237 results about "Power line inspection" patented technology

The invention relates to the technical field of power line inspection, and provides a line holographic anomaly detection method and system based on cross-modal intelligent cooperation. The method comprises the following steps: acquiring multi-modal data; performing cross-modal fusion to generate an association tensor; the abnormal joint reasoning uses a time sequence diagram neural network and reinforcement learning to output abnormal confidence; the dynamic knowledge driven decision adaptively adjusts a detection threshold through Bayesian calculation and transfer learning; local real-time response is realized through layered edge calculation; and multi-target collaborative optimization feedback improves the detection precision. The system is composed of a multi-mode perception fusion layer, an intelligent analysis layer, an edge execution layer and an optimization control layer. According to the method, the problems of multi-modal information isolation, response delay and environmental adaptability are solved, and the defect detection rate and the system robustness are remarkably improved.

The invention discloses an unmanned aerial vehicle airport site selection method suitable for large-scale electric power line inspection, and the method comprises the steps: S1, carrying out the primary selection of a geographic space, constructing a geographic constraint evaluation model, carrying out the processing of elements and historical disaster data through discrete coding, generating a safe and suitable region through the multi-factor weighted stacking space analysis, and carrying out the calculation of an evaluation result; and a K-Means clustering algorithm based on Euclidean distance is combined to screen a primary selection region. S2, risk assessment preselection: constructing an entropy weight-TOPSIS risk assessment model, determining a risk factor weight by using an entropy evaluation method, calculating a scheme closeness degree through a TOPSIS algorithm, and screening a low-risk preselection region; and S3, constructing a binary relationship between a demand center set and a pre-selected site set by combining an electric power inspection target and taking an electric power inspection task target as a demand node, measuring weighted traffic cost through Euclidean distance, designing a double-layer nested 0-1 decision variable, and realizing optimization of an airport deployment area by taking minimization of a total weighted distance as a target function.

The invention discloses a line planning method and system, and relates to the technical field of electric power line inspection. According to the line planning method, a plurality of feasible inspection paths are generated by obtaining a list of tasks to be inspected and three-dimensional coordinates of an electric tower and an inspection part of the electric tower, combining regional environment data, constructing a semantic knowledge graph and a spatial topological structure chart, determining an optimal path through evaluation, screening and analysis, and transmitting the optimal path to an unmanned aerial vehicle to execute an inspection task. According to the method, the flight state is monitored in real time, the abnormal condition is found in time, and the residual path is dynamically replanned in the inspection process, so that the smooth execution of the inspection task is ensured. The flight state and the environment interference of the unmanned aerial vehicle are monitored in real time, and the dynamic path is replanned according to the actual condition; according to the method, the flexibility and emergency response capacity of the inspection task are remarkably improved, the efficiency and safety of the inspection task are effectively improved by dynamically adjusting the path, and the adaptive capacity of the unmanned aerial vehicle in a complex environment is enhanced.

The invention relates to the technical field of intelligent operation inspection of power transmission lines, in particular to an intelligent tower detection method and a satellite image processing system, and the method comprises the steps: obtaining tower space information through constructing a tower information database, carrying out the complexity judgment of the tower space information, and carrying out the labeling processing of the tower space information according to a judgment result, the method comprises the following steps: acquiring tower marking data, training a tower identification model according to the acquired tower marking data, then performing correction fusion on tower detection information, acquiring correction detection information, identifying the correction detection information according to the tower identification model, and outputting a tower identification result; through the method, the problems of low identification precision, low efficiency and high marking cost of a traditional tower identification method in a large-scale and high-complexity scene are effectively solved, the tower detection precision is improved, the adaptability of a model in a complex environment is enhanced, and the intelligent level and the operation and maintenance efficiency of electric power inspection are improved.

The invention discloses a method and a system for processing route planning data for electric power line inspection. The method comprises the following steps: acquiring line distribution and topographic feature data; gridding the power network region, determining the patrol priority, and generating a region priority sequence; the overall patrol task is decomposed, a plurality of task subsets are generated, one task is allocated to each unmanned aerial vehicle, and each task subset comprises an initial position and a target line section; generating an initial route path based on the topographic features; comprehensively analyzing all initial paths, identifying potential airspace intersections and performing conflict prediction, and generating a conflict-free route path set by performing height hierarchical adjustment on related paths; and calculating predicted flight time and energy consumption of each unmanned aerial vehicle, redistributing boundary tasks according to a load balancing principle, and finally generating a balanced multi-unmanned aerial vehicle cooperative route scheme. According to the method, the safe and efficient multi-machine collaborative inspection route can be automatically generated through systematic task decomposition, conflict prediction and load balancing optimization.

The invention discloses an electric power AI intelligent auxiliary control and inspection system, and relates to the technical field of electric power, and the system comprises a data collection module which is used for collecting the operation state data and appearance image data of electric power equipment, and carrying out the preprocessing; the image processing and analysis module is used for generating an appearance fault prediction result; the health assessment and fault prediction module is used for generating an equipment health state and an operation fault prediction result; the inspection and control module is used for scheduling and executing inspection tasks, the inspection tasks comprise ground equipment inspection and power line inspection, the ground equipment inspection is executed through a robot, the power line inspection is executed through an unmanned aerial vehicle, and the inspection tasks are scheduled according to priorities; and the data security and transparency module is used for encrypting and storing all data in the system through the block chain. According to the invention, comprehensive monitoring of the operation state of the power equipment, accurate prediction of faults and efficient inspection can be realized, and the security and transparency of data are ensured at the same time.

The invention provides an unmanned aerial vehicle intelligent power line inspection system, and relates to the technical field of power line inspection. The unmanned aerial vehicle intelligent power line inspection system comprises an unmanned aerial vehicle platform which comprises an unmanned aerial vehicle body, a flight control system, a power system and a navigation system; the sensor module comprises a high-definition camera, an infrared camera, a laser radar and an ultrasonic sensor; the data processing module comprises an embedded processor, a data storage unit and a data transmission module and is used for processing and analyzing the data acquired by the sensor; the intelligent algorithm module is used for intelligently analyzing and judging the state of the power line; and the communication module comprises a wireless communication module and a satellite communication module and is used for realizing data transmission and remote control between the unmanned aerial vehicle and a ground control center. The unmanned aerial vehicle is used for replacing manual inspection, so that the work of personnel in dangerous environments such as high altitude and complex terrains is reduced, and the personnel risk is reduced.

The invention relates to an intelligent tree barrier detection method and system based on multi-modal perception, and the method employs an unmanned plane to carry a sensor to collect data, including RGB images, LIDAR point clouds, multispectral data and the like. Firstly, data is preprocessed to form standardized input. Hough transformation is carried out on the LIDAR point cloud, the position of the power line is extracted, and a three-dimensional line model is generated. And then positioning a tree bounding box from the RGB image by using a deep learning algorithm, and screening a high-risk point cloud region in combination with a line model. And constructing a three-dimensional tree model, calculating the minimum safety clearance distance between the power line and the tree in real time through a collision detection algorithm, obtaining the three-dimensional tree model with a safety distance mark, and finally carrying out space risk assessment based on the model. According to the invention, the reliability and efficiency of power line inspection can be significantly improved.

The invention discloses a power line inspection unmanned aerial vehicle pose estimation method and device based on multi-modal data fusion, and the method comprises the steps: obtaining detection data of a binocular vision sensor, an IMU and a TOF sensor, and obtaining multi-modal detection data; using TOF sensor data to initialize a flight height and performing scale alignment with a speedometer; determining a linear velocity reference value and an angular velocity reference value, and calculating adaptive weights of the binocular vision sensor and the IMU; according to the binocular vision sensor, the detection data of the IMU and the calculated adaptive weight, performing pose estimation based on an adaptive extended Kalman filtering algorithm to obtain a pose estimation result of the binocular IMU odometer; and fusing the pose estimation results of the monocular odometer and the binocular IMU odometer to obtain the final pose of the unmanned aerial vehicle. According to the method, the pose estimation precision, the environment adaptability and the robustness in the electric power line inspection process can be improved.

The invention discloses a remote-controlled power transmission line inspection device which comprises a tower, the upper end of the tower is fixedly connected with a connecting rod, the top of the connecting rod is fixedly connected with an insulator, the top of the insulator is provided with a power transmission cable, the outer surface of the connecting rod is fixedly connected with a fixed base, and the top of the power transmission cable is slidably provided with an inspection device. According to the scheme, multi-section inspection is achieved, the problem that long-distance inspection cannot be achieved due to long-distance flight of an unmanned aerial vehicle and insufficient power is solved, meanwhile, a background can independently select one section of a power transmission cable for inspection, and the inspection efficiency is improved. Operation and management of the inspector are facilitated, active inspection is achieved, the location and position of a fault are found in the first time, the inspector can conveniently repair the fault in time, loss expansion is avoided, the position of the fault and hidden danger is rapidly found, and the inspection and maintenance efficiency is improved.

The present invention relates to an intelligent tree barrier detection method and system based on multimodal perception. The method uses sensors carried by drones to collect data, including RGB images, LIDAR point clouds, multispectral data, etc. First, the data is preprocessed to form a standardized input. Then, a Hough transform is performed on the LIDAR point cloud to extract the position of the power line and generate a three-dimensional line model. Then, a deep learning algorithm is used to locate the tree bounding box from the RGB image, and the high-risk point cloud area is screened in combination with the line model. Then, a three-dimensional tree model is constructed, and the minimum safe clearance distance between the power line and the tree is calculated in real time through a collision detection algorithm to obtain a three-dimensional tree model with a safety distance annotation. Finally, a spatial risk assessment is performed based on this model. The present invention can significantly improve the reliability and efficiency of power line inspections.

The invention is suitable for the technical field of electric power line inspection, and provides an improved YOLO network-based electric power line metal corrosion detection and region segmentation method, which comprises the following steps of: aligning a collected visible light image and an infrared spectrum image at spatial positions; after carrying out defogging processing on the visible light image, judging whether the defogged image has a corrosion region or not based on the color shift feature and the texture distortion feature; if yes, inputting the visible light image and the infrared spectrum image into an improved YOLO-MA network for processing, and outputting the classification probability and confidence of the corrosion area; inputting the corrosion area with the classification probability and the confidence higher than a preset threshold value into a preset segmentation network, and determining a corrosion level corresponding to the corrosion area; and generating a visual report according to the corrosion level, and superposing the corrosion region bounding box, the segmentation mask and the corrosion level on the original image. The detection efficiency is improved while the omission ratio is reduced, and the real-time monitoring and maintenance requirements of complex industrial scenes are met.

The invention provides an unmanned aerial vehicle electric power inspection image defect intelligent identification method and system, and the method comprises the steps: collecting an electric power line image through an unmanned aerial vehicle, carrying out the preprocessing of enhancement, noise reduction, region-of-interest extraction and the like, inputting an improved YOLOv8 model deployed on Atlas 200I DK edge calculation equipment carrying mercuric chloride 310NPU, and carrying out the defect detection reasoning, and finally outputting an identification result and generating a defect report. The improved YOLOv8 model enhances small target feature extraction through a PCFocalNeXt-C2f trunk module, optimizes feature fusion through an AFPN neck network, improves classification and positioning precision through ATFL and PIOU loss functions, and adapts edge end deployment in combination with technologies such as pruning and quantification. The method can achieve the high-precision and low-delay recognition of the defects of the power line, effectively reduces the data return amount, improves the inspection efficiency, reduces the labor cost, and has an important engineering application value.

The invention discloses a power line collaborative inspection system based on an unmanned aerial vehicle cluster, and relates to the technical field of power correlation, and the system comprises a hypergraph topology modeling module which is used for constructing a power line hypergraph model, and extracting a hypergraph topology feature matrix; the fractal region division module is used for dividing the region to be inspected into a plurality of sub-regions by minimizing space coverage balance and boundary complexity under the condition that constraint conditions are met; the dynamic task allocation module is used for constructing an adaptation degree matrix, realizing optimal matching of the unmanned aerial vehicle based on the adaptation degree matrix, and carrying out power line collaborative inspection according to an optimal matching result; and the autonomous fault-tolerant recovery module is used for performing task redistribution when a fault occurs during cooperative inspection of the power line. Through hypergraph modeling, a region division algorithm and adaptive task matching, accurate modeling of a power system structure and risk factors, efficient division of an inspection region and intelligent scheduling of unmanned aerial vehicle resources are realized, and the efficiency of power line inspection and the resource utilization rate are improved.

The invention discloses an unmanned aerial vehicle inspection device and method for electric power inspection, and relates to the technical field of electric power inspection. A mounting base is fixed to the front side of the unmanned aerial vehicle body, a second camera is mounted on the front side of the mounting base, a buffer assembly is mounted on the lower side of the unmanned aerial vehicle body, and the cable is shot by arranging the second camera; the moving assembly comprises fixing frames, a T-shaped block, a threaded rod, a limiting rod and a first motor, the two corresponding fixing frames are fixed to the lower side of the unmanned aerial vehicle body, the T-shaped block is arranged between the two fixing frames, a threaded hole is formed in the edge of the right side of the T-shaped block, and the threaded rod is in threaded connection with the interior of the threaded hole; and the threaded rod is rotationally connected to the interior of the fixing frame on the right side, a first motor is installed on the lower side of the fixing frame on the right side, residues attached to the surface of the cable can be cleaned, and meanwhile damage points of the cable can be marked.

The invention provides a power line inspection method and system based on an unmanned aerial vehicle, and relates to the field of unmanned aerial vehicle technology and power line automatic inspection, and the method comprises the steps: obtaining the basic data of an unmanned aerial vehicle power inspection road, so as to construct a two-dimensional environment diagram, the basic data comprises a specific layout of the power line, topographic and geomorphic information and an obstacle position; according to the two-dimensional environment diagram, path planning is carried out, and according to the cost of each node in the path and the actual distance, a two-dimensional power line path plan is obtained; establishing a three-dimensional topographic map according to the two-dimensional power line path planning; constructing an inspection cost function according to the three-dimensional topographic map; and according to the cost function, evaluating the cost of different inspection paths to obtain an evaluated inspection path. According to the electric power line inspection method based on the unmanned aerial vehicle, efficient, safe, low-cost and high-quality electric power line inspection is realized through accurate path planning, optimization and evaluation.

The invention relates to the technical field of robot control, in particular to a mounting type power transmission line inspection robot self-adaptive control method, system and device based on deep reinforcement learning and a storage medium. Establishing a multi-modal fusion state vector by synchronously collecting visual sensing, inertial measurement and actuator feedback data, and inputting the multi-modal fusion state vector into a deep reinforcement learning strategy network to dynamically generate a state weight and a control weight adjustment amount of a linear quadratic controller; performing positive qualitative verification, amplitude limitation adjustment and constraint projection processing on the candidate weight matrix; a feedback gain matrix is obtained by solving an algebraic Riccati equation, and control output is calculated according to the state deviation to drive the double mechanical arms to execute inspection actions; designing a multi-target reward function of the comprehensive task completion quality and the energy consumption efficiency, and iteratively updating strategy network parameters by adopting a near-end strategy optimization algorithm; the problem that a fixed weight controller is difficult to adapt to dynamic working conditions of a power transmission line is solved, and a technical scheme is provided for intelligent control of a power inspection robot.

The invention provides a small-scale target detection method and device for power line inspection, a medium and a terminal. The method comprises the steps of obtaining a power transmission line data set and performing small-scale target labeling; the reference Faster-RCNN algorithm model is improved, and an improved Faster-RCNN algorithm model is obtained; and training and evaluating the improved Faster-RCNN algorithm model by using the marked data set, so as to perform small-scale target detection on the power transmission line image of the inspection area acquired in real time by using the unmanned aerial vehicle end. The improved Faster-RCNN algorithm model adopts an AdvResNet network combined with a feature pyramid structure to improve the feature extraction efficiency, a guide feature refining module improves the detection capability of a small-scale target, and an ROI alignment network improves the positioning precision of a bounding box, so that the reliability and safety of power transmission line inspection are improved.

The utility model discloses a power transmission line inspection unmanned aerial vehicle which comprises an unmanned aerial vehicle body, four connecting columns are arranged on the lower side face of the unmanned aerial vehicle body in a rectangular array mode, a mounting plate is arranged on the lower side faces of the four connecting columns, a camera is arranged on the lower side face of the mounting plate, and a mounting frame is arranged on the upper side face of the mounting plate. An upper cavity and a lower cavity are vertically formed in the mounting frame through a partition plate, two first telescopic pipes are symmetrically arranged in the lower cavity left and right, the upper side faces of the first telescopic pipes are connected with the lower side face of the partition plate, and second telescopic pipes are slidably connected to the outer side faces of the first telescopic pipes in a sealed mode; two small air pumps are arranged in the upper cavity in a bilateral symmetry mode, the air conveying ends of the two small air pumps are inserted into the two first telescopic pipes respectively, and a plurality of air spraying holes facing the camera are formed in the outer side face of the second telescopic pipe in a penetrating mode. Two groups of lifting assemblies for respectively driving the two second telescopic pipes to move up and down are arranged in the upper cavity; the utility model has the advantage of eliminating water mist on the surface of the camera.

The invention discloses a high-voltage power line inspection robot, and belongs to the technical field of high-voltage line detection, the high-voltage power line inspection robot comprises a head pipe, a tail pipe and a plurality of middle pipes arranged between the head pipe and the tail pipe, the head pipe and the middle pipes are arranged in the axial direction, every two adjacent middle pipes are connected through hinge pieces, and the middle pipes and the tail pipe are connected through hinge pieces. A displacement driving device for driving the head tube to move along the power line and a stop device for fixing the head tube on the power line are mounted on the inner side of the head tube, and an elastic guide device in contact with the outer side of the power line is arranged in the middle tube; a rotating ring and a rotation driving device for driving the rotating ring to rotate around the axial direction of the power line are rotationally mounted on the tail pipe, a detection head is mounted on the rotating ring, and the detection head extends to the inner side of the tail pipe in the radial direction of the rotating ring and is used for detecting the surface of the power line. According to the robot, the influence of strong wind weather on device inspection can be reduced, the smoothness during inspection can be ensured, and the inspection efficiency is improved.

The invention relates to the technical field of integrated circuits, in particular to an electron migration violation combined repair method and system, which comprises the following steps: after double-through-hole processing is completed, judging whether a power line inspection result meets a power line current standard or not, if not, adding a power grid to a damaged power line area, and if yes, adding a power grid to the damaged power line area; if yes, after high-layer metal wiring and buffer insertion are completed, large driving unit forbidding is carried out on a signal line to be repaired, a signal line inspection result, a unit path conversion value set and a unit clock conversion value set are recognized, whether the signal line inspection result meets a signal line current standard or not is judged, and if not, after buffer segmentation and damaged line widening are completed, the signal line to be repaired is repaired. And judging whether the unit path conversion value set and the unit clock conversion value set accord with a conversion value standard or not, and if not, disabling the non-standard driving unit until the signal line inspection result accords with a signal line current standard. According to the invention, the repair efficiency of electron migration violation can be improved.

The invention discloses a power line image super-resolution recovery system and method for power transmission line patrol, and relates to the technical field of unmanned aerial vehicle image processing. The system provided by the invention comprises three core modules, namely a physical perception generator, a differentiable physical discriminator and a spatial transformation network (STN), wherein the generator is combined with a U-Net and a residual structure, and the output is ensured to accord with the geometry and material characteristics of a power line through physical constraint loss; the discriminator adopts a double-branch structure, and synchronously evaluates image authenticity and physical rationality; and the STN module corrects perspective distortion by using the pose data of the unmanned aerial vehicle to realize geometric alignment of the power line. The system integrates advanced components such as Swin Transformer, ViT and differentiable RANSAC, supports end-to-end processing from original Bayer data to a high-definition image, and outputs a defect detection confidence map with enhanced physical constraints. Compared with a traditional data driving method, the method strictly guarantees the physical credibility of a reconstruction result while improving the resolution, and is suitable for a power line inspection task in a complex environment.

The invention discloses an electric power line inspection method and device based on cooperation of an unmanned aerial vehicle unit, and belongs to the field of electric power inspection, and the method comprises the steps: building a three-dimensional equivalent model of an electric power inspection region, planning a plurality of electric power inspection routes, and configuring an electric power inspection task; configuring an unmanned aerial vehicle group, executing a power inspection task, obtaining multi-modal detection data of the power line, and locking a line abnormal point when detecting that the line is abnormal; and determining a three-dimensional model of the abnormal region, performing abnormal evolution, obtaining an abnormal development prediction result, and performing power line maintenance on the line abnormal points. The technical problems that in the prior art, power line maintenance is performed only when obvious abnormity occurs in power line inspection, early abnormity is not maintained in time, and the operation reliability of the power line is affected are solved, the early abnormity of the power line is found in time, the future development trend is predicted through abnormity evolution, and the reliability of the power line is improved. Early maintenance of the power line is carried out, and the operation reliability of the power line is improved.

The invention relates to the field of power transmission line inspection, and discloses a laser-assisted focusing system of a power transmission line inspection camera, and the system comprises a laser ranging module, a visual sensor, a main control module, a target analysis module and a strategy optimization module, and the laser ranging module measures the distance between a target part on a power transmission line and a high-speed zoom lens in real time; the vision sensor synchronously collects definition characteristic data of a target part, and the main control module fuses the two types of data to generate an initial focusing parameter matrix and controls the high-speed zoom lens to adjust the focal length; the target analysis module determines a focusing strategy instruction based on the current detection task and a target priority database; the strategy optimization module corrects the initial parameter matrix according to the instruction, generates an optimized parameter matrix and feeds back the optimized parameter matrix to the main control module; the system improves the accuracy, pertinence and efficiency of focusing in a complex environment through the cooperation of multiple modules, and is suitable for the intelligent inspection work of a power transmission line.

The invention discloses a power tower component detection method for an unmanned aerial vehicle scene. According to the method, a target detection model comprising a self-adaptive multi-receptive field module, a hierarchical wavelet interaction unit and a distributed perception confidence optimization head is constructed, so that key technical problems in unmanned aerial vehicle power inspection are solved. In the feature extraction stage, a parallel multi-expansion deep convolution and channel attention mechanism is adopted, and the multi-scale feature perception capability is enhanced; in the feature fusion stage, semantic alignment and edge detail retention are realized through wavelet transform decomposition and cross-scale channel interaction; in the prediction stage, the classification confidence coefficient is adaptively calibrated based on statistical analysis of bounding box distribution, and the consistency of the positioning precision and the classification score is improved. According to the method, the problems of missing detection and false detection caused by large component scale difference and complex background are effectively solved, the accuracy and reliability of a detection result are remarkably improved, and an effective technical support is provided for unmanned aerial vehicle power inspection.

The invention provides an unmanned aerial vehicle aerial photography small target detection method based on a YOLO11 backbone network, a fusion feature enhancement module, a lightweight up-sampling module and a self-adaptive cross-layer fusion strategy, and aims to solve the problems of low detection recall rate, positioning drift and the like caused by small, dense and easy-to-shield targets, large scale change and motion blur in an aerial photography scene. And the small target detection precision and robustness are improved. The method specifically comprises the four main steps that firstly, an aerial photography data set is obtained, and an adaptive training data set is constructed through specification division, multi-scale zooming, Mosaic enhancement and input standardization preprocessing; inputting the preprocessed image into an improved backbone network, extracting enhanced features through a convolution and feature enhancement module, and optimizing a global receptive field in combination with a fast spatial pyramid pooling module and a cross-stage partial space attention module; then carrying out lightweight up-sampling and adaptive fusion to generate double 80 * 80 and single 40 * 40 resolution feature branches; and finally, outputting a target category and a bounding box through a detection head. The method provided by the invention can improve the small target detection performance of aerial photography of the unmanned aerial vehicle, and can provide technical support for scenes such as security and protection monitoring, electric power inspection, agricultural remote sensing and the like.

The invention discloses a satellite-ground enhanced signal conversion method and a satellite-ground enhanced signal conversion model based on PPP-RTK, which are used for solving the problems that conventional RTK in a ground-free network area fails, and satellite-based PPP-RTK depends on a special terminal and is slow in convergence. Generating an RTCM-OSR correction number by analyzing the SSR correction number, fusing the orbital clock correction, ionosphere and troposphere correction and performing weighted interpolation; the ambiguity is solved in combination with the observation value of the base station, dual-path verification and weighted fusion are carried out in combination with M-W and LAMBDA algorithms, and the wide lane ambiguity fixing reliability is improved; and when the ground-based signal is interrupted for more than 1 second, inheriting and maintaining Kalman filtering continuity by using the state vector and generating a switching identifier. The model integrates a signal processing module, a correction generation module, an ambiguity verification module and a switching control module, so that a conventional RTK terminal obtains centimeter-level positioning (level 2.3 cm and elevation 4.1 cm) without a public network, the interruption recovery is less than or equal to 1.5 seconds, and the scene upgrading cost of power inspection, power transmission monitoring, wind power plant monitoring and the like is reduced.

The invention discloses an unmanned aerial vehicle power line inspection anomaly detection method based on point cloud data, and belongs to the field of point cloud information processing, and the method comprises the following steps: A, data collection: obtaining three-dimensional point cloud space position information and color information of a power transmission tower; b, filtering, down-sampling and registering the obtained point cloud data, removing noise, improving precision and ensuring data consistency; c, target segmentation is carried out on the point cloud data according to color features of the insulator chain, and an insulator chain area is extracted; d, fitting the insulator chain point cloud data by using a least square method, determining an axial plane and generating a two-dimensional plane projection; and E, performing time sequence anomaly detection, and identifying and marking the position of the self-explosion insulator. Through the combination of point cloud data processing and an intelligent algorithm, the method achieves the efficient detection of the abnormality of the insulator of the power line through the unmanned aerial vehicle, remarkably improves the automation and precision of power inspection, and is suitable for the inspection task of large-scale power equipment.

The invention relates to the field of unmanned aerial vehicles, in particular to a power line inspection unmanned aerial vehicle adaptive flight control system, which comprises a data acquisition module arranged on power equipment, and a communication module, a sensing module, an unmanned aerial vehicle attitude adjustment module and a central data processing module which are arranged on an unmanned aerial vehicle, the data acquisition module is used for acquiring environmental parameters near the power equipment; the communication module is used for realizing signal communication between the unmanned aerial vehicle and the data acquisition module; the sensing module is used for collecting parameters of the environment nearby the unmanned aerial vehicle, and the central data processing module is used for generating adjustment parameters and controlling all the modules of the unmanned aerial vehicle according to the adjustment parameters. According to the scheme, the current attitude of the unmanned aerial vehicle is acquired through the shooting unit, and the attitude of the unmanned aerial vehicle is adjusted in combination with the roll angle and the pitch angle acquired by the unmanned aerial vehicle, so that the accuracy of the adjustment value of the attitude of the unmanned aerial vehicle is improved.

The invention discloses a power line inspection system and method for an unmanned aerial vehicle. The system comprises a central control module, a data acquisition module and an inspection planning module, relates to the technical field of power line inspection, and solves the technical problem that the efficiency of inspecting a power line by an unmanned aerial vehicle in the prior art is not high. The method comprises the following steps: acquiring historical icing information of a power line; dividing the power line into a plurality of inspection sections based on the iron tower distribution information; calculating a fault risk coefficient of each inspection section based on the historical icing information and the tree feature information; obtaining a fault risk level of each inspection section based on the fault risk coefficient; and determining the inspection frequency corresponding to each inspection section based on the fault risk coefficient and the fault risk level. The fault risk coefficient of the high-risk inspection section is substituted into the formula to calculate the inspection frequency, and the inspection frequency can be dynamically adjusted along with the change of the fault risk of the inspection section, so that the inspection efficiency of the unmanned aerial vehicle on the power line can be improved.