3072 results about "Laser scanning" patented technology

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention relates to the technical field of bridge bearing capacity evaluation, and discloses an existing continuous beam bridge bearing capacity comprehensive evaluation method which comprises the steps that multi-source data are collected to construct a three-dimensional damage voxel field, and an initial finite element model is generated; simulating a heavy load identification control section, and designing an equivalent static load test scheme according to an equivalent internal force principle; executing a field static load test, collecting elastic response data and constructing an actual measurement response vector; calculating errors and iteratively adjusting parameters in a voxel field constraint range to finish model correction; and generating an operation space-time security envelope based on the correction model, and comparing the state in real time to output a control instruction. A three-dimensional damage voxel field is constructed, voxels are used as a normalized data carrier, discrete apparent and internal damage data such as three-dimensional laser scanning point cloud, ultrasonic velocity and rebound strength are mapped into a continuously distributed space field, and the space variability of the rigidity of an existing bridge material can be represented in a refined mode. And the accuracy of describing the physical current situation of the structure by the initial finite element model is improved.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a three-dimensional reconstruction method for the peripheral topography and landform of an electric power facility based on a three-dimensional laser point cloud. The method comprises the following steps: performing electric flight laser scanning on the periphery of an electric power facility through an unmanned aerial vehicle, and synchronously collecting near-field point clouds around the electric power facility for space-time synchronous registration and morphological filtering denoising to obtain preprocessed three-dimensional point cloud data around the electric power facility; obtaining a point cloud geometric feature set corresponding to the periphery of the electric power facility through the electric power facility periphery preprocessing three-dimensional point cloud data, and performing electric power facility ground feature point classification and surface triangulation network dense and uniform reconstruction to generate an electric power facility periphery topography three-dimensional initial model; and performing reconstruction smoothing and triangular mesh simplification processing on the three-dimensional initial model of the topography and the landform around the electric power facility to obtain a three-dimensional optimization model of the topography and the landform around the electric power facility. According to the invention, the high-precision three-dimensional reconstruction process of the electric power facility and the surrounding landform thereof can be realized.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention relates to the technical field of data processing, in particular to a multi-source data fusion side slope safety monitoring method, which comprises the following steps: acquiring a side slope surface three-dimensional model generated by three-dimensional laser scanning and a two-dimensional radar deformation cloud picture acquired by a side slope radar; extracting a cloud picture space feature vector through an encoder of the self-supervised deep learning model, and outputting three-dimensional heat map coordinate data through a decoder; screening topographic feature points of slope shoulders, scarps and crack areas from the vertexes of the triangular mesh based on a topographic curvature threshold; matching the three-dimensional heat map feature points with topographic feature points, and mapping to a three-dimensional model coordinate system through affine transformation to generate a dynamic three-dimensional deformation heat map with a timestamp; comparing each timestamp displacement value to calculate a displacement variation; and marking a displacement over-limit region on the three-dimensional model by using a color gradient and outputting a three-dimensional visualization result. According to the method, mapping from the two-dimensional deformation data to the three-dimensional space is realized, the positioning fuzziness of an existing two-dimensional cloud picture is eliminated, and the slope deformation position identification precision is improved.

The invention discloses a slope cutting house building deformation monitoring method based on unmanned aerial vehicle remote sensing, and the method comprises the steps: carrying out the periodic observation of an artificial slope through an unmanned aerial vehicle carrying a three-dimensional laser scanner, and obtaining slope three-dimensional point clouds in at least two different periods; performing denoising, down-sampling and coordinate normalization on the point cloud of each period to obtain a preprocessed data set; generating a global feature descriptor of each period by using a PointNet feature extraction unit; on the basis of a Lucas-Kanade iterative optimization algorithm, the descriptors of all periods are matched with the first period as the reference, and optimal rigid body transformation parameters are obtained; completing registration of the cross-period point clouds in a unified coordinate system according to the parameters; calculating displacement by adopting point-by-point difference and forming a differential deformation field; and comparing the displacement with a preset safety threshold, positioning an abnormal area and giving a risk level. According to the method, centimeter order registration and deformation identification of the cross-period point cloud under the unified stable reference are realized, and early warning and hidden danger assessment of a slope cutting and house building scene are supported.

The invention relates to the technical field of metal structure detection, in particular to an intelligent special-shaped aluminum plate structure parameter detection method and system based on spectral analysis, and solves the problems of multi-parameter splitting, poor complex structure adaptability and inaccurate risk assessment in traditional detection. According to the method, data are synchronously collected through multi-spectral imaging and line laser scanning, surface component parameters such as the thickness gradient of an oxide film are inverted, the local curvature is calculated based on three-dimensional point cloud, a spiral encryption or grid sparse scanning track is dynamically switched, differential detection of a complex structure and a plane area is achieved, and the detection precision is improved. And then a space correlation model of the oxidation film thickness and the hole deformation quantity is constructed, a structure failure risk area is accurately recognized by combining a three-level risk judgment rule and cross-regional influence analysis, and the system effectively improves the accuracy and detection efficiency of defect recognition of the special-shaped aluminum plate through cooperation of dynamic scanning control, component deformation correlation and a composite structure decision unit.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention relates to the technical field of road repair state monitoring, in particular to a road repair state monitoring method and system based on edge calculation. The method comprises the following steps: acquiring planning information of a repaired road, deploying 3D laser scanning vibration meters at a plurality of repairing points, collecting pavement vibration signals in different time periods, and performing disordered distribution time domain feature analysis to obtain a vibration signal disordered distribution time domain graph; then, structure weakening behavior simulation is carried out based on the graph, periodic structure weakening data is quantified, and a pavement water damage cumulative gradient is obtained through water damage cumulative gradient estimation; and finally, in combination with the periodic structure weakening data and the water damage cumulative gradient, carrying out service life evaluation on the restoration state, and sending an evaluation result to the terminal. According to the invention, the road repair state monitoring technology is optimized, so that the road repair state monitoring technology is more perfect.

The invention relates to the technical field of building structure health monitoring, and particularly discloses a building group safety inspection method based on external structure size displacement detection, and the method comprises the steps: obtaining three-dimensional laser scanning point cloud data of a target building group at a plurality of time points, adding timestamp information, and constructing a time sequence point cloud data set; initial registration is carried out by adopting a feature matching and motion recovery structure technology, and the registration precision and stability in a dynamic environment are improved in combination with a global optimization model fused with space-time consistency constraints; identifying and eliminating pseudo displacement point clusters through track consistency analysis, and extracting a static background structure; finally, key part deformation identification is carried out on the differential point cloud data, and a structure health state evaluation report is generated. According to the invention, full-process automatic processing from data acquisition, optimization registration to intelligent evaluation is realized, the dynamic interference influence is effectively inhibited, the precision and reliability of structural deformation monitoring are improved, and technical support is provided for long-term safety inspection of building groups.

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

The invention discloses a three-dimensional noise mapping method and system based on point cloud, and belongs to the technical field of urban environmental noise prediction.The method comprises the steps that point cloud data driven three-dimensional modeling and parameter extraction are carried out, an urban three-dimensional model with a geometric topological structure is constructed through laser scanning point cloud data, and related parameters are extracted; based on sound field calculation of ISO 9613 standards, a multi-path propagation model including an atmospheric effect, a ground effect and obstacle diffraction is established, a green infrastructure noise reduction effect is especially considered, and propagation attenuation of noise in a three-dimensional space is simulated; and performing visual expression of three-dimensional noise, and constructing fusion expression of a sound pressure level contour surface and a three-dimensional city model. In view of difficulty in simulation of three-dimensional reconstruction and sound waves in a spatial propagation process in the prior art, the method improves the precision of quantitative prediction, and fuses a green infrastructure noise reduction effect to realize rapid and efficient regional three-dimensional reconstruction and noise mapping.

The invention discloses an image recognition and correction system based on an optical model and environment perception, and relates to the technical field of image processing, and the system comprises an environment perception module which is used for collecting multi-medium refractive indexes, interface three-dimensional forms and initial image data through a refractive index sensor, a three-dimensional laser scanner and a visual camera; an environment perception data set is formed through dynamic correction and multi-source fusion; the optical model is constructed on the basis of environment sensing data, the light propagation path is calculated in combination with the interference compensation sub-model, and the interference compensation sub-model can quantify the interference influence by analyzing related data acquired by the environment sensing module for interference factors such as impurities and temperature gradient in a medium, so that the interference compensation precision is improved. The compensation term is generated and fused into the light propagation equation, so that the light propagation path is corrected, the calculation accuracy of the light propagation path is ensured, a firm and reliable data support is provided for subsequent image recognition and correction, and the precision and quality of image recognition and correction are improved.

The invention provides a wheeled robot trafficability prediction method and system in a complex scene, and relates to the technical field of robos.The method comprises the steps that indoor three-dimensional point cloud data are collected in advance through a three-dimensional laser scanner, and an indoor point cloud map is generated; processing the indoor point cloud map by using a three-dimensional semantic segmentation network based on sparse convolution to generate a multi-layer semantic map; extracting a target point coordinate of the task instruction, and searching a global reference path on the multilayer semantic map by adopting a path planning engine; constructing a local real-time dynamic environment model; obtaining a comprehensive feasibility score; and executing a hierarchical decision based on the comprehensive feasibility score to generate a motion adjustment scheme. The technical problems that dynamic and variable trafficability challenges in complex indoor scenes are difficult to deal with, the environment characterization capability is insufficient, the perception dimension is single, and the decision-making mechanism is not intelligent enough are solved.

The invention discloses a tunnel face back break visual analysis method based on laser point cloud, and the method comprises the steps: carrying out the scanning operation of a tunnel through a laser scanner, and collecting the specific original rock three-dimensional point cloud model data in a tunnel construction circulation process; exporting the acquired original rock mass three-dimensional point cloud model data, and performing denoising preprocessing on the original rock mass three-dimensional point cloud model data to obtain tunnel rock mass three-dimensional point cloud model data; the rock mass three-dimensional point cloud model data also need to be subjected to space compression processing, so that the quality of the rock mass three-dimensional point cloud model data is improved; based on the rock mass three-dimensional point cloud model data, a tunnel actually-measured excavation section after construction is obtained; and weighting and calculating tunnel over-excavation and under-excavation values based on the point-to-pole distance between the tunnel design section and the tunnel actually-measured excavation section. According to the method, the back-break depth is directly calculated through the point cloud data and is bound as the point cloud attribute, so that the analysis efficiency and the result intuition are remarkably improved.

The invention discloses a nondestructive testing method for a welding seam of a steel structure. The method comprises the steps that firstly, morphology data of the surface of a building steel structure are obtained through a high-precision laser scanner and an electromagnetic induction technology; coating thickness distribution and weld surface roughness are analyzed based on the morphology data, and a surface information matrix is constructed; then automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using the adaptive parameter regulation and control model and executing detection to obtain a preliminary ultrasonic echo signal; the time-frequency domain difference between the preliminary echo and the reference signal is calculated through an echo signal compensation model, and secondary compensation is completed to obtain an optimized ultrasonic echo signal; fusing the optimized echo signal, the morphology data and the surface information matrix to construct a detection data set, and adopting a pre-trained steel structure detection model to realize defect detection; and finally, evaluating the confidence of a detection result by adopting a three-dimensional confidence evaluation model, performing grading, and performing local re-detection and parameter adjustment on a low-confidence region to form closed-loop optimization. According to the method, the surface state of the steel structure can be adaptively matched, the accuracy of echo signals and the defect detection precision are improved, and the detection reliability is guaranteed through three-dimensional confidence evaluation.

The invention relates to the technical field of building templates, and particularly provides a building template flatness threshold partition detection method based on normal vector constraint RANSAC, which comprises the following steps: performing three-dimensional laser scanning on a building template to obtain high-density point cloud data; preprocessing operations such as denoising and downsampling are carried out on the collected point cloud data, so that the data quality is improved; fitting a reference surface of the template based on the preprocessed point cloud data by using an improved RANSAC algorithm, and introducing a unit normal vector to ensure the correct direction of the reference surface; calculating the vertical distance from each point cloud point to the reference surface to obtain a deviation value; independently setting a deviation threshold value according to a building specification, dividing the deviation value into different grades, and distinguishing and displaying the deviation value on the three-dimensional point cloud model through colors; and a visual result containing the three-dimensional point cloud model and the color identification is output, the flatness condition of the template is visually displayed, and the method has the effects of automatic measurement, high efficiency and high precision.

The invention provides a self-adaptive spraying mechanical arm based on multi-mode perception and a control method of the self-adaptive spraying mechanical arm. The method comprises the steps that multi-source perception data of a to-be-repaired area is obtained through a dual-mode vision system and a laser scanning device, a two-dimensional repairing area segmentation map is generated, and space registration and local geometric modeling are achieved in combination with three-dimensional point cloud; on the basis of local geometric features extracted by the model, retrieving matched process parameters from the knowledge graph, generating a digital spraying instruction set containing tracks, postures and dynamic process parameters, and performing simulation verification in a digital twin environment; the mechanical arm state and the virtual model are synchronized in real time in the execution process, the paint film thickness is dynamically monitored, and compensation adjustment is triggered; and after spraying is completed, the coating quality is detected, and a result is fed back to the knowledge graph to update the parameter mapping relation. According to the method, closed-loop control from sensing, decision making, execution, evaluation to learning is achieved, and the spraying uniformity, the self-adaptability and the intelligent level under the complex working condition are improved.

The invention discloses a panel vertical seam water stop structure repair effect evaluation method, which comprises the following steps of: firstly, synchronously obtaining surface crack and internal defect data through cooperative detection of infrared thermal imaging and ultrasonic flaw detection; and carrying out space alignment on point cloud data generated by three-dimensional laser scanning and an ultrasonic detection result, and establishing a BIM model containing a crack three-dimensional form. A practical engineering environment is simulated in a laboratory to carry out material accelerated aging and test key indexes such as an elastic recovery rate, a base surface state is evaluated through a temperature and humidity sensor array in a construction stage, a six-axis grouting robot is adopted to execute accurate filling, a curing process is monitored by a synchronous pre-embedded optical fiber sensor, and after aging is simulated by combining a hydraulic test with climate, a construction period is shortened. A distributed optical fiber network is used for continuously collecting deformation data, a comprehensive evaluation model is constructed based on mechanics, durability and sealing indexes, and finally material selection and process parameters are optimized through machine learning.

The invention relates to the technical field of data processing, in particular to an integrated multi-mode culture resource intelligent data governance and management system. The system comprises a data acquisition module, an intelligent processing module, a data management module, a knowledge organization module and an interactive display module. Synchronous acquisition of videos, audios, images and three-dimensional point clouds is realized through camera equipment, audio acquisition equipment and a laser scanner, and cultural elements are extracted through action recognition, speech recognition model processing dialect transcription, image semantic segmentation and named entity recognition by adopting a convolutional neural network. The system realizes structured description and semantic mapping of data on the basis of non-abandoned domain ontology, and constructs a knowledge graph by means of a graph database and a graph neural network. And finally, realizing three-dimensional visual display of the cultural resources by combining a virtual reality technology. According to the method, the problems of non-abandoned multi-modal data processing splitting, insufficient semantic organization and weak display interactivity in the prior art are solved, and the digital governance level and the propagation capability of cultural resources are improved.

The invention discloses a three-dimensional curved surface pattern layering processing method based on parameterized segmentation, and aims to solve the problem of high-precision pattern processing on the surface of a curved surface by using a laser scanning galvanometer. The method comprises the following steps: firstly, importing a three-dimensional model, and carrying out parameterization processing to obtain a two-dimensional parameter domain; positioning a to-be-processed plane pattern in the two-dimensional parameter domain to form a complete two-dimensional geometric grid covering a pattern area, generating a three-dimensional pattern grid attached to a curved surface, and calculating a center normal vector of the three-dimensional pattern grid; then, generating an equidistant plane group on the surface of the curved surface, and calculating an intersection line of the plane and the three-dimensional pattern grid as a laser scanning track; and the five-axis linkage platform-galvanometer system is started for machining. Through the parameterization domain, distortion of curved surface pattern machining is effectively solved, machining uniformity is improved, and the method is particularly suitable for laser machining of large-scale or high-complexity curved surface patterns.

The invention provides a prefabricated bridge pier column installation quality automatic evaluation method based on a three-dimensional laser point cloud. The prefabricated bridge pier column installation quality automatic evaluation method is used for measuring the perpendicularity of a high pier and the distance between pier columns. The method comprises the following steps: an unmanned aerial vehicle laser scanning system collects pier point cloud data according to an optimized flight path, and pre-processes the pier point cloud data to improve data quality; quickly segmenting the point cloud by using an improved adaptive octree and a machine learning algorithm, and extracting a pier point cloud; and based on the finely segmented point cloud, calculating the included angle between the center axis of the bridge pier and the plane normal of the capping beam and the coordinate of the center point of the top surface of the bridge pier to realize accurate evaluation of the perpendicularity of the bridge pier and the distance between adjacent bridge piers. The optimization of the flight path of the unmanned aerial vehicle is combined with the point cloud data preprocessing, so that the data quality can be effectively improved, and the data volume is reduced. Meanwhile, point cloud segmentation is performed by fusing an improved adaptive octree and a machine learning algorithm, so that piers, capping beams and backgrounds can be accurately distinguished, and geometric features of pier studs are completely extracted.

The invention relates to the technical field of intelligent monitoring, in particular to a municipal building engineering construction progress monitoring method based on an unmanned aerial vehicle and laser scanning, and the method comprises the following steps: obtaining point cloud data through carrying laser scanning by the unmanned aerial vehicle, calibrating a space coordinate, binding a design coordinate system, carrying out Gaussian filtering denoising processing, and analyzing point cloud through coordinate transformation. The method comprises the following steps: extracting a main axis vector by adopting a principal component analysis method, dividing a sectioning region, calculating a cosine value of a normal included angle, classifying and identifying a difference region by Euclidean distance, carrying out secondary scanning clustering segmentation, and calculating an offset judgment state label set. According to the method, a three-dimensional reference is established by adopting laser scanning and coordinate calibration, Gaussian filtering is combined to eliminate noise, PCA is used to extract a geometric main axis, a normal included angle matching degree is calculated, Euclidean distance classification detection deviation is carried out, geometric difference is quantitatively identified, graph-model matching precision is improved, millimeter-level capture is realized, an automatic monitoring system is constructed, and manual errors are reduced. And controlling the power-assisted progress.

The invention provides a mine safety training method and system based on digital twinning and virtual reality, and the method comprises the steps: obtaining laser scanning point cloud and geological exploration data of an underground roadway of a mine, and constructing a high-precision virtual underground environment model based on the data; on the basis of the virtual underground environment model, combined with explosion condition judgment, disaster simulation is triggered, and a virtual training environment is generated by applying real-time rendering and dynamic labeling technologies; obtaining operation behavior data of trainees in the virtual training environment, and evaluating the operation behavior data to obtain a behavior evaluation result; and based on the behavior evaluation result and the environmental parameter change data, constructing and training an evaluation model by utilizing machine learning, and generating customized training content in combination with geological conditions and mining process characteristics of different mining areas. According to the method, a high-reduction-degree virtual scene can be constructed, the problem that a traditional training scene is single is solved, quantitative evaluation of the training effect is achieved, and training pertinence and effectiveness are improved.

The invention discloses a laser scanning optimization system for high-precision ellipticity and diameter measurement, and relates to the technical field of cylindrical workpiece geometric dimension measurement. Comprising a laser contourgraph assembly, the laser contourgraph assembly comprises four laser contourgraphs, the four laser contourgraphs are arranged in the circumferential direction of the cross section of a to-be-measured workpiece at equal intervals, the included angle between every two adjacent laser contourgraphs is 90 degrees, and the laser contourgraphs are used for synchronously collecting contour point cloud data of the surface of the to-be-measured workpiece; and the data acquisition module is used for receiving and summarizing the contour point cloud data. The four laser contourgraphs are arranged at equal intervals in the circumferential direction of the cross section of the workpiece and collect synchronously, so that the problems that a traditional single laser module is incomplete in rotary scanning coverage and has timeliness deviation are solved; and the point cloud processing module improves the condition of insufficient point cloud processing refinement through splicing, direction correction, section extraction, adaptive region division and arc length interception processing.

The invention discloses a test pit volume measurement system based on three-dimensional laser scanning, relates to the technical field of engineering measurement, and mainly solves the technical problems that a traditional test pit volume measurement method is low in efficiency, poor in precision and greatly influenced by human factors. According to the technical scheme, the system is characterized in that a three-dimensional laser scanner with the scanning distance being 0.5-150 m and the point cloud density being 10000-500000 points per square meter is adopted to scan a test pit area from a plurality of sites; recognizing four symmetrically arranged targets through a point cloud registration module in the control processing unit to realize multi-site cloud data fusion; after denoising processing is carried out by using the point cloud filtering module, a surface model is constructed by using the triangulation network modeling module, and finally, the closed volume is calculated based on the spatial relationship between the triangulation network model and the fitted reference plane through the volume calculation module. The system is mainly used for accurately measuring the earthwork volume in filling and excavation projects in the fields of water conservancy, traffic, mining industry and the like.