639results about "Photogrammetry/videogrammetry" patented technology

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

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

The invention relates to the technical field of track foreign matter real-time detection, and particularly discloses a track foreign matter real-time detection method based on YOLOv5 improvement. Comprising the steps of video vibration synchronous acquisition, space offset compensation parameter generation, dynamic feature enhancement processing, feature weight thermodynamic diagram generation, hierarchical perception feature aggregation, cross-scale target verification, foreign matter positioning instruction packaging and multistage early warning execution control. Image distortion caused by mechanical vibration and illumination fluctuation is effectively overcome by synchronously acquiring track video and vibration time sequence signals, constructing a spatial offset compensation parameter set and combining with a dynamic feature enhancement template to correct video frames, a feature weight thermodynamic diagram is generated through visual saliency detection, features are aggregated through a layered perception model, and the visual saliency detection accuracy is improved. Early warning levels are dynamically matched according to the sizes and the positions of the foreign matters, and a sound-light alarm and braking system is linked; according to the invention, the reliability of data transmission is ensured, the operation and maintenance cost is greatly reduced, and the safety requirement of millisecond response of modern rail transit is met.

The invention relates to the technical field of flight path planning, in particular to an autonomous flight path planning method and system for a road slope inspection unmanned aerial vehicle, and the method comprises the following steps: controlling the unmanned aerial vehicle to fly in a road slope region according to a preset flight path, collecting the aerial image data of the unmanned aerial vehicle, analyzing the aerial inspection adaptive feature value of the unmanned aerial vehicle, and determining the flight path of the unmanned aerial vehicle. Judging whether the feature value is lower than a preset inspection adaptation feature threshold value or not; analyzing a field-of-view missing feature set of the unmanned aerial vehicle based on the aerial image data of the unmanned aerial vehicle if the field-of-view missing feature set is lower than the preset routing inspection adaptive feature value threshold; performing flight path planning processing on the unmanned aerial vehicle based on the field-of-view missing feature set; according to the method, the unmanned aerial vehicle is subjected to flight path planning processing on the basis of the field-of-view missing feature set, so that when an aerial picture is deviated, fuzzy or blind areas, judgment and automatic adjustment are performed in real time, self-adaptive optimization of the flight path is realized, and the road slope inspection efficiency is remarkably improved.

The invention provides a lodging area determination method and system based on a lodging monitoring spectral index image, and relates to the field of crop form prediction.The method comprises the steps that firstly, a target farmland remote sensing image and a digital surface model are obtained, radiation and geometric correction are completed in combination with GNSS positioning data and meteorological data, and a standardized orthoimage is generated; calculating a vegetation index on the image and carrying out difference to obtain change information; executing opening and closing operation by adopting a structural element adaptive to the image resolution and the crop row spacing, filtering noise according to the area of a connected domain or a pixel number threshold value, and extracting a spectral index abnormal region and a boundary thereof; and then fusing the boundary and the digital surface model under a unified coordinate reference, accumulating the area according to the pixel resolution, and outputting the area of the abnormal region. The method has parameter self-adaption and multi-source data fusion capabilities, can stably obtain a consistent abnormal region area in a multi-resolution and complex field environment, and provides reliable technical support for agricultural condition monitoring and disaster assessment.

The invention relates to the technical field of unmanned aerial vehicle cooperative surveying and mapping, and discloses an unmanned aerial vehicle cluster cooperative space-space-ground integrated surveying and mapping system which comprises a space-time reference construction module, a multi-source data acquisition module, a distributed fusion optimization module, a system adaptive reconstruction module and a four-dimensional model generation module. An internal high-precision space-time reference independent of the GNSS is constructed; uniformly modeling the whole surveying and mapping task into a space-time factor graph through a distributed fusion optimization module, and solving the space-time factor graph; and closed-loop adaptive adjustment is performed on the geometric configuration of the beacon network and the measurement data quality through a system adaptive reconstruction module. The technical problem that a traditional surveying and mapping system is poor in precision and reliability in a GNSS disturbed environment is solved, the robustness, autonomy and environment adaptability of the system are remarkably improved by constructing a self-consistent internal reference and introducing intelligent feedback control, and a globally consistent high-precision four-dimensional space-time model can be efficiently generated.

The invention provides a ship distance estimation method and device based on an AIS (Automatic Identification System) supervision signal, relates to the technical field of intelligent shipping and water traffic supervision, and solves the problems that the prior art depends on a single sensor or a complex multi-source fusion scheme, so that supervision blind areas, inaccurate distance measurement and system complexity are caused, and the cost is low. And effective distance estimation cannot be carried out on the AIS-free ship. The method comprises the following steps: acquiring and preprocessing AIS data and video data; obtaining an approximate predicted position of the ship at the current moment through the AIS trajectory prediction model, and obtaining a ship target detection frame of the current field angle through the target detection model; dividing the current field angle of the camera into equal credible areas and carrying out space-time matching; and the matched visual features and AIS distance information are fused, an AIS distance is used as a supervision signal to construct a distance regression module, and a target detection model is trained to output a ship distance estimation value. The method is used in the other ship distance estimation process of the shore-based and ship bridge view angle.

The invention relates to the technical field of intelligent surveying and mapping, and discloses an intelligent surveying and mapping system for urban surveying and mapping based on Internet of Things, which comprises a data acquisition module, a surveying and mapping data processing and integration module, a surveying and mapping data analysis module, an intelligent identification module and a result output module, through the arrangement of the surveying and mapping data analysis module, point cloud data and image data in surveying and mapping data are processed and integrated to improve the accuracy of the surveying and mapping data, the surveying and mapping data of different data sources and data types are analyzed in corresponding modes, and then a point cloud processing model and an image processing model are constructed to improve the accuracy of the surveying and mapping data. According to the method, the point cloud data and the image data are analyzed respectively, and then the point cloud data and the image data are combined to obtain the final earth surface object category, so that the accuracy of urban earth surface object recognition by the surveying and mapping data is improved, and meanwhile, a foundation is laid for subsequent intelligent recognition of suspicious targets.

The invention relates to a non-inductive security monitoring method and system based on a radio frequency identification technology, and relates to the technical field of data processing. The method mainly comprises the following steps: acquiring original radio frequency data and original video data of a target area in a current time period; respectively carrying out feature extraction on the original radio frequency data and the original video data to obtain a radio frequency feature vector and a visual feature vector; performing feature vector fusion on the radio frequency feature vector and the visual feature vector to obtain a fusion feature vector; inputting the fusion feature vector of the current time period and the fusion feature vectors of N time periods before the current time period into a behavior classification model to obtain a behavior prediction result; if the behavior prediction result is limb movement, determining a limb recognition result corresponding to the current time period according to a limb recognition model; and if the behavior prediction result is gesture motion, determining a gesture recognition result corresponding to the current time period according to a gesture recognition model.

The invention provides a light and heat-dissipating aerospace small-sized calibration lamp mounting seat, which relates to the technical field of aerospace optical small-sized calibration lamp structure fixation, and comprises a substrate mounting seat module, a conductive module, an insulating module and a heat conduction module. The conductive module comprises two chamfered semi-cylindrical conductive copper blocks and two wires with soldering lugs; the insulating module comprises an insulating polyimide isolating layer; the heat conduction module comprises a heat conduction insulation pad which is installed above two copper blocks, the positive electrode and the negative electrode of the two copper blocks are separated through an insulation polytetrafluoroethylene isolation layer, and the semi-cylindrical conductive copper blocks, the polytetrafluoroethylene isolation layer and the substrate installation base are positioned through pin holes. The soldering lug wire is installed on the left semi-cylindrical conductive copper block and the right semi-cylindrical conductive copper block in a pressing mode through top screws. The structure distribution is friendly, the anti-seismic performance is good, a good heat dissipation way is provided for the calibration lamp, and an effective and feasible implementation scheme is provided for fixing the small aerospace calibration lamp.

The invention relates to the technical field of unmanned aerial vehicle surveying and mapping, and discloses a pressure pipeline axonometric mapping system based on an unmanned aerial vehicle, and the system uses a pipeline anchoring relative navigation module to guide the unmanned aerial vehicle to fly along a line with a target pipeline as a reference; the outer diameter and three-dimensional center point coordinates of the pipeline are obtained in real time through an orthogonal contour scanning and parameter resolving module; a self-adaptive flight and measurement control module is used for adjusting the flight distance according to the measured outer diameter feedback, and adjusting sensor parameters according to the imaging quality feedback; meanwhile, the coordinates of the center point are incrementally constructed into a topological graph through an airborne real-time topological graph construction and node processing module, pipe fitting nodes are autonomously detected and recognized, and then a preset strategy is called to conduct refined scanning on the nodes. According to the invention, autonomous navigation independent of global positioning, self-adaptive closed-loop control of measurement and flight, and airborne integrated real-time modeling of pipe network topology and geometric information are realized, and the surveying and mapping precision, the automation level and the operation efficiency are remarkably improved.

The invention relates to an observation device and method for simulating GIS equipment switch action metal particle motion, the device comprises a GIS simulation cavity, a vibration applying mechanism and a particle observation mechanism, the vibration applying mechanism applies mechanical vibration to the GIS simulation cavity; the particle observation mechanism collects the motion trail of the metal particles in real time. According to the device, the vibration of a shell is actually measured by means of a vibration detection mechanism when GIS equipment is switched on and off, mechanical vibration and transient overvoltage conditions generated by the GIS equipment switching on and off operation are equivalently simulated by means of a vibration applying mechanism and a voltage applying mechanism, and the movement conditions of sunken metal particles are recorded in the whole process by means of a particle observation mechanism. Furthermore, the influence of the GIS equipment vibration parameters on metal particle escape is quantitatively analyzed, the influence mechanism of the GIS equipment switch operation on the sinking particle escape phenomenon is obtained, and an important research basis and data support are provided for an optimization scheme for suppressing sinking particle movement under the GIS equipment switch operation.

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 provides a space camera exterior orientation element thermal stability correction method and system, and the method comprises the steps: S1, obtaining an uncontrolled positioning error and a satellite temperature under multiple scenes, and carrying out the fitting calculation of a temperature correlation coefficient of the exterior orientation element thermal stability of a space camera; s2, acquiring uncontrolled positioning errors and satellite temperature rise rates corresponding to different imaging times, and calculating a time-varying coefficient of thermal stability of exterior orientation elements of the space camera; s3, acquiring image auxiliary data in a specified imaging task, and determining reference time and satellite temperature under the reference time according to correction requirements; and S4, generating an exterior orientation element correction matrix of the space camera, and correcting the attitude of the space camera. According to the method, the problem of low satellite positioning precision caused by element change of exterior orientation of the space camera in different thermal environments is solved, and the satellite uncontrolled absolute positioning precision and the relative positioning precision of single-scene long-time imaging can be greatly improved.

The invention discloses a dynamic monitoring and management system for land subsidence of an oil pipeline in a loess area. The dynamic monitoring and management system comprises a basic sensing module, an intelligent analysis module and a decision execution module, the basic sensing module is used for monitoring ground surface deformation, soil humidity and pipeline surrounding environment parameters along a pipeline in a collapsible loess area in real time; the intelligent analysis module is used for analyzing the data acquired by the basic sensing module to judge whether the ground subsidence occurs or not; if the ground subsidence occurs, calculating the subsidence amount, further calculating the suspension length of the pipeline, analyzing the stress condition of the pipeline, and calculating a comprehensive risk index R; and the decision execution module implements automatic risk assessment and emergency response according to a grading early warning strategy based on a result output by the intelligent analysis module. According to the management system, all-weather dynamic monitoring of loess collapse deformation along the oil pipeline is achieved, early warning is issued in time when potential hazards occur, and emergency management is executed.

The invention discloses a sighting point track measuring method of a sighting telescope. The method comprises the following steps: acquiring at least one sighting scene image comprising a light spot position on a target; for each aiming scene image in the at least one aiming scene image, intercepting a region of interest in each aiming scene image according to the proportion of the light spot pixel number in each aiming scene image to obtain at least one region of interest; and determining the aiming point track according to the at least one region of interest and the firing action of the shooting body. According to the method, the region of interest is dynamically determined according to the proportion of the number of light spot pixels in the first aiming scene image, precise matching with target features can be self-adapted, further, row-column accumulation operation is performed on pixels higher than the proportion, background interference is effectively inhibited, track core features are reserved, and meanwhile, the accuracy of the target features is improved. And the redundant calculation amount is greatly reduced.

This invention provides a method and system for inspecting wall decoration, relating to the field of building inspection technology. The method includes acquiring wall decoration data using a laser level and camera equipment, the data including height, level, and image information; sending the data to a wall flatness model for flatness calculation to obtain flatness error data; performing edge calculation based on the flatness error data and decoration data to obtain crack data, including location and shape information; analyzing the data using a data analysis model to obtain smoothness and color data; and finally, sending the flatness error data, crack data, smoothness, and color data to a qualification analysis model for analysis to obtain the wall decoration inspection result. This invention achieves a comprehensive assessment of wall decoration quality through the analysis and detection of multiple data sources, accurately determining whether a wall is qualified, and is characterized by high efficiency, accuracy, and a high degree of automation.

The invention provides a photogrammetry integrated control method and system, and relates to the technical field of photogrammetry control, and the method comprises the steps: obtaining multi-source original data, such as a sequence image, an initial point cloud and a real-time attitude parameter; carrying out combined preprocessing of timestamp alignment, point cloud denoising and image distortion correction, and uploading key logs and quality indexes through a consensus mechanism for evidence storage; performing bundle adjustment to solve internal and external orientation elements, and calling block chain historical control points for verification and dynamic adjustment; generating an initial three-dimensional point cloud through dense matching; verifying the point cloud precision by using a high-precision check point, and feeding back a correction instruction when the point cloud exceeds the limit; gridding and texture mapping are carried out on the effective point cloud to generate a three-dimensional model, and achievements are partitioned and stored in edge nodes and are chained for evidence storage; the system comprises a multi-source data acquisition module, a data preprocessing module, an adjustment resolving module, a point cloud generation module, a point cloud verification module and a three-dimensional modeling module. The efficiency, precision and reliability of photogrammetry are effectively improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle adaptive shooting method and system based on multiple sensors and AI, and the method comprises the steps: multi-sensor data collection, deep learning fusion processing, multi-modal exception processing, decision-making layer path planning reinforcement, adaptive shooting precision improvement, and a re-planning mechanism. Compared with the defects that environmental perception is incomplete and the shooting quality is suddenly reduced under complex illumination or severe weather due to dependence on a single sensor in the prior art, according to the scheme of the invention, a multi-mode sensor array (laser radar / millimeter wave radar / illumination sensor and the like) and Transform-GNN deep learning fusion architecture is adopted; joint modeling of obstacle positions, dynamic tracks and illumination distribution is realized; therefore, the dimensionality and precision of environmental perception are remarkably improved, and the unmanned aerial vehicle can still output clear and stable high-quality pictures under extreme conditions of strong light, rain and fog and the like.

The invention discloses a water conservancy project slope measurement method based on deep learning, and particularly relates to the technical field of water conservancy project measurement. Acquiring a remote sensing image and digital elevation model data of a to-be-measured area, and constructing a multi-scale remote sensing and topographic feature data set; performing gradient prediction by using a deep neural network model containing a multilayer convolution structure and residual connection, and generating a pixel-level gradient value distribution diagram; performing curved surface fitting and abnormal point correction on the key section to form a continuous and consistent gradient measurement result map; and finally, performing error comparison on a measurement result and historical measured data, outputting an error analysis report, and automatically adjusting model parameters based on error feedback to complete continuous optimization. The method has high precision, high adaptability and intelligent optimization capability, is especially suitable for gradient extraction tasks of complex landforms or engineering control areas, and significantly improves the measurement efficiency and result reliability in water conservancy projects.

The present application relates to a kind of robot system high-precision hand-eye calibration method.The present application includes by planning multiple random robot poses;During the process of running robot pose, the coordinate values of target ball points are measured by laser tracker, end effector tool coordinate system is constructed, and is converted to robot base coordinate system, and the measured rotation matrix and displacement vector are calculated.Combined with theoretical rotation matrix and displacement vector, the pose compensation is realized by the rotation operator and the translation operator of trace adjustment, and the robot target posture and displacement are adjusted;After compensation, the measured pose is repeatedly measured, residual error is calculated, and it is used as the compensation value of next iteration, until residual error meets preset precision threshold or reaches maximum iteration number.Finally, the pose of calibration plate in 3D camera coordinate system and the corresponding relationship of robot theoretical pose are recorded, for completing robot hand-eye calibration process.The method improves calibration accuracy, meets high-precision machining and assembly requirements.

The invention discloses an excavator operation loading capacity and unloading capacity monitoring method, equipment and a product, and relates to the field of volume monitoring, the method comprises the following steps: using a monocular camera to obtain an operation video of an excavator operation device, and determining images of continuous time points according to the operation video; annotating the images at different time points and in different operation states; acquiring laser point cloud data corresponding to the image at each time point; performing camera internal and external parameter calibration on the marked image, and obtaining a three-dimensional point cloud data set of the excavator operation device according to the corresponding laser point cloud data; according to the three-dimensional point cloud data set of the excavator operation device and the marked image, laser point cloud data of the excavator bucket in different operation states are extracted; and the position and the loading variation of the excavator bucket are determined based on the laser point cloud data of the excavator bucket in different spatial forms in the same operation state period. According to the invention, the quality and precision of engineering machinery operation dynamic monitoring can be improved.

The invention discloses a landslide hidden danger associated element identification method and device based on multi-modal fusion and pruning, and relates to the technical field of geological disaster identification. The landslide hidden danger associated element identification method based on multi-modal fusion and pruning comprises the following steps: acquiring initial data, wherein the initial data comprises aviation optical atlas data, InSAR deformation data and topographic data; carrying out waveband level superposition on the initial data to generate a multi-channel fusion data matrix; performing channel pruning, network reconstruction and fine tuning learning processing on the first lightweight model network based on the multi-channel fusion data matrix to obtain a target lightweight model network; and obtaining to-be-identified data, inputting the to-be-identified data into the target lightweight model network, and outputting landslide hidden danger associated element data. According to the invention, multi-modal data fusion and lightweight pruning technologies can be combined, and the identification precision and real-time processing capability of landslide hidden danger associated elements are improved.

The invention provides a foundation pit monitoring method and system, and the method comprises the steps: collecting a multi-view foundation pit image of a target foundation pit in real time through a preset shooting device, and generating a corresponding sparse point cloud in real time according to the multi-view foundation pit image through a preset SfM algorithm; calling a three-dimensional Gaussian display expression scene, and performing fitting processing on the sparse point cloud through preset optimization parameters and preset BIM attributes to output a corresponding Revit model in real time; standard key parameters corresponding to the target foundation pit are obtained in real time, and the standard key parameters are mapped into corresponding Revit family parameters in real time through a Revit model; and determining a risk level corresponding to the target foundation pit in real time according to the Revit parameter, and creating a corresponding monitoring platform in real time according to the risk level so as to correspondingly complete real-time monitoring of the foundation pit through the monitoring platform. According to the method, the manual surveying and mapping process can be omitted, meanwhile, accurate monitoring can be completed, and the monitoring efficiency is correspondingly improved.

The invention relates to the technical field of fire detection, in particular to a highway tunnel fire fixed and mobile cooperative detection system and method, and the method comprises the steps: presetting each monitoring point in a to-be-detected tunnel, shooting an infrared image of each monitoring point in a monitored road section in the tunnel in real time, and dividing the infrared image into each region, obtaining each high-temperature area in each infrared image at each moment; acquiring a temperature change sequence and a trend change coefficient of each high-temperature area, acquiring a temperature drift coefficient of each high-temperature area in combination with the change condition of the temperature values of each high-temperature area and each preset adjacent area in each infrared image at each moment in a previous preset time period, acquiring a time-space characteristic value of each infrared image at each moment, and acquiring a time-space characteristic value of each infrared image at each moment; obtaining a fire possibility coefficient of each high-temperature area, and screening each suspected fire area; and transferring the mobile detection unmanned vehicle to shoot a video for evaluating the fire condition in the to-be-detected tunnel. The invention aims to improve the accuracy of fire monitoring in the highway tunnel.

The invention discloses an underwater topographic measurement method. In order to solve the problems that in an existing physical model experiment, contact type measurement destroys the terrain, optical measurement is interfered by refraction, and acoustic equipment is high in cost, a measurement scheme integrating flight time correction and refraction geometric compensation is provided. According to the method, a depth camera is arranged to collect scoured terrain point cloud data in real time, Gaussian filtering denoising and outlier elimination are carried out in sequence, a double-medium light path correction model is constructed based on the Snell law, flight time errors caused by light velocity differences and axial and radial offsets caused by air-water interface refraction are calculated respectively, and the accuracy and the reliability of the two-medium light path correction model are improved. And finally, high-precision three-dimensional terrain coordinates are generated. According to the method, low-cost hardware of the depth camera is combined with algorithm compensation, errors caused by refraction and light velocity differences in underwater topographic survey are reduced, experimental results show that the average relative error is smaller than 3%, non-contact and high-precision real-time monitoring of the physical model scoured topography is achieved, and reliable technical support is provided for hydraulic structure safety assessment.

The invention discloses a mining area ecological soil erosion rate monitoring method, and relates to the technical field of soil monitoring. The method comprises the following steps: dividing a mining area monitoring area and arranging a monitoring network; collecting multi-source data based on the monitoring network; performing setting processing and compliance detection on the multi-source data to obtain preprocessed data; inputting the preprocessed data into a soil erosion rate prediction model to obtain a soil erosion rate prediction result; the soil erosion rate prediction model is constructed by adopting a random forest algorithm, terrain parameters, vegetation coverage and soil property parameters are used as input variables, and the soil erosion rate is used as an output variable; and setting an early warning threshold value, triggering early warning information when the soil erosion rate prediction result exceeds the early warning threshold value, and transmitting the early warning information to an equipment end of a supervisor. The mining area ecological soil erosion rate monitoring precision can be improved.

The invention relates to a geographic information surveying and mapping method and device based on a laser radar, equipment and a medium. The method comprises the following steps: according to hardware parameters of sensors such as a laser radar and a multispectral camera, carrying out space-time calibration processing on the sensors deployed on a mobile platform to obtain space-time synchronous multi-source data; through semantic segmentation and three-dimensional association processing, dynamic semantic reference points are extracted, and a dynamic semantic reference point space-time constraint database is constructed; according to the dynamic semantic reference point, IMU measurement data and wheel speedometer speed data, pose resolving processing is carried out through an adaptive filtering algorithm; and performing hierarchical modeling according to the platform pose and the original point cloud, and fusing satellite geographic data to output a multi-scale geographic information result. According to the method, through the technical means of multi-source data space-time collaborative collection, dynamic semantic datum point extraction, self-adaptive pose correction and the like, the precision and efficiency of geographic information surveying and mapping are remarkably improved, and the adaptability to the dynamic complex environment in the drawing process is enhanced.

The invention discloses a method for generating an unmanned aerial vehicle autonomous inspection oblique photography route, and relates to the technical field of unmanned aerial vehicle autonomous control, and the method comprises the steps: setting route parameters: obtaining and processing target region data: obtaining an imported target region boundary coordinate file or obtaining coordinates of a framed target region on a map; the actual flight height is adjusted according to the three-dimensional elevation data, the constant ground resolution is kept, and according to the orthoimage route generation rule, the route direction is parallel to the long side of the target area, and the routes are in parallel S shapes; the inclined image route is expanded around the target area; after the target area is expanded, corresponding S-shaped inclined routes are generated according to the inclined photography course spacing and the inclined photography lateral spacing in the front, back, left and right directions. The route planning algorithm realizes high-precision autonomous planning of the inspection route of the unmanned aerial vehicle in a complex environment.