613 results about "Photogrammetry" patented technology

The invention relates to an air-ground integrated bridge disease monitoring method and an application system thereof, and the method comprises the steps: controlling an unmanned plane to take off from a nest, and transmitting a bridge initial inspection task to the unmanned plane; sending the bridge image data to a vehicle-mounted high-performance edge server for primary three-dimensional modeling; carrying out monomer splitting on the general three-dimensional model of the bridge, and carrying out close photogrammetry on the bridge by an unmanned aerial vehicle based on a close inspection route to obtain a monomer high-resolution image of each part of the bridge; the unmanned aerial vehicle automatic inspection and data processing platform executes disease target detection and disease semantic segmentation on the single high-resolution images; and associating and binding disease target detection and disease semantic segmentation results with the bridge refined three-dimensional model to generate a bridge disease detection report. A worker only needs to draw an inspection area, and the unmanned aerial vehicle automatic inspection and data processing platform automatically generates an unmanned aerial vehicle inspection route according to the drawn inspection area, so that the unmanned aerial vehicle autonomously and safely inspects a bridge.

The invention discloses a building health state detection system, and relates to the technical field of photogrammetry and image processing, the system firstly obtains four-dimensional space-time coordinate data of a plurality of monitoring points on a target building based on satellite image analysis, constructs a four-dimensional space-time network model containing spatial topology and time sequence evolution information according to the four-dimensional space-time coordinate data, and then obtains a four-dimensional space-time network model containing space topology and time sequence evolution information; the system maps network model nodes to a predefined standard reference network associated with a local gravity direction, then, the system analyzes dynamic relevance in the four-dimensional network model by adopting a graph self-attention network, and meanwhile, calculates relative relation changes of monitoring points relative to the predefined standard reference network through geometric analysis; the deformation interpretation module distinguishes and determines internal relative deformation, overall displacement and absolute posture change (including settlement and inclination) of the building according to the two types of analysis results and by referring to surrounding environment information, and the state evaluation module integrates the information to generate a health state report containing risk levels.

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 provides an unmanned aerial vehicle oblique photogrammetry data intelligent processing method and system, and relates to the technical field of unmanned aerial vehicle photogrammetry, and the method comprises the steps: carrying out the preprocessing of an oblique photogrammetry original image, obtaining the exterior orientation elements of a camera, carrying out the scene semantic segmentation through a deep convolutional neural network, adaptively determining the optimal three-dimensional reconstruction parameters of each region, and obtaining the optimal three-dimensional reconstruction parameters of each region. On the basis of multi-view image matching, feature point parallax information is calculated to generate dense point clouds, different areas are processed by adopting an adaptive grid division strategy and a corresponding noise reduction algorithm, and finally, a three-dimensional scene model with real textures is constructed, so that high-precision three-dimensional reconstruction of different scene areas is realized.

The invention discloses a method for measuring an attitude angle of a free throwing object in a wind tunnel test through a binocular system, and belongs to the field of aerodynamics tests based on a photogrammetry technology. Comprising the steps that a binocular system is arranged in a wind tunnel, and internal and external parameters of the binocular system are calibrated through a cross scale; reconstructing the surface mark points of the calibration plate by photogrammetry outside the wind tunnel and establishing a local coordinate system; placing a calibration plate in the wind tunnel, wherein each vector of a local coordinate system is parallel or vertical to an incoming vector, and calibrating a wind tunnel coordinate system by adopting a resection method; carrying out photogrammetry and initial pose installation on mark points on the surface of a thrown object; acquiring each state image of the thrown object and reconstructing the three-dimensional coordinates of the surface mark points; taking the attitude angle of the thrown object as 0 degree as a reference state, and comparing the rigid body transformation relationship between the blowing state point cloud and the reference state point cloud to obtain attitude angle data. Alignment of a wind tunnel coordinate system and a binocular system coordinate system can be achieved, limitation of the appearance of the thrown object is avoided, and attitude angle data of the thrown object are calculated in a non-contact and high-precision mode.

The invention relates to the field of urban road modeling, in particular to an aerial photography data processing method and system for urban road modeling. The invention discloses an aerial photography data processing system for urban road modeling. The aerial photography data processing system comprises an aerial photography video acquisition module, a road modeling module, a photogrammetry module, a spatial registration module and a rendering synthesis module. According to the invention, on the basis of a spatial calibration mechanism of the original aerial image and the BIM model, redundant processes such as point cloud acquisition, data fusion and manual correction are eliminated, and linear processing from data acquisition to dynamic result output is realized; according to the architecture, the hardware resource dependency and the operation complexity are greatly reduced, and designers are endowed with the capability of completing large-scale scene visualization processing in a conventional engineering environment; according to the method, a transmission chain from original data to decision support is remarkably shortened, and important engineering scheme deduction and dynamic review have unprecedented timeliness.

The invention provides a dynamic change monitoring method and system based on a multi-temporal river channel image, and relates to the technical field of image monitoring, and the method comprises the steps: obtaining a multi-temporal multispectral remote sensing image and an unmanned plane oblique photogrammetry image; identifying a riverway water body boundary and constructing a water area mask; dense point cloud data are generated and cut; generating a balanced riverbed point cloud through density compensation sampling and local curvature constraint; constructing a terrain model to generate a section sequence; calculating hydrodynamic correlation strength to construct an evolution propagation sequence; and recursively calculating the erosion and deposition change rate and performing early warning. According to the invention, high-precision monitoring and early warning of river form changes are realized.

The invention discloses a prediction method for quantifying the development degree of undermining diseases of rammed earth cultural relics, and relates to the field of cultural relic protection. The method comprises the steps of obtaining three-dimensional laser point cloud data and close photography three-dimensional model data of the rammed earth cultural relics; fusion of the three-dimensional laser point cloud model and the close photography three-dimensional model is realized, and a rammed earth cultural relic undermining disease three-dimensional model is constructed; based on the rammed earth cultural relic undermining disease stereoscopic model, quantitative indexes of undermining disease development are extracted, and an undermining disease development prediction model is constructed and optimized; and generating and outputting a quantitative report of the development degree of the undermining disease of the rammed earth cultural relics. According to the method, three-dimensional laser scanning and close photogrammetry are combined, differentiated routes are designed for areas with different heights, alignment of cross section contours of different models is realized based on Fourier description, a Poisson curved surface reconstruction technology is combined with a hole filling algorithm to complement missing parts of the models, spatial distribution and morphological characteristics of undercut diseases are intuitively presented, and the method is suitable for large-scale popularization and application. The change rule of undercut diseases along with time is quantified, and protection work is promoted to be changed from passive repair to active prevention.

The invention belongs to the technical field of glacier identification, and provides a system for monitoring glacier dynamics by an unmanned aerial vehicle low-altitude flight photogrammetry method, which comprises the following steps that: a glacier area unmanned aerial vehicle low-altitude photogrammetry module selects an unmanned aerial vehicle as required, and plans a route and parameters; the image preprocessing module is used for extracting key feature points from each image by using Pi * 4Dcap; fine three-dimensional information, an ortho-image and a glacier digital surface model DSM are obtained through point cloud generation and densification, and glacier dynamic information can be identified by utilizing the glacier digital surface model DSM after correction and interpolation processing are carried out on the three-dimensional information, the ortho-image and the glacier digital surface model DSM; calculating the elevation, volume, speed and mass change of the glacier based on a digital surface model (DSM) of the glacier; the glacier fissure is identified by utilizing the orthoimage and the glacier digital surface model DSM, and the system can comprehensively and accurately monitor the dynamic information of the glacier.

The invention discloses a field-ditch-pond extraction method based on unmanned aerial vehicle photogrammetry and deep learning, and belongs to the technical field of remote sensing technology and agricultural informationization, and the method comprises the steps: employing an unmanned aerial vehicle to carry a multispectral camera and an optical lens, and obtaining original image data through an oblique photogrammetry technology; preprocessing the obtained original image data to generate a multispectral image map (DOM) and a digital surface model (DSM); starting from the spectrum, terrain and texture features of a small watershed field-ditch-pond system, a U-net algorithm with a built-in attention mechanism, an image segmentation model supporting zero sample learning, a multi-scale segmentation technology and a multiple extraction framework of a shape feature extraction algorithm are constructed, image segmentation, classification and interpretation are carried out, and a multi-scale image segmentation model is constructed. Fine extraction of the small watershed field-ditch-pond system is realized; the design can improve the extraction precision and efficiency under complex terrains, and is suitable for current small watershed agricultural non-point source pollution migration trajectory simulation and farmland fine management.

The invention discloses a space-space-ground-well integrated carbon dioxide sequestration leakage monitoring method and system, and relates to the technical field of carbon dioxide sequestration leakage monitoring, and the method comprises the steps: carrying out the remote sensing measurement of a target region through a synthetic aperture radar and a satellite, and determining a carbon dioxide sequestration leakage monitoring boundary; performing photogrammetry by using an unmanned aerial vehicle, and constructing a three-dimensional real scene model Surveying the target area on site to obtain surveying and mapping data; based on the key monitoring area, an absorption spectrum and earth surface leakage flux are obtained through infrared spectrum monitoring and vorticity monitoring; gravity monitoring data, storage area underground water parameters, carbon dioxide plume distribution conditions and logging sound wave frequency distribution characteristic graphs are obtained through gravity monitoring, underground water monitoring, optical fiber monitoring and sound wave logging monitoring. And through comprehensive analysis, according to a comprehensive analysis result, whether carbon dioxide leaks is judged, and a carbon dioxide sequestration leakage monitoring result is obtained. The accuracy of carbon dioxide sequestration monitoring can be effectively improved.

The invention provides an attitude measurement camera coordinate system calibration method, apparatus and device, and a storage medium, which can be applied to the technical field of measurement calibration. The method comprises the steps that a calibration plate is constructed, a conversion target seat and at least one reflective mark point are arranged on the calibration plate, the conversion target seat is used for placing a cube-corner prism target ball and a photogrammetry target ball, the cube-corner prism target ball and the photogrammetry target ball are the same in diameter, and the calibration plate is placed at different positions in a view field of an attitude measurement camera; the method comprises the following steps: acquiring three-dimensional coordinates of a cube-corner prism target ball by using a laser tracker, replacing a photogrammetric target ball, shooting an image of a calibration plate by using an attitude measurement camera to obtain the three-dimensional coordinates and a plurality of images of the cube-corner prism target ball in a laser tracker coordinate system, and calculating the three-dimensional coordinates of the cube-corner prism target ball according to the three-dimensional coordinates and the plurality of images of the cube-corner prism target ball in the laser tracker coordinate system. And calibrating a coordinate system of the attitude measurement camera. The extraction precision of the two-dimensional image coordinates of the target spot can be improved, and then the calibration precision of the coordinate system is improved.

The invention provides a seawall terrain extraction method and system fusing a vehicle-mounted laser point cloud and an inclination model. The method comprises the following steps: forming a seawall range according to a seawall vector diagram and a satellite image; dividing a seawall range into a vehicle-mounted laser radar acquisition range and an unmanned aerial vehicle oblique photogrammetry range; acquiring a vehicle-mounted laser point cloud collected by a vehicle-mounted laser radar, and performing preprocessing and precision optimization on the vehicle-mounted laser point cloud; obtaining oblique photography image data collected by a five-splicing camera carried by the unmanned aerial vehicle, and generating an oblique photography model and an oblique three-dimensional model point cloud; extracting edge line elements and angular point feature points of a common overlapping area in the inclined three-dimensional model point cloud and the vehicle-mounted laser point cloud, and performing point cloud fusion based on the edge line elements and the angular point feature points; filtering the fused point cloud to screen seawall terrain point cloud data; according to seawall terrain point cloud data, resolution-adaptive digital elevation models are constructed for different areas of a seawall, and seawall structure features can be accurately represented.

Systems and methods for generating patient-specific surgical guides comprising: capturing a first and second images of an orthopedic element in different reference frames using a radiographic imaging technique, detecting spatial data defining anatomical landmarks on or in the orthopedic element using a neural network, applying a mask to the orthopedic element defined by an anatomical landmark, projecting the spatial data from the first image and the second image to define volume data, applying the neural network to the volume data to generate a reconstructed three-dimensional (“3D”) model of the orthopedic element; and calculating dimensions for a patient-specific surgical guide configured to abut the orthopedic element.

The invention discloses a slope monitoring and early warning method based on oblique photography and radar technologies. The method comprises the following steps: acquiring three-dimensional point cloud data and surface texture data of a slope; constructing a risk identification model; inputting the three-dimensional point cloud data and the surface texture data into a registration fusion module for registration fusion to obtain three-dimensional image data of the slope; the three-dimensional image data of the side slope are input into an image recognition module for crack recognition, and a crack image is obtained and comprises crack positions, sizes and the number; the crack image is input into a comparison module, and whether risk warning is carried out or not is judged based on a preset threshold value; and further dynamically adjusting the scanning frequency of the synthetic aperture radar through the crack image. According to the method, the three-dimensional point cloud data acquired by the multi-line laser radar and the surface texture data acquired by the oblique photography unmanned aerial vehicle are fused, so that high-precision and comprehensive identification of the slope crack can be realized.

The invention relates to the technical field of digital twinborn simulation, in particular to a digital twinborn simulation system for a sleeper maintenance period, which comprises a multi-source data integration module for integrating point cloud data of a laser scanner and a photogrammetric image sequence of an unmanned aerial vehicle, extracting steel rail profile point position coordinates and sleeper position geometric parameters of a sleeper, and generating a digital twinborn simulation result; and generating an integrated structure attribute set. According to the invention, by integrating the point cloud data of the laser scanner and the photogrammetric image sequence of the unmanned aerial vehicle, the space coordinates of the steel rail profile and the geometric parameters of the sleeper are obtained, and a unified space coordinate system is constructed, so that the accuracy and stability of data matching are improved; and the displacement vector and attitude difference between the actually measured data and the digital model are taken as an adjustment basis, so that the adjustment operation of the steel rail node coordinate and the sleeper rotation matrix is more in line with the actual track facility state, the consistency of the simulation geometric structure and the actual track structure is realized, and the credibility of model simulation analysis is improved.

The invention provides an aerial survey unmanned aerial vehicle route planning method and system, and the method mainly comprises four steps: map coordinate conversion, polygon processing, polygon decomposition, and path planning, and through the four steps, the aerial survey unmanned aerial vehicle route planning method employs a full-coverage path planning algorithm. After the boundary of the operation area and the coordinate information of the no-fly area are obtained, the air route is automatically generated to avoid the no-fly area and the path is shortest, so that the method is really applied to aerial photogrammetry operation, the reasonable shortest path can be planned for the operation area in any shape, the operation area cannot fly to the no-fly area, the turning frequency is reduced, and the working efficiency is improved. The phenomenon of repeated flight is avoided, and the operation efficiency of the aerial survey unmanned aerial vehicle is improved.

The invention discloses a landscape pattern classification method combining global-local multi-order deep and shallow layer features, and relates to the technical field of photogrammetry data processing, and the method comprises the steps: firstly introducing a deep and shallow layer feature cross fusion module between an encoder and a decoder to relieve the semantic gap between different levels of features generated by a Swin Transform; three global-local attention modules are introduced into a decoder part to aggregate global context information and local detail information, and identification of intra-class feature consistency and inter-class feature difference is enhanced. According to the method, the advantages of the CNN and the Swin Transform in local and global information extraction are fully utilized, global context information and local fine-grained information are aggregated, the problems of spectral feature similarity and intra-class difference between ground feature classes in a complex landscape pattern scene are solved, and the method is of great significance to dynamic monitoring of an ecological system.

An autonomous drone swarm system and method utilizing artificial intelligence for coordinated operations comprises command drones equipped with large language model processors and subordinate drones coordinated through a hierarchical Queen-Worker architecture. The system processes natural language commands, generates autonomous mission plans, and coordinates multi-drone operations through an encrypted self-healing mesh communication network utilizing laser, radio frequency, and visual communication channels. Multi-modal sensor integration including electro-optical, infrared, LiDAR and photogrammetry, radio frequency, and chemical detection provides comprehensive environmental awareness while federated learning algorithms enable distributed coordination in signal-denied environments. The system implements configurable operational modes spanning tactical (15 minutes-2 hours over 2 to 5 square kilometers), operational (6-24 hours over 20 to 50 square kilometers), and strategic (7-30+days over 200 to 500 square kilometers) mission profiles for military and commercial applications. Fault-tolerant protocols ensure continued operation despite individual failures through automatic task redistribution and leader election procedures.

The invention discloses an unmanned aerial vehicle multispectral system error calibration and correction method based on a Boolean model, and belongs to the technical field of photogrammetry and remote sensing. Comprising the following steps: acquiring a multispectral image, acquiring original POS data, and performing band registration preprocessing on the image to generate a first orthoimage to confirm a system error magnitude; carrying out bundle method strict adjustment by using ground control points to obtain reference POS data, and solving seven-parameter conversion parameters; correcting the original POS data by using seven parameters and generating a high-precision orthoimage; and generating a simplified multispectral camera calibration and verification formula according to the difference value of the POS data before and after correction. Complex coordinate conversion is simplified into fixed value addition and subtraction operation, one-button calibration and batch correction of system errors of the multi-spectral camera of the unmanned aerial vehicle are achieved, the positioning precision is improved to the centimeter level from the meter level, dependence on ground control points is greatly reduced, and an efficient standardized solution is provided for large-scale engineering data processing.

The invention provides an unmanned aerial vehicle image matching method and device, equipment, a storage medium and a program product, and relates to the technical field of photogrammetry and computer vision, and the method comprises the steps: obtaining a global feature vector of an unmanned aerial vehicle image, and carrying out the image index processing of the global feature vector, and obtaining an image matching pair; performing data scheduling processing of iterative matrix bandwidth reduction on the image matching pair to generate an image scheduling block; and performing feature matching on the image scheduling block, and eliminating matching error data in a feature matching result to obtain an unmanned aerial vehicle image matching result. According to the method, based on a data scheduling strategy of matrix bandwidth reduction, the sparsely connected image matching graph is divided into compact sub-blocks, the sub-blocks can be loaded and processed more efficiently under the condition that the GPU memory is limited, meanwhile, the feature matching speed is increased by combining feature matching and gross error elimination of cascade hash, and the matching efficiency is improved. Therefore, the rapid feature matching requirement of large-scale unmanned aerial vehicle images is met.

The invention discloses a tidal flat vegetation pattern information extraction method based on unmanned aerial vehicle photogrammetry. The method roughly comprises the following steps of (1) task planning, (2) data acquisition, (3) data preprocessing, (4) feature extraction, (5) classification and identification, (6) pattern analysis and (7) precision verification. According to the method, a multi-spectrum and RGB fused unmanned aerial vehicle image acquisition scheme is adopted, the acquisition opportunity is optimized in combination with tide forecast data, accurate extraction of multi-level information such as tidal flat vegetation types, coverage and biomass can be achieved through integration of deep learning and a traditional remote sensing classification method, and the method is high in pertinence, easy to operate and suitable for popularization.

The invention discloses a calibration board and wind tunnel feature point-based binocular system wind tunnel coordinate system calibration method, and belongs to the field of aerodynamic tests. Comprising the following steps: reconstructing surface mark points of a calibration plate by photogrammetry outside a hole and establishing a local coordinate system to obtain coordinates under the local coordinate system; a binocular system is arranged in the hole, and binocular internal and external parameters are calibrated through a special cross calibration ruler; arranging a plurality of circular feature mark points on the surface of the hole wall with overlapped visual fields of the binocular system; the calibration plate is correctly placed according to the geometrical relationship, so that each vector of the local coordinate system is parallel or perpendicular to an incoming vector, and the binocular system reconstructs global points of the calibration plate and registers the global points to achieve alignment of a measurement coordinate system of the binocular system and a wind tunnel coordinate system; reconstructing a three-dimensional coordinate of the fixed point in a wind tunnel coordinate system through a binocular system; and in a subsequent test, adopting resection to realize the recalibration of the wind tunnel coordinate system. According to the invention, high-precision and high-efficiency alignment requirements of a measurement coordinate system and a wind tunnel coordinate system of a binocular system can be met.

The invention relates to the field of rock mass structural surface recognition, in particular to a high and steep slope rock mass structural surface intelligent recognition method based on double clustering. According to the method, high-precision image data of a high and steep slope is obtained through an unmanned aerial vehicle approaching photogrammetry technology, and a three-dimensional digital model of the high and steep slope is constructed by utilizing a three-dimensional reconstruction and calculation technology. And calculating normal vectors of all triangular surfaces based on the triangular surface vertex information of the model, and performing clustering analysis to divide structural surface groups with similar occurrence. And further performing clustering analysis according to the surface center coordinates of the triangular surface, and dividing to obtain the structural surface. And finally, calculating the inclination angle, inclination direction and trend data of the structural surface through an accurate mathematical model. According to the method, the flexibility and the safety of high and steep slope structural plane measurement operation are enhanced, a measurement area of any scale can be completely covered, objective, accurate, rapid and low-cost structural plane intelligent identification and attitude data acquisition are realized, and the method has important significance in engineering application related to high and steep slopes.

The invention relates to the technical field of photogrammetry, in particular to a settlement monitoring method and system for road exploration, and the method comprises the steps: obtaining settlement schematic diagrams in the same road exploration surface area at different sampling moments, and forming a settlement change curve corresponding to any pixel point, dividing a plurality of communicated areas in the settlement volume schematic diagram at the last sampling moment, and determining adjacent working face roadbed settlement correlation factors of the communicated areas and roadbed collapse factors of each pixel point according to the difference between the settlement volume change curves corresponding to the pixel points in the communicated areas; and determining a roadbed damage factor of the connected region, thereby obtaining the total surface settlement damage actually caused by current excavation in the road exploration region, and dividing the damage degree of the road exploration surface region. According to the method, the accurate and credible total surface settlement destructiveness actually caused by current excavation is obtained, so that appropriate treatment measures are used for repairing.

The invention discloses an underwater target volume measurement method, device and equipment based on three-dimensional reconstruction and a medium, and belongs to the technical field of photogrammetry, underwater image processing and point cloud volume calculation cross, and the method comprises the steps: carrying out the video data collection of an underwater target through video collection equipment, and obtaining an original image with image format data; the underwater image enhancement method model based on depth estimation performs image enhancement on the original image to obtain an underwater enhanced image; reconstructing a real scene containing a target based on the underwater enhanced image; and based on the three-dimensional reconstruction point cloud, segmenting to obtain the to-be-measured target, and obtaining the volume parameter of the underwater target. According to the method, a three-dimensional reconstruction technology is utilized, a real target structure is restored, the measurement accuracy of the underwater target defect volume is improved, a reference standard is provided for later repair, the maintenance cost is reduced, and the operation safety of a hydraulic structure is guaranteed.

The invention relates to the technical field of slope stability analysis, in particular to a joint slope parameterized modeling and analysis method, which comprises the following steps of: performing photogrammetry by an unmanned aerial vehicle to obtain a surface grid model; constructing a controllable high-precision curved surface on the model; generating a three-dimensional joint slope model and a support model; and constructing a numerical model, and carrying out slope stability analysis. According to the method, parameterization application of the process is realized based on a Rhino-Griddle platform and a Python language through the modeling process of three-dimensional real terrain, curved surface construction, block generation, grid model construction and support structure model construction; a complex three-dimensional joint slope model and a support model can be quickly generated based on existing information, a model file which can be exported to 3DEC software is generated, obvious advantages are achieved in multiple aspects, and a real, efficient and universal technical path is provided for three-dimensional joint slope modeling and support effect analysis.

The invention relates to a multi-mode power transmission line state monitoring method and system based on thunder and shooting fusion, and belongs to the technical field of circuit monitoring. The method comprises the following steps: acquiring historical multi-mode power transmission line data, and constructing a lightning-shooting fusion power transmission line monitoring set according to the historical multi-mode power transmission line data; a power transmission line state monitoring model is obtained through multi-sensor fusion modeling according to the lightning shooting fusion power transmission line monitoring set; the real-time multi-mode power transmission line data is acquired, the real-time multi-mode power transmission line data is processed through the power transmission line state monitoring model to obtain the power transmission line state label, high-precision, all-weather and low-cost power transmission line state monitoring is achieved, and efficient and reliable technical guarantee is provided for safe operation of a power grid.

The invention discloses an arbitrary three-dimensional terrain visual coverage unmanned aerial vehicle energy optimization trajectory generation method, which comprises the following steps of: generating visual coverage air route points: selecting a mountain region, acquiring a rough digital elevation model by utilizing a geographic information system or level flight shooting, calculating the air route points based on photogrammetry constraints, and calculating the visual coverage air route points; traversing the waypoints according to a predetermined sequence to complete a coverage task; modeling the energy consumption of the motor of the unmanned aerial vehicle: taking the brushless direct current motor as an object, carrying out mathematical modeling in combination with torque dynamics and electromagnetic equations of the motor, constructing a physical information neural network model, and training and optimizing flight data; and track optimal prediction control input mapping: designing an optimal prediction control method based on unmanned aerial vehicle dynamics, and calculating a mapping relation between the track and control input. According to the invention, the coverage efficiency can be obviously improved, so that the single flight coverage range of the unmanned aerial vehicle is expanded.

The present invention discloses a robot adaptive force control and admittance control method and system for wall panel assembly. First, a high-precision photogrammetry system is combined to measure the position and posture of the robot end in real time, and the trajectory is corrected according to the measurement result and the desired position and posture to achieve the positioning of the robot in the position control direction. In the force control direction, a mapping relationship between the robot end force and the contact surface contact force is established through a heteroscedastic neural network mapping model, and an inverse model is jointly trained to map the desired contact force back to the desired end force, so that the contact force on the contact surface is controlled by adjusting the end force, and an adaptive force control and admittance control model is constructed; by interconnecting a virtual energy tank and the adaptive force control and admittance control model, the update of the control model parameters is guided to ensure the passivity of the system; at the same time, the parameters of the virtual energy tank are dynamically adjusted according to the system state, which can maintain the passivity and maximize the assembly accuracy and performance of the robot.