Transmission line channel monitoring method and system

Abstract

The invention relates to a power transmission line channel monitoring method and system. The method comprises the following steps: acquiring satellite remote sensing image data of a power transmission line channel, and judging whether a suspected easy floating object exists in the power transmission line channel; if yes, the suspected easy-to-float objects are identified, and a suspected easy-to-float object distribution map is obtained; a task line of the unmanned aerial vehicle is generated based on the suspected easy floating object distribution map, and the unmanned aerial vehicle is controlled to fly according to the task line and collect multi-sensor image data of the power transmission line channel; judging whether an easily floating object exists in the power transmission line channel or not; if yes, the type of the easy-to-float object is identified based on the multi-sensor image data, and early warning information of the easy-to-float object is generated based on a type identification result and the meteorological monitoring data. The system for executing the method comprises a first judgment module, a suspected easy floating object identification module, a first generation module, a second judgment module and a second generation module which are connected in sequence. The safety of the power transmission line can be improved.

Description

Technical Field

[0001] This invention relates to the field of image detection technology, and specifically to a method and system for monitoring power transmission line channels based on unmanned aerial vehicles (UAVs). Background Technology

[0002] With the continuous development of power systems and the ever-expanding scale of transmission networks, the safety monitoring of transmission line corridors has become a crucial guarantee for the stable operation of the power grid. Traditional transmission line monitoring mainly employs methods such as manual inspection and helicopter inspection, which have played an important role in practical applications. In recent years, the rapid development of remote sensing technology, drone technology, and intelligent identification technology has provided new technical means and solutions for transmission line monitoring.

[0003] However, existing monitoring technologies are significantly inadequate in addressing the threat of easily floating objects within transmission line corridors. Traditional monitoring methods primarily focus on detecting defects in the transmission equipment itself, lacking effective identification and early warning capabilities for easily floating objects within the line corridors that may pose a threat to power transmission safety. They also lack specialized identification technologies for easily floating objects within transmission line corridors, making it impossible to effectively identify and locate floating object targets that may threaten transmission lines. These easily floating objects may shift under specific meteorological conditions, posing safety hazards to transmission lines, but existing technologies lack targeted monitoring and early warning mechanisms, failing to promptly detect and assess such risks.

[0004] Furthermore, existing technologies primarily rely on single data sources, lacking effective fusion technologies for satellite remote sensing and UAV data, thus failing to achieve complementary advantages between macro and micro monitoring. Intelligent identification algorithms lack specificity, lacking identification models and algorithms tailored to the characteristics of easily floating objects, resulting in lower accuracy and efficiency. Simultaneously, existing technologies lack systematic data processing and risk assessment techniques, failing to establish a complete technological chain from data collection, processing, and analysis to risk warning. The technologies also lack preventative monitoring capabilities, failing to identify and warn of potential risks in advance, thus impacting the safe and stable operation of transmission lines. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for monitoring transmission line channels, which can fill the monitoring gap of easily floating objects in transmission line channels, realize effective monitoring and early warning of easily floating objects in transmission line channels, and thus avoid the risk of easily floating objects threatening power transmission safety.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Firstly, a method for monitoring transmission line corridors is provided, which includes the following steps: Step S1: Acquire satellite remote sensing image data of the power transmission line channel in real time, and determine whether there are any suspected easily floating objects in the power transmission line channel based on the satellite remote sensing image data. If there are, proceed to step S2. Step S2: Identify the suspected easily floating objects based on the satellite remote sensing image data to obtain a distribution map of the suspected easily floating objects; Step S3: Generate the mission route of the UAV based on the distribution map of the suspected easily floating objects, and control the UAV to fly along the mission route and collect multi-sensor image data of the power transmission line channel; Step S4: Based on the multi-sensor image data, determine whether there are any easily floating objects in the transmission line channel. If so, proceed to step S5. Step S5: Identify the type of the easily floating object based on the multi-sensor image data, and generate an easily floating object early warning information based on the identification result of the easily floating object type and meteorological monitoring data.

[0007] In one possible implementation, step S1, the method for determining whether there are suspected easily floating objects in the power transmission line channel based on the satellite remote sensing image data, includes: The satellite remote sensing image data is corrected to obtain preprocessed image data; The preprocessed image data is subjected to feature extraction processing to obtain image feature data; The change detection results are determined based on the image feature data and historical image data; The change detection results are processed based on the criteria for identifying suspected easily floating objects to determine whether the suspected easily floating objects exist.

[0008] The correction processing performed on the satellite remote sensing image data includes radiometric correction, geometric correction, and atmospheric correction; feature extraction processing is performed on the preprocessed image data based on spectral features, texture features, and shape features.

[0009] In one possible implementation, step S2, which involves identifying the suspected easily floating objects based on the satellite remote sensing image data to obtain a distribution map of the suspected easily floating objects, includes the following method: The satellite remote sensing image data is processed by region segmentation to obtain monitoring unit data; The monitoring unit data is subjected to multi-feature fusion recognition processing to obtain the easily floating object recognition result; The attribute information of the easily floating object identification result is extracted to obtain the attribute data of the easily floating object; Based on spatial analysis technology, the attribute data and transmission line location information are processed by distance calculation to obtain the threat level assessment result of the easily floating object; Based on the threat level assessment results, transmission line routing information, and topographic features, spatial annotation processing is performed using a geographic information system to obtain the distribution map of the suspected easily floating objects.

[0010] Specifically, the satellite remote sensing image data is segmented based on image resolution and terrain features; the monitoring unit data is fused and identified based on spectral reflectance and geometric shape features; and the attribute data of the easily floating objects includes location coordinates, area size, shape features, and spectral characteristics.

[0011] In one possible implementation, step S3, which involves generating a mission route for the UAV based on the suspected easily floating object distribution map and controlling the UAV to fly along the mission route and collect multi-sensor image data of the power transmission line channel, includes: Spatial distribution feature analysis was performed on the suspected easily floating object distribution map to obtain data on key detection areas; Based on the threat level assessment results of the suspected easily floating objects, the key detection areas are prioritized to obtain a detection task priority sequence. The detection task priority sequence is subjected to path optimization processing to obtain flight path planning results; The flight path planning results are processed to generate flight commands, resulting in flight command data that includes takeoff point, track point, shooting point and landing point. Based on the flight command data, the UAV is controlled to perform flight missions, and the flight status data of the UAV is obtained; The flight status data of the UAV is simultaneously acquired and processed using a visible light camera, an infrared thermal imager, and a lidar system to obtain the multi-sensor image data.

[0012] Among them, the priority sequence of the detection tasks is optimized based on flight safety factors, detection efficiency factors, and meteorological parameters.

[0013] In one possible implementation, step S4, the method for determining whether there are easily floating objects in the transmission line channel based on the multi-sensor image data, includes: The multi-sensor image data is spatiotemporally registered to obtain registered multi-sensor data. Visual feature extraction processing is performed on the visible light image data in the registered multi-sensor data to obtain visible light feature data; The infrared thermal image data and lidar data in the registered multi-sensor data are subjected to feature extraction processing to obtain infrared laser feature data; Based on the multi-sensor feature fusion discrimination mechanism, the infrared thermal image data and lidar data in the registered multi-sensor data are comprehensively evaluated and processed to obtain the target evaluation result; The target evaluation results are processed based on multi-dimensional discrimination thresholds to obtain a judgment result on whether the easily floating object exists.

[0014] Specifically, visual feature extraction processing is performed on the visible light image data in the registered multi-sensor data based on color, texture, and edge features; feature extraction processing is performed on the infrared thermal image data and lidar data in the registered multi-sensor data based on thermal radiation characteristics and three-dimensional geometric information.

[0015] In one possible implementation, step S5, which involves identifying the type of the easily floating object based on the multi-sensor image data and generating an easily floating object warning message based on the identification result and meteorological monitoring data, includes: The multi-sensor image data is subjected to feature fusion analysis to obtain the feature data of easily floating objects; The easily floating object feature data is classified and identified based on the easily floating object type identification system to obtain the easily floating object type identification result. The meteorological monitoring data is acquired and trend analyzed to obtain meteorological forecast data; Based on the identification results of the easily floating object type and the meteorological forecast data, motion trajectory prediction processing is performed to obtain the motion trajectory prediction results of the easily floating object. Based on the threat level calculation rules, the predicted trajectory of the easily floating object and the safe distance of the transmission line are subjected to threat assessment processing to obtain the threat level assessment data of the easily floating object. The warning information is constructed and processed based on the threat level assessment data of the easily floating objects and the warning information generation rules to obtain the warning information of the easily floating objects.

[0016] The multi-sensor image data is subjected to feature fusion analysis based on spectral features, geometric features, thermal radiation features, and three-dimensional shape features.

[0017] Secondly, a transmission line corridor monitoring system is provided, comprising: The first judgment module acquires satellite remote sensing image data of the transmission line channel in real time, and judges whether there are any suspected easily floating objects in the transmission line channel based on the satellite remote sensing image data. A suspected floating object identification module, which identifies suspected floating objects based on satellite remote sensing image data when they are present, and obtains a distribution map of suspected floating objects; The first generation module generates a mission route for the UAV based on the distribution map of the suspected easily floating objects, and controls the UAV to fly along the mission route and collect multi-sensor image data of the power transmission line channel. The second judgment module determines whether there are easily floating objects in the power transmission line channel based on the multi-sensor image data. The second generation module identifies the type of easily floating objects based on the multi-sensor image data when they are present, and generates easily floating object early warning information based on the identification result of the easily floating object type and meteorological monitoring data. The first judgment module, the suspected floating object identification module, the first generation module, the second judgment module, and the second generation module are connected in sequence.

[0018] Due to the application of the above technical solutions, the present invention has the following advantages compared with the prior art: the present invention can effectively monitor and warn of easily floating objects in the transmission line channel, thereby helping to improve the safety of the transmission line and reduce the threat of easily floating objects to the safety of power transmission. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Appendix Figure 1 This is a flowchart of the transmission line channel monitoring method of the present invention.

[0021] Appendix Figure 2 This is a structural block diagram of the power transmission line channel monitoring system of the present invention.

[0022] Appendix Figure 3 A structural block diagram of a terminal device provided for an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0025] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0028] Example 1: Please refer to Figure 1 A method for monitoring transmission line corridors, comprising the following steps S1 to S5.

[0029] Step S1: Acquire satellite remote sensing image data of the transmission line channel in real time, and determine whether there are any suspected floating objects in the transmission line channel based on the satellite remote sensing image data. If so, proceed to step S2.

[0030] Satellite remote sensing imagery data, selecting optical satellite images with a resolution of at least 1 meter, is used to ensure the identification of major ground features within the corridor. Simultaneously, a multi-temporal remote sensing data acquisition plan is developed based on seasonal changes and meteorological conditions to monitor dynamic environmental changes within the corridor.

[0031] Specifically, this step involves establishing communication connections with multiple satellites, including optical remote sensing satellites and synthetic aperture radar satellites. Using pre-set geographic coordinates of the power transmission line, image acquisition commands are automatically sent to the satellites to obtain high-resolution remote sensing images covering the power transmission line corridor and its surrounding area. The range and frequency of image acquisition are dynamically adjusted based on the power transmission line's direction and terrain features; for power transmission line sections crossing rivers, lakes, coastlines, or other water bodies, the image acquisition density is increased. Image data from different time phases is acquired, and newly appearing objects are identified through temporal comparative analysis. After preprocessing the acquired satellite remote sensing image data, including radiometric, geometric, and atmospheric corrections, objects differing from their surroundings are identified by analyzing spectral, textural, and shape features. Comparative analysis with historical image data identifies newly appearing or relocated objects, and the judgment results are automatically output by establishing criteria for identifying suspected easily floating objects. The beneficial effect of this step is that it enables all-weather, large-scale monitoring of power transmission line channels through multi-satellite collaborative work, and effectively identifies dynamically changing suspected easily floating objects through time-series comparative analysis, providing a reliable data foundation for subsequent accurate detection.

[0032] Step S2: Identify suspected floating objects based on satellite remote sensing image data to obtain a distribution map of suspected floating objects.

[0033] Specifically, this step is a refined identification procedure initiated immediately upon detection of suspected floating objects in step S1. In this step, satellite remote sensing imagery is segmented into several monitoring units, with the size of each unit adaptively adjusted based on image resolution and terrain features. Areas within the safe distance of the transmission line are prioritized. A multi-feature fusion method is used to classify and identify suspected floating objects. By analyzing the spectral reflectance characteristics of the objects, floating objects of different materials are distinguished, and their geometric features, such as aspect ratio, area, and perimeter, are analyzed. An attribute database of suspected floating objects is established, recording the location coordinates, size, shape features, and spectral characteristics of each identified object. Spatial analysis techniques are used to calculate the distance relationship between each suspected floating object and the transmission line, assessing its potential threat level. A distribution map of suspected floating objects is generated. This map, based on a geographic information system, marks the location, type, and threat level of each suspected floating object, including detailed information on the transmission line's direction and surrounding terrain features. The beneficial effect of this step is that it improves the accuracy of identifying suspected easily floating objects through refined identification and multi-feature fusion analysis, and the generated distribution map provides detailed spatial reference information for UAV mission planning.

[0034] Change detection and analysis were performed using multi-temporal remote sensing data. Algorithms such as the difference method, ratio method, and principal component analysis were employed to identify newly added abnormal targets within power transmission line corridors. Special attention was paid to areas prone to floating debris, such as near water bodies, riverbanks, and farmland. By setting change thresholds and morphological filtering, normal ground feature changes caused by seasonal variations and agricultural activities were eliminated, while potential floating debris targets were retained.

[0035] Step S3: Generate the drone's mission route based on the distribution map of suspected easily floating objects, and control the drone to fly along the mission route and collect multi-sensor image data of the power transmission line channel.

[0036] Specifically, in this step, upon receiving the suspected floating object distribution map generated in step S2, the UAV mission planning program is immediately initiated. The spatial distribution characteristics of the suspected floating objects in the map are analyzed to identify areas requiring focused detection. Priority is given based on the threat level of the suspected floating objects, with detection tasks assigned to high-threat areas first. During mission route planning, flight safety, detection efficiency, and weather conditions are comprehensively considered, and flight altitude and speed are adjusted based on meteorological parameters such as wind speed, wind direction, and visibility. After mission route planning is completed, detailed flight instructions are generated, including the coordinates of key nodes such as takeoff point, waypoint, shooting point, and landing point. Each shooting point corresponds to a specific location on the suspected floating object distribution map. The UAV executes the flight mission according to the planned route, with its multi-sensor system working synchronously. The visible light camera acquires high-resolution color images, the infrared thermal imager detects the temperature characteristics of objects, and the lidar system provides accurate three-dimensional spatial information. The UAV's flight status and data acquisition quality are monitored in real time. The beneficial effect of this step is that it enables the accurate location and detection of suspected easily floating objects through intelligent task planning and multi-sensor collaborative work, providing high-quality multi-dimensional data support for subsequent accurate judgment.

[0037] A dynamic task allocation algorithm is employed to optimize flight paths and task assignments based on target distribution density, importance, and UAV endurance. For high-risk areas and areas with concentrated suspected targets, detection frequency and flight density are increased to ensure no potential threat is overlooked. A multi-UAV collaborative operation mode is used, equipped with various sensors including high-definition visible light cameras, infrared thermal imagers, and LiDAR. The visible light cameras utilize zoom lenses, automatically adjusting the focal length according to target size and distance to ensure clear, detailed images of the targets. Infrared thermal imagers detect the temperature distribution characteristics of targets and identify metallic and non-metallic floating objects. LiDAR acquires the three-dimensional geometric information of targets, accurately measuring parameters such as height and volume. UAV flight path planning employs an adaptive route generation algorithm based on the power transmission line's alignment, ensuring full coverage scanning of the transmission line corridor. Flight altitude is controlled within 50-100 meters of the power line, and flight speed is controlled within 15-25 m / s to ensure sufficient resolution and clarity in the acquired image data.

[0038] Step S4: Determine whether there are easily floating objects in the transmission line channel based on multi-sensor image data. If so, proceed to step S5.

[0039] Specifically, in this step, after the multi-sensor image data (including visible light image data, infrared thermal image data, and lidar data) collected by the UAV is transmitted back to the ground processing system, it immediately enters the precise judgment stage. Spatiotemporal registration is performed on the multi-sensor data, ensuring accurate correspondence between data of the same target acquired by different sensors. Visible light image data provides detailed appearance features of the object. By analyzing visual features such as color, texture, and edges, typical features such as the transparency or translucency of plastic film, the metallic luster of corrugated steel plates, and the irregular shape of tree branches can be identified. Infrared thermal image data reveals the thermal radiation characteristics of the object; different materials of buoyant objects exhibit different temperature characteristics in the infrared band, helping to distinguish real buoyant objects from the environmental background. LiDAR data provides precise three-dimensional geometric information of the object, measuring parameters such as height, volume, and surface roughness. By analyzing the three-dimensional shape features of the object, it is determined whether the object has the characteristic of being easily carried by wind. A multi-sensor feature fusion discrimination mechanism is established to comprehensively analyze visible light, infrared, and lidar data, and to comprehensively evaluate each suspected target. By setting multi-dimensional discrimination thresholds, real buoyant objects and misjudged targets can be effectively distinguished. The beneficial effect of this step is that it significantly improves the accuracy and reliability of identifying easily floating objects through multi-sensor data fusion and a precise discrimination mechanism, effectively reducing false positives and false negatives.

[0040] The intelligent recognition algorithm employs a deep learning model, combining the advantages of convolutional neural networks and recurrent neural networks to construct a multimodal fusion model for recognizing easily floating objects. The model input includes visible light images, infrared images, and LiDAR point cloud data. Feature extraction networks extract feature information from each modality separately, and then feature fusion is performed through an attention mechanism. The recognition model can accurately distinguish different types of easily floating objects, including plastic products, metal products, textiles, and plant debris, achieving an accuracy rate of over 95%.

[0041] Step S5: Identify the types of easily floating objects based on multi-sensor image data, and generate easily floating object early warning information based on the identification results of easily floating object types and meteorological monitoring data.

[0042] Specifically, in this step, after confirming the presence of easily floating objects, the process proceeds to the type identification and early warning generation stage. Based on the multi-sensor image data acquired in step S4, an easily floating object type identification system is established. By comprehensively analyzing the spectral characteristics, geometric characteristics, thermal radiation characteristics, and three-dimensional shape characteristics of the objects, easily floating objects are classified into different types, such as plastic films, metal sheets, plant branches and leaves, and textiles. Each type of easily floating object has unique physical properties and flight characteristics. Real-time meteorological monitoring data, including parameters such as wind speed, wind direction, temperature, humidity, and air pressure, is acquired simultaneously. By analyzing the changing trends of meteorological data, weather conditions for a future period are predicted. Combining the type characteristics of easily floating objects and current location information, an easily floating object trajectory prediction model is established to calculate the possible movement paths of different types of easily floating objects under specific meteorological conditions. The early warning information generation module comprehensively considers factors such as the type of easily floating object, location, meteorological conditions, and safe distance from power transmission lines to calculate the threat level and early warning level. It predicts the possible movement direction of the easily floating object and the time it will reach the power transmission line. The early warning information includes the detailed location coordinates, type description, threat level, and recommended handling measures for easily floating objects, and is promptly sent to relevant personnel through various communication methods. The beneficial effect of this step is that, through precise type identification and intelligent early warning mechanisms, it provides power operation and maintenance personnel with timely and accurate threat warning information, effectively ensuring the safe and stable operation of transmission lines.

[0043] Risk level assessments are conducted on identified targets, calculating their threat level to power transmission lines based on factors such as material, size, location, and meteorological conditions. Satellite remote sensing identification results and UAV-based refined detection results are fused to establish a comprehensive database of easily floating debris risks along power transmission line corridors. Data fusion employs a multi-level fusion strategy, including data-level fusion, feature-level fusion, and decision-level fusion. Data-level fusion unifies the processing of raw data from different data sources through spatial registration and temporal synchronization. Feature-level fusion extracts feature information from each data source, improving the reliability of target identification through weighted fusion and complementary fusion. Decision-level fusion synthesizes the results of various identification algorithms, determining the final identification result through a voting mechanism and confidence level weighting.

[0044] A floating object risk assessment model was established, comprehensively considering multiple dimensions such as target characteristics, environmental factors, and meteorological conditions. Target characteristics include parameters such as material type, geometric dimensions, weight estimation, and surface roughness. Environmental factors include information such as the distance between the target and transmission lines, topography, and vegetation cover. Meteorological conditions include real-time meteorological data such as wind speed and direction, precipitation, and temperature. A multi-factor risk assessment model was established using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, classifying risk levels into four levels: low, medium, high, and extremely high. A risk early warning system was constructed to monitor changes in meteorological conditions in real time. When extreme weather such as strong winds or heavy rain is predicted, the risk level of easily floating objects in the corresponding area is automatically upgraded. A risk propagation model was established to simulate the movement trajectory of easily floating objects under different wind conditions and predict the transmission line sections that may be affected. Risk distribution maps and early warning information were generated and visualized through a GIS platform to provide decision support for the power sector's emergency response. Based on the risk assessment results, a tiered early warning mechanism and an automated emergency response system were established. The early warning system employs a combination of real-time monitoring and dynamic assessment to continuously track changes in the status of identified targets and the emergence of new targets. By setting different early warning thresholds, it achieves a four-level early warning response: blue alert (general risk), yellow alert (significant risk), orange alert (major risk), and red alert (extremely serious risk).

[0045] The intelligent early warning system integrates meteorological forecast data and establishes a meteorological-risk correlation model. When meteorological conditions that may trigger the movement of easily floating objects are predicted, early warning information is issued in advance. The system automatically calculates the risk propagation path, identifies potentially affected transmission lines and critical equipment, and generates detailed risk reports and emergency response recommendations. Early warning information is disseminated through multiple channels, including SMS, email, mobile apps, and dispatch system interfaces, ensuring that relevant personnel receive early warning information in a timely manner.

[0046] The emergency response system automatically initiates corresponding handling procedures based on the risk level. For low-risk targets, the system records and incorporates them into the routine inspection plan; for medium-risk targets, the system automatically assigns inspection tasks and increases monitoring frequency; for high-risk and extremely high-risk targets, the system immediately notifies relevant departments and initiates emergency response procedures. The system also has intelligent scheduling capabilities, automatically allocating handling tasks and optimizing handling routes based on the availability of personnel and equipment, thereby improving emergency response efficiency.

[0047] In some embodiments, the method for determining whether there are suspected easily floating objects in the transmission line channel based on satellite remote sensing image data in step S1 includes: The satellite remote sensing image data is corrected, including radiometric correction, geometric correction and atmospheric correction, to obtain preprocessed image data; Feature extraction is performed on the preprocessed image data based on spectral features, texture features, and shape features to obtain image feature data. The change detection results are determined based on image feature data and historical image data; The change detection results are processed based on the criteria for identifying suspected easily floating objects to determine whether the suspected easily floating objects exist.

[0048] Specifically, radiometric correction eliminates the influence of sensor self-scattering and atmospheric scattering on image radiometric values; geometric correction eliminates geometric distortions caused by satellite attitude and terrain undulations; and atmospheric correction eliminates the influence of atmospheric absorption and scattering on the spectrum of ground objects. The combined effect of these three correction processes yields high-quality preprocessed image data. Spectral feature extraction obtains the spectral response characteristics of ground objects by analyzing reflectance values ​​in different bands; texture feature extraction obtains the roughness and uniformity information of ground object surfaces by calculating the gray-level co-occurrence matrix of the image; and shape feature extraction obtains the geometric shape parameters of ground objects through edge detection and contour analysis. By comparing and analyzing the current image feature data with historical image feature data from the same period, newly appearing, disappearing, or displaced ground object targets are identified, forming change detection results. The criteria for identifying suspected easily floating objects comprehensively consider multiple discrimination factors such as spectral differences, shape irregularities, and positional variability of ground objects, determining the existence of suspected easily floating objects through multi-factor comprehensive evaluation. The beneficial effect of this embodiment is that through systematic image preprocessing and multi-feature fusion analysis, it significantly improves the accuracy and reliability of suspected easily floating object detection, laying a solid foundation for subsequent refined identification.

[0049] In some embodiments, the method for identifying suspected easily floating objects based on satellite remote sensing image data and obtaining a distribution map of suspected easily floating objects in step S2 includes: Based on image resolution and terrain features, satellite remote sensing image data is segmented into regions to obtain monitoring unit data. Based on spectral reflectance and geometric shape features, multi-feature fusion recognition processing is performed on the monitoring unit data to obtain the identification results of easily floating objects; The attribute information of the easily floating object identification results is extracted and processed to obtain the attribute data of the easily floating object. The attribute data includes location coordinates, area size, shape features and spectral characteristics. Based on spatial analysis technology, distance calculations are performed on attribute data and transmission line location information to obtain the threat level assessment results of easily floating objects. Based on the geographic information system, spatial annotation processing of threat level assessment results, transmission line route information and topographic features was performed to obtain a distribution map of suspected easily floating objects.

[0050] Specifically, the region segmentation process determines the size of the smallest segmentation unit based on the spatial resolution of the image, and adjusts the segmentation strategy according to topographic features such as terrain undulation, water distribution, and vegetation cover to form monitoring unit data of appropriate size. The multi-feature fusion recognition process uses spectral reflectance characteristics as the main basis for material identification and geometric shape features as an important parameter for object type identification, achieving accurate identification of easily floating objects through feature weight allocation and fusion algorithms. The attribute information extraction process extracts key attribute information such as spatial coordinates, area size, shape contour features, and spectral response features of each easily floating object target from the identification results, forming structured attribute data. The distance calculation process uses spatial analysis techniques to calculate the straight-line, horizontal, and vertical distances between each easily floating object target and the nearest transmission line, and assesses its threat level to the transmission line by combining the type characteristics of the easily floating object and environmental risk factors. The spatial annotation process visualizes the threat level assessment results using different colors and symbols on the geographic information system platform, overlaying transmission line route information and topographic features to generate an intuitive and easy-to-understand distribution map of suspected easily floating objects. The beneficial effect of this embodiment is that it improves the detection accuracy and spatial positioning capability of easily floating objects through fine regional division and multi-feature fusion recognition. The generated distribution map intuitively reflects the spatial distribution and threat status of easily floating objects, providing a scientific basis for UAV mission planning and risk warning.

[0051] In some embodiments, step S3, the method for generating a drone's mission route based on a suspected easily floating object distribution map, and controlling the drone to fly along the mission route and collect multi-sensor image data of the power transmission line channel, includes: Spatial distribution characteristic analysis was performed on the distribution map of suspected easily floating objects to obtain data on key detection areas; Based on the threat level assessment results of suspected easily floating objects, the key detection areas are prioritized to obtain a priority sequence of detection tasks. Based on flight safety factors, detection efficiency factors, and meteorological parameters, the priority sequence of detection tasks is optimized to obtain flight path planning results. The flight path planning results are processed to generate flight commands, resulting in flight command data that includes takeoff point, track point, shooting point and landing point; The flight status data of the UAV is obtained by controlling the UAV to perform flight missions based on flight command data. The flight status data of the UAV is collected and processed simultaneously using a visible light camera, an infrared thermal imager, and a lidar system to obtain multi-sensor image data.

[0052] Specifically, by analyzing the spatial clustering characteristics of targets in the distribution map of suspected easily floating objects, areas with a high concentration of such objects are identified as key detection areas. Combined with threat level assessment, these key detection areas are prioritized according to threat level, forming a priority sequence for detection tasks. Path optimization considers the UAV's endurance, safe flight altitude, obstacle distribution, and real-time meteorological parameters such as wind speed and direction to calculate the optimal flight path, improving detection efficiency and reducing flight risks. The flight command generation module generates detailed instructions including takeoff point, track point, shooting point, and landing point based on the path planning results, ensuring the UAV executes the task according to the predetermined route. During the flight mission, the multi-sensor system simultaneously collects image data, fusing detailed information from visible light images, temperature characteristics from infrared thermal images, and three-dimensional spatial data from lidar to ensure data richness and diversity. The beneficial effect of this embodiment is that through scientific task planning and simultaneous multi-sensor data acquisition, it achieves comprehensive coverage and efficient detection of suspected easily floating objects, improving the accuracy and reliability of monitoring operations.

[0053] In some embodiments, step S4, the method for determining whether there are easily floating objects in the transmission line channel based on multi-sensor image data, includes: Spatiotemporal registration processing is performed on multi-sensor image data to obtain registered multi-sensor data; Visual feature extraction processing is performed on the visible light image data in the registered multi-sensor data based on color, texture and edge features to obtain visible light feature data; Based on thermal radiation characteristics and three-dimensional geometric information, feature extraction processing is performed on infrared thermal imaging data and lidar data in the registered multi-sensor data to obtain infrared laser feature data. Based on the multi-sensor feature fusion discrimination mechanism, the infrared thermal image data and lidar data in the registered multi-sensor data are comprehensively evaluated and processed to obtain the target evaluation result; The target evaluation results are processed based on multi-dimensional discrimination thresholds to determine whether buoyant objects exist.

[0054] Specifically, the data collected by multiple sensors is first precisely spatiotemporally registered to ensure high consistency in spatial location and time between the data from different sensors. Visible light data is analyzed to extract information for identifying surface features by examining the target's color distribution, texture details, and edge shape. Infrared thermal imaging data, based on differences in the object's thermal radiation, assists in identifying targets with temperatures different from the ambient temperature. LiDAR data provides spatial structure information by measuring the object's three-dimensional shape, volume, and surface features. The feature data extracted from each sensor are fused, and a preset discrimination mechanism is used to comprehensively evaluate the target, eliminating misidentification and background interference. Finally, the comprehensive evaluation result is judged based on a set discrimination threshold to determine whether any easily floating objects exist. The beneficial effect of this embodiment is that by fusing and comprehensively discriminating multi-sensor data, it significantly improves the accuracy of easily floating object identification, effectively reduces the false positive rate, and enhances the reliability of monitoring results.

[0055] In some embodiments, step S5, the method for identifying the type of easily floating objects based on multi-sensor image data and generating easily floating object early warning information based on the easily floating object type identification result and meteorological monitoring data, includes: Feature fusion analysis is performed on multi-sensor image data based on spectral features, geometric features, thermal radiation features, and three-dimensional shape features to obtain feature data of easily floating objects; Based on the easy-to-float object type identification system, the characteristic data of easy-to-float objects are classified and identified to obtain the easy-to-float object type identification results. Meteorological monitoring data is acquired and processed through trend analysis to obtain meteorological forecast data; Based on the identification results of easily floating objects and meteorological forecast data, motion trajectory prediction processing is performed to obtain the motion trajectory prediction results of easily floating objects. Based on the threat level calculation rules, the predicted trajectory of easily floating objects and the safe distance of transmission lines are used to perform threat assessment processing to obtain threat level assessment data of easily floating objects. Early warning information is constructed and processed based on threat level assessment data of easily floating objects and early warning information generation rules to obtain easily floating object early warning information.

[0056] Specifically, the system integrates and analyzes the spectral, geometric, and thermal radiation characteristics of visible light, infrared, and lidar data to form a comprehensive description of easily floating objects. A type identification system, based on an established classification model, categorizes the feature data, identifies different types of easily floating objects, and records their attributes. A meteorological monitoring system continuously acquires parameters such as wind speed, wind direction, temperature, humidity, and air pressure, and performs trend analysis on the meteorological data to predict future weather changes. Combining the identified type and location of easily floating objects, a kinematic model is used to predict their possible trajectories under future weather conditions. Based on threat level calculation rules and parameters such as the safe distance from transmission lines, the potential threat level of easily floating objects to transmission lines is assessed. The early warning information generation module constructs early warning information including threat description, location, and recommended measures based on the threat level and early warning rules. The beneficial effect of this embodiment is that it achieves accurate type identification of easily floating objects and dynamic threat early warning, providing a scientific basis and timely response means for the safety management of transmission lines.

[0057] Example 2: Please refer to Figure 2 A power transmission line channel monitoring system 100 includes a first judgment module 110, a suspected floating object identification module 120, a first generation module 130, a second judgment module 140, and a second generation module 150 connected in sequence.

[0058] The first judgment module 110 is used to acquire satellite remote sensing image data of the power transmission line channel in real time, and to judge whether there are any suspected floating objects in the power transmission line channel based on the satellite remote sensing image data.

[0059] The suspected floating object identification module 120 is used to identify suspected floating objects based on satellite remote sensing image data when suspected floating objects are present, and to obtain a distribution map of suspected floating objects; The first generation module 130 is used to generate the mission route of the UAV based on the distribution map of suspected easily floating objects, and control the UAV to fly along the mission route and collect multi-sensor image data of the power transmission line channel. The second judgment module 140 is used to determine whether there are easily floating objects in the power transmission line channel based on multi-sensor image data; The second generation module 150 is used to identify the type of easily floating objects based on multi-sensor image data when easily floating objects are present, and to generate easily floating object early warning information based on the easily floating object type identification result and meteorological monitoring data.

[0060] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and each module described above can be referred to the corresponding processes in the aforementioned embodiments of the transmission line channel monitoring method, and will not be repeated here.

[0061] The transmission line corridor monitoring method provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 3 The terminal device 200 shown is running on it.

[0062] Please see Figure 3 The terminal device 200 includes a processor 201 and a memory 202, which are connected via a device bus 203. The memory 202 may include a non-volatile storage medium and internal memory.

[0063] The non-volatile storage medium can store a computer program. The computer program includes program instructions that, when executed by the processor 201, cause the processor 201 to perform any of the aforementioned UAV-based power transmission line channel monitoring methods.

[0064] The processor 201 provides computing and control capabilities to support the operation of the entire terminal device 200.

[0065] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor 201, the processor 201 can execute any of the above-mentioned UAV-based transmission line channel monitoring methods.

[0066] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal device 200 involved in the present application. The specific terminal device 200 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0067] It should be understood that processor 201 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0068] In some embodiments, the processor 201 is configured to run a computer program stored in memory to perform the following steps: Real-time acquisition of satellite remote sensing image data of power transmission line channels, and determination of whether there are suspected easily floating objects in the power transmission line channels based on the satellite remote sensing image data; If they exist, the suspected floating objects are identified based on the satellite remote sensing image data to obtain a distribution map of the suspected floating objects; The mission route of the UAV is generated based on the distribution map of the suspected easily floating objects, and the UAV is controlled to collect multi-sensor image data of the power transmission line channel along the mission route. Based on the multi-sensor image data, determine whether there are easily floating objects in the power transmission line channel; If present, the type of easily floating object is identified based on the multi-sensor image data, and an easily floating object warning message is generated based on the identification result of the easily floating object type and the meteorological monitoring data.

[0069] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the terminal device 200 described above can be referred to the corresponding process of the aforementioned UAV-based power transmission line channel monitoring method, and will not be repeated here.

[0070] This application also provides a computer-readable storage medium storing a computer program that, when executed by one or more processors, causes the one or more processors to implement the UAV-based transmission line channel monitoring method provided in this application.

[0071] The computer-readable storage medium can be an internal storage unit of the terminal device 200 in the aforementioned embodiments, such as a hard disk or memory of the terminal device 200. The computer-readable storage medium can also be an external storage device of the terminal device 200, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided with the terminal device 200.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of monitoring a power line right-of-way, the method comprising: The power transmission line channel monitoring method comprises the following steps: Step S1: Real-time satellite remote sensing image data of the power transmission line channel is acquired, and whether there is a suspected easy-to-float object in the power transmission line channel is determined based on the satellite remote sensing image data. If there is, step S2 is performed; Step S2: The suspected easy-to-float object is identified based on the satellite remote sensing image data to obtain a suspected easy-to-float object distribution map; Step S3: A task line of a UAV is generated based on the suspected easy-to-float object distribution map, and the UAV is controlled to fly along the task line and collect multi-sensor image data of the power transmission line channel; Step S4: Whether there is an easy-to-float object in the power transmission line channel is determined based on the multi-sensor image data. If there is, step S5 is performed; Step S5: The type of the easy-to-float object is identified based on the multi-sensor image data, and easy-to-float object warning information is generated based on the type identification result of the easy-to-float object and meteorological monitoring data.

2. The power line right-of-way monitoring method of claim 1, wherein: In step S1, the method for determining whether there is a suspected easy-to-float object in the power transmission line channel based on the satellite remote sensing image data comprises: The satellite remote sensing image data is corrected to obtain preprocessed image data; The preprocessed image data is subjected to feature extraction processing to obtain image feature data; A change detection result is determined based on the image feature data and historical image data; The change detection result is subjected to discrimination processing based on suspected easy-to-float object discrimination criteria to obtain a determination result of whether there is a suspected easy-to-float object.

3. The method of transmission line corridor monitoring of claim 2, wherein: The correction processing of the satellite remote sensing image data comprises radiation correction, geometric correction and atmospheric correction; The feature extraction processing of the preprocessed image data is based on spectral features, texture features and shape features.

4. The method of claim 1, wherein: In step S2, the method for identifying the suspected easy-to-float object based on the satellite remote sensing image data to obtain a suspected easy-to-float object distribution map comprises: The satellite remote sensing image data is subjected to region segmentation processing to obtain monitoring unit data; The monitoring unit data is subjected to multi-feature fusion recognition processing to obtain easy-to-float object recognition results; The easy-to-float object recognition results are subjected to attribute information extraction processing to obtain attribute data of the easy-to-float object; The attribute data and power transmission line position information are subjected to distance calculation processing based on spatial analysis techniques to obtain threat level evaluation results of the easy-to-float object; The threat level evaluation results, power transmission line orientation information and topographic features are subjected to spatial annotation processing based on geographic information systems to obtain the suspected easy-to-float object distribution map.

5. The method of transmission line corridor monitoring of claim 4, wherein: The region segmentation processing of the satellite remote sensing image data is based on image resolution and topographic features; The multi-feature fusion recognition processing of the monitoring unit data is based on spectral reflection features and geometric shape features; The attribute data of the easy-to-float object comprises position coordinates, area size, shape features and spectral characteristics.

6. The method of transmission line corridor monitoring of claim 4, wherein: In step S3, the method for generating a task line of a UAV based on the suspected easy-to-float object distribution map, controlling the UAV to fly along the task line and collecting multi-sensor image data of the power transmission line channel comprises: The spatial distribution characteristics of the suspected easily floating object distribution map are analyzed and processed to obtain key detection area data; The key detection areas are prioritized based on the threat level evaluation results of the suspected easily floating objects to obtain a detection task priority sequence; The detection task priority sequence is subjected to path optimization processing to obtain flight path planning results; The flight path planning results are subjected to flight instruction generation processing to obtain flight instruction data including takeoff points, track points, shooting points, and landing points; The flight instruction data are used to control the UAV to perform a flight task to obtain flight state data of the UAV; The flight state data of the UAV are synchronously collected and processed based on a visible light camera, an infrared thermal imager, and a laser radar system to obtain multi-sensor image data.

7. The power line right-of-way monitoring method of claim 6, wherein: The detection task priority sequence is subjected to path optimization processing based on flight safety factors, detection efficiency factors, and meteorological parameters.

8. The method of transmission line corridor monitoring of claim 6, wherein: In the step S4, the method for determining whether there is an easily floating object in the power transmission line channel based on the multi-sensor image data includes: The multi-sensor image data are subjected to spatio-temporal registration processing to obtain registered multi-sensor data; The visible light image data in the registered multi-sensor data are subjected to visual feature extraction processing to obtain visible light feature data; The infrared thermal image data and laser radar data in the registered multi-sensor data are subjected to feature extraction processing to obtain infrared light laser feature data; The infrared thermal image data and laser radar data in the registered multi-sensor data are subjected to comprehensive evaluation processing based on a multi-sensor feature fusion discrimination mechanism to obtain target evaluation results; The target evaluation results are subjected to discrimination processing based on multi-dimensional discrimination threshold values to obtain a determination result of whether the easily floating object exists.

9. The method of transmission line corridor monitoring of claim 8, wherein: The visible light image data in the registered multi-sensor data are subjected to visual feature extraction processing based on color, texture, and edge features; The infrared thermal image data and laser radar data in the registered multi-sensor data are subjected to feature extraction processing based on thermal radiation characteristics and three-dimensional geometric information.

10. The method of transmission line corridor monitoring of claim 6, wherein: In the step S5, the method for identifying the type of the easily floating object based on the multi-sensor image data and generating easily floating object early warning information based on the type identification result of the easily floating object and meteorological monitoring data includes: The multi-sensor image data are subjected to feature fusion analysis processing to obtain easily floating object feature data; The easily floating object feature data are subjected to classification identification processing based on an easily floating object type identification system to obtain easily floating object type identification results; The meteorological monitoring data are subjected to acquisition and trend analysis processing to obtain meteorological prediction data; The motion trajectory prediction result of the easily floating object is subjected to threat evaluation processing based on a threat level calculation rule and a power transmission line safety distance to obtain threat level evaluation data of the easily floating object; ​ The warning information construction processing is performed based on the threat level evaluation data of the easy-to-float object and a warning information generation rule to obtain the easy-to-float object warning information.

11. The method of transmission line corridor monitoring of claim 10, wherein: The multi-sensor image data is subjected to feature fusion analysis processing based on spectral features, geometric features, thermal radiation features and three-dimensional shape features.

12. A power line corridor monitoring system characterized by: The method comprises the steps of: The first judgment module acquires satellite remote sensing image data of the power transmission line channel in real time, and judges whether there is a suspected easy-to-float object in the power transmission line channel based on the satellite remote sensing image data; The suspected easy-to-float object identification module identifies the suspected easy-to-float object based on the satellite remote sensing image data when there is a suspected easy-to-float object, and obtains a suspected easy-to-float object distribution map; The first generation module generates a task line of a UAV based on the suspected easy-to-float object distribution map, and controls the UAV to fly along the task line and collect multi-sensor image data of the power transmission line channel; The second judgment module judges whether there is an easy-to-float object in the power transmission line channel based on the multi-sensor image data; The second generation module identifies the type of the easy-to-float object based on the multi-sensor image data when there is an easy-to-float object, and generates easy-to-float object warning information based on the type identification result of the easy-to-float object and meteorological monitoring data; The first judgment module, the suspected easy-to-float object identification module, the first generation module, the second judgment module and the second generation module are connected in sequence.

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