Power conductor galloping track extraction and vibration parameter automatic fitting method
By combining video preprocessing and spectrum analysis, the galloping trajectory of transmission lines is automatically extracted and vibration parameters are fitted, which solves the problems of insufficient robustness and automation in existing technologies and realizes efficient and accurate monitoring of transmission line galloping.
Patent Information
- Application Number
- CN202511360242.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-23
- Publication Date
- 2026-02-03
AI Technical Summary
Existing transmission line galloping monitoring methods lack robustness in complex environments, have low utilization of multispectral information, rely on initial values for frequency estimation, and lack automated processing procedures, resulting in low identification efficiency and a lack of confidence in the results.
By combining visible light video preprocessing with color space separation, edge detection, and multi-point trajectory tracking, and using a combination of spectrum estimation and nonlinear fitting, the system automatically extracts the conductor region and node positions, and performs dance trajectory fitting.
It can stably identify node positions under different lighting and background conditions, automatically track motion trajectories, and accurately calculate parameters such as frequency, amplitude, and phase, thereby improving the automation and accuracy of monitoring.
Smart Images
Figure CN121458622A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power transmission line condition monitoring technology, and in particular to a method for extracting the galloping trajectory of power conductors and automatically fitting vibration parameters. Background Technology
[0002] Currently, video detection technology based on visible light and multispectral imaging is mainly used. Its basic principle is to use the changes in the spatial position of the power transmission line in the video image sequence over time, and to identify the position of the power transmission line and key nodes such as spacers and clamps through image processing algorithms. Then, the motion trajectory of these nodes is tracked, and finally the trajectory data is fitted into a simple harmonic vibration model to obtain parameters such as the frequency and amplitude of the vibration.
[0003] The typical workflow of existing visible light and multispectral video galloping detection methods includes: video acquisition: acquiring visible light or multispectral video sequences of power transmission lines using fixed monitoring or drone platforms; feature extraction: extracting the outline of the power transmission line or the location of key nodes through edge detection, Hough transform, and other methods; trajectory tracking: establishing a time series of node positions between video frames; and harmonic function fitting: fitting the change of node displacement over time to a simple harmonic vibration function to obtain parameters such as the galloping frequency B, amplitude A, phase C, and baseline position D.
[0004] However, this existing method has the following shortcomings: 1. It lacks robustness to environments with complex backgrounds, changing lighting, and snowy weather, and is prone to node loss or false detection; 2. The fusion and utilization rate of multispectral and visible light information is low, and the advantages of multi-band data in background suppression and feature enhancement are not fully utilized; 3. Frequency estimation depends on the initial values of the fit. If the initial values are inaccurate, it may lead to large deviations in the results of the harmonic fitting. 4. Lack of automated processing workflows, often requiring manual intervention, resulting in low batch processing efficiency. For example, the identification process typically requires manually selecting the conductor region and target node position in the first or key frame, which is particularly difficult to automatically lock spacers or clamps against complex backgrounds. Output results such as frequency and amplitude lack confidence indicators, requiring manual filtering of outlier data, thus limiting processing efficiency and large-scale application. Summary of the Invention
[0005] The purpose of this invention is to provide a method for extracting the galloping trajectory of power conductors and automatically fitting vibration parameters, in order to solve the problem that existing methods for monitoring power line galloping cannot quickly and accurately monitor the galloping of power lines.
[0006] The above-mentioned objective of this application is achieved through the following technical solution: S1: Acquire visible light video of the transmission line and preprocess it to obtain an enhanced image; S2: Perform Canny edge detection, contour extraction and filtering on the enhanced image to obtain the conductor region; S3: Within the conductor area, determine the observation positions of each node of the transmission line; S4: Multi-point trajectory tracking based on nearest neighbor distance matching, combined with the observed position of the node, establishes the mapping relationship between ID-frame-coordinate; S5: By combining a trajectory analysis method based on spectrum estimation and nonlinear fitting, and incorporating the ID-frame-coordinate mapping relationship, the galloping trajectory of the transmission line is fitted.
[0007] Optionally, step S1 includes: Visible light video of power transmission lines is collected through fixed monitoring or drones; Convert visible light video to the HSV color space; Based on intra-frame color distribution statistics, the hue range of the green region is dynamically estimated, and a background mask is constructed by combining saturation and brightness thresholds. Based on the background mask, an adaptive Gaussian blur operation guided by brightness gradient is introduced to obtain the blur result; Adaptive thresholding based on local contrast enhancement is performed on the blurred results to preserve high-response regions.
[0008] Optionally, step S2 includes: Canny edge detection is performed on the enhanced image using a preset detection threshold to obtain an edge image; Based on the edge map, a probabilistic Hough linear transformation is performed, and line segment direction consistency and spacing constraints are introduced to filter candidate conductor clusters with directions within a ±10° window; Construct a confidence matrix for candidate conductor clusters, and combine the differences in conductor segment length, density and direction to determine the conductor structure to which the segment belongs and generate a conductor region mask; By combining wire region masking and morphological operations, connected component filtering and break repair are performed to obtain the repaired wire region. Within the conductor region, valid targets are screened by using the contour area and aspect ratio, and isolated false detection conductor structures are removed.
[0009] Optionally, step S3 includes: S31: Based on the binary mask of the wire profile in the conductor region, calculate the centerline of the conductor according to the conductor direction; With the central axis of the conductor as the center, extend upwards and downwards by a preset pixel distance to construct a strip-shaped region of interest; Within the conductor area, affine transformation or rotation clipping is performed according to the conductor angle to ensure that the envelope structure closely follows the conductor outline and eliminates interference areas far away from the conductor. The interference area includes: vegetation and towers; the nodes include: spacers, fittings or clamps; S32: Within the wire region of each video frame, combined with the strip region of interest, perform connected component analysis to extract the white foreground contours of all nodes; calculate the area of each contour and filter according to the dynamic threshold, retaining the contours that meet the conditions as node candidates. Calculate the area of each white foreground outline. By setting a dynamic area threshold They are filtered as follows:
[0010] in This is a scaling factor used to eliminate small-sized noisy targets; This represents the arithmetic mean of all elements in set S. Represents the set of pixel areas of all white foreground outlines in the current frame; Calculate the geometric centroid coordinates of the white foreground outline after filtering, denoted as . ,in Indicates the current frame The first in A valid node, whose coordinates are used as the node's position in the current frame. The observation location.
[0011] Optionally, step S4 includes: Each node is assigned a unique ID. A mapping relationship between ID, frame, and coordinates is established based on the observed positions of each node in the current frame, as follows: Based on the previous frame trajectory Observation coordinates Predict its position in the current frame Possible location And for the candidate nodes detected in the current frame coordinate Perform Euclidean distance matching:
[0012] like , Consider as a node With the current frame node match, This represents the maximum matching radius threshold. Represents a node In frame The predicted coordinates, i.e., pixel coordinates; The Hungarian algorithm is used to perform multi-point global matching on the distance cost matrix generated by the node positions of the previous and current frames, thereby optimizing the overall matching cost. Set up an occlusion tolerance and trajectory preservation mechanism: When the motion trajectory of a tracked node, that is, the time-series position path formed by the node in consecutive frames, fails to match successfully within a short period of time, retain a tracking window of up to K frames, where K is a positive integer and the unit is frames, and continue to update the trajectory position through linear prediction.
[0013] Optionally, step S5 includes: A time series trajectory is constructed by mapping the ID, frame, and coordinates of each node; The trajectory of each node, with the frame number as the time reference, records the change of its vertical direction over time; For each node trajectory, a sinusoidal function model is used for fitting:
[0014] Where A represents amplitude, i.e., the intensity of the dancing; B represents frequency, i.e., the speed of the dancing; C represents the initial phase; D represents the vertical offset; and T represents the period. The fitting process employs a two-stage strategy, which includes: estimating the main frequency using Fast Fourier Transform (FFT) as the initial value and performing nonlinear fitting of the galloping trajectory of the transmission line within the local frequency constraint range. Initial values include: frequency, amplitude, phase, and baseline.
[0015] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform a method for extracting the galloping trajectory of a power conductor and automatically fitting vibration parameters.
[0016] A computer-readable storage medium storing instructions that, when executed, perform a method for extracting the galloping trajectory of a power conductor and automatically fitting vibration parameters.
[0017] The beneficial effects of the technical solution provided in this application are: This invention utilizes a combination of video image processing and frequency domain analysis to automatically extract the galloping trajectories of spacers or nodes in high-voltage transmission lines. Vibration parameters, including frequency, amplitude, and phase, are automatically calculated through sine curve fitting. Compared to existing methods, this invention significantly reduces vegetation interference through color space separation and background suppression; employs a two-stage frequency estimation approach combining spectral analysis and curve fitting for more robust frequency fitting; boasts a high degree of automation, enabling batch processing of node trajectories without manual intervention; and maintains high recognition and fitting accuracy under varying lighting conditions. Attached Figure Description
[0018] The present application will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of an embodiment of this application; Figure 2 This is a schematic diagram of background removal and wire detection in an embodiment of this application; Figure 3 This is a schematic diagram of node tracking in an embodiment of this application; Figure 4 This is a fitting diagram of the dancing trajectory of node 6 in the embodiments of this application; Figure 5 This is a fitting diagram of the dancing trajectory of node 7 in the embodiments of this application; Figure 6 This is a schematic diagram of the electronic device structure in the embodiments of this application. Detailed Implementation
[0019] To provide a clearer understanding of the technical features, objectives, and effects of this application, the specific embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0020] The embodiments of this application provide a method for extracting the galloping trajectory of power conductors and automatically fitting vibration parameters.
[0021] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for extracting the galloping trajectory of power conductors and automatically fitting vibration parameters according to an embodiment of this application, including: S1: Acquire visible light video of the transmission line and preprocess it to obtain an enhanced image; S2: Perform Canny edge detection, contour extraction and filtering on the enhanced image to obtain the conductor region; S3: Within the conductor area, determine the observation positions of each node of the transmission line; S4: Multi-point trajectory tracking based on nearest neighbor distance matching, combined with the observed position of the node, establishes the mapping relationship between ID-frame-coordinate; S5: By combining a trajectory analysis method based on spectrum estimation and nonlinear fitting, and incorporating the ID-frame-coordinate mapping relationship, the galloping trajectory of the transmission line is fitted.
[0022] As one embodiment, this invention provides a robust and highly automated method for monitoring power line galloping. It can stably identify the position of power line nodes under different lighting and background conditions, automatically track and extract their motion trajectories, and accurately calculate key parameters such as the frequency, amplitude, and phase of the galloping through spectral analysis combined with nonlinear curve fitting, achieving online monitoring and rapid assessment. The main components include: 1. A green background suppression method based on color space; 2. A power line detection method combining Hough linear transform and contour screening; 3. A node trajectory tracking method based on multi-point nearest neighbor matching; 4. A galloping parameter extraction method combining FFT frequency estimation and nonlinear sine fitting; 5. A process for visualizing and batch outputting the trajectory and fitting results.
[0023] As one example, parameter output and visualization include: outputting A (pixels), B (Hz), C (rad), D (pixels), period T, and goodness of fit for each node. Draw a comparison graph of the original trajectory and the fitted curve; save the results to an image and a CSV file.
[0024] Step S1 includes: Visible light video of power transmission lines is collected through fixed monitoring or drones; Convert visible light video to the HSV color space; Based on intra-frame color distribution statistics, the hue range of the green region is dynamically estimated, and a background mask is constructed by combining saturation and brightness thresholds. Based on the background mask, an adaptive Gaussian blur operation guided by brightness gradient is introduced to obtain the blur result; Adaptive thresholding based on local contrast enhancement is performed on the blurred results to preserve high-response regions.
[0025] As one example, visible light video of power transmission lines is collected using fixed monitoring or drones, with a frame rate ≥25fps and a resolution ≥1080p. Combining image enhancement and color separation strategies, precise suppression of background vegetation areas in complex scenes is achieved, enhancing the recognizability of key nodes. Video frames are converted to the HSV color space, and the hue range of the green area is dynamically estimated based on intra-frame color distribution statistics. A background mask is constructed by combining saturation and brightness thresholds. Based on the mask, an adaptive Gaussian blur operation guided by brightness gradient is introduced, with the kernel size automatically adjusted according to image resolution. Adaptive threshold segmentation based on local contrast enhancement is performed on the blurred result, preserving high-response areas to highlight the contours of key nodes such as spacers.
[0026] Through the above technical solution, this application has a stable background suppression capability under different lighting and vegetation disturbance conditions, effectively improving the accuracy and robustness of subsequent node detection.
[0027] Step S2 includes: Canny edge detection is performed on the enhanced image using a preset detection threshold to obtain an edge image; Based on the edge map, a probabilistic Hough linear transformation is performed, and line segment direction consistency and spacing constraints are introduced to filter candidate conductor clusters with directions within a ±10° window; Construct a confidence matrix for candidate conductor clusters, and combine the differences in conductor segment length, density and direction to determine the conductor structure to which the segment belongs and generate a conductor region mask; By combining wire region masking and morphological operations, connected component filtering and break repair are performed to obtain the repaired wire region. Within the conductor region, valid targets are screened by using the contour area and aspect ratio, and isolated false detection conductor structures are removed.
[0028] This application, through the above-mentioned technical solution, addresses the geometric characteristics of conductor targets such as slenderness, parallelism, and consistent orientation. It integrates spatial constraints and clustering strategies on the basis of traditional edge detection, and achieves adaptive extraction of conductor contours through multi-constraint clustering and confidence judgment mechanisms, thereby improving the detection accuracy in complex backgrounds and realizing high-precision conductor contour extraction.
[0029] Step S3 includes: S31: Based on the binary mask of the wire profile in the conductor region, calculate the centerline of the conductor according to the conductor direction; With the central axis of the conductor as the center, extend upwards and downwards by a preset pixel distance to construct a strip-shaped region of interest; As one embodiment, the strip-shaped region of interest is used as a priority search strip during node matching (step S4) to limit the priority search range of node matching (step S4), thereby suppressing background interference far from the conductor and improving matching accuracy.
[0030] Within the conductor area, affine transformation or rotation clipping is performed according to the conductor angle to ensure that the envelope structure closely follows the conductor outline and eliminates interference areas far away from the conductor. The interference area includes: vegetation and towers; the nodes include: spacers, fittings or clamps; S32: Within the wire region of each video frame, combined with the strip region of interest, perform connected component analysis to extract the white foreground contours of all nodes; calculate the area of each contour and filter according to the dynamic threshold, retaining the contours that meet the conditions as node candidates. Calculate the area of each white foreground outline. By setting a dynamic area threshold They are filtered as follows:
[0031] in This is a scaling factor used to eliminate small-sized noisy targets; This represents the arithmetic mean of all elements in set S. Represents the set of pixel areas of all white foreground outlines in the current frame; Calculate the geometric centroid coordinates of the white foreground outline after filtering, denoted as . ,in Indicates the current frame The first in A valid node, whose coordinates are used as the node's position in the current frame. The observation location.
[0032] Step S4 includes: Each node is assigned a unique ID. A mapping relationship between ID, frame, and coordinates is established based on the observed positions of each node in the current frame, as follows: Based on the previous frame trajectory Observation coordinates Predict its position in the current frame Possible location And for the candidate nodes detected in the current frame coordinate Perform Euclidean distance matching:
[0033] like , Consider as a node With the current frame node match, This represents the maximum matching radius threshold. Represents a node In frame The predicted coordinates, i.e., pixel coordinates; The Hungarian algorithm is used to perform multi-point global matching on the distance cost matrix generated by the node positions of the previous and current frames, thereby optimizing the overall matching cost. In one specific embodiment of this application, the rows of the matrix represent the nodes tracked in the previous frame (arranged by trajectory ID), the columns represent the nodes detected in the current frame, and the matrix elements are the Euclidean distance between nodes or its square.
[0034] Set up an occlusion tolerance and trajectory preservation mechanism: When the motion trajectory of a tracked node, that is, the time-series position path formed by the node in consecutive frames, fails to match successfully within a short period of time, retain a tracking window of up to K frames, where K is a positive integer and the unit is frames, and continue to update the trajectory position through linear prediction.
[0035] As one embodiment, the aim is to identify galloping nodes, such as spacers, fittings, or clamps, within the detected power conductor area and perform time-series tracking, providing high-quality input data for frequency domain analysis and fitting of galloping parameters.
[0036] Step S5 includes: A time series trajectory is constructed by mapping the ID, frame, and coordinates of each node; The trajectory of each node, with the frame number as the time reference, records the change of its vertical direction over time; For each node trajectory, a sinusoidal function model is used for fitting:
[0037] Where A represents amplitude, i.e., the intensity of the dancing; B represents frequency, i.e., the speed of the dancing; C represents the initial phase; D represents the vertical offset; and T represents the period. The fitting process employs a two-stage strategy, which includes: estimating the main frequency using Fast Fourier Transform (FFT) as the initial value and performing nonlinear fitting of the galloping trajectory of the transmission line within the local frequency constraint range. Initial values include: frequency, amplitude, phase, and baseline.
[0038] This invention employs a two-stage fitting strategy to extract the dominant frequency features of the galloping trajectory of transmission lines and achieve accurate modeling.
[0039] In the first stage, the node trajectory is analyzed by Fast Fourier Transform (FFT) to extract the main frequency f0 as the initial frequency estimate for nonlinear fitting.
[0040] In the second stage, within the local frequency constraint range [f0-δ, f0+δ], the nonlinear least squares method is used to fit the dancing trajectory, and the model form is as follows:
[0041] Where A represents amplitude, i.e., the intensity of the dancing; B represents frequency, i.e., the speed of the dancing; C represents the initial phase; D represents the vertical offset; and T represents the period. For example, in the observed trajectory of a certain node, FFT analysis yields a dominant frequency of f0 = 0.45 Hz, and the frequency constraint range is set to [0.40, 0.50] Hz. The final fitting result is:
[0042] As one example, the main frequency is estimated using Fast Fourier Transform (FFT) as the initial value, and then nonlinear fitting is performed within the local frequency constraint range to improve convergence stability and accuracy.
[0043] As one embodiment, this invention proposes a trajectory analysis method based on a combination of spectrum estimation and nonlinear fitting for the dynamic dancing behavior of wire nodes in videos. This method includes steps such as trajectory acquisition, initial frequency estimation, sinusoidal model fitting, and fitting quality evaluation, enabling the stable extraction of node motion parameters in complex backgrounds.
[0044] The fitting was performed on 8 nodes (IDs 1–8), and the results are shown in the table below:
[0045] In one embodiment, Figure 4 , Figure 5 The original trajectories and fitted curves of nodes 6 and 7 are shown respectively. Among them, point ID=1 shows the best fitting effect, with a goodness of fit of [missing value]. =0.867, the fitted curve is in high agreement with the original trajectory, verifying the effectiveness of the method of the present invention in real-world scenarios.
[0046] This application also discloses an electronic device. (See reference...) Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.
[0047] The communication bus 502 is used to enable communication between these components.
[0048] The user interface 503 may include a display screen, and optionally, the user interface 503 may also include a standard wired interface or a wireless interface.
[0049] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).
[0050] This application also discloses a computer-readable storage medium storing multiple instructions adapted for loading by a processor to execute the above-described method for extracting the galloping trajectory of a power conductor and automatically fitting vibration parameters.
[0051] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure.
[0052] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.
Claims
1. A method for extracting the galloping trajectory of power conductors and automatically fitting vibration parameters, characterized in that, The method includes the following steps: S1: Acquire visible light video of the transmission line and preprocess it to obtain an enhanced image; S2: Perform Canny edge detection, contour extraction and filtering on the enhanced image to obtain the conductor region; S3: Within the conductor area, determine the observation positions of each node of the transmission line; S4: Multi-point trajectory tracking based on nearest neighbor distance matching, combined with the observed position of the node, establishes the mapping relationship between ID-frame-coordinate; S5: By using a trajectory analysis method that combines spectrum estimation and nonlinear fitting, and combining the ID-frame-coordinate mapping relationship, the galloping trajectory of the transmission line is fitted.
2. The method for extracting the galloping trajectory and automatically fitting vibration parameters of power conductors as described in claim 1, characterized in that, Step S1 includes: Visible light video of power transmission lines is collected through fixed monitoring or drones; Convert visible light video to the HSV color space; Based on intra-frame color distribution statistics, the hue range of the green region is dynamically estimated, and a background mask is constructed by combining saturation and brightness thresholds. Based on the background mask, an adaptive Gaussian blur operation guided by brightness gradient is introduced to obtain the blur result; Adaptive thresholding based on local contrast enhancement is performed on the blurred results to preserve high-response regions.
3. The method for extracting the galloping trajectory and automatically fitting vibration parameters of power conductors as described in claim 1, characterized in that, Step S2 includes: Canny edge detection is performed on the enhanced image using a preset detection threshold to obtain an edge image; Based on the edge map, a probabilistic Hough linear transformation is performed, and line segment direction consistency and spacing constraints are introduced to filter candidate conductor clusters with directions within a ±10° window; Construct a confidence matrix for candidate conductor clusters, and combine the differences in conductor segment length, density and direction to determine the conductor structure to which the segment belongs and generate a conductor region mask; By combining wire region masking and morphological operations, connected component filtering and break repair are performed to obtain the repaired wire region. Within the conductor region, valid targets are screened by using the contour area and aspect ratio, and isolated false detection conductor structures are removed.
4. The method for extracting the galloping trajectory and automatically fitting vibration parameters of power conductors as described in claim 1, characterized in that, Step S3 includes: S31: Based on the binary mask of the wire profile in the conductor region, calculate the centerline of the conductor according to the conductor direction; With the central axis of the conductor as the center, extend upwards and downwards by a preset pixel distance to construct a strip-shaped region of interest; Within the conductor area, affine transformation or rotation clipping is performed according to the conductor angle to ensure that the envelope structure closely follows the conductor outline and eliminates interference areas far away from the conductor. The interference area includes: vegetation and towers; the nodes include: spacers, hardware or clamps; S32: Within the wire region of each video frame, combined with the striped region of interest, perform connected component analysis to extract the white foreground contours of all nodes; Calculate the area of each white foreground outline. By setting a dynamic area threshold They are filtered as follows: in This is a scaling factor used to eliminate small-sized noisy targets; This represents the arithmetic mean of all elements in set S. This represents the set of pixel areas of all white foreground outlines in the current frame; Calculate the geometric centroid coordinates of the white foreground outline after filtering, denoted as . ,in Indicates the current frame The first in A valid node, whose coordinates are used as the node's position in the current frame. The observation location.
5. The method for extracting the galloping trajectory and automatically fitting vibration parameters of power conductors as described in claim 4, characterized in that, Step S4 includes: Each node is assigned a unique ID. A mapping relationship between ID, frame, and coordinates is established based on the observed positions of each node in the current frame, as follows: Based on the previous frame trajectory Observation coordinates Predict its position in the current frame Possible location And for the candidate nodes detected in the current frame coordinate Perform Euclidean distance matching: like , Consider as a node With the current frame node match, This represents the maximum matching radius threshold. Represents a node In frame The predicted coordinates, i.e., pixel coordinates; The Hungarian algorithm is used to perform multi-point global matching on the distance cost matrix generated by the node positions of the previous and current frames, thereby optimizing the overall matching cost. Set up an occlusion tolerance and trajectory preservation mechanism: When the motion trajectory of a tracked node, that is, the time-series position path formed by the node in consecutive frames, fails to match successfully within a short period of time, retain a tracking window of up to K frames, where K is a positive integer and the unit is frames, and continue to update the trajectory position through linear prediction.
6. The method for extracting the galloping trajectory and automatically fitting vibration parameters of power conductors as described in claim 1, characterized in that, Step S5 includes: A time series trajectory is constructed by mapping the ID, frame, and coordinates of each node; The trajectory of each node, with the frame number as the time reference, records the change of its vertical direction over time; For each node trajectory, a sinusoidal function model is used for fitting: Where A represents amplitude, i.e., the intensity of the dancing; B represents frequency, i.e., the speed of the dancing; C represents the initial phase; D represents the vertical offset; and T represents the period. The fitting process employs a two-stage strategy, which includes: estimating the main frequency using Fast Fourier Transform (FFT) as the initial value and performing nonlinear fitting of the galloping trajectory of the transmission line within the local frequency constraint range. Initial values include: frequency, amplitude, phase, and baseline.
7. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-6.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the method as described in any one of claims 1-6.