjacent line topology.

[0035] Specifically, multiple adjacent lines of the transmission line in the preset area are obtained, and other lines adjacent to the target transmission line are identified. The adjacent lines and the target line are physically close to each other or have a connection structure. For example, there are three other transmission lines near a certain transmission line, and the distance between them may be 50 meters, 80 meters, etc.

[0036] Next, based on the connection structures and distances between multiple adjacent lines and the target transmission conductor, abnormal influence analysis is carried out to generate multiple abnormal influence coefficients. The connection structure refers to the physical or electrical connection relationship between the lines, and the distance is their relative position in space, evaluating the potential influence of adjacent lines on the target line. For example, if the distance between two lines is relatively close and there is strong electrical coupling, the abnormal influence coefficient may be relatively high. Suppose the influence coefficient of a certain line is 0.8, while that of a farther line may be only 0.2.

[0037] Subsequently, with the transmission conductors in a preset area as the center, an adjacent line topology is established according to the abnormal influence coefficients. Topology is a diagram that describes the connection relationships between elements in a system. By using the abnormal influence coefficients, an influence network between the target line and adjacent lines is constructed. For example, lines that are closer and have a high influence coefficient may have larger nodes in the topology diagram, indicating higher importance.

[0038] Finally, linkage early warning is carried out according to the adjacent line topology. Linkage early warning means that when an abnormality occurs in an adjacent line, an early warning is issued according to the topological relationship to predict the possible influence of the abnormality on the target line. For example, if a fault occurs in an adjacent line and its influence coefficient is 0.8, an early warning is issued in a timely manner, indicating that the fault may have a significant impact on the target line.

[0039] By obtaining the adjacent lines of the target transmission conductor, carrying out abnormal influence analysis based on the connection structure and distance, generating multiple abnormal influence coefficients, constructing the topological structure of the adjacent lines, and carrying out linkage early warning through the topological relationship, the safety monitoring ability of the transmission conductor is improved.

[0040] Furthermore, this application also includes:

[0041] Based on the wind deviation and galloping data and the adjacent line topology, the wind deviation and galloping data of the multiple adjacent lines are speculated; linkage early warning is carried out with the speculation results of the wind deviation and galloping data of the multiple adjacent lines.

[0042] Specifically, based on the wind deviation and galloping data and the adjacent line topology, the wind deviation and galloping data of the multiple adjacent lines are speculated. The wind deviation and galloping data of the target transmission conductor and the topological structure of the adjacent lines are used to speculate the galloping conditions of the adjacent lines. By analyzing the actual galloping data of the target line and combining the connection relationships and influence coefficients in the topology, the wind deviation conditions of the adjacent lines under the same or similar environmental conditions are estimated. For example, if the wind deviation amplitude of the target line is 5 cm, and the adjacent line is closely connected to it and has a high influence coefficient, then it may be speculated that the wind deviation amplitude of the adjacent line is 4 cm.

[0043] Next, a linkage warning is issued based on the speculated wind-induced galloping data results of adjacent lines. Linkage warning means that when wind-induced galloping occurs on the target line, not only a warning is issued for the target line, but also based on the speculation results of adjacent lines, warnings are issued for possible abnormal conditions of these lines. For example, if it is speculated that the wind-induced galloping amplitude of a certain adjacent line is 4 cm and exceeds the set safety threshold, a linkage warning is issued to indicate that there is also a galloping risk for this line.

[0044] By speculating the wind-induced galloping data of multiple adjacent lines according to the wind-induced galloping data of the target transmission conductor and the topological structure of adjacent lines, a linkage warning is issued based on the speculation results to indicate the possible risks faced by adjacent lines, thereby improving the overall monitoring and warning capabilities.

[0045] Furthermore, as Figure 2 shown, this application further includes:

[0046] The wind-induced galloping image set includes multiple pairs of images. Each pair of images includes a first image collected by the first camera in the binocular vision device and a second image collected by the second camera, and the first image and the second image are images with different perspectives but in the same image acquisition area; the two-dimensional image coordinates of the transmission conductor are identified based on the multiple pairs of images; it is determined whether the two-dimensional image coordinates of the transmission conductor coincide with the center of the crosshair laser, and the coincidence comparison distance is obtained; an abnormal probability is assigned according to the coincidence comparison distance to generate the first wind-induced abnormal probability.

[0047] Specifically, the wind-induced galloping image set contains multiple pairs of images, and each pair of images is collected by two cameras in the binocular vision device respectively. Among them, the image collected by the first camera is the first image, and the image collected by the second camera is the second image. The first image and the second image have different perspectives but the same acquisition area, and the two cameras capture the same transmission conductor area from different angles. For example, the first camera is located on the left and the second camera is located on the right. Through dual-angle acquisition, more comprehensive three-dimensional information can be obtained.

[0048] Next, the two-dimensional image coordinates of the transmission conductor are identified through multiple pairs of images. The two-dimensional image coordinates refer to determining the position of the transmission conductor on the image plane, such as the X and Y coordinates of the transmission conductor, for subsequent analysis.

[0049] Subsequently, according to the two-dimensional image coordinates of the transmission conductor, it is determined whether the two-dimensional image coordinates coincide with the center position of the crosshair laser. Coincidence means that the position of the transmission conductor is exactly the same as the preset laser center point. If they do not coincide, the coincidence comparison distance will be calculated to represent the degree of deviation of the line position from the laser center. If they coincide, the distance is 0, and there will be corresponding values for deviation.

[0050] Based on the coincidence comparison distance, an abnormal probability is assigned. If the deviation distance is large, a higher abnormal probability is assigned, while when the distance is small, the abnormal probability is low, and it is then used to predict the abnormal degree of the transmission line conductor's galloping due to wind deflection.

[0051] Multiple pairs of images are collected by a binocular vision device, and the coordinates of the transmission line conductor are identified in the images. Subsequently, a comparison is made with the crosshair laser center, the coincidence comparison distance is calculated, and finally, an abnormal probability is assigned according to the distance, generating the first abnormal probability of galloping due to wind deflection, which can better monitor the wind deflection behavior of the transmission line conductor.

[0052] Furthermore, this application also includes:

[0053] A limit assignment distance threshold constraint is established; the coincidence comparison distance is assigned an abnormal probability according to the constraint that the coincidence comparison distance is proportional to the first wind deflection abnormal probability and the limit assignment distance threshold constraint, generating the first wind deflection abnormal probability.

[0054] Specifically, a limit assignment distance threshold constraint is established. The limit assignment distance threshold is the maximum allowable value for the coincidence comparison distance. A distance exceeding the limit assignment distance threshold is considered abnormal, and its abnormal probability is directly set to 100%. For example, if the set limit assignment distance threshold is 5 millimeters, when the offset of the transmission line conductor exceeds 5 millimeters, a 100% abnormal probability is directly assigned to this situation, indicating severe galloping due to wind deflection.

[0055] Next, according to the constraint that the coincidence comparison distance is proportional to the first wind deflection abnormal probability, an abnormal probability is assigned to the coincidence comparison distance. As the coincidence comparison distance increases, the probability of wind deflection abnormality will also increase accordingly. For example, if the distance is 2 millimeters, the assigned abnormal probability is 10%; if the distance is 4 millimeters, the abnormal probability increases to 80%.

[0056] Then, combined with the limit assignment distance threshold constraint, if the offset distance is greater than the set threshold (for example, 5 millimeters), a 100% abnormal probability is directly assigned to this situation, ensuring that when the galloping due to wind deflection of the transmission line conductor reaches a severe level, an alarm is issued in a timely manner and intervention is carried out.

[0057] By establishing a limit assignment distance threshold, assigning values according to the proportional relationship between the size of the coincidence comparison distance and the wind deflection abnormal probability, and combining the threshold constraint to ensure that abnormal situations exceeding the limit distance are directly marked with a 100% abnormal probability, the risks of the transmission line conductor in different galloping due to wind deflection situations are effectively identified.

[0058] Furthermore, this application also includes:

[0059] Obtain the line design information of the transmission line, where the line design information includes line structure and line material; obtain the environmental data of a preset area, where the environmental data is the environmental periodic change data of the preset area; perform line deformation fitting with the environmental periodic change data, the line structure, the line material, and the service life of the transmission line; perform wind resistance analysis on the line deformation fitting result through a resistance fitting model, and compensate the first wind deviation anomaly probability with the resistance analysis result to generate the second wind deviation anomaly probability.

[0060] Specifically, obtain the line design information of the transmission line, and collect the design parameters related to the transmission line, including the structure and material of the transmission line. The line structure refers to the shape of the transmission line, the erection method, and the arrangement of the conductors, and the line material refers to the material used for the conductors that make up the transmission line, such as aluminum alloy or copper.

[0061] Next, the preset area is custom-set by the workers in the field according to the actual situation. Obtain the environmental data of the preset area, and the environmental data is the environmental periodic change data of the area where the line is located, such as the climate conditions, wind speed, humidity, etc. in the preset area changing over time.

[0062] Then, combine the environmental periodic change data with the line structure, the line material, and the service life of the transmission line to perform fitting analysis of line deformation. The service life refers to the number of years since the transmission line was built. For example, the self-weight of the transmission line and the long-term wind force cause the sag of the conductor to increase.

[0063] Subsequently, analyze the line deformation fitting result through a resistance fitting model to calculate the wind resistance of the line under the action of wind. The resistance analysis is to evaluate the wind resistance of the transmission line at different wind speeds. For example, if the wind resistance of a line with an increased sag decreases by 10%, the probability of wind deviation anomaly will also increase accordingly. The resistance analysis result is used to compensate the first wind deviation anomaly probability. For example, if the wind resistance decreases and the first wind deviation anomaly probability is 10%, after compensation by the resistance analysis, the second wind deviation anomaly probability may increase to 15%.

[0064] By collecting the design information of the transmission line and the environmental periodic change data, combining the service life of the line and the material characteristics to perform fitting analysis of line deformation, and compensating the wind deviation anomaly probability through the calculation of wind resistance, it can more accurately reflect the wind deviation risk of the line.

[0065] Furthermore, this application also includes:

[0066] Call the resistance identification sample library through the resistance fitting model to screen the matching resistance identification sample with the highest similarity to the fitting result of the line deformation; wherein, the resistance identification sample library includes multiple groups of resistance identification samples, and any group of resistance identification samples includes a line deformation sample and identification information marking the resistance of the wind; generate the resistance analysis result according to the matching resistance identification sample.

[0067] Specifically, through the resistance fitting model, call the resistance identification sample library to screen out the resistance identification sample with the highest similarity to the line deformation fitting result. The resistance fitting model refers to comparing the calculation model with the pre-stored resistance identification samples according to the specific situation of the line deformation. For example, the sag deformation amplitude of a line is 1.5 meters, and find the resistance identification sample closest to it in the sample library.

[0068] Next, the resistance identification sample library contains multiple groups of samples, and each group of resistance identification samples consists of a line deformation sample and the corresponding identification information of the wind resistance. The line deformation sample describes the deformation situation of the transmission wire under different conditions, and the identification information records the wind resistance of the line under these deformation samples. For example, a resistance identification sample includes a line with a sag increase of 1 meter and marks its wind resistance as 90%.

[0069] Then, generate the resistance analysis result according to the screened matching resistance identification sample. Deduce the wind resistance of the current line through the similarity with the deformation sample. For example, if the matching resistance identification sample shows that the wind resistance is 85% when the sag increases by 1.5 meters, generate the corresponding resistance analysis result.

[0070] By screening out the resistance identification sample closest to the line deformation fitting result from the resistance identification sample library and generating the resistance analysis result through the identification information of the sample, it provides a more accurate basis for the subsequent analysis of wind-induced galloping.

[0071] Furthermore, this application also includes:

[0072] Scan the transmission wire multiple times through the cross-line laser, and at the same time start the binocular vision device to perform synchronous image acquisition to generate multiple pairs of scanned images; identify multiple scanning points based on the multiple pairs of scanned images to generate a wire trajectory point set, wherein the wire trajectory point set has a time sequence identifier; identify the wind deflection angle, galloping amplitude, vertical galloping amplitude, horizontal galloping amplitude and elliptical tilt angle data based on the wire trajectory point set to generate the wind deflection and galloping data.

[0073] Specifically, a crosshair laser is a device that can project a precise cross-shaped laser beam and is used to mark the position of the transmission line in space. By using the crosshair laser to scan the transmission line multiple times, it is equivalent to drawing light rays on the wire with a laser pointer for subsequent analysis. For example, the laser may scan the wire 10 times in 1 second. At the same time, a binocular vision device is activated for synchronous image acquisition. The binocular vision device can capture images from different angles to simulate the visual effect of a three-dimensional space. As a result, multiple pairs of scanned images can be generated, which record the positions of the wire at different scanning times.

[0074] Next, based on multiple pairs of scanned images, multiple scanned points are identified. These scanned points are the marks left by the laser on the wire. Through these points, a set of wire trajectory points can be generated. The trajectory point set is the set of the movement paths of the wire over a period of time, and each point has its specific position information. These point sets also have a time sequence identifier, which means that each point is marked with the acquisition timestamp. For example, the timestamp of the first point is 0 seconds, the second point is 0.1 seconds, and so on.

[0075] Then, data identification is performed based on the set of wire trajectory points, including the wind deflection angle, the amplitude of galloping, the vertical amplitude of galloping, the horizontal amplitude of galloping, and the elliptical tilt angle. The wind deflection angle refers to the offset angle of the wire relative to the vertical direction. The amplitude of galloping is the maximum swing distance of the wire in the wind. The vertical and horizontal amplitudes of galloping are the components of these swings in the vertical and horizontal directions respectively. The elliptical tilt angle describes the inclination degree of the elliptical shape of the wire galloping trajectory relative to the horizontal plane. Through these data, wind deflection and galloping data are finally generated, which can detail the dynamic behavior of the wire in the wind.

[0076] The transmission line is scanned and images are acquired by the crosshair laser and the binocular vision device to generate a set of wire trajectory points with timestamps. Then, based on these point sets, key parameters such as the wind deflection angle and the amplitude of galloping of the wire are calculated, and finally wind deflection and galloping data are obtained.

[0077] In summary, the wind deflection and galloping monitoring method for transmission lines based on crosshair lasers and binocular vision provided by this application has the following technical effects:

[0078] By setting the scanning parameters of the cross-line laser under the non-wind-deflection state for the transmission wires within the preset area, a preset cross-laser beam propagation path is generated; aiming at the preset cross-laser beam propagation path, controlling the cross-line laser to perform laser profile scanning on the transmission wires, and at the same time continuously and dynamically monitoring the laser profile based on the binocular vision device to generate a set of wind-deflection dancing images; calculating the probability of wind-deflection anomaly based on the set of wind-deflection dancing images to generate the first wind-deflection anomaly probability; performing wind resistance analysis on the transmission wires within the preset area, and compensating the first wind-deflection anomaly probability with the result of the resistance analysis to generate the second wind-deflection anomaly probability; when the second wind-deflection anomaly probability is greater than or equal to the preset anomaly probability, activating the multivariate comprehensive analysis model to analyze the set of wind-deflection dancing images to generate wind-deflection dancing data; warning according to the wind-deflection dancing data to achieve the technical goal of real-time and accurate monitoring of the wind-deflection dancing of transmission wires, and achieving the technical effects of improving the monitoring coverage, reducing the maintenance cost, and enhancing the warning response speed and accuracy.

[0079] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A monitoring method for wind-induced galloping of transmission lines based on crosshair laser and binocular vision, characterized in that Including: Set the scanning parameters of the cross-line laser for the transmission wire in the preset area under the non-wind-deflection state to generate a preset cross-laser beam propagation path; Taking the preset cross-laser beam propagation path as the target, control the cross-line laser to perform laser profile scanning on the transmission wire, and at the same time, based on the binocular vision device, continuously monitor the laser profile dynamically to generate a set of wind-deflection dancing images; Calculate the probability of wind-deflection anomaly based on the set of wind-deflection dancing images to generate a first wind-deflection anomaly probability; Conduct a wind resistance analysis on the transmission wire in the preset area, and compensate the first wind-deflection anomaly probability with the result of the resistance analysis to generate a second wind-deflection anomaly probability; When the second wind-deflection anomaly probability is greater than or equal to the preset anomaly probability, conduct a wind-deflection dancing analysis on the transmission wire based on the cross-line laser and the binocular vision device to generate wind-deflection dancing data; Give an early warning according to the wind-deflection dancing data; Calculating the probability of wind-deflection anomaly based on the set of wind-deflection dancing images to generate a first wind-deflection anomaly probability, including: The set of wind-deflection dancing images includes multiple pairs of images. Each pair of images includes a first image collected by the first camera in the binocular vision device and a second image collected by the second camera, and the first image and the second image are images with different perspectives but the same image acquisition area; Identify the two-dimensional image coordinates of the transmission wire based on the multiple pairs of images; Judge whether the two-dimensional image coordinates of the transmission wire coincide with the cross-line laser center based on the two-dimensional image coordinates of the transmission wire, and obtain the coincidence comparison distance; Assign an anomaly probability according to the coincidence comparison distance to generate the first wind-deflection anomaly probability; Conduct a wind-deflection dancing analysis on the transmission wire based on the cross-line laser and the binocular vision device to generate wind-deflection dancing data, including: Scan the transmission wire multiple times through the cross-line laser, and at the same time start the binocular vision device to perform synchronous image acquisition to generate multiple pairs of scanned images; Identify multiple scanning points based on the multiple pairs of scanned images to generate a set of wire trajectory points, where the set of wire trajectory points has a time sequence identifier; Identify the data of the wind-deflection angle, dancing amplitude, vertical dancing amplitude, horizontal dancing amplitude, and elliptical tilt angle based on the set of wire trajectory points to generate the wind-deflection dancing data.

2. The method for monitoring the wind-induced galloping of a transmission line based on crosshair laser and binocular vision according to claim 1, characterized in that The wind-deflection dancing data includes the wind-deflection angle, dancing amplitude, vertical dancing amplitude, horizontal dancing amplitude, and elliptical tilt angle data.

3. The power transmission line wind-induced galloping monitoring method based on crosshair laser and binocular vision according to claim 1, characterized in that Also including: Obtain multiple adjacent lines of the transmission wire in the preset area; Conduct an abnormal influence analysis according to the connection structure and distance between the multiple adjacent lines and the transmission wire to generate multiple abnormal influence coefficients; Taking the transmission wire in the preset area as the center, establish an adjacent line topology according to the multiple abnormal influence coefficients; Give a linkage early warning according to the adjacent line topology.

4. The method for monitoring the wind-induced galloping of a transmission line based on crosshair laser and binocular vision according to claim 3, wherein, Giving a linkage early warning according to the adjacent line topology, including: Speculate the wind-deflection dancing data of the multiple adjacent lines based on the wind-deflection dancing data and the adjacent line topology; Give a linkage early warning based on the speculation results of the wind-deflection dancing data of the multiple adjacent lines.

5. The monitoring method for wind-induced galloping of transmission lines based on crosshair laser and binocular vision according to claim 1, characterized in that, Assigning an abnormal probability value according to the coincidence comparison distance to generate the first wind deviation abnormal probability, including: Establishing a limit assignment distance threshold constraint; Assigning an abnormal probability value to the coincidence comparison distance according to the constraint that the coincidence comparison distance is proportional to the first wind deviation abnormal probability and the limit assignment distance threshold constraint, to generate the first wind deviation abnormal probability.

6. The method for monitoring the wind-induced galloping of a transmission line based on crosshair laser and binocular vision according to claim 1, wherein Performing a wind resistance analysis on the transmission line in the preset area, and compensating the first wind deviation abnormal probability with the wind resistance analysis result to generate a second wind deviation abnormal probability, including: Obtaining the line design information of the transmission line, where the line design information includes the line structure and the line material; Obtaining the environmental data of the preset area, where the environmental data is the environmental periodic change data of the preset area; Performing line deformation fitting with the environmental periodic change data, the line structure, the line material, and the service life of the transmission line; Performing a wind resistance analysis through a resistance fitting model according to the line deformation fitting result, and compensating the first wind deviation abnormal probability with the wind resistance analysis result to generate the second wind deviation abnormal probability.

7. The method for monitoring the wind-induced deviation and galloping of a transmission line based on crosshair laser and binocular vision according to claim 6, characterized in that, Performing a wind resistance analysis through a resistance fitting model according to the line deformation fitting result, including: Calling a resistance identification sample library through a resistance fitting model to screen the matching resistance identification sample with the highest similarity to the line deformation fitting result; Wherein, the resistance identification sample library includes multiple groups of resistance identification samples, and any group of resistance identification samples includes a line deformation sample and identification information marked with the wind resistance; Generating the wind resistance analysis result according to the matching resistance identification sample.

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