263 results about "Aerial survey" patented technology

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 present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention discloses an air-ground integrated measurement method for an original terrain, and relates to the technical field of engineering surveying, and the method comprises the steps: S1, carrying multi-sensor aerial survey by an unmanned aerial vehicle, obtaining an image and POS positioning data, scanning a blind area of the unmanned aerial vehicle by using a three-dimensional laser scanner, obtaining laser point cloud data, and carrying out the scanning of the laser point cloud data; constructing a multi-source data set including optical images, POS positioning data and laser point cloud, and forming an air-ground complementary data acquisition mode; s2, geometric correction and aerial triangulation encryption are carried out on the unmanned aerial vehicle image, denoising and multi-station registration are carried out on the laser point cloud data, and the data quality is improved; s3, unifying a multi-source data space reference through coordinate system unification, feature fusion and decision-level fusion, and realizing feature complementation; s4, constructing a digital elevation model and a digital surface model based on the fused data, and generating a topographic mapping map; the method breaks through the limitation of a single technology through air-ground technology complementation, and ensures the global data coverage of complex terrains.

Disclosed in the present invention are a multi-source aviation remote sensing data acquisition apparatus, system and control method, which are applied to the technical field of aerial survey remote sensing. The apparatus comprises: a flight platform and an airborne device disposed on the flight platform. The airborne device comprises: a multi-sensor ultra-compact integrated device, an airborne integrated synchronization control device, and a GNSS receiving antenna. The multi-sensor ultra-compact integrated device and the airborne integrated synchronization control device are connected by means of cables, and the GNSS receiving antenna is disposed on an upper end face of the flight platform. The multi-sensor ultra-compact integrated device is used for acquiring multi-source remote sensing data; and the airborne integrated synchronization control device is used for controlling the operation of the multi-sensor ultra-compact integrated device. The present invention ultra-compactly integrates a LiDAR, a hyperspectral camera, a thermal infrared camera, and a visible-light camera on the basis of a high-precision inertial navigation system, and achieves efficient synchronous acquisition of texture, spectral, temperature, and geometric information by means of using an integrated control subsystem.

The invention belongs to the technical field of hydraulic engineering measurement, and discloses a seawall measurement method and system based on multi-source data fusion, and the method comprises the steps: collecting geological data and hydrological data through a data collection module; the route planning module formulates a route; the overwater data acquisition equipment and the underwater data acquisition equipment are used for monitoring; the auxiliary equipment provides a high-precision positioning reference; the meteorological data module obtains meteorological data; the data processing module processes the data; the three-dimensional modeling module carries out point cloud registration and DEM model construction; the comprehensive analysis module performs comprehensive evaluation and predicts potential problems and risks; when an abnormal condition or a potential risk is monitored, the early warning module gives an alarm. Multi-source data acquisition equipment such as unmanned aerial vehicle aerial survey, a laser radar, a multi-beam depth finder, a side-scan sonar, a shallow profiler and a micro-motion exploration instrument are integrated, seawall overwater and underwater full-dimensional data acquisition, processing, modeling and volume calculation are realized, and high precision and reliability of measurement results are ensured.

The invention relates to the technical field of landslide geological disaster hidden danger point identification, in particular to a landslide hidden danger point identification method based on an air-space remote sensing fusion neural network, which comprises the following steps: acquiring satellite SAR data, a digital elevation model, a vector boundary map and a precision orbit ephemeris of a research area; carrying out interference processing on the InSAR data, and carrying out deformation rate inversion, terrain residual error correction and atmospheric delay phase modeling and correction; preprocessing an original high-resolution remote sensing image, interpreting a high-resolution image, and outputting spatial distribution of suspected landslide hidden danger points in a research area; planning an aerial survey route of the unmanned aerial vehicle, and performing distortion correction, feature matching, dense point cloud generation and orthoimage generation processing; performing multi-dimensional feature analysis, and performing comprehensive interpretation in combination with an expert knowledge base; according to the method, the key effect of the multi-source remote sensing fusion technology in landslide hidden danger monitoring is displayed from data acquisition to hidden danger recognition.

A method and system for constructing a three-dimensional (3D) aerial survey of a city street scene include obtaining a plurality of video frames from a calibrated multi-camera setup covering a 360-degree view mounted on a moving vehicle. The plurality of video frames is split into a plurality of 3D parts containing a subset of the plurality of video frames and preprocessing the subset of the plurality of video frames of each part of the plurality of parts to obtain a calculated information. Further, constructing, by the processing circuitry, a 3D representation of each part of the plurality of parts based on the calculated information to obtain a plurality of local 3D reconstructed scene intervals. The method includes stitching and filtering, by the processing circuitry, the plurality of local 3D reconstructed scene intervals to construct the 3D city street scene.

The invention relates to the technical field of tunnel construction, and discloses a coal mine goaf karst tunnel construction method which comprises the following steps: S1, performing three-dimensional scanning on the axis and the periphery of a tunnel by adopting a high-frequency geological radar in combination with aerial survey of an unmanned aerial vehicle, and accurately positioning the boundary of a goaf, the development form of a karst cave and filler distribution; and S2, integrating a seismic wave reflection method, drilling and coring data and a geological sketch result, constructing a three-dimensional geological model, and dynamically predicting a potential water inrush and mud inrush risk area. According to the method, a high-frequency geological radar is combined with aerial survey of an unmanned aerial vehicle, three-dimensional scanning is conducted on the axis and the periphery of a tunnel, geological radar data, a point cloud model and tunnel design BIM are integrated, the boundary of a goaf (the error is smaller than or equal to 0.3 m) and the karst cave form are marked, and the collapse risk level is predicted based on rock mass mechanical parameters (compressive strength and fracture density) and hydrological data (seepage paths). Therefore, the method has the advantages of advanced detection, multi-source data fusion and the like, and the resolution of geological forecast is improved.

The invention relates to the technical field of ecological restoration and carbon sink evaluation, and discloses a river and lake ecological buffer zone carbon sink potential evaluation and restoration construction system which comprises a carbon sink potential evaluation module, an intelligent restoration construction module and a collaborative verification module. The carbon sink potential evaluation module is used for collecting soil and vegetation data and calculating the carbon sink potential; the intelligent restoration construction module executes ecological restoration based on a carbon sink potential evaluation result; and the collaborative verification module verifies the repair effect through multi-source data fusion, and returns an optimization instruction to the carbon sink potential evaluation module and the intelligent repair construction module. The vegetation coverage index and the root biomass density are acquired by using a multispectral sensor array, a carbon sink model is constructed by combining the organic carbon content of soil, dynamic weight adjustment is introduced, and the weight is updated according to aerial survey data of an unmanned aerial vehicle in each quarter, so that a more accurate and more economical carbon sink potential evaluation basis can be provided for ecological restoration, and the ecological restoration efficiency is improved. And resource waste and invalid repair are avoided.

The invention discloses an adjacent foundation pit deformation prediction method for a dense building group and a support deformation prediction method, and the method comprises the steps: obtaining SAR satellite images of a to-be-processed region in a period of time sequence, and generating an image stack after registration; screening points of which the amplitude deviation indexes are lower than a threshold value as PS points; calculating the relative displacement sequence of each PS point, inverting the displacement variation and accumulating to obtain the accumulated settlement; before construction, a digital surface model (DSM) is generated through aerial survey of an unmanned aerial vehicle, feature points with stable displacement are recognized by combining SAR images, and the feature points are anchored to an absolute coordinate system of the unmanned aerial vehicle for correction. Therefore, permanent scatterer (PS) identification and time sequence analysis are carried out by utilizing SAR images of a long time sequence, absolute geographic reference is provided by a high-precision digital surface model (DSM) generated by aerial survey of an unmanned aerial vehicle, and reference correction is carried out on a relative displacement field acquired by the InSAR.

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 water-land integrated all-terrain DEM surveying method and system based on UAV-USV-GIS, and belongs to the field of terrain surveying. The method comprises the steps of determining a research area range, planning an unmanned aerial vehicle route, and laying image control points; performing aerial survey on the planned route by using an unmanned aerial vehicle to obtain ground point cloud data and images, and respectively processing the ground point cloud data and images into a land DEM and an orthoimage; the boundaries of the subsidence water area are extracted through the orthoimages, the measurement route of the unmanned ship is planned, and underwater topographic data are measured; processing the underwater topographic data, generating a water area DEM, uniformly selecting inspection points by using RTK and sounding equipment to carry out sampling measurement on the underwater elevation, and verifying the accuracy of the water area DEM underwater topographic data; and fusing the land DEM and the water area DEM to generate a complete water-land integrated all-terrain DEM. The technical bottlenecks that the efficiency is low, the data at the junction of water and land is not accurate, and only the elevation data of a single area of the land or the water can be obtained in the subsidence water area terrain modeling by a traditional single measurement means are effectively solved.

The invention relates to the field of unmanned aerial vehicle aerial survey, and particularly discloses a regional geology and landform processing method based on unmanned aerial vehicle aerial survey, and the method comprises the following steps: S1, carrying out the preliminary division of the geology and landform of a to-be-measured region through intelligent region planning, and forming a plurality of basic grids; s2, intelligently and dynamically adjusting the size of the grid; S2, performing terrain complexity evaluation: performing terrain complexity evaluation on each position in the to-be-detected region by using a terrain complexity evaluation function; s22, dynamically adjusting the size of the grid; through intelligent area planning and dynamic grid adjustment, the method can reasonably allocate aerial survey resources according to actual conditions, and in a complex or key area, grid division is finer so as to ensure high density and precision of data; and in a flat or secondary area, the grids are relatively sparse so as to improve the overall efficiency of aerial survey, and the dynamic adjustment mode avoids unnecessary repeated shooting and resource waste.

The invention relates to the technical field of ecological monitoring, in particular to a resident-free island resource monitoring method based on a spatial three-dimensional model, and the method comprises the following steps: S1, building a remote sensing image interpretation information base; s2, based on the remote sensing image interpretation information base, generating an island space three-dimensional model containing island land terrain and shoreline distribution; s3, generating a total element three-dimensional dynamic map; s4, the interpretation result and the total element three-dimensional dynamic map in the S3 are superposed, and a reef area comprehensive distribution map is generated; s5, calculating the island resource development intensity and the ecological risk level, and outputting an island ecological assessment report; and S6, generating an island resource monitoring report. According to the invention, unmanned aerial vehicle aerial survey, GNSS measurement, depth sounding technology, image interpretation and ecological assessment are combined, a high-precision total-factor three-dimensional dynamic monitoring system is constructed, accurate analysis and visual expression of the current island resource utilization situation are realized, and scientific decision support is provided for island management and ecological protection.

The invention discloses an electric power aerial patrol operation data mining method, and belongs to the technical field of intelligent patrol. Coordinate values and temperature data of target equipment are synchronously collected through an infrared thermal imager, a visible light camera and a vibration sensor carried by power equipment, infrared coordinates and visible light coordinates are projected to the same geographic coordinate system through affine transformation, space registration is achieved, continuous wavelet transformation is conducted on collected vibration signals, and the temperature of the target equipment is obtained. The method comprises the following steps: extracting vibration feature vectors including global vibration intensity, wavelet energy spectrum and wavelet entropy, calculating the weight of each feature through image quality, carrying out weighted fusion to calculate a state index representing the health state of equipment, judging the state index according to a dynamic threshold value, judging that the equipment is abnormal if the state index exceeds the threshold value, and judging that the equipment is abnormal if the state index does not exceed the threshold value. And a feedback mechanism is triggered to collect data again to monitor the trend, otherwise, the data is stored for subsequent analysis. According to the invention, the precision and reliability of abnormal state identification in electric power aerial patrol operation and the robustness to a complex field environment are significantly improved.

The invention belongs to the technical field of water environment treatment, and particularly relates to a polluted river purification method which comprises the following steps: arranging solid floating object intercepting buoys in a river, and carrying out regular mechanical cleaning through a water surface floating object cleaning boat; arranging a water quality monitoring sensor, collecting multi-dimensional water quality parameters, and establishing a three-dimensional pollution information map by combining an aerial survey image of an unmanned aerial vehicle; identifying pollution and establishing a water quality change model; predicting key indexes such as ammonia nitrogen concentration, COD (Chemical Oxygen Demand) concentration and dissolved oxygen in future 6 hours by using a water quality change model; the adjustable trash holding system is started according to the prediction result, and a purification medium is scattered; intelligent regulation and storage are achieved, and the pollution value is reduced. According to the invention, automatic sensing, accurate research and judgment, intelligent decision making and cooperative treatment of polluted river treatment are realized, and an intelligent river purification system is constructed.

The invention provides an efficient riverway management range demarcation method based on multi-source data fusion and spatial characteristic difference, and the method comprises the steps: constructing a whole-process technical system of law and regulation analysis, multi-source data fusion, characteristic parameter extraction and dynamic optimization, integrating the multi-source data of satellite remote sensing, unmanned aerial vehicle aerial survey, hydrological monitoring, etc. Key parameters such as a central line, a gradient and a curvature of a river channel are extracted by utilizing an intelligent algorithm, and accurate delimiting of a management line is realized by combining design flood level calculation and Kriging interpolation optimization. The invention aims to break through the limitation of the traditional method, improve the demarcation efficiency and precision, provide standardized and executable technical support for scientific management of complex rivers, and assist the cooperative promotion of flood control safety and water resource protection.

The invention discloses a visual imaging method and system combined with unmanned aerial vehicle multi-temporal automatic comparison, and the system comprises an unmanned aerial vehicle aerial survey module which is used for carrying out the repeated aerial survey of the same target region at different time points, and obtaining multi-period image data; the data transmission and storage module is used for transmitting the multi-period image data acquired by the unmanned aerial vehicle aerial survey module in real time; the method has the beneficial effects that the aerial survey of the unmanned aerial vehicle supports dynamic route adjustment, multi-sensor cooperative acquisition is realized, the overlap ratio of multiple aerial survey paths is high, the image data integrity and consistency are high, and the manual intervention and data supplementary acquisition cost is greatly reduced; multi-stage image automatic registration, feature extraction and change detection are realized through deep learning, the processing precision is high, a large amount of manual operation is not needed, and the efficiency is remarkably improved; three-dimensional modeling and multi-mode comparison are supported, dynamic changes of ground objects can be clearly displayed, interaction functions are rich, and the requirements for single-person detail checking and multi-person collaborative rechecking can be met.

The invention belongs to the technical field of marine environment monitoring, and particularly relates to an air-sea cooperation-based enteromorpha identification and density estimation method and system, and the method comprises the steps: carrying out the aerial survey of a target sea area through an unmanned plane, collecting a sequence image, processing the sequence image based on a deep learning model, and generating an enteromorpha distribution probability graph and a density estimation graph; carrying out marine navigation and fixed-point sampling through an acoustic sensor carried by an unmanned ship to obtain actually measured density data of enteromorpha; the unmanned aerial vehicle sends the generated enteromorpha distribution probability graph and / or density estimation graph to the unmanned ship, the unmanned ship receives the enteromorpha distribution probability graph and / or density estimation graph, plans a navigation path and sampling points according to the enteromorpha distribution probability graph and / or density estimation graph, and goes to a high-probability or high-uncertainty area for fixed-point sampling; and the unmanned aerial vehicle receives actually measured density data returned by the unmanned ship and fuses the actually measured density data with own identification and estimation results so as to update and optimize the deep learning model and form an air-sea collaborative monitoring closed loop.

The invention relates to the technical field of earthwork calculation, in particular to an earthwork calculation method based on an unmanned aerial vehicle aerial survey technology. According to the technical scheme, the method comprises the steps of image control point layout and data acquisition, unmanned aerial vehicle image data acquisition, aerial triangulation processing and model generation. Based on an unmanned aerial vehicle aerial survey technology, image control points are scientifically arranged to collect high-precision coordinates, high-quality image data are obtained, a high-precision model is generated through aerial triangulation processing, the precision is strictly verified, data are extracted through professional software to construct an optimized DTM model, and precise earth-rock volume calculation is achieved; a calculation result is encrypted and stored by means of a block chain and is in butt joint with a construction management system, and a GIS platform and a BIM model are combined to generate a visual progress diagram, simulate slag yard capacity early warning and the like, so that the precision and efficiency of earth-rock calculation are comprehensively improved, powerful support is provided for engineering construction management, auditing tracing and ecological restoration decision, and the method is suitable for popularization and application. And the scientificity and informatization level of project management are obviously improved.

The invention relates to the technical field of engineering area risk inspection, in particular to an intelligent engineering area risk inspection method and system based on unmanned aerial vehicle aerial survey, and discloses an unmanned aerial vehicle regular inspection module, a risk identification and analysis module and a risk early warning feedback module. According to the invention, the unmanned aerial vehicle aerial survey technology is combined with the hyperspectral imaging technology to carry out regular risk inspection on the target engineering area, and the damage position in the inspection process is further analyzed; according to the method, the damage position is deeply analyzed in combination with the basic risk, the risk time sequence and the risk relevance of the damage position and the risk variability under each load condition, the damage risk existing in the engineering area is accurately locked, whether the corresponding damage position needs to be repaired or not is efficiently judged, accurate risk control over the engineering area is guaranteed, and the engineering safety is improved. And it can be guaranteed that limited resources can be accurately input to the damaged position really needing to be repaired, and the resource utilization efficiency is improved.

The application discloses a high-precision earthwork measurement algorithm and system for constructing a three-dimensional model based on unmanned aerial vehicle aerial survey, and the method comprises the following steps: arranging image control points and check points in a to-be-measured area in advance; starting unmanned aerial vehicle aerial survey image data collection, and performing supplementary measurement on an obstacle-shielded area to obtain supplementary data; importing the aerial survey image data to generate a real scene three-dimensional model; finding an image control point mark position on the three-dimensional model and corresponding puncture points of actual measured image control points, converting model latitude and longitude coordinates into project coordinates after matching all the image control points, regenerating a three-dimensional model, and importing check point coordinate data to verify the model precision. Seed points are added to remove obstacles and restore the ground terrain, and then drawings and design elevations are imported to perform high-precision earthwork calculation, so that the problems of complex calculation operation, easy errors and large time consumption of manual operation combined with other software are solved, accurate data support is provided for earthwork construction, and the project construction period is saved.

The invention relates to the technical field of aerial survey of unmanned aerial vehicles, in particular to a mountainous area aerial survey data processing method and device based on an unmanned aerial vehicle and a storage medium. The aerial survey assembly comprises a main control module, a high-precision positioning module, a laser radar module, a multispectral imaging module, a hardware synchronization module, a data fusion processing module and a wireless transmission module. The system has the advantages that the synchronization of data acquisition is ensured through aerial survey equipment integrating the high-precision positioning module, the laser radar module and the multi-spectral imaging module and the hardware synchronization module, the problem of space-time inconsistency of multi-source remote sensing data in a mountainous area complex environment is solved, and the multi-source remote sensing data is obtained through a combined adjustment processing method. The synchronously collected multispectral image and laser point cloud data are subjected to combined processing, precise three-dimensional coordinates provided by the laser point cloud constrain aerial triangulation of the multispectral image, and texture information of the multispectral image corrects a point cloud splicing error.

The invention discloses a geothermal water multi-factor remote sensing detection method, system and device, a medium and a product, and the method comprises the steps: obtaining a thermal radiation anomaly index and a surface temperature anomaly index through multi-temporal thermal infrared preliminary screening and surface temperature inversion; the method comprises the following steps: extracting a vegetation stress index VIBS by utilizing the characteristic that terrestrial heat inhibits vegetation, and obtaining a comprehensive altered mineral index MI according to mineral alteration characteristics and a spectral unmixing method; based on ALOS PALSAR, a fracture-fracture zone is extracted, and a construction advantage degree SF is quantified; gRACE gravity image data are introduced to form hydrological favorable degree HF; geothermal water indication characteristics are integrated, multi-factor scoring and grading are fused to obtain a comprehensive geothermal anomaly distribution map, and finally boundary refinement and field verification are carried out through unmanned aerial vehicle thermal infrared / multispectral aerial survey. According to the method, false detection and missing detection can be remarkably reduced, and the accuracy and field efficiency of geothermal anomaly recognition are improved.

The invention provides a contaminated site remediation progress management method fusing multi-source data, which comprises the following steps: collecting unmanned aerial vehicle aerial survey data and soil remediation real-time data of a contaminated site, and carrying out semantic association to obtain a multi-source data set after semantic fusion; based on an association rule algorithm, analyzing an association relationship among data in the multi-source data set, and screening out a key factor set according to the association relationship and the correlation in combination with calculation and a repair progress; inputting data in the key factor set into a pre-trained restoration progress prediction model for real-time progress prediction, and outputting a predicted value of the current restoration progress of the polluted site; and performing deviation analysis on the predicted value and an actual value of the repair progress uploaded by the construction side, calculating an error between the predicted value and the actual value, analyzing a variation trend of the error through a sliding window algorithm, and judging whether the repair progress is abnormal or not based on the variation trend. According to the invention, the defect of inaccurate prediction of the repair progress is overcome.

The invention discloses a geological disaster hidden danger point rapid identification method based on aerial survey of an unmanned aerial vehicle, and relates to hidden danger point rapid identification. An airborne camera image sequence, the acceleration and angular velocity of an inertial measurement unit, the propeller rotating speed, the flight height, the wind speed and direction and a machine body vibration signal are collected, and image exposure time is used as a unified time axis; the method comprises the following steps: forming a time sequence sample, establishing rotor underwash near-earth boundary layer momentum entrainment prior based on the time sequence sample, calculating underwash dynamic pressure field time sequence estimation, a particle mobilization probability time sequence and an equivalent micro-attitude disturbance spectrum, performing sub-pixel level optical flow and phase accumulation analysis on an image sequence, performing time cross-correlation with the equivalent micro-attitude disturbance spectrum, and obtaining a target object. Coupling evidence is obtained, joint judgment of the triggering intensity and the coupling degree is carried out, a flight parameter self-adaptive fine tuning strategy is triggered, space risk analysis is carried out on the image after compensation reconstruction, a space risk calibration graph is generated, and geological disaster hidden danger points in a critical stable state can be effectively recognized.

The invention discloses a multi-dimensional construction safety risk management and control method for a high-altitude highway, particularly relates to the field of construction risk management and control, and is used for solving the problems of slope monitoring lag and passive safety management and control caused by complex terrain in the existing high-altitude highway construction process. The method comprises the following steps: acquiring aerial survey images and laser point cloud data of a construction slope area according to a fixed period, generating a time sequence three-dimensional live-action model sequence, identifying and tracking natural feature points and artificial structure feature points in the model sequence, calculating a feature point displacement field based on precision registration among multiple periods of models, and obtaining a feature point displacement field. The method comprises the following steps: identifying risk deformation data in space-time association with a construction activity in combination with historical construction activity records, performing spatial clustering analysis on the risk deformation data to mark a potential instability region, and generating a prospective construction risk grading control signal according to the space range of the potential instability region and construction information data; and dynamic pre-judgment and graded response of high-altitude highway construction risks are realized.

The utility model relates to the technical field of unmanned plane flight state switching, especially to a state switching structure of amphibious unmanned plane, including land walking mechanism for land walking and aerial flight mechanism for aerial flight, and the aerial flight mechanism includes state turnover assembly and drive assembly, the utility model combines two application fields of land tunnel survey and aerial survey, land free steering, fuselage structure change, land-air flight state free switching control, through changing the body structure, reducing the volume, possessing the ability of crossing limited space, can realize the monitoring and measurement to the underground and aboveground environment, the fuselage structure and the flight state change make that the unmanned plane can obtain the lift of multiple angles, when unmanned plane and bottom level, can provide the lift upwards / downwards, and the unmanned plane can execute aerial survey task, when unmanned plane and bottom vertical, can provide the thrust forwards / backwards, and the unmanned plane can execute tunnel survey task.

The invention aims to provide an intelligent surveying and mapping method and system based on aerial survey of an unmanned aerial vehicle in order to solve the problem that in the prior art, timeliness requirements of scenes such as disaster emergency and engineering monitoring are difficult to meet, and the intelligent surveying and mapping method comprises the steps that S1, a geographic boundary of a target area is input, and an anti-wind-disturbance self-adaptive route is generated based on a digital elevation model (DEM) and real-time meteorological data; s2, synchronously acquiring point cloud, image and spectral data through a laser radar, a five-lens oblique camera and a hyperspectral sensor carried by the unmanned aerial vehicle; s3, fusing GPS / I MU positioning data and visual SLAM feature points, and performing dynamic pose correction on the point cloud data; s4, registering multi-period point clouds by adopting an improved I CP algorithm, and extracting a ground object vector boundary through a semantic segmentation neural network (U-Net + + variant); and S5, fusing the vector boundary with the oblique photography model, outputting a three-dimensional live-action model with an attribute tag, reducing the number of supplementary flight times by adopting the adaptive route, and preprocessing data in real time by an edge calculation carrier plate.

The present invention discloses a deformation monitoring method based on DSM data. The method comprises the following steps: performing aerial survey of the object to be measured, generating orthophotos using photogrammetric modeling software, and extracting at least two phases of DSM data; determining a resampling interval d, and resampling the DSM data; cropping the resampled DSM data, calculating similar points, and obtaining displacement change data in the x, y, and z directions; filtering the obtained displacement change data to remove noise from the data and obtain displacement data; and uniformly performing error correction on the displacement data using correction data to obtain the displacement amount. The present invention utilizes current aerial photogrammetry results, has a low overall cost, quickly obtains large-area data, has a wide monitoring range, and can monitor three-dimensional surface deformation data. While its accuracy is somewhat lower than that of traditional and GNSS measurements, it is higher than that of InSAR measurements, and its error can be controlled to the centimeter and decimeter levels.