1800 results about "Vehicle inspection" patented technology

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

An inspection application may display, on a user device, a user interface including at least a portion of a vehicle inspection report including a plurality of inspection categories. The inspection application may configure the user device to obtain inspection information associated with a vehicle, the inspection information comprising photographs, videos, audio, and / or text. The inspection application and / or a network-accessible inspection assistant system may generate a prompt including at least a portion of the inspection information and information indicating potential vehicle defects. The prompt may be transmitted to a large language model that returns a response indicating any potential vehicle defects identified in the inspection information. The vehicle inspection report may then be updated to indicate the potential vehicle defects identified by the language model.

The invention relates to the technical field of photovoltaic power station inspection, and provides a distributed photovoltaic power station unmanned aerial vehicle inspection method and system, and the method comprises the following steps: generating an inspection task list based on the multi-station distribution characteristics of a distributed photovoltaic power station; according to the inspection task list, performing scanning modeling on each photovoltaic sub-power station in sequence through a laser radar, and generating a photovoltaic power station three-dimensional map; dynamically planning an inspection path according to the characteristics of each photovoltaic sub-power station; according to the inspection path, performing multi-angle scanning on each photovoltaic module in different installation environments through a multi-source sensor, and obtaining photovoltaic module image data with position information in combination with the real-time position coordinates of the unmanned aerial vehicle; performing preprocessing, feature extraction and fault identification on the photovoltaic module image data by using a fault identification model, and establishing a fault feature library; and carrying out fusion analysis on the multi-source data, generating a fault detection report of each photovoltaic sub-power station based on the inspection task list, and formulating a corresponding maintenance scheme according to the fault detection report. According to the invention, multi-source data fusion analysis of the photovoltaic module image data, the fault feature library, the position coordinates and the three-dimensional map is realized, and the inspection efficiency and the fault detection accuracy of the distributed photovoltaic power station are improved.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle inspection control system and method based on data analysis, and the method comprises the steps: receiving environment feature data of a target region, and constructing a static environment model; obtaining kinetic parameters of the unmanned aerial vehicle, and calculating a feasible flight envelope in combination with the static environment model; after taking off, the unmanned aerial vehicle collects environment data in real time, and a multi-level environment topology model is constructed; acquiring state data in real time, and judging an obstacle avoidance level; if yes, local path planning is carried out, and an optimal flight path is generated; acquiring trajectory deviation and energy consumption data, dynamically calibrating the flight envelope, and optimizing the flight envelope; according to the method, environment perception, trajectory optimization and online learning are combined, the method is adaptive to a complex environment, and the intelligent level of unmanned aerial vehicle inspection is improved.

The invention provides an unmanned aerial vehicle autonomous flight path planning and image acquisition method and system, and relates to the technical field of unmanned aerial vehicle inspection. Firstly, through intelligent three-dimensional grid division and dynamic route planning, the coverage rate and efficiency of feature region monitoring are remarkably improved; secondly, due to the integration of real-time meteorological data and historical feature data, the pertinence and adaptability of acquisition are enhanced, and high-quality images can be obtained in a complex and changeable environment; through combination of real-time processing and ground deep learning analysis, the accuracy and efficiency of feature extraction are greatly improved; and on the basis of a self-adaptive supplementary sampling mechanism of the region of interest, quick response and accurate positioning of the suspected feature region are realized, and reliable data support is provided for subsequent detailed analysis and decision making. The method is not only suitable for identification tasks of various preset features, but also has good expansibility and flexibility, and can be adjusted and optimized according to different application requirements.

The invention belongs to the technical field of unmanned aerial vehicle inspection, and particularly provides a photovoltaic power station inspection method and system based on an unmanned aerial vehicle and a GIS, and the method comprises the steps: obtaining initial geographic information data of a photovoltaic power station, enabling the unmanned aerial vehicle to execute initial surveying and mapping flight, collecting high-resolution image data, and constructing a three-dimensional model of the photovoltaic power station; fusing the high-resolution image data with the initial geographic information data to generate a multi-factor initial intelligent inspection path; the unmanned aerial vehicle flies along the initial intelligent inspection path, collects multi-modal data of the photovoltaic module, and transmits the multi-modal data to a GIS for data fusion and updating to obtain fused data; generating a fault distribution thermodynamic diagram and a maintenance priority map; the inspection path is dynamically adjusted, and the three-dimensional model of the photovoltaic power station is updated. According to the method and the system, comprehensive inspection can be carried out on the photovoltaic power station, the inspection accuracy and efficiency are improved, the inspection path can be dynamically adjusted, unnecessary repeated inspection is reduced, and the inspection cost is effectively reduced.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention relates to the technical field of constructional engineering, in particular to a steel structure construction intelligent monitoring method based on unmanned aerial vehicle inspection and point cloud processing, which comprises the following steps: S1, collecting point cloud data of geometric shape, size and position information of each part of a steel structure; s2, registering and fusing the point cloud data from different visual angles to form a three-dimensional point cloud model; s3, geometric features of the steel structure are extracted from the three-dimensional point cloud model, and a steel structure three-dimensional model is established; s4, comparing the steel structure three-dimensional model established in the S3 with a construction drawing, and identifying geometric differences; and S5, analyzing the construction deviation report, and identifying potential quality problems in construction. According to the invention, by combining the unmanned aerial vehicle inspection, the point cloud processing and the graph neural network algorithm, the quality problem in steel structure construction can be efficiently and accurately identified, the safety risk can be evaluated, and the automation and intelligence level of construction quality monitoring can be significantly improved.

The invention discloses a reservoir bank slope crack identification method based on unmanned aerial vehicle inspection and digital images, and relates to the technical field of reservoir bank slope inspection. 2, image preprocessing: preprocessing the data by adopting processing methods of graying, gray enhancement and bilateral filtering; 3, crack identification: identifying reservoir bank slope cracks in the image by adopting methods of threshold segmentation, edge detection and supervised classification; 4, crack pixel filtering: performing filtering processing on the extracted crack image by adopting morphological closed operation, length filtering and direction filtering; and 5, model fusion: fusing the threshold segmentation model, the edge detection model and the supervised classification model together by adopting a minimum-risk Bayesian probability method. The method shows remarkable applicability and effect in large-range and multi-form crack identification and investigation, and shows high robustness when processing crack images with different scales, shapes and backgrounds, and the accuracy of the method can stably reach more than 90%.

The invention discloses a photovoltaic field area unmanned aerial vehicle inspection system, which relates to the field of unmanned aerial vehicle inspection, and specifically comprises a data acquisition and preprocessing module, a data fusion module, a data processing module, a data storage module, a data processing module and a data processing module, and is characterized in that the data acquisition and preprocessing module is used for acquiring environmental parameter data and carrying out dynamic data cleaning and preprocessing to obtain preprocessed environmental parameter data; carrying out weight adjustment on the basis of the preprocessed environmental parameter data, and carrying out cross-modal attention mechanism processing and synchronous fusion processing on the basis of the environmental parameter data subjected to weight adjustment to obtain cross-modal fusion features and synchronous fusion features; by introducing adaptive adjustment of environmental parameter data, cross-modal data fusion, anomaly detection and a self-learning optimization module, the high-precision inspection capability in a complex environment is realized, a data acquisition strategy is dynamically adjusted based on different environmental states, weight distribution and time synchronization fusion are performed on video images, thermal imaging and audio data, and the accuracy of inspection is improved. And the fusion precision of the multi-modal data and the accuracy of anomaly detection are effectively improved.

The invention relates to the technical field of mechanical arm control, in particular to an unmanned aerial vehicle mechanical arm control system for power transmission line inspection. The method comprises the following steps: the distortion correction module is used for collecting and processing a patrol video and generating a corrected image sequence, the pose estimation module is used for performing three-dimensional pose estimation on a corrected image and performing motion trail fitting, and the extension evolution module is used for performing entity reconstruction and extension evolution on a fitting trail, so as to obtain a corrected image sequence; the extension trajectory data generation module is used for generating extension trajectory data, the constraint analysis module is used for performing constraint analysis based on a trajectory model, generating a feasible trajectory and synchronizing with an inspection video to obtain a synchronous trajectory positioning point, and the control planning module is used for performing inspection route planning according to the feasible trajectory and controlling an unmanned aerial vehicle mechanical arm in real time to complete an intelligent inspection task. According to the invention, the reliability of unmanned aerial vehicle inspection is improved.

The invention relates to the technical field of electrical equipment defect detection, in particular to an electrical equipment defect detection system and method based on deep learning, which are characterized in that a three-dimensional model of a power grid region is constructed, and based on historical defect data, a neural network model is adopted to mark an importance score of an inspection object in the three-dimensional model; quantitative evaluation of the equipment fault risk level is completed, and the matching degree of inspection resources and defect risk distribution is improved. A power grid three-dimensional model and importance scores are combined, a reinforcement learning model is utilized to construct an unmanned aerial vehicle inspection route planning strategy, the inspection route comprehensively considers power equipment defect risks and space factors in the planning stage, task allocation is optimized, and the problem that a static route cannot adapt to equipment changes is solved. In the inspection execution process, the unmanned aerial vehicle is dispatched according to the planning strategy, and the image flow is synchronously acquired for defect detection, so that the linkage of the inspection action and the detection process is realized, and the response efficiency and the detection quality of potential defects are improved.

The inventors have developed technology to facilitate the inspection of vehicles, such as cars, for the presence of defects. The technology may be used to facilitate detection of any defects of one or more vehicle defects before, during, and / or after inspection of the vehicle. The technology includes software and trained machine learning models for performing analyses to determine likely vehicle defects prior to completion of a vehicle inspection, to determine whether any vehicle defects are present based on data acquired during the vehicle inspection, and / or to determine, after completion of the vehicle inspection, whether there are any discrepancies between any defects identified by an inspector during the vehicle inspection and defects automatically detected by analyzing data collected during the inspection of the vehicle.

The invention discloses a power station photovoltaic field area automatic inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection, and the unmanned aerial vehicle comprises the following components: a meteorological self-adaptive intelligent inspection flight system, a power supply module, a rotor wing device and a communication module; the power supply module is used for providing power support for each component of the unmanned aerial vehicle; the communication module is used for realizing data interaction between the unmanned aerial vehicle and the ground control station and between the unmanned aerial vehicle and the photovoltaic field area monitoring equipment; by carrying the meteorological self-adaptive intelligent inspection flight integrated system, meteorological conditions of a photovoltaic field area can be perceived and analyzed in real time, and an inspection path and a flight attitude are dynamically optimized according to the meteorological conditions, so that the unmanned aerial vehicle can preferentially inspect an area where a problem is most likely to occur, and the inspection efficiency is remarkably improved; meanwhile, the unmanned aerial vehicle can keep stable flight under complex meteorological conditions such as strong wind, flight accidents are avoided, and the accuracy of inspection data is ensured.

The invention discloses a VR-based unmanned aerial vehicle inspection method and system, a storage medium and a program product, and the method comprises the steps: calculating a low-polygon terrain model, and carrying out the subdivision smoothing processing, and obtaining a three-dimensional terrain image; matching and registering the panoramic image sequence and the three-dimensional topographic image to obtain a point cloud map; generating a virtual mining area environment map; calculating image quality parameters; when the image quality parameter is lower than a preset threshold value, generating a semi-real and semi-virtual fusion display picture; transmitting the semi-real and semi-virtual fusion display picture to a VR helmet for display, and synchronizing the attitude information of the unmanned aerial vehicle to the VR helmet; when it is determined that a fatigue feature appears according to the brain wave data, the unmanned aerial vehicle is controlled to be switched to a preset autonomous flight mode; constructing a virtual anti-collision boundary; and obtaining an optimized flight path according to the virtual anti-collision boundary, and controlling the unmanned aerial vehicle to inspect according to the optimized flight path. The continuity and reliability of the inspection task are improved, and the safety risk of the inspection task is reduced.

The invention relates to power transmission line unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection path dynamic planning system, which comprises a quantum high-precision positioning unit, an environment perception and electromagnetic field modeling unit, a dynamic path planning unit, an unmanned aerial vehicle flight control unit and a ground station task management unit. The quantum positioning unit fuses quantum inertial navigation, GNSS and quantum magnetometer data to improve the positioning precision under strong interference; the environment sensing unit obtains a three-dimensional environment and electromagnetic field distribution of the power transmission line; the path planning unit dynamically optimizes an inspection path based on positioning and environment information; the flight control unit performs accurate execution; and ground station management tasks and data analysis. The anti-interference positioning precision of the unmanned aerial vehicle is remarkably improved, accurate path planning and electromagnetic field avoidance are realized, the inspection risk is effectively reduced, and the efficiency, the safety and the adaptability are improved.

The invention discloses a transformer substation inspection unmanned aerial vehicle path planning method based on a fusion A *-grey wolf algorithm, and aims to solve the problem of unmanned aerial vehicle path planning in a transformer substation inspection scene, and the method fully considers coupling of task point traversal and obstacle avoidance, thereby ensuring that an inspection path allocated for unmanned aerial vehicle inspection accords with a real transformer substation scene. A transformer substation virtual environment model is established through a grid map of an A * algorithm, and path implementation of the algorithm is simplified; a population is initialized by improving Tent chaotic mapping, a grey wolf algorithm is improved by introducing an adaptive position updating strategy and an elite grey wolf optimization strategy, and an initial traversal path is generated; and refining through an A * algorithm dynamically weighted by an evaluation function to obtain a final path. Simulation results prove that the feasibility and effectiveness of the improved fusion algorithm in obstacle avoidance and path planning in a substation simulation scene, and the improved algorithm is more suitable for path planning in a complex environment with multiple task targets and multiple obstacles.

The invention discloses an unmanned aerial vehicle inspection and power transmission operation control method and system based on path planning. The method comprises the following steps: constructing a three-dimensional environment model of a target area; constructing a total cost function at the cost of distance, weather risk, energy consumption and electromagnetic interference; taking the minimum total cost function as a target, and obtaining an inspection path of the unmanned aerial vehicle in the target area, so that the unmanned aerial vehicle flies based on the inspection path; in the flight process of the unmanned aerial vehicle, the inspection path is corrected in combination with the dynamic obstacle influence factors; the dynamic obstacle influence factor is determined by the distance between the unmanned aerial vehicle and the dynamic obstacle, the speed of the dynamic obstacle and the acceleration of the dynamic obstacle; in the flight process of the unmanned aerial vehicle, the airborne electricity testing device is controlled to measure the line voltage, and the airborne ground wire hanging and dismounting device is controlled to carry out ground wire hanging and dismounting operation according to the measured line voltage. According to the embodiment of the invention, dynamic obstacles encountered by the unmanned aerial vehicle in the flight process can be avoided, and collision is avoided.

The invention relates to the technical field of unmanned aerial vehicle inspection, in particular to an intelligent unmanned aerial vehicle bridge slope crack detection and early warning system, which comprises an unmanned aerial vehicle cruise unit and a detection and early warning management unit, and is characterized in that the unmanned aerial vehicle cruise unit is used for performing autonomous flight inspection on a bridge slope in a target area according to a preset period; the visible light image, the infrared thermal imaging and the three-dimensional laser point cloud data of the target area are synchronously obtained; the detection and early warning management unit is used for processing the image data acquired by the unmanned aerial vehicle cruise unit in real time through a pre-trained crack intelligent analysis algorithm, extracting geometric features and thermodynamic features of the crack, constructing a time sequence model to predict a crack expansion trend and a structure safety risk level, and outputting the crack expansion trend and the structure safety risk level. A multi-level early warning signal is generated based on the risk level and is pushed to the maintenance terminal; the geometrical characteristics comprise crack depth and strike angle, and the thermodynamic characteristics comprise abnormal temperature gradient distribution. The problems of long detection period, high cost and terrain limitation in the prior art are solved.

The invention relates to the field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle automatic flight control method and system for underground cable trench inspection, and the system comprises an environment sensing subsystem and an underground tunnel navigation subsystem. The visual sensor module obtains image data, the multi-sensor joint calibration module processes laser radar and camera data, the multi-layer fusion algorithm module fuses the calibration data, the underground tunnel navigation subsystem solves the problem of navigation obstacle avoidance, map construction of a tunnel environment and accurate estimation of the pose of an unmanned aerial vehicle are achieved, and an inspection route is planned and optimized. All the modules are integrated to achieve efficient and safe inspection of the underground cable trench, the inspection safety and efficiency are remarkably improved, the defects of a traditional manual inspection method are overcome, wide application prospects are achieved, safe operation of the underground cable can be effectively guaranteed through implementation of the system, and powerful support is provided for stability of an electric power system.

The invention discloses a low-altitude unmanned aerial vehicle intelligent inspection system and method based on deep learning, and relates to the technical field of unmanned aerial vehicle intelligent control. An unmanned aerial vehicle information set and a task information set are collected in advance, an anomaly detection model is constructed based on the task information set, and unmanned aerial vehicle real-time flight parameter data are collected; based on the anomaly detection model, the real-time flight parameter data and the unmanned aerial vehicle information set, constructing a parameter adjustment optimization model, solving and outputting a flight parameter adjustment strategy, sending the flight parameter adjustment strategy to the unmanned aerial vehicle, and collecting anomaly detection information collected in the flight process of the unmanned aerial vehicle in real time; performing dynamic parameter adjustment; and the intelligent level and the operation efficiency of unmanned aerial vehicle inspection are obviously improved.

The invention discloses a vehicle illegal parking detection system based on unmanned aerial vehicle inspection shooting, and relates to the technical field of vehicle illegal parking inspection. The vehicle illegal parking detection platform is in communication connection with an unmanned aerial vehicle image acquisition module, an image preprocessing module, a vehicle detection analysis module, an illegal parking judgment module, a license plate recognition module and an early warning notification module, and all the modules are in electric signal connection. According to the method, a large area can be rapidly covered through unmanned aerial vehicle inspection shooting, real-time monitoring and detection of vehicle illegal parking behaviors are achieved, compared with a traditional manual inspection mode, unmanned aerial vehicle inspection greatly shortens the detection period, the detection accuracy is remarkably improved, and the detection efficiency is improved through a high-precision image recognition algorithm. The vehicle illegal parking detection system can accurately recognize the license plate number, the vehicle type and the illegal parking position, and subjectivity and errors of manual judgment are effectively avoided.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic power station fault supervision system based on a neural network model, and the system comprises a data collection module which is used for collecting string current data and voltage data; the IV analysis module is used for obtaining a fault diagnosis result; the digital power station modeling module is used for identifying and numbering the coordinates of the photovoltaic string; an inspection route planning module; the unmanned aerial vehicle inspection module is used for performing inspection control on the unmanned aerial vehicle to obtain inspection data; an inspection data uploading module; the CV analysis module is used for performing defect identification on the inspection data to obtain a defect identification result; the fusion inspection analysis module is used for outputting a fusion diagnosis result and also used for selecting a final fusion diagnosis result; the visual presentation module is used for carrying out visual presentation; the report module is used for generating a report; and the navigation defect elimination module is used for carrying out navigation defect elimination. According to the invention, the automation level and inspection efficiency of fault diagnosis are improved.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and device, and the method comprises the steps: obtaining a three-dimensional routing inspection map of a target routing inspection region, the three-dimensional routing inspection map comprising routing inspection equipment and obstacles; constructing a target function based on the inspection coverage rate, the inspection energy consumption and the inspection efficiency of the unmanned aerial vehicle in the target inspection area; constructing constraint conditions based on the inspection speed and the inspection time of the unmanned aerial vehicle and the shooting limitation of the unmanned aerial vehicle; based on the objective function and the constraint condition, adopting a local optimization strategy to obtain an initial planning path; and optimizing the initial planning path by adopting a global optimization strategy to complete the path planning of the unmanned aerial vehicle inspection. Through the method, automatic, efficient and accurate route planning of the inspection unmanned aerial vehicle in the target inspection area is realized, blind areas of inspection equipment are reduced to the maximum extent, the integrity and reliability of inspection are enhanced, the inspection efficiency is improved, and the operation risk is reduced.

The invention discloses a dam crack identification method and system based on unmanned aerial vehicle inspection. The method comprises the steps that a multispectral image and point cloud data of the surface of a to-be-detected dam are collected through an unmanned aerial vehicle; carrying out preprocessing and spatial registration on the multispectral image and the point cloud data; fusing the multispectral enhanced image and the point cloud calibration data to obtain a six-channel fusion tensor, and constructing a fused image by the six-channel fusion tensor; extracting fusion features through the six-channel fusion tensor, performing multi-scale context aggregation on the fusion features to obtain crack aggregation features, and performing crack segmentation on the fusion image according to the crack aggregation features to obtain a dam crack recognition model; obtaining a dam crack identification result and a crack size based on the dam crack identification model; according to the invention, data are collected through the unmanned aerial vehicle, multispectral and point cloud information are fused, the model is constructed to accurately identify the dam crack and the measurement size, the detection efficiency and accuracy are improved, and the dam safety is guaranteed.

The invention provides a railway anomaly monitoring method and system based on unmanned aerial vehicle routing inspection, and relates to the field of data processing, the method comprises the following steps: obtaining railway routing inspection data collected by an unmanned aerial vehicle, extracting a monitoring instruction in the railway routing inspection data, carrying out parameter analysis on the monitoring instruction, and obtaining an anomaly monitoring parameter set corresponding to the monitoring instruction; performing spatio-temporal feature extraction processing on the plurality of spatio-temporal frame sequences, generating a spatial feature vector and a time feature vector of each spatio-temporal frame sequence, screening out at least one abnormal region from the plurality of spatio-temporal frame sequences based on a dynamic matching relationship between the abnormal monitoring parameter set and the spatial feature vector and the time feature vector, and performing abnormal region extraction processing on the abnormal region. And calling an anomaly identification model to perform conjoint analysis on the spatial-temporal characteristics of the abnormal region, generating a railway anomaly monitoring result, and mapping the railway anomaly monitoring result to a physical coordinate system of the railway line to generate an anomaly positioning report. According to the invention, the accuracy and intelligent decision of railway inspection anomaly detection can be improved.

The invention discloses an unmanned aerial vehicle fan inspection route autonomous planning method, and relates to the technical field of unmanned aerial vehicle inspection, and the method comprises the following specific steps: real-time data acquisition: establishing a data sharing link with a professional marine monitoring mechanism to obtain tide data, deploying local monitoring equipment in an offshore fan group area, and establishing a data sharing link; according to the method, through fusion of dynamic tide compensation, tide prediction and real-time monitoring data can be obtained in real time, and a dynamic marine environment model is constructed, so that marine environment conditions at different tide moments are accurately reflected; therefore, the unmanned aerial vehicle can avoid a risk area where seawater rises and floaters are increased during flood tide, select a proper flight height and ensure safety, and can be close to a fan foundation for detailed inspection during ebb tide, and meanwhile, a safety buffer area is set by utilizing dangerous area information in the model, so that the inspection safety is further improved.

The invention discloses an intelligent photovoltaic station unmanned aerial vehicle AI inspection management method and system, and relates to the technical field of unmanned aerial vehicle inspection. Marking the photovoltaic module image shot by the unmanned aerial vehicle; and carrying out fault detection on the shot photovoltaic module image. According to the intelligent photovoltaic station unmanned aerial vehicle AI inspection management method provided by the invention, through combination of unmanned aerial vehicle inspection and AI diagnosis technologies, efficient inspection and fault detection of photovoltaic station equipment are realized. The unmanned aerial vehicle automatically flies to shoot visible light and infrared images of the assembly, the AI algorithm synchronously analyzes physical damage and temperature abnormity of the assembly, faults are accurately recognized, and a diagnosis report is generated. And meanwhile, a reinspection route can be automatically planned, a fault area can be polled emphatically, and troubleshooting after maintenance is ensured. According to the invention, the inspection efficiency and the fault identification accuracy are significantly improved, the manual inspection cost and risk are reduced, and the operation and maintenance management level and the power generation benefit of the photovoltaic station are improved.

The invention discloses an overhead distribution line topology mapping method and system based on unmanned aerial vehicle inspection image density clustering, and the method comprises the steps: shooting a visible light image of an overhead distribution line through an unmanned aerial vehicle; extracting image feature information of the visible light image, including detecting tower equipment and defect hidden dangers of the visible light image, and extracting coordinate position information of the visible light image; based on the image feature information of the visible light image, determining a density center point and a neighborhood point of the visible light image, and constructing a density cluster of spatial distribution of the visible light image; based on density clustering of spatial distribution of visible light images, mapping of overhead distribution line equipment and defects and a line topological structure is realized, and latest inspection data is obtained; according to the method, an original standing book is compared with latest inspection data, coordinate position deviation and equipment corresponding conditions are calculated, the original standing book is compared with a newly constructed standing book according to the type and number of equipment, and inconsistency is recognized and reminded.