Transmission tower deformation monitoring and early warning method based on image feature analysis

Abstract

The invention discloses a transmission tower deformation monitoring and early warning method based on image feature analysis, and relates to the technical field of image processing. The method comprises the following steps: monitoring a power transmission tower to obtain a power transmission tower monitoring image; performing time sequence analysis to obtain time sequence image features; performing fusion to generate a plurality of fused tower monitoring images; performing feature extraction to obtain a plurality of structure reference points; generating a plurality of transmission tower three-dimensional structure models, performing deformation analysis, and obtaining deformation data and temperature change data; when the temperature change data is larger than a preset temperature threshold value, positioning is carried out based on a corresponding relation, and an abnormal structure point is obtained; and carrying out deformation trend analysis on the abnormal structural points in combination with the deformation data to obtain deformation, and when the deformation is greater than a preset deformation early warning threshold, generating an early warning signal. The technical problem that the deformation monitoring accuracy of the transmission tower in the complex environment is low is solved, and the technical effect of improving the deformation monitoring accuracy of the transmission tower in the complex environment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method for monitoring and early warning of transmission tower deformation based on image feature analysis. Background Art

[0002] In today's rapidly developing power system, transmission towers are key infrastructure for power transmission, and their stable operation is crucial to ensuring the reliability and continuity of power supply. However, with the expansion of the scale of the power grid and the influence of natural environmental factors, transmission towers face many challenges, such as structural deformation caused by extreme weather conditions, material fatigue under long-term load accumulation, and overheating of electrical connection components. If these problems are not discovered in time and effective measures are not taken, they may cause serious power accidents and affect the safe and stable operation of the power grid. Traditional monitoring methods often focus on the monitoring of a single parameter, but in a complex and changing environment, it is difficult to fully reflect the actual operating status of the tower, resulting in low monitoring accuracy. Summary of the invention

[0003] The present application provides a method for monitoring and early warning of transmission tower deformation based on image feature analysis, which solves the technical problem of low accuracy in monitoring transmission tower deformation in complex environments.

[0004] In view of the above problems, the present application provides a transmission tower deformation monitoring and early warning method based on image feature analysis.

[0005] The present application provides a method for monitoring and early warning of transmission tower deformation based on image feature analysis, the method comprising:

[0006] A plurality of sensor clusters are deployed in the target area where the transmission tower is located. Based on the sensor clusters, the transmission tower is monitored to obtain a plurality of groups of transmission tower monitoring images at different time points, wherein the transmission tower monitoring images include standard tower monitoring images and infrared tower monitoring images; a time series analysis is performed on the plurality of groups of transmission tower monitoring images at different time points to obtain time series image features; based on the time series image features, the standard tower monitoring images and the infrared tower monitoring images are fused to generate a plurality of fused tower monitoring images, wherein the fused tower monitoring images have a temperature mark; feature extraction is performed on the plurality of fused tower monitoring images respectively , obtain multiple structural reference points of multiple groups of transmission towers; based on the structural reference points, generate multiple three-dimensional structural models of transmission towers using three-dimensional reconstruction technology, and perform deformation analysis on the multiple three-dimensional structural models of transmission towers to obtain deformation data and temperature change data of the transmission towers, wherein the deformation data and the temperature change data have a one-to-one correspondence; when the temperature change data is greater than a preset temperature threshold, locate based on the correspondence to obtain abnormal structural points; perform deformation trend analysis on the abnormal structural points in combination with the deformation data to obtain the deformation amount, and generate an early warning signal when the deformation amount is greater than a preset deformation early warning threshold.

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

[0008] First, multiple sensor clusters are deployed in the target area where the transmission tower is located. Based on the sensor clusters, the transmission tower is monitored to obtain multiple groups of transmission tower monitoring images at different time points, including standard tower monitoring images and infrared tower monitoring images. Then, time series analysis is performed on multiple groups of transmission tower monitoring images at different time points to obtain time series image features. Further, according to the time series image features, the standard tower monitoring image and the infrared tower monitoring image are fused to generate multiple fused tower monitoring images, and the fused tower monitoring images have temperature labels. Then, feature extraction is performed on multiple fused tower monitoring images respectively to obtain multiple structural reference points of multiple groups of transmission towers. Based on the structural reference points, three-dimensional reconstruction technology is used to generate multiple three-dimensional structural models of transmission towers, and deformation analysis is performed on multiple three-dimensional structural models of transmission towers to obtain deformation data and temperature change data of transmission towers, wherein the deformation data and temperature change data have a one-to-one correspondence. When the temperature change data is greater than the preset temperature threshold, positioning is performed based on the corresponding relationship to obtain abnormal structural points; finally, deformation trend analysis is performed on the abnormal structural points in combination with the deformation data to obtain the deformation amount, and when the deformation amount is greater than the preset deformation warning threshold, a warning signal is generated. The technical problem of low accuracy in deformation monitoring of transmission towers in complex environments is solved, and the technical effect of improving the accuracy of deformation monitoring of transmission towers in complex environments is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a method for monitoring and early warning of transmission tower deformation based on image feature analysis provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the process of performing layered fusion in a method for monitoring and early warning of transmission tower deformation based on image feature analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application solves the technical problem of low accuracy in deformation monitoring of transmission towers in complex environments by providing a deformation monitoring and early warning method for transmission towers based on image feature analysis.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0015] Examples, such as Figure 1 As shown, the embodiment of the present application provides a method for monitoring and early warning of transmission tower deformation based on image feature analysis, wherein the method includes:

[0016] Multiple sensor clusters are deployed in the target area where the transmission towers are located. Based on the sensor clusters, the transmission towers are monitored to obtain multiple groups of transmission tower monitoring images at different time points. The transmission tower monitoring images include standard tower monitoring images and infrared tower monitoring images.

[0017] In the target area where the transmission tower is located, multiple sensor clusters are deployed. That is, the deployment location of the sensor cluster is reasonably selected according to the specific location, height, surrounding environment, etc. of the transmission tower. Usually, the sensor should be installed in a position that can fully cover all key parts of the tower and is not easily blocked. The sensor cluster includes various types of sensors, such as visual sensors (used to obtain standard tower monitoring images) and infrared sensors (used to obtain infrared tower monitoring images). The sensor cluster is responsible for continuously monitoring the status of the transmission tower to obtain multiple groups of transmission tower monitoring images at different time points. The transmission tower monitoring images include standard tower monitoring images and infrared tower monitoring images. Among them, the standard tower monitoring image refers to the appearance image of the transmission tower, which contains intuitive information such as the structural details and surface conditions of the tower; the infrared tower monitoring image reflects the temperature changes of the tower caused by current, environment or internal faults and possible tower defects.

[0018] Furthermore, multiple sensor clusters are deployed in the target area where the transmission tower is located, and the transmission tower is monitored based on the sensor clusters to obtain multiple groups of transmission tower monitoring images at different time points. The method includes:

[0019] Based on the target area where the transmission tower is located, multiple deployment points are determined; at the multiple deployment points, a sensor cluster consisting of visual sensors and infrared sensors is deployed; according to a preset start interval, the sensor cluster is started to monitor the transmission tower, and multiple groups of transmission tower monitoring images at different time points are obtained.

[0020] Specifically, a detailed geographical, climatic and environmental analysis is conducted on the target area where the transmission tower is located to determine which areas are the focus of monitoring. Based on the analysis results, multiple deployment points are selected that can fully cover the key parts of the transmission tower and are not easily obstructed. At each deployment point, a sensor cluster consisting of a visual sensor (such as a high-definition camera) and an infrared sensor (such as an infrared thermal imager) is deployed. These two sensors are used to obtain standard monitoring images and infrared monitoring images of the transmission tower, respectively. According to the monitoring requirements and system resources, the start-up interval of the sensor cluster is preset. This interval can be a fixed time interval (such as once an hour) or can be triggered based on specific conditions (such as weather changes, grid load changes, etc.); according to the preset start-up interval, the sensor cluster is started to monitor the transmission tower. During the monitoring process, the visual sensor and the infrared sensor obtain the standard monitoring image and infrared monitoring image of the transmission tower, respectively.

[0021] Furthermore, based on the target area where the transmission tower is located, multiple deployment points are determined, and the method includes:

[0022] Acquire geographic information data of the target area; perform fault frequency analysis on historical deformation monitoring data of the transmission tower to determine multiple risk areas; and determine multiple deployment points based on the geographic information data and the risk areas.

[0023] Specifically, geographic information system (GIS) and remote sensing technology are used to collect geographic information data of the target area, which may include topography, soil type, vegetation cover, meteorological conditions, hydrological conditions, etc.; the collected geographic information data are sorted and analyzed to extract key information related to the stability of transmission towers, such as geological structure, terrain slope, groundwater level, etc.; the historical deformation monitoring data of transmission towers are obtained from the database, and the collected historical deformation monitoring data are statistically analyzed to identify areas with frequent deformation or high failure rates. These areas may be more prone to deformation or failure due to geological conditions, environmental factors or design defects; based on the results of the failure frequency analysis, the target area where the transmission tower is located is divided into multiple risk areas; the geographic information data is combined with the analysis results of the risk area, and a comprehensive assessment is conducted by an expert team. Based on the comprehensive assessment results, multiple deployment points are selected, which can fully cover the key parts and potential risk areas of the transmission tower, while avoiding blind spots.

[0024] Time series analysis is performed on the multiple groups of transmission tower monitoring images at different time points to obtain time series image features.

[0025] Time series analysis is performed on multiple groups of transmission tower monitoring images at different time points, aiming to extract time-varying features from the image sequence, namely, time series image features. Time series image features can reflect the state changes of transmission towers at different time points, providing an important basis for subsequent monitoring, evaluation and early warning.

[0026] Furthermore, time series analysis is performed on the plurality of groups of transmission tower monitoring images at different time points to obtain time series image features, and the method includes:

[0027] Perform image registration on the multiple groups of transmission tower monitoring images at different time points to obtain multiple groups of aligned transmission tower monitoring images; perform time series analysis on the multiple groups of aligned transmission tower monitoring images to extract time series image features of the transmission tower deformation trend.

[0028] Preferably, feature points are detected in each monitoring image, which are usually easily identifiable and stable points in the image, such as corner points, edge intersections, etc.; the detected feature points are described to generate feature descriptors so as to match similar feature points between different images; matching feature point pairs are found between monitoring images at different time points using feature descriptors; based on the matching feature point pairs, the transformation matrix (such as affine transformation, perspective transformation, etc.) between the images is calculated, which describes the spatial transformation relationship from one image to another; the transformation matrix is ​​applied to transform all monitoring images into a unified coordinate system to obtain multiple sets of aligned transmission tower monitoring images; the difference image between the aligned images at adjacent time points is calculated to highlight the changes of the transmission towers at different time points; features are extracted from the difference image, which are temporal image features, and may include the shape, size, direction, texture changes, etc. of the deformation area.

[0029] According to the temporal image features, the standard pole tower monitoring image and the infrared pole tower monitoring image are fused to generate a plurality of fused pole tower monitoring images, wherein the fused pole tower monitoring images have temperature marks.

[0030] According to the temporal image characteristics, the standard pole tower monitoring image and the infrared pole tower monitoring image are preprocessed to eliminate useless features in the image. The preprocessed standard pole tower monitoring image and the infrared pole tower monitoring image are fused by using image fusion algorithms, such as weighted average method, multi-resolution fusion method (such as pyramid fusion, wavelet transform fusion), etc., to generate a fused pole tower monitoring image with temperature identification. The fused pole tower monitoring image not only contains the appearance information of the pole tower in the standard monitoring image, but also contains the temperature information and defect information in the infrared image.

[0031] Furthermore, according to the temporal image feature, the standard pole tower monitoring image and the infrared pole tower monitoring image are fused to generate a fused pole tower monitoring image, wherein the fused pole tower monitoring image has a temperature mark, and the method includes:

[0032] Based on the temporal image features, the structural change features and temperature features in the standard pole tower monitoring image and the infrared pole tower monitoring image are extracted; feature matching is performed on the structural change features and the temperature features, and based on the feature matching results, the standard pole tower monitoring image and the infrared pole tower monitoring image are layered fused to generate a fused pole tower monitoring image.

[0033] Specifically, based on the temporal image features, the structural features of the pole tower and its surrounding environment, such as edges, corners, textures, etc., are extracted from the standard pole tower monitoring image, and the temperature features are extracted from the infrared pole tower monitoring image; the extracted structural change features and temperature features are matched based on spatial position, shape similarity or other advanced features (such as features obtained through machine learning training). In the matching process, the temporal image features can be used to help determine which structural changes are associated with temperature changes; based on the results of feature matching, the standard pole tower monitoring image and the infrared pole tower monitoring image are layered and fused. In the fusion process, different weights can be given to different features or image layers to highlight important information. A variety of fusion techniques can be used, such as multi-resolution fusion, region-based fusion or pixel-based fusion, to ensure the quality and accuracy of the fused image, and finally generate a fused pole tower monitoring image.

[0034] Furthermore, if Figure 2 As shown, based on the feature matching result, the standard pole tower monitoring image and the infrared pole tower monitoring image are layered and fused to generate a fused pole tower monitoring image. The method includes:

[0035] According to the structural change characteristics and the temperature characteristics, the standard pole tower monitoring image and the infrared pole tower monitoring image are divided into multiple feature layers; based on the feature layers and the feature matching results, the corresponding feature layers of the standard pole tower monitoring image and the infrared pole tower monitoring image are mapped to obtain mapping results; based on the mapping results, the corresponding feature layers of the standard pole tower monitoring image and the infrared pole tower monitoring image are weighted fused, and the fused image is reconstructed to generate the fused pole tower monitoring image.

[0036] Specifically, according to the extracted structural change features and temperature features, the standard pole tower monitoring image and the infrared pole tower monitoring image are divided into multiple feature layers respectively. These feature layers can be divided based on different feature types (such as edges, textures, temperature distribution, etc.) or spatial regions (such as different parts of the pole tower, different areas of the surrounding environment); based on the result of feature matching, the corresponding feature layers of the standard pole tower monitoring image and the infrared pole tower monitoring image are mapped so as to find the corresponding parts in space and features in the two images. The mapping result is a corresponding relationship, which indicates which feature layer in the standard image corresponds to which feature layer in the infrared image; for each pair of mapped feature layers, according to the needs and purposes of fusion, an appropriate fusion strategy is selected for weighted fusion so as to effectively combine the information in the two images while retaining their respective important features; the weighting coefficient can be determined according to the importance, reliability or user preference of the feature, for example, for areas with rich structural details, a higher weight can be given to the standard image; and for areas with significant temperature changes, a higher weight can be given to the infrared image; all fused feature layers are recombined according to the structure of the original image to form a fused pole tower monitoring image.

[0037] Feature extraction is performed on the multiple fused tower monitoring images respectively to obtain multiple structural reference points of multiple groups of transmission towers.

[0038] Perform necessary preprocessing on each fused tower monitoring image, including denoising, contrast enhancement, brightness adjustment, etc., to improve image quality and make features more obvious; select appropriate feature extraction strategies based on the structural characteristics of the transmission tower and the characteristics of the fused image, including shape-based feature extraction (such as edge detection, corner detection), texture-based feature extraction, template matching-based methods, or deep learning-based feature extraction; apply the selected feature extraction strategy to extract features from each fused tower monitoring image, with the goal of identifying multiple structural reference points on the tower; edge detection algorithms can be used to identify the edges and contours of the tower, and then locate intersections or support points; verify the extracted structural reference points to ensure that they are accurate, reliable, and consistent with the actual structure of the transmission tower.

[0039] Based on the structural reference points, a plurality of three-dimensional structural models of transmission towers are generated using three-dimensional reconstruction technology, and deformation analysis is performed on the plurality of three-dimensional structural models of transmission towers to obtain deformation data and temperature change data of the transmission towers, wherein the deformation data and the temperature change data have a one-to-one correspondence.

[0040] Using 3D reconstruction technology, such as methods based on multi-view geometry, the extracted structural reference points are converted into point clouds in 3D space, and the surface of the point clouds is reconstructed to generate a 3D structural model of the transmission tower. This model can accurately reflect the shape, size and structural characteristics of the tower; obtain the 3D structural model of the same transmission tower at different time points; compare the 3D models at different time points, and measure the deformation of the tower in various directions, which can be obtained by calculating the distance change, angle change or volume change between corresponding points on the model; the deformation data should include parameters such as displacement, rotation, strain, etc., and can accurately reflect the deformation of the tower in different time periods; extract temperature information from the fused image, which corresponds to the structural reference points or areas in the 3D structural model, and match the deformation data with the temperature change data to ensure that they have a one-to-one correspondence in time and space.

[0041] Furthermore, the deformation analysis is performed on the three-dimensional structural models of the plurality of transmission towers, and the method includes:

[0042] Deviation analysis is performed on the three-dimensional structural models of the multiple transmission towers to obtain temperature deviation data and displacement deviation data; when the temperature deviation data is greater than a temperature deviation threshold, the temperature deviation data is added to the temperature change data; when the displacement deviation data is greater than a displacement deviation threshold, the displacement deviation data is added to the deformation data.

[0043] Specifically, a benchmark three-dimensional structural model is selected as a reference. The benchmark model is usually obtained under normal conditions (such as standard temperature and no external force), or a representative model selected at the beginning of the analysis. For each three-dimensional structural model of the transmission tower to be analyzed, it is compared with the benchmark model, and the temperature deviation and displacement deviation of each corresponding point or area are calculated. The temperature deviation can be obtained by comparing the temperature values ​​in the infrared monitoring data, while the displacement deviation can be obtained by calculating the spatial distance difference between the corresponding points in the three-dimensional model. According to the design specifications, historical data or expert experience of the transmission tower, the temperature deviation threshold and the displacement deviation threshold are set. For each temperature deviation data, if it is greater than the set temperature deviation threshold, it is considered to be a significant temperature change and needs to be added to the temperature change data. For each displacement deviation data, if it is greater than the set displacement deviation threshold, it is considered to be a significant displacement change, that is, the tower has undergone significant deformation. At this time, the displacement deviation data needs to be added to the deformation data. Through the above steps, the three-dimensional structural models of multiple transmission towers can be deeply analyzed for deformation, and valuable temperature change and displacement change data can be obtained, providing strong support for the safe operation of the transmission tower.

[0044] When the temperature change data is greater than a preset temperature threshold, positioning is performed based on the corresponding relationship to obtain abnormal structure points.

[0045] When performing deformation analysis on the three-dimensional structural model of a transmission tower, if the temperature change data is detected to be greater than the preset temperature threshold, this usually means that a certain area on the tower may be subject to abnormal thermal stress or other temperature-related influences. Based on the correspondence between this temperature change and the three-dimensional structural model, it can be located to obtain abnormal structural points. Specifically, when it is found that the temperature change of a certain area or point exceeds the preset threshold, it is marked as an abnormal temperature change area; the abnormal temperature change area is mapped to the three-dimensional structural model using the previously established correspondence between the three-dimensional structural model and the temperature monitoring data; after locating the abnormal temperature change area on the three-dimensional structural model, the structural characteristics of the area are further analyzed to determine which structural points or components may be affected, thereby obtaining abnormal structural points.

[0046] The deformation trend of the abnormal structural point is analyzed in combination with the deformation data to obtain the deformation amount, and when the deformation amount is greater than a preset deformation warning threshold, a warning signal is generated.

[0047] Collect and integrate the deformation data in the three-dimensional structural model of the transmission tower and the information of abnormal structural points previously identified by temperature monitoring. These data include the displacement measurement values ​​of each abnormal structural point at different time points, such as horizontal displacement, vertical displacement or angle change, etc.; arrange the deformation data in chronological order and observe its changing trend over time; select a suitable mathematical model (such as a linear model, a polynomial model or an exponential model) to fit the deformation trend based on the smoothed data; calculate the total deformation of the abnormal structural point at the current time point based on the results of trend analysis. This deformation can be the cumulative displacement relative to the initial state, or the average deformation rate or maximum deformation in a specific time period; set a reasonable deformation warning threshold based on the design specifications, historical data and expert experience of the transmission tower; compare the calculated deformation with the preset deformation warning threshold, and generate a warning signal if the deformation is greater than the warning threshold.

[0048] Furthermore, after generating the warning signal, the method further includes:

[0049] Defect recognition is performed based on the fused tower monitoring image to obtain defect recognition results; a defect analysis model is constructed to quantitatively analyze the defect recognition results to generate defect assessment indicators; when the defect assessment indicator is greater than a defect warning threshold, the warning signal is generated.

[0050] Preferably, image processing technology and machine learning algorithms (such as convolutional neural network CNN, support vector machine SVM, etc.) are used to identify defects in the fused image. Defect identification may include but is not limited to wire breakage or loosening, tower tilt or cracks, etc.; a machine learning model is trained based on historical defect data, and a defect analysis model is obtained after the training is completed. The defect analysis model can evaluate the severity of the defect, the impact on the safety of the tower structure, and the possible consequences; the defect assessment index is compared with a preset defect warning threshold. The preset defect warning threshold is set according to the design specifications, historical data, expert experience and safety standards of the transmission tower, and is used to determine whether the defect has reached the level that requires an early warning. If the defect assessment index is greater than the preset defect warning threshold, an early warning signal is generated.

[0051] In summary, the embodiments of the present application have at least the following technical effects:

[0052] First, multiple sensor clusters are deployed in the target area where the transmission tower is located. Based on the sensor clusters, the transmission tower is monitored to obtain multiple groups of transmission tower monitoring images at different time points, including standard tower monitoring images and infrared tower monitoring images. Then, time series analysis is performed on multiple groups of transmission tower monitoring images at different time points to obtain time series image features. Further, according to the time series image features, the standard tower monitoring image and the infrared tower monitoring image are fused to generate multiple fused tower monitoring images, and the fused tower monitoring images have temperature labels. Then, feature extraction is performed on multiple fused tower monitoring images respectively to obtain multiple structural reference points of multiple groups of transmission towers. Based on the structural reference points, three-dimensional reconstruction technology is used to generate multiple three-dimensional structural models of transmission towers, and deformation analysis is performed on multiple three-dimensional structural models of transmission towers to obtain deformation data and temperature change data of transmission towers, wherein the deformation data and temperature change data have a one-to-one correspondence. When the temperature change data is greater than the preset temperature threshold, positioning is performed based on the corresponding relationship to obtain abnormal structural points; finally, deformation trend analysis is performed on the abnormal structural points in combination with the deformation data to obtain the deformation amount, and when the deformation amount is greater than the preset deformation warning threshold, a warning signal is generated. The technical problem of low accuracy in deformation monitoring of transmission towers in complex environments is solved, and the technical effect of improving the accuracy of deformation monitoring of transmission towers in complex environments is achieved.

[0053] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0055] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the 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 equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for monitoring and early warning of transmission tower deformation based on image feature analysis, characterized in that: The method comprises: Multiple sensor clusters are deployed in the target area where the transmission towers are located. Based on the sensor clusters, the transmission towers are monitored to obtain multiple groups of transmission tower monitoring images at different time points. The transmission tower monitoring images include standard tower monitoring images and infrared tower monitoring images. Performing time series analysis on the multiple groups of transmission tower monitoring images at different time points to obtain time series image features; According to the temporal image features, the standard pole tower monitoring image and the infrared pole tower monitoring image are fused to generate a plurality of fused pole tower monitoring images, wherein the fused pole tower monitoring images have temperature marks; Performing feature extraction on the multiple fused tower monitoring images respectively to obtain multiple structural reference points of multiple groups of transmission towers; Based on the structural reference points, a plurality of three-dimensional structural models of transmission towers are generated by using three-dimensional reconstruction technology, and deformation analysis is performed on the plurality of three-dimensional structural models of transmission towers to obtain deformation data and temperature change data of the transmission towers, wherein the deformation data and the temperature change data have a one-to-one correspondence; When the temperature change data is greater than a preset temperature threshold, positioning is performed based on the corresponding relationship to obtain an abnormal structure point; The deformation trend of the abnormal structural point is analyzed in combination with the deformation data to obtain the deformation amount, and when the deformation amount is greater than a preset deformation warning threshold, a warning signal is generated.

2. A method for monitoring and early warning of transmission tower deformation based on image feature analysis as claimed in claim 1, characterized in that: A plurality of sensor clusters are deployed in the target area where the transmission tower is located, and the transmission tower is monitored based on the sensor clusters to obtain a plurality of groups of transmission tower monitoring images at different time points. The method includes: Determine multiple deployment points based on the target area where the transmission towers are located; Deploying a sensor cluster consisting of visual sensors and infrared sensors at the plurality of deployment points; According to the preset start-up interval, the sensor cluster is started to monitor the transmission tower, and multiple groups of transmission tower monitoring images at different time points are obtained.

3. A method for monitoring and early warning of transmission tower deformation based on image feature analysis as claimed in claim 2, characterized in that: Based on the target area where the transmission tower is located, a plurality of deployment points are determined, and the method includes: Acquiring geographic information data of the target area; Performing fault frequency analysis on historical deformation monitoring data of the transmission tower to determine multiple risk areas; A plurality of deployment points are determined based on the geographic information data and the risk area.

4. The method for monitoring and early warning of transmission tower deformation based on image feature analysis according to claim 1, characterized in that: Performing time series analysis on the plurality of groups of transmission tower monitoring images at different time points to obtain time series image features, the method comprising: Performing image registration on the multiple groups of transmission tower monitoring images at different time points to obtain multiple groups of aligned transmission tower monitoring images; A time series analysis is performed on the multiple groups of aligned transmission tower monitoring images to extract the time series image features of the transmission tower deformation trend.

5. The method for monitoring and early warning of transmission tower deformation based on image feature analysis according to claim 4, characterized in that: According to the temporal image feature, the standard pole tower monitoring image and the infrared pole tower monitoring image are fused to generate a fused pole tower monitoring image, wherein the fused pole tower monitoring image has a temperature mark, and the method includes: Extracting structural change features and temperature features in the standard pole tower monitoring image and the infrared pole tower monitoring image based on the temporal image features; Feature matching is performed on the structural change feature and the temperature feature, and based on the feature matching result, the standard pole tower monitoring image and the infrared pole tower monitoring image are layered fused to generate a fused pole tower monitoring image.

6. A method for monitoring and early warning of transmission tower deformation based on image feature analysis as claimed in claim 5, characterized in that: Based on the feature matching result, the standard pole tower monitoring image and the infrared pole tower monitoring image are layered and fused to generate a fused pole tower monitoring image. The method includes: According to the structural change characteristics and the temperature characteristics, the standard pole tower monitoring image and the infrared pole tower monitoring image are divided into a plurality of feature layers; Based on the feature layer and the feature matching result, corresponding feature layers of the standard pole tower monitoring image and the infrared pole tower monitoring image are mapped to obtain a mapping result; Based on the mapping result, weighted fusion is performed on the corresponding feature layers of the standard pole tower monitoring image and the infrared pole tower monitoring image, and the fused image is reconstructed to generate the fused pole tower monitoring image.

7. The method for monitoring and early warning of transmission tower deformation based on image feature analysis according to claim 1, characterized in that: Performing deformation analysis on the three-dimensional structural models of the plurality of transmission towers, the method comprising: Performing deviation analysis on the three-dimensional structural models of the plurality of transmission towers to obtain temperature deviation data and displacement deviation data; When the temperature deviation data is greater than a temperature deviation threshold, adding the temperature deviation data to the temperature change data; When the displacement deviation data is greater than a displacement deviation threshold, the displacement deviation data is added to the deformation data.

8. The method for monitoring and early warning of transmission tower deformation based on image feature analysis according to claim 1, characterized in that: After generating the warning signal, the method further includes: Perform defect recognition based on the fused tower monitoring image to obtain a defect recognition result; Constructing a defect analysis model to quantitatively analyze the defect identification results and generate defect assessment indicators; When the defect assessment index is greater than the defect warning threshold, the early warning signal is generated.

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