1366results about "Picture taking arrangements" patented technology

The invention discloses an integrated Beidou monitoring machine system integrating panoramic photographing and machine vision deformation monitoring, and relates to the technical field of safety monitoring. The system comprises a hardware structure unit and a software function unit, the hardware structure unit comprises a Beidou positioning module, an inertial navigation compensation module, a panoramic vision acquisition module, a machine vision processing module, a multi-mode communication transmission module and a power management module; the software function unit comprises a multi-source data fusion engine, a panoramic three-dimensional modeling sub-module, an intelligent early warning decision module and a cloud collaborative resolving interface. According to the invention, through real-time fusion of Beidou positioning data and panoramic vision imaging, an improved dense optical flow method is adopted to track local crack propagation, and global displacement monitoring of Beidou is combined, so that cross-scale synchronous sensing from macroscopic displacement to microscopic deformation is realized, and the comprehensive monitoring precision is improved.

The invention provides a geographic information visualization intelligent analysis system based on unmanned aerial vehicle surveying and mapping, and belongs to the field of surveying and mapping, and the system comprises an unmanned aerial vehicle aerial survey module which carries out regional aerial survey based on a multi-source sensor carried by an unmanned aerial vehicle, obtains multi-modal geographic data, and carries out the preprocessing of the multi-modal geographic data; the data processing module is used for forming an initial geographic data set under a standard coordinate system; the three-dimensional modeling module is used for obtaining the fused geographic feature data, constructing a three-dimensional geographic information model, optimizing the three-dimensional geographic information model and embedding time dimension information to form a four-dimensional spatio-temporal data set; the analysis and decision module is used for generating an analysis report comprising a terrain evolution trend and disaster risk assessment; and the visual platform module is used for visualizing the analysis report through a preset visual interaction interface and adjusting the rendering precision of the optimized three-dimensional geographic information model in real time according to the change of the visual angle of the user. The system provides an efficient and intelligent solution for geographic surveying and mapping and disaster early warning.

The invention provides a three-dimensional modeling processing method based on unmanned aerial vehicle oblique photography. The method is applied to the technical field of three-dimensional modeling, and comprises the following steps: obtaining a serialized image set containing geographical coordinate information according to original image data collected by an oblique photography camera carried by a multi-rotor unmanned aerial vehicle; according to time-space synchronization parameters of the serialized image set, determining a multi-view image matching relation matrix with an overlapping degree quantitative index; determining mixed three-dimensional point cloud data fusing sparse point cloud and dense point cloud according to geometric constraint conditions of the multi-view image matching relation matrix; determining an initial three-dimensional grid model with multi-level details according to the topological connection relationship of the mixed three-dimensional point cloud data; and determining an optimized three-dimensional model based on adaptive texture mapping according to the surface curvature distribution characteristics of the initial three-dimensional mesh model. In this way, the efficiency of three-dimensional modeling can be improved.

The invention discloses an unmanned aerial vehicle navigation reasoning method and system based on visual perception and a large language model, and belongs to the field of unmanned aerial vehicle navigation and artificial intelligence, and the method comprises the steps: collecting an image of a target area through a camera carried by an unmanned aerial vehicle; pixel-level perception is carried out on the image through a pre-trained visual perception model, and visual information is converted into structured data; converting the structured data into key statements which can be processed by a large language model by adopting retrieval enhancement generation and combining with a predefined knowledge base; adopting a prompt project and thinking chain technology, and outputting a navigation instruction by the large language model according to the key statement and the current task; when a target of an unknown type is encountered, an active learning process is started, and real-time updating of an edge end knowledge base is realized through human intervention; according to the method, the problems of understanding and decision making of the unmanned aerial vehicle on a dynamic scene in a complex environment are effectively solved, data-driven intelligent support is provided for autonomous flight of the unmanned aerial vehicle, and the method has the advantages of being high in real-time performance, high in environmental adaptability and good in decision making reliability.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

The present invention relates to the field of dam area deformation early warning technology, and proposes a dam area deformation early warning method and system based on a laser scanner, which includes the following steps: selecting a drone, equipping the drone with a laser scanner and a Beidou positioning system; planning the monitoring flight path of the drone, continuously emitting lasers and receiving reflected signals through the laser scanner, and using the Beidou positioning system to record the scanning position information of the laser scanner in real time to obtain original three-dimensional point cloud data; integrating the three-dimensional point cloud data obtained by multiple scans of the laser scanner into a unified coordinate system through a registration algorithm to obtain a three-dimensional model of the dam area; using a statistical method and a preset threshold to perform a data consistency check on the original deformation data set, screening out abnormal values ​​in the original deformation data set, and obtaining a deformation data set; identifying the deformation data set through a dam area deformation risk assessment model, obtaining a dam area deformation risk level, and taking corresponding early warning measures based on different dam area deformation risk levels. The present invention improves the accuracy of deformation data, realizes accurate judgment of deformation risk levels, and takes corresponding early warning measures according to different risk levels, thereby enhancing the safety of the dam area.

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention relates to an unmanned aerial vehicle multi-modal visual positioning method and system in a GNSS denial environment, and the method comprises the steps: constructing a visual inertial mileage calculation method based on deep learning feature extraction and matching, calculating the relative positioning of an unmanned aerial vehicle, and outputting to obtain high-frequency 6-DoF pose information; performing scene matching of infrared RGB image multi-modal fusion based on the Officient-LoFTR, and obtaining absolute positioning in a world system by solving a relative pose between a current key frame picture and a matched satellite image; a factor graph optimization mode is adopted to carry out self-adaptive weight information fusion according to the confidence coefficient of absolute positioning and relative positioning under a world system, and 6-DoF pose information with high frequency and higher precision is output; based on high-frequency and higher-precision 6-DoF pose information, after the unmanned aerial vehicle is recovered, all aerial images and satellite images are fused and updated. The system comprises a calculation module and a fusion updating module. According to the invention, more accurate and high-frequency pose output can be provided.

The invention discloses a multi-sensor fusion-based position and posture estimation method for a working device of a mining robot, which realizes accurate position and posture estimation by installing a body posture sensor and an environment sensing sensor and combining a kinematic model, visual inertia state estimation and a nonlinear least square optimization algorithm. The method comprises the following steps: firstly, carrying out preliminary prediction on a kinematic model through feedback data of an encoder and an inclinometer, and constructing a local visual inertia estimator by utilizing a camera and an MEMS to correct a prediction result; and then, a combined constraint modeling and optimization method is adopted to improve the precision of the pose of each working mechanism. Besides, geometric features of the bucket are extracted through a semantic segmentation network and a template matching technology, six-degree-of-freedom pose estimation of the bucket is completed, finally data fusion of a body pose sensor and an environment perception sensor is performed through a factor graph optimization algorithm, and comprehensive pose estimation of the whole working device is achieved. The working pose of the mining robot can be monitored in real time, the operation precision is improved, human errors are reduced, the working efficiency and safety can be optimized through data analysis, and more effective support is provided for path planning of a working device of the mining robot.

The invention relates to a surface collapse monitoring system based on multi-parameter distributed optical fiber sensing, and the system comprises a monitoring module which comprises a plurality of fully distributed optical fiber sensors laid in a monitoring area, and an optical signal transmission and demodulation module which comprises a continuous spectrum laser, an optical switch, an optical amplifier, an optical filter, and a demodulation module. The data processing and analysis center comprises a data acquisition module and a collapse model modeling module, and the data acquisition module is used for performing acquisition and digital conversion on the optical signal demodulated by the demodulation module; the collapse model modeling module constructs a model according to the monitoring data and can predict the development trend of surface collapse; and the early warning and communication module communicates with a remote monitoring center. The monitoring system provided by the invention has the advantages of multi-parameter monitoring, high monitoring precision, suitability for large-scale laying, real-time response and the like, and can comprehensively and accurately reflect the real condition of surface collapse.

Systems, methods, and computer-readable media for aerial localization in the absence of satellite (e.g., global navigation satellite system (GNSS)) signals while flying over heterogeneous environments. The present embodiments provide a training strategy for robust cross-seasonal image matching in complex, real-world environments. The challenge put forward is to successfully localize a UAV flying over both densely and sparsely populated areas by means of image registration. To this end, the focus of the present embodiments can be on devising loss components to a visual recognition network that adequately embeds the diverse feature types on the ground. A normalized error metric can be provided as well in order to render a conceptually different method comparable to the present approach. The present technical contributions can provide robust visual recognition algorithms in challenging natural environments.

The invention provides an intelligent barn automatic inspection system and method, and relates to the technical field of intelligent inspection, the system comprises an inspection robot, a 5G communication transmission module and a remote computer, the inspection robot comprises an environment sensing module for generating and updating an environment map and identifying obstacle information; the autonomous navigation control module comprises a path planning unit for generating an inspection path and performing local path adjustment; the obstacle avoidance unit is used for generating an obstacle avoidance strategy; the motion control unit controls the movement of the inspection robot; the data acquisition module is used for acquiring information in the granary; the data processing and analyzing module is used for processing and analyzing the collected multi-source data and outputting a monitoring report and early warning information; wherein the 5G communication transmission module is connected with the inspection robot and the remote computer, and is used for transmitting data output by each module in the inspection robot and receiving an instruction sent by the remote computer. According to the invention, real-time sensing, intelligent obstacle avoidance and autonomous inspection of the granary environment are realized.

The invention belongs to the technical field of water quality remote sensing monitoring, and particularly relates to a sky-ground remote sensing integrated lake and reservoir water quality parameter quantitative inversion method. Qualitative inversion is carried out on water quality parameters through medium-high resolution satellite remote sensing images, and ground sampling points are reasonably arranged. And carrying out ground and air collaborative operation, including on-site water body sampling and unmanned aerial vehicle flight, so as to ensure the consistency of sampled water quality parameters and unmanned aerial vehicle image spectral characteristics. The sampling point water body sample is sent to a laboratory to be tested, and measured data are obtained. An unmanned aerial vehicle image is subjected to preprocessing including splicing and radiation correction, and a reflectivity image map is obtained. Water vector data are extracted by using an unmanned aerial vehicle image, a remote sensing quantitative inversion model of water quality parameters is established, and the model is analyzed and optimized through an optimal wave band combination. And in combination with the unmanned aerial vehicle image, the inversion model and the water body vector data, obtaining a water quality parameter quantitative inversion result and spatial distribution characteristics in the lake and reservoir range. The method can improve ground sampling rationality and time coordination of lake and reservoir water quality monitoring, and supports long-term high-precision dynamic monitoring.

The invention provides a geological BIM modeling and engineering quantity calculation method based on point cloud sparse optimization, and relates to the technical field of geological engineering, and the method comprises the steps: obtaining high-resolution point cloud data through an unmanned aerial vehicle oblique photography technology, combining the high-resolution point cloud data with drilling exploration data, and carrying out the denoising, classification, segmentation, lithology discrimination and registration, thereby obtaining a point cloud point cloud model; a geological BIM model before excavation is constructed; area point cloud data are obtained again after excavation, the point cloud precision of the slope toe area is optimized through an inverse distance weighted interpolation algorithm, and a geological BIM model after excavation is generated; and in combination with a classified and optimized digital elevation model, detecting the elevation difference of the model before and after excavation pixel by pixel by adopting a robust elevation difference calculation method based on a neighborhood window, and accurately calculating the volume change of each lithologic region to obtain the engineering quantity. Through the point cloud sparse optimization and elevation change detection technology, the precision of the geological BIM model and the reliability of volume calculation are improved, and the method is widely suitable for geological engineering and earth volume calculation scenes.

The invention discloses a photovoltaic panel defect detection system and method based on unmanned aerial vehicle and machine vision, the system comprises an unmanned aerial vehicle, a visible light lens and an EL lens, the EL lens is connected with the input end of an image collection module through a wireless network, and the output end of the image collection module is connected with the input end of an image processing module; the output end of the central processor is connected with the defect discrimination module; the unmanned aerial vehicle firstly carries a visible light lens and is used for automatically planning a route according to a given area range to carry out primary high-altitude aerial photography and simulated mesoscale aerial photography; secondly, carrying an EL lens, and carrying out multi-view adaptive directional high-precision shooting according to a route planning path; the image processing module is used for correcting a photovoltaic panel image collected by an EL lens, defect features are extracted through a SimAM-OfficientNetV2 network model, a sample set is classified into a defect-free sample and a defect sample, high-precision real-time detection is carried out on various defects existing in the image through a YOLOV9 network model, and rapid discrimination of the defects is achieved.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to a terrain recognition and three-dimensional reconstruction method based on a remote sensing surveying and mapping image, which comprises the following steps of: recognizing and selecting a key spectral band based on a remote sensing image, adjusting the configuration of a spectral filter, extracting key terrain data according to configuration data, and analyzing terrain change by utilizing terrain feature data. And recording related wave bands and positions, forming a comparison table, and combining natural colors and surface texture analysis to generate a three-dimensional model of the terrain. According to the method, the key spectrum sections are screened by adopting the preferable waveband index, the specificity and efficiency of data acquisition are improved, the spectral response section is accurately adjusted, it is ensured that a spectral region with remarkable earth surface reflection characteristics is focused on, and the practical value of spectral data is improved; the spectral configuration data optimizes the response performance of the sensor so that the data collected from the terrain is more sensitive and accurate.

The embodiment of the invention provides a multi-unmanned aerial vehicle cooperative surveying and mapping method and system for areas along a railway, and belongs to the technical field of railway surveying and mapping. The method comprises the steps that the unmanned aerial vehicle collects monitoring data in the flight process; the relative position and posture between the aircrafts are dynamically adjusted, and an observation configuration with variable baseline parameters is constructed; laser ranging modules carried by the unmanned aerial vehicles are controlled to execute synchronous ranging operation, and multiple sets of laser point cloud data covering the railway structure area are generated; performing coordinate registration and fusion on the collected laser point cloud data, and combining position information and attitude information of each unmanned aerial vehicle; and extracting geometric dimension parameters of the railway structure from the compensated three-dimensional model, and outputting the geometric dimension parameters as measurement results. According to the scheme, high-precision non-contact measurement of geometric dimension parameters such as the track gauge, the height difference and the roll angle of a railway track structure is achieved.

The invention discloses a side slope stability intelligent monitoring and supporting optimization method based on digital twinning, which comprises the following steps: extracting gradient, curvature and geological layering characteristics from a holographic terrain model, calculating a preliminary change trend of stress distribution of a rock-soil body in combination with a finite element analysis method, and determining spatial position distribution of a potential sliding surface of a side slope; according to the space coordinate set of the real-time monitoring point position, the data acquisition frequency of the sensor is adjusted through a time sequence analysis method, and a high-frequency monitoring data set reflecting dynamic changes of stress and underground water is obtained; extracting stress peak values and underground water level fluctuation characteristics from the high-frequency monitoring data set, and generating a multi-dimensional information matrix containing terrain, stress and water dynamics by combining spatial characteristics of the holographic terrain model and adopting a data fusion algorithm; according to the state parameters of the digital twinborn body, if it is judged that the stress distribution exceeds a preset threshold value, the stress distribution of the supporting structure is recalculated through finite element analysis, and an adjusted slope supporting design scheme is obtained.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention discloses a multi-unmanned aerial vehicle cooperative positioning method and system, and belongs to the technical field of unmanned aerial vehicle positioning, a global coordinate system is defined firstly, a task starting point is taken as an original point, a Z axis is vertically upward, and unmanned aerial vehicle coordinate systems take respective inertial measurement units as centers; the unmanned aerial vehicle A is controlled to perform hovering motion at the indoor closed space laser radar feature point; controlling the unmanned aerial vehicle B to receive the pose covariance matrix and the local feature points of the unmanned aerial vehicle A in real time; laser SLAM output of the unmanned aerial vehicle B and the reference pose of the unmanned aerial vehicle A are fused through tight coupling Kalman filtering, accumulative errors are dynamically corrected, and an optimized pose and a covariance matrix of the unmanned aerial vehicle B under a global coordinate system are output; and unifying observation of the unmanned aerial vehicle A and observation of the unmanned aerial vehicle B to the same coordinate system by sharing pose constraints, and dynamically aligning local and global features to realize multi-unmanned aerial vehicle cooperative positioning. The method and the device are used for solving the technical problems of accumulated drifting and the like caused by long-time operation of laser SLAM in perception weakening scenes such as galleries and pipe galleries.

A method, system, medium and program product for measuring a volume based on elevation measurement, the method comprising: determining historical topographic feature data; elevation values of all positions in the mining area in different time periods are extracted; calculating a topographic relief change rule according to the elevation value, and establishing a topographic evolution model according to the topographic relief change rule; extracting terrain boundary feature points and ponding area contour lines from the unmanned aerial vehicle inclined image data; dividing a plurality of local terrain units according to the terrain boundary feature points, and calculating an elevation difference to obtain a gradient change coefficient; determining a terrain prediction model of each local terrain unit; splicing and reconstructing the terrain prediction model of each local terrain unit to obtain underwater terrain estimation data of the ponding area; constructing a mining area terrain model according to the underwater terrain estimation data and the terrain data; and calculating the earthwork excavation volume in the mining area according to the mining area terrain model. According to the invention, the accuracy of mining volume measurement is improved.

The invention relates to the technical field of robot navigation and artificial intelligence, in particular to an autonomous navigation robot system based on multi-modal perception, which comprises a visual perception module, a laser radar module, a voice instruction module, a central processing unit and an execution control unit, the visual perception module is used for collecting an environment RGB-D image; the laser radar module is used for acquiring an environment distance point cloud; the voice instruction module is used for receiving and analyzing a user voice instruction; the central processing unit generates a navigation path and an obstacle avoidance strategy through a multi-modal data fusion algorithm and a path planning algorithm; the execution control unit drives the robot chassis to move according to the navigation path and the obstacle avoidance strategy.

The invention relates to the technical field of pavement disease automatic detection, and particularly discloses a pavement disease automatic detection method and system based on deep learning, and the system comprises a municipal pavement data collection module, a pavement image quality analysis module, a pavement image re-collection module, a pavement disease evaluation feedback module and a database. Flight parameters (speed and height) are dynamically adjusted through an environmental quality index (EQI), the image acquisition quality is ensured, the detection precision in a complex environment is improved, a multi-disease coupling risk model is constructed, region weight and traffic volume dynamic weight are integrated, a disease risk level is output, and finally a disease development trend is predicted through a one-time exponential smoothing method. The system achieves the dual functions of current situation evaluation and trend early warning, predicts the disease evolution trend based on historical data, remarkably improves the scientificity and timeliness of municipal road risk management, reduces the maintenance cost, and prevents safety accidents.

Scanning Tele cameras (STCs) based on two optical path folding element (OPFE) field-of-view scanning and mobile devices including such STCs. A STC may comprise a first OPFE (O-OPFE) for folding a first optical path OP1 to a second optical path OP2, an O-OPFE actuator, a second OPFE (I-OPFE) for folding OP2 to a third optical path OP3, an I-OPFE actuator, a lens, a lens actuator and an image sensor, wherein the STC has a STC native field-of-view (n-FOVT), wherein the O-OPFE actuator is configured to rotate the O-OPFE around a first axis and the I-OPFE actuator rotates the I-OPFE around a second axis for scanning a scene with the n-FOVT, wherein the lens actuator is configured to move the lens for focusing along a third axis, and wherein the first axis is perpendicular to the second axis and parallel to the third axis.

The invention provides a map building and path planning method based on multi-modal SLAM, a robot system collects data through a multi-modal sensor, a data fusion module fuses the collected data by using an AI model, a map building module carries out real-time map building according to the fused data, and a path planning module carries out path planning according to the fused data. The global path planning module performs global path planning through an AI model, the actual path planning module outputs a local path by using the AI model, and the robot system dynamically adjusts and outputs an execution path according to the local path in combination with the latest data of the multi-modal sensor. The motion execution module controls the robot to move to a task position according to the output execution path, monitors the execution condition of the path in real time and feeds back the execution condition to the positioning and posture estimation module and the path planning module, and the positioning and posture estimation module updates the current positioning and adjusts the posture of the robot according to feedback data. And the path planning module finely adjusts the global path and the local path according to feedback data to improve the path planning quality.

The present invention relates to the field of mine surveying and mapping technology, and in particular to a mine surveying and mapping method and system based on unmanned aerial vehicle remote sensing technology, the method comprising: obtaining terrain data of a mine; generating a flight trajectory of an unmanned aerial vehicle; using an unmanned aerial vehicle to collect image data and hyperspectral data of the mine; constructing a three-dimensional surface model of the mine; obtaining geological structure data and mineral distribution data of the mine; and generating a three-dimensional structural model of the mine based on the geological structure data, mineral distribution data and the three-dimensional surface model. The present invention generates a flight trajectory of an unmanned aerial vehicle to ensure that the unmanned aerial vehicle takes photos at a reasonable flight altitude and speed in different areas, thereby reducing repeated coverage and maximizing regional coverage. By generating a three-dimensional structural model, detailed geological structure and mineral distribution data can be obtained without actually entering the mining area, greatly reducing the safety risks and economic costs that may be caused by on-site surveys.

The invention belongs to the technical field of gas concentration detection, and discloses a three-dimensional tomography method and system for gas concentration space distribution detection based on an unmanned aerial vehicle, and the method comprises the steps: 1, building a space rectangular coordinate system, and converting latitude and longitude coordinates into XYZ coordinates; 2, dividing view field grids and grid numbers; 3, calculating the number of intersecting grids of the light beams and the view field and the length of intersecting line segments; 4, calculating a weight coefficient matrix and an integral concentration vector; and 5, reconstructing gas three-dimensional concentration distribution by using an SART algorithm. The system combines the unmanned aerial vehicle and the telemetry technology, realizes gas cloud scanning and three-dimensional gas cloud concentration distribution reconstruction based on the unmanned aerial vehicle, has the characteristics of non-contact measurement, high imaging speed, high precision, autonomous detection, strong environmental adaptability and the like, can monitor and predict the gas diffusion trend in real time, and has a wide application prospect. And a new solution is provided for realizing accurate traceability of methane gas leakage and reconstruction of leakage space distribution in a complex scene.

The invention discloses a substation unmanned aerial vehicle inspection dynamic route planning method and device. The method comprises the steps of collecting and constructing a substation 3DGS model, optimizing details, dividing areas, recognizing equipment parts, inversely calculating waypoint parameters, planning waypoints of a safety channel and a flight channel, generating a basic model data set, and dynamically generating an inspection route in combination with tasks. The device comprises a model construction module, a region division module and the like. On the basis of a 3DGS model and equipment part identification, through a multi-level waypoint system and a dynamic planning algorithm, full-process automation from model construction to route generation is achieved, it is ensured that unmanned aerial vehicle routing inspection is safe and efficient, equipment updating and task changing are adapted, the problems that traditional two-dimensional planning is insufficient in precision, and static route flexibility is poor are solved, and the unmanned aerial vehicle routing inspection efficiency is improved. And the automation and intelligence level of substation inspection is improved.

A forest land landslide collapse area detection method based on unmanned aerial vehicle visual remote sensing is characterized by comprising the following steps: step 1, constructing a forest land landslide collapse area detection system based on unmanned aerial vehicle visual remote sensing; 2, the unmanned aerial vehicle shoots remote sensing image data; 3, an image preprocessing module performs preprocessing operation on the historical satellite image data and the remote sensing image data to obtain a remote sensing image and a historical image; step 4, matching the feature points of the remote sensing image and the historical image by an image alignment module, calculating a transformation matrix, and completing alignment of the images before and after the landslide through the transformation matrix; step 5, identifying a landslide area by a landslide area identification module; step 6, a landslide area identification module extracts a landslide area contour; and 7, calculating the average gradient of the landslide area by a landslide area calculation module, and calculating the actual area of the slope of the landslide in combination with the plane projection area and the average gradient. The method has the advantage that the forest land landslide collapse area can be rapidly and accurately detected.

The invention relates to the technical field of automatic driving, and discloses an unmanned vehicle navigation method based on multi-modal fusion, which comprises the following steps: S1, acquiring and preprocessing sensor data, acquiring multi-modal sensor data, and performing data alignment, filtering and normalization processing; s2, constructing an optimization framework of the fusion weight of the sensor based on a variational inference method, defining an optimization objective function by using the preprocessed data as input, and solving an optimal solution of the fusion weight through the variational inference method; and S3, on the basis of the variational reasoning optimization model, optimizing a sensor weight updating process by utilizing information geometric measurement. A cross-modal learning and reasoning mechanism is introduced, weight updating is accelerated by using an information geometric optimization method, so that the unmanned vehicle can quickly adapt to sudden environmental changes, and compared with a traditional neural network model needing a large amount of training data, the method does not need large-scale data set pre-training, can adaptively adjust decisions, and improves generalization ability.