526results 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 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 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 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 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 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 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 relates to the technical field of remote sensing data processing, in particular to an unmanned aerial vehicle remote sensing data processing system and a data processing method. The method comprises the following steps: collecting remote sensing data of an original measurement area through an unmanned aerial vehicle, eliminating a relative error caused by a ground height difference through elevation normalization, constructing a measurement area coordinate system, obtaining a trajectory sequence and attitude disturbance information of the unmanned aerial vehicle, projecting the trajectory sequence and attitude disturbance information to the unified measurement area coordinate system, constructing a three-dimensional disturbance trajectory network, and obtaining a three-dimensional disturbance trajectory network. The point cloud space offset deviation is identified through the network, each point cloud unit is dynamically compensated in combination with a timestamp so as to correct the remote sensing data of the measurement area, the corrected remote sensing data is matched with the coordinates of the corresponding point cloud units, a three-dimensional measurement area frame is reconstructed, and high-precision topographic feature description is realized. The three-dimensional disturbance trajectory network is built by combining the trajectory sequence and the attitude disturbance information of the unmanned aerial vehicle, and complex surveying and mapping tasks and environmental changes are effectively coped with.

The invention discloses a soil heavy metal portable XRF intelligent detection and early warning system integrated with a risk assessment function, and relates to the technical field of environment monitoring. According to the invention, data are collected through a portable equipment system integrating multispectral sensing and an environment probe, a pollution area is accurately locked in combination with an improved clustering algorithm, accurate positioning of a pollution point location is realized by means of cooperative operation of the unmanned aerial vehicle, and the problem of blindness of traditional sampling is effectively solved; a dynamic interference correction and multi-stage calibration mechanism is established, a deep learning and machine learning fusion model is adopted to extract an environmental interference factor, and spectral line processing and an environmental compensation algorithm are combined, so that the accuracy of a detection result in a complex environment is remarkably improved; the efficiency and precision of soil heavy metal pollution treatment are greatly improved by realizing full-process intelligence from risk evaluation to treatment decision, establishing a multi-dimensional risk evaluation system, automatically generating an optimal operation path and intelligently recommending a treatment scheme based on pollution characteristic data.

The invention discloses a mixed tower structure deflection measurement method based on a high-precision digital deflectometer, and belongs to the field of dynamic deflection monitoring, and the method comprises the following steps: S1, deploying a deflectometer, an acoustic emission sensor and a dual-camera vision station; s2, collecting multi-source data, and preprocessing the multi-source data by adopting a differential preprocessing strategy; s3, compensating the preprocessed multi-source data; s4, based on a Bayesian fusion framework, fusing the multi-source data after deflection compensation, and outputting an optimal deflection value; and S5, in combination with a composite material damage identification technology, constructing a CNN-LSTM hybrid model, realizing real-time early warning of deflection abnormity, and identifying the health state of the mixed tower structure. By the adoption of the mixed tower structure deflection measurement method based on the high-precision digital deflectometer, high-precision and full-process monitoring and intelligent early warning of mixed tower structure deflection are achieved by integrating multi-modal sensing, dynamic error compensation, Bayesian data fusion and the CNN-LSTM deep learning model.

The present invention relates to the technical field of real-time detection of foreign objects on track, and specifically discloses an improved real-time detection method for foreign objects on track based on YOLOv5, comprising: synchronous acquisition of video vibration, generation of spatial offset compensation parameters, dynamic feature enhancement processing, generation of feature weight heat maps, hierarchical perception feature aggregation, cross-scale target verification, foreign object positioning instruction encapsulation, and multi-level warning execution control. The present invention synchronously acquires track video and vibration time series signals, constructs a spatial offset compensation parameter set, and corrects video frames in combination with a dynamic feature enhancement template, effectively overcoming image distortion caused by mechanical vibration and light fluctuations. Feature weight heat maps are generated through visual saliency detection, features are aggregated through a hierarchical perception model, and warning levels are dynamically matched according to the size and position of the foreign object, thereby linking an audible and visual alarm with a braking system. The present invention ensures data transmission reliability, significantly reduces operation and maintenance costs, and meets the safety requirements of millisecond-level response in modern rail transit.

The present application relates to the technical field of irrigation control automation, in particular to a kind of irrigation area flow full node measurement method based on remote sensing interpretation and Internet of Things fusion, in the present application, through remote sensing interpretation plot pixel determination gray scale difference and reconstruct boundary connected structure, combined with multi-scale residual network identifies low reflection water area, effectively improve the identification precision of water area in complex background, make the boundary of identified area have closed integrity, through combining plot center and actual node coordinate error analysis, realize the spatial binding of node and canal section, optimize the spatial fragmentation and response delay problem caused by traditional segmented irrigation information acquisition mode, through the structural association between disturbance information by graph neural network, extract water pressure response consistent path combination, improve the ability of connectivity identification, water pressure evolution capture, path closed loop mining and scheduling node identification in distributed irrigation system.

This invention discloses a capture device for unmanned underwater vehicles (UUVs), comprising a submersible fixed by a mounting frame. The mounting frame is symmetrically mounted with a grasping mechanism for capturing the UUV via a base. The grasping mechanism includes a drive arm driven by the base, with the other end of the drive arm rotatably connected to four corner support frames. The four corner support frames are driven by connecting arms to contact claws for gripping. A groove is provided on the side of the drive arm near the base, and a servo motor is mounted on the base. The servo motor is connected to a gear at one end of a limiting link, transmitting power through the other end of the limiting link to the groove on the drive arm to achieve the movement of the grasping mechanism. This invention also discloses a method for launching and recovering UUVs in conjunction with the above-mentioned launch and recovery device. This invention simplifies precise hook docking operations in high sea states, reduces the dependence of UUVs on computing power during launch and recovery, and improves the automation level of the launch and recovery process.

The invention provides a surface water body dynamic monitoring method based on multi-source remote sensing and spatial similarity reconstruction, and relates to the technical field of remote sensing detection. The method comprises the following steps: acquiring a to-be-reconstructed water body distribution diagram sequence collected by an optical remote sensor; searching a reference image with the smallest spatial structure difference with the to-be-reconstructed water body distribution map in the reference image set, and taking the reference image as an optimal reference water body map of the to-be-reconstructed water body distribution map; and carrying out pixel replacement on a missing region in each to-be-reconstructed water body distribution diagram by utilizing a pixel category in the optimal reference water body diagram, and finally obtaining a reconstructed water body distribution diagram sequence. According to the method, pixel-level completion is carried out on a to-be-reconstructed water body distribution diagram acquired by an optical remote sensor by using a pre-constructed missing-free reference image set, so that the integrity of a time sequence water body information image acquired by the optical remote sensor within a period of time is ensured, and the continuity of water body information in time sequence is further ensured; the monitoring requirements of large-range and high-dynamic change water bodies can be met.

An imaging device includes a display control unit configured to display a first indicator indicating a recommended imaging position and a second indicator indicating a current imaging position in a superimposed manner on an imaging monitor image, and perform processing for changing display of the first indicator in accordance with image obtaining processing for obtaining image data, and an imaging control unit configured to perform the image obtaining processing in accordance with a result of a matching determination between the first indicator and the second indicator on a screen.

The invention discloses a mark point layout method and device for rotation monitoring and a medium. The method comprises the steps of obtaining a curved surface normal vector and a curved surface center point of a curved surface structure to be monitored; acquiring the optimal position of the binocular camera; obtaining a threshold value of a sight angle of the binocular camera according to the eccentricity of the elliptical distortion of the circular mark point; obtaining the relationship between the visibility of points on each position of the curved surface and the rotation angle of the curved surface structure; obtaining the relationship between the elliptical distortion and the rotation angle of the circular mark point; obtaining an image available frame of each point location under the sight line of the current rotation angle; and constructing mark point selection logic to obtain an optimal layout position of the mark points. The positioning method of the binocular camera and the positioning method of the position of the mark point are adopted to optimize the layout of the mark point, the elliptical distortion of the mark point is reduced, the precision of rotation monitoring is improved, the layout of the mark point can be optimized, the measurement blind area is avoided, the number of the mark point is reduced, the time for pasting and removing the mark point in the measurement operation is shortened, and the measurement efficiency is improved.

The present application relates to the field of satellite navigation and positioning technology, and more particularly to a Beidou GNSS and visual image information fusion deformation monitoring method, comprising: collecting and preliminarily screening the surface periodic images of a monitoring body to determine a time adding period; sub-pixel refining the images in the time adding period, extracting texture and identification features to obtain a visual displacement field; constructing a carrier phase time difference observation equation to eliminate the integer ambiguity, introducing the visual displacement as a priori constraint and jointly solving multiple epochs to obtain optimized coordinates; and combining RTK data to jointly determine the deformation and adjust the real-time static filter. The present application improves the spatial continuity, trigger reliability and adaptability to harsh environments of deformation monitoring, and realizes the technical upgrade from single-point monitoring to full-field millimeter-level high-precision monitoring.

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.