66967results about "Using optical means" patented technology

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention provides a defect detection method and system for integrated circuit manufacturing. Wherein in the integrated circuit packaging stage, packaged chips are divided into a first type of chips meeting a preset physical characteristic threshold value and a second type of chips exceeding the physical characteristic threshold value through a pneumatic sorting device; performing scanning electron microscope imaging on the surface of the second type of chip, generating a three-dimensional topological structure by using an image reconstruction technology, and positioning a micron-sized particle defect area by comparing reference type differences; synchronously applying high-frequency electric signal excitation in the sorting process, detecting impedance response of the second type of chips, and recording an abnormal signal sequence exceeding a preset frequency domain range; performing space-time correlation matching on the micron-sized particle defect area and the abnormal signal sequence, and judging that the defect is a multi-layer composite defect when a preset coupling condition is met; and dynamically adjusting the mesh diameter and vibration frequency parameters of the pneumatic sorting device. According to the technical scheme provided by the invention, accurate identification of the multi-layer composite defect of the chip is realized, and the defect interception efficiency is remarkably improved.

The invention relates to a steel truss girder digital pre-assembly method and system based on three-dimensional laser scanning, and belongs to the technical field of constructional engineering. The method comprises the following steps: calculating a three-dimensional coordinate and a scanning angle of a scanning observation station based on a steel truss girder design model and field environment point cloud data to obtain observation station layout position parameters; controlling a three-dimensional laser scanner to scan the steel truss girder section by section according to the arrangement position parameters of the observation station, and acquiring original point cloud data of each section of the steel truss girder; performing spatial alignment and noise reduction processing on the original point cloud data to obtain standardized point cloud data; based on the standardized point cloud data, feature parameters of all the segments are segmented and extracted, digital splicing is carried out, and a steel truss girder three-dimensional point cloud model is generated; based on the steel truss girder design model and the steel truss girder three-dimensional point cloud model, the displacement deviation value and the angle deviation value between the segments are calculated, and an assembly error result is obtained; and generating a construction correction parameter set based on the assembly error result. The assembling precision of the steel truss girder can be improved.

The embodiment of the invention discloses a visual inspection and quality evaluation method for a small structural component of an airplane, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and carrying out the preprocessing of the three-dimensional point cloud data; s2, carrying out multi-angle two-dimensional image acquisition; s3, mapping the two-dimensional image texture obtained in the S2 to the surface of a three-dimensional model, and generating the three-dimensional model comprising surface information; s4, extracting geometric features and surface texture features from the three-dimensional model; s5, fusion and comparative analysis are carried out to identify potential anomalies and defects; s6, outputting specific abnormal types and positions; and S7, performing comprehensive quality evaluation according to an anomaly detection result in the step S6, and generating a quality evaluation report. According to the technical scheme provided by the embodiment of the invention, by fusing three-dimensional scanning and high-resolution two-dimensional image technologies and adopting the classification model based on machine learning, the comprehensiveness, accuracy and efficiency of detection are remarkably improved, so that the production quality and safety are effectively improved.

The invention relates to the technical field of wall surface detection, in particular to a wall surface flatness detection method and system based on a three-dimensional scanning technology, and provides the following scheme: obtaining global point cloud data through a laser scanner, calculating macroscopic flatness and marking a problem area; performing local scanning on the problem area by using a structured light scanner to obtain local point cloud data; identifying defect types by applying a deep learning algorithm, and calculating local flatness; registering the global point cloud data and the local point cloud data, and calculating a correction coefficient based on an optimal registration result; and weighting the local flatness by using the correction coefficient, and fusing the local flatness with the macroscopic flatness to generate a wall flatness report. The report includes overall flatness evaluation, local defect details, and a flatness profile. The invention provides a high-precision global and local flatness fusion analysis method, and the accuracy and comprehensiveness of wall flatness detection are effectively improved.

The invention provides a flexible circuit board three-dimensional shape laser scanning measurement and reconstruction method and system, and relates to the technical field of three-dimensional measurement, and the method comprises the steps: obtaining a strain distribution diagram of a flexible circuit board through applying a preset stress field and constructing a strain monitoring point array, and dividing a deformation sensitive region; arranging a plurality of groups of binocular laser scanning devices based on the distribution of the deformation sensitive area, collecting an orthogonal grid projection image, and obtaining a space mapping relation matrix through a self-adaptive multi-step calibration algorithm; constructing a depth information mapping model in combination with the laser stripe center line, and generating initial three-dimensional point cloud data; point cloud registration fusion is carried out through a local feature preserving algorithm, noise reduction and abnormal point elimination are carried out through a local curvature adaptive algorithm, and optimized point cloud data are obtained; and block fitting is carried out based on the deformation sensitive area, a fitting result is optimized and corrected by combining the strain distribution diagram and the curvature distribution, and a three-dimensional geometrical morphology of the flexible circuit board is generated.

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

The invention provides a time division multiplexing multi-parameter synchronous sensing method and system based on a multi-core optical fiber. A multi-core optical fiber containing three heterogeneous fiber cores is embedded into a measured object, and a distributed reflection grating array forming an inclined angle with the axial direction is inscribed on the surface of each fiber core; energy exchange data between modes of all fiber cores and reflection grating wavelength shift signals are synchronously collected by adopting a time division multiplexing technology, phase difference signals are extracted through a cross correlation algorithm to serve as curvature reference values, dynamic compensation operation is carried out on the wavelength shift data and the phase signals, and accurate strain distribution is obtained after temperature interference is effectively eliminated; a coordinate transformation model is established by combining the three-dimensional geometric parameters of the spiral optical fiber, vector synthesis is performed on the curvature reference and the strain parameters, and accurate reconstruction of the three-dimensional deformation field of the measured object is realized. According to the technical scheme provided by the invention, through multi-core fiber multi-parameter synchronous sensing and three-dimensional coordinate mapping, temperature self-compensation high-precision three-dimensional deformation field real-time reconstruction is realized.

The invention relates to the technical field of optical detection, in particular to a workpiece surface defect detection method and system based on the optical technology, and the method comprises the following steps: S1, generating a curvature model of a workpiece; s2, dynamically adjusting the irradiation angle and the light spot shape of a light source according to the curvature model in the S1; s3, adjusting the focal length of the detection equipment in real time; s4, imaging acquisition is carried out on the workpiece at the same time through multiple angles; s5, correcting image distortion caused by surface curvature change, and fusing the multi-angle images into a high-precision workpiece surface image; s6, defect features of the surface of the workpiece are extracted, defects are classified, positions are marked, and a defect detection report is generated; according to the invention, by dynamically adjusting the light source and the focal length and combining multi-view imaging and advanced image processing technologies, efficient and accurate defect detection is realized, and the product quality and the production efficiency are remarkably improved.

The invention discloses a building deformation analysis method and system based on three-dimensional laser scanning and deep learning, and the method comprises the steps: obtaining original point cloud data, carrying out the hierarchical processing through a feature decomposition method, obtaining the hierarchical feature data, carrying out the fusion analysis in combination with environment parameter data, and eliminating the environment influence. Obtaining high-fidelity point cloud data through quality evaluation optimization and spatial-temporal feature fusion; component segmentation and deformation feature extraction are carried out on the high-fidelity point cloud data, and fusion deformation feature data are obtained through multi-scale analysis; inputting the deformation feature data into a hybrid neural network for feature enhancement and deformation evolution analysis to generate deformation prediction data; and performing physical rule verification, multi-source data cross verification and space-time continuity optimization on the prediction result, and outputting final optimization data and a reliability report. According to the invention, through a multi-level data processing and verification mechanism, the accuracy and reliability of building deformation monitoring are improved.

The invention provides a cast workpiece three-coordinate structured light rapid measurement system, and relates to the technical field of industrial automatic measurement, and the system comprises a calibration parameter module which is used for calibrating a standard ball, building an initial conversion relation between a structured light coordinate system and a three-coordinate measuring machine world coordinate system, and collecting temperature, humidity and vibration data in real time; dynamically updating the conversion relation to form calibration parameters; and the measurement path planning module is used for dividing the casting workpiece into a plurality of measurement areas based on the calibration parameters, planning a motion path of the three-coordinate measuring machine and driving the motion platform to move to each measurement position. According to the invention, full-process automation from calibration, measurement to modeling analysis of the casting workpiece and the advantage of comprehensive quality analysis are realized, and a reliable three-dimensional measurement and quality control solution is provided for the casting industry.

The invention provides a curtain wall deformation condition safety detection method and device based on laser point clouds, and relates to the technical field of constructional engineering, and the method comprises the steps: 1, carrying out the coordinate system unification processing of a three-dimensional point cloud data set covering the whole surface of a curtain wall, generating a curtain wall surface digital twin model, and carrying out the coordinate system unification processing of a three-dimensional point cloud data set covering the whole surface of the curtain wall in the digital twin model; defining a first detection target point and a second detection target point according to geometrical characteristics and material joint characteristics of the curtain wall sheet; 2, according to the digital twinborn model, surface normal vector distribution characteristics are extracted, actually-measured wind pressure load distribution data and temperature field parameters are fused, a dynamic flexural deformation coupling calculation model is constructed, and global deformation parameters and local deformation parameters are output. According to the invention, the digital twinborn model is constructed through point cloud data processing, deformation and displacement parameters are extracted, the safety coefficient is evaluated, and high-precision, omnibearing and dynamic detection and safety risk early warning of the flatness of the building outer wall are realized.

The invention belongs to the technical field of structure monitoring systems, and discloses a structure monitoring system and method for civil engineering, and the method comprises the steps that a data collection module collects structure data; the cruise monitoring mechanism collects a high-definition image, an infrared image and laser point cloud data; the environment data acquisition module acquires environment data; the image analysis module identifies abnormal conditions; the infrared data analysis module detects internal defects; the laser data analysis module accurately measures the deformation of the structure; the comprehensive analysis module performs comprehensive evaluation; the risk prediction module fuses multi-source data and predicts potential problems and risks; when an abnormal condition or a potential problem risk is monitored, the alarm module gives an alarm in time. According to the invention, through combination of sensor monitoring and unmanned aerial vehicle cruise monitoring, the structure of civil engineering is monitored in an omnibearing manner; the comprehensive analysis module is used for integrating the multi-source monitoring data to carry out comprehensive evaluation and abnormity judgment; and the risk prediction module fuses monitoring results of multiple modules and predicts potential problems and risks.

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention belongs to the technical field of industrial measurement, and discloses a pump machine metal shell size detection method based on image analysis, which comprises the following steps: synchronously acquiring image data of a pump machine metal shell in a visible light wave band and a near-infrared wave band; the contrast ratio of image data is optimized through a dynamic exposure control technology, and a high-dynamic-range multispectral image is obtained; three-dimensional geometric information of the pump machine metal shell is obtained, registration fusion is carried out on the three-dimensional geometric information and the high-dynamic-range multispectral image, a dense three-dimensional point cloud model with multispectral textures is constructed, and a multi-frequency heterodyne algorithm is adopted to process a high-reflection area in the dense three-dimensional point cloud model; the method comprises the following steps: collecting a multi-angle reflection image of a pump machine metal shell by rotating a linear polarizer, calculating a Stokes vector to extract a diffuse reflection component, carrying out diffuse reflection component constraint and brightness suppression on a high-reflection area based on a CIE-Lab color space, and outputting a dense three-dimensional point cloud model after texture enhancement; and the size detection precision of the pump machine metal shell is improved.

A system for navigating a host vehicle, includes at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to perform operations comprising: receiving from a camera onboard the host vehicle at least one captured image representative of an environment of the host vehicle; detecting, based on analysis of the at least one captured image, a presence of a road covering material on a road segment surface; determining, based on analysis of the at least one captured image, a distance between the camera onboard the host vehicle and the road covering material; and determining a thickness profile of the road covering material based on the determined distance between the camera onboard the host vehicle and the road covering material, and also based on stored three-dimensional profile information associated with the road segment surface.

The invention relates to a part surface defect detection and process optimization method and system, and solves the problems that defect detection has defects, missing detection and erroneous judgment are easy to occur, and subsequent process improvement faces huge challenges even if defects are detected, and the method comprises the following steps: inputting a feature set into a double-branch fusion deep learning model, the first branch identifies defect types and quantization parameters by fusing three-dimensional features and two-dimensional features, and the second branch calculates the correlation degree between the defect features and each process through association rule mining and a random forest algorithm; when the three-dimensional features and the two-dimensional features both meet a preset defect threshold value and the association degree of a certain process exceeds a preset value, determining that the process is a root process; and analyzing a deviation value between the key parameter of the source process and the defect quantization parameter, and correcting the parameter through a dynamic adjustment mechanism according to the deviation degree. The method has the advantages that the defects of the part are accurately detected, the procedure is traced, parameters are dynamically adjusted, closed-loop optimization is formed, and the quality of the part is improved.

The invention belongs to the technical field of three-coordinate measurement, and particularly discloses a three-coordinate measurement method based on incomplete curved surface fitting, which comprises the following steps: hierarchically extracting geometric features from incomplete point clouds, and dividing a workpiece to be measured into a plurality of ordered curved surfaces; adding a curved surface label to each point cloud data, and generating a plurality of ordered point cloud sets based on the curved surface labels; generating virtual points based on the geometric features to fill the missing region; reconstructing a three-dimensional sub-model of each ordered curved surface based on the ordered point cloud set, the virtual points and the edge point cloud; and fitting the three-dimensional sub-model to generate a continuous curved surface model, and obtaining workpiece measurement parameters based on the continuous curved surface model. By extracting geometric features, classifying point clouds and generating virtual points, the limitation that a traditional method depends on a prior model is broken through, unknown or non-standard geometry can be processed, global continuous modeling of incomplete point clouds is achieved through ordinal curved surface division, three-dimensional sub-model reconstruction and continuous curved surface fitting, and the accuracy of measurement parameters is ensured.

The invention discloses a building measurement method and system based on three-dimensional scanning, a medium and a product, and relates to the field of exploration, and the method comprises the steps: obtaining BIM model data and a measurement parameter threshold value of a target building, and determining a subarea scanning path according to the BIM model data; scanning the target building according to the subarea scanning path to obtain a point cloud data set of the target building; generating a three-dimensional digital model of the target building according to the point cloud data set, and dividing the three-dimensional digital model into a plurality of feature regions; each feature region comprises at least one feature component; extracting a feature point set in the feature region, and establishing a mapping relationship with a corresponding point set of a corresponding region in the BIM model data; calculating actual measurement parameters of the feature region based on the mapping relation; and comparing the actual measurement parameter with the measurement parameter threshold, determining an abnormal area and generating a measurement technical report. By implementing the method, the building data can be more comprehensively acquired, and the precision of building measurement is improved.

The invention provides a color steel plate coating flatness evaluation method and system based on artificial intelligence. According to the method, the three-dimensional point cloud data is generated by collecting the interference fringe image, and the surface fluctuation characteristics are quantified; capturing a multi-dimensional vibration spectrum of the transmission roller shaft, generating a servo motor compensation control signal, driving a multispectral scanning head to perform reverse displacement compensation, and generating real-time compensation data; inputting the surface topography features in the three-dimensional point cloud and the real-time compensation data into a lightweight convolutional neural network, and outputting fusion features; and dynamically classifying and identifying surface defects and uneven areas based on the fusion result, adjusting a classification threshold in combination with the speed of the production line, outputting a flatness evaluation result, and synchronizing the flatness evaluation result to a speed regulation system of the production line to realize closed-loop optimization. According to the method, laser interference, vibration compensation and lightweight AI technologies are fused, dynamic high-precision evaluation of the surface flatness of the color steel plate of the high-speed production line is achieved, and the problems of defect misjudgment and measurement distortion caused by vibration interference are solved.

A computer implemented system configured for receiving obtaining sensor data comprising one or more displacements as a function of time of one or more targets attached to a structure in response to point loads applied to the structure; optionally receiving image data representing images of the vehicles comprising the point loads traversing the structure; and determining, from the displacements, at least one of a health status of the structure, or forces applied by one or more of the point loads.

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention discloses a bridge appearance disease detection method and system based on laser point cloud and visual image fusion, and the method comprises the steps: carrying out the synchronous data collection through employing a calibrated laser radar and a camera, and obtaining a three-dimensional laser point cloud and a two-dimensional visual image of a bridge appearance; projecting the three-dimensional laser point cloud to a two-dimensional visual image plane through a mapping model to generate a point cloud front view; identifying diseases in the two-dimensional visual image by adopting a preset target detection model, and generating a segmentation mask for each disease; screening out points in the segmentation mask from the point cloud front view to obtain a potential disease three-dimensional point cloud; error points generated by local shielding in the potential disease three-dimensional point cloud are filtered through ray tracing, and a real disease three-dimensional point cloud is obtained; distributing the depth information of the real disease three-dimensional point cloud to corresponding pixel points, generating a depth map of a disease segmentation mask region, and performing quantitative calculation on the physical size of the disease; and displaying the real disease three-dimensional point cloud data and the disease physical size. The method is high in efficiency and precision.

The invention relates to the technical field of stamping part detection, and discloses a stamping part size and defect synchronous detection method and system. The method comprises the steps that the multi-sensor measurement module is used for collecting line laser scanning morphology data, infrared thermal image strain data and structured light projection contour data of a stamping part; multi-sensor data registration is achieved through a feature point matching algorithm, and a three-dimensional space coordinate mapping relation is generated; calculating the thermal expansion compensation amount of the material in combination with a thermal deformation correction model; filtering the structured light projection contour data, and extracting key contour feature points and defect region boundaries; inputting the related data into a multi-source data fusion model to obtain a dimensional deviation and defect fusion detection result; based on this, a measurement path is updated through a dynamic path planning algorithm, and a synchronous detection scheme is output. The device can synchronously detect the size and defect of the stamping part, improves the detection precision and efficiency, achieves the quality grade classification, and is high in adaptability.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The invention discloses a single tree dynamic monitoring method and system based on multi-stage point cloud and deep learning, and the method comprises the steps: collecting multi-stage multispectral images and laser point cloud data of a research region, and carrying out the preprocessing; performing registration fusion on the preprocessed multi-period multispectral image and the laser point cloud data to generate a multi-period fusion point cloud; based on the multi-period fusion point cloud, performing individual tree segmentation by using a deep learning model; and based on the individual tree segmentation result, inverting the crown breadth and the tree height of the individual tree, and analyzing growth parameters of the individual tree according to the inversion result of the crown breadth and the tree height. By fusing the multi-temporal point cloud and the multi-spectral image data and combining the deep learning model, accurate segmentation and dynamic growth monitoring of the single tree are realized, and the defects of low single tree segmentation accuracy, insufficient multi-source data fusion and incapability of capturing forest tree growth changes in the prior art are overcome. The crown breadth and tree height inversion model is adopted, the precision of growth parameter estimation is effectively improved, and a scientific basis is provided for forest resource management.

The invention discloses a bridge structure detection method based on an unmanned aerial vehicle, a product, a medium and equipment, and relates to the technical field of bridge structure detection, and the method comprises the steps: obtaining the basic data of a bridge structure, and constructing a three-dimensional model according to the basic data; layering the three-dimensional model of the bridge structure according to a preset interval, and calculating an initial path of the unmanned aerial vehicle; optimizing the initial path by adopting a gradient descent method to obtain an optimal path of the unmanned aerial vehicle; acquiring to-be-detected data of the bridge structure based on the optimal path of the unmanned aerial vehicle; preprocessing the to-be-detected data to obtain preprocessed to-be-detected data; and performing crack detection and deformation detection based on the preprocessed to-be-detected data to realize bridge structure detection. According to the bridge structure detection method based on the unmanned aerial vehicle, the product, the medium and the equipment, efficient and accurate detection of the bridge structure can be realized.

The invention discloses an existing line state comprehensive analysis method based on a three-dimensional mobile scanning system. The method comprises the steps that fine scanning is conducted on a steel rail based on a high-precision three-dimensional mobile laser scanning system, and whole-line data of an existing line are rapidly collected; collecting field data; local point cloud data of the steel rail and a point cloud result of a full-section large scene are obtained based on preprocessing and fusion registration calculation of original point cloud data, inertial navigation data, GNSS data and encoder mileage data; establishing a whole line mileage calibration ledger based on the point cloud result; performing multi-term detection based on the whole line mileage calibration ledger and the scanning data, and outputting an existing line comprehensive detection graph and comprehensively evaluating the steel rail state in a segmented manner; the multiple detection items comprise linear design adjustment detection, track geometric state detection, fastener detection, track surface disease detection, profile abrasion detection, limit detection, industrial and electrical equipment detection and existing line linear fine design detection. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

A shape-sensing system can include a stylet, an optical interrogator, a console, and a display screen. The stylet can include an optical fiber with fiber Bragg grating (“FBG”) sensors along a length of the optical fiber. The optical interrogator can be configured to send input optical signals into the optical fiber and receive FBG sensor-reflected optical signals from the optical fiber. The console can be configured to convert the reflected optical signals with the aid of filtering algorithms of some optical signal-converter algorithms into plottable data for displaying plots thereof on the display screen. The plots can include a plot of curvature vs. time for each FBG sensor of a selection of the FBG sensors for identifying a distinctive change in strain of the optical fiber as the stylet is advanced into a superior vena cava of a patient.