5108 results about "Laser scanning" patented technology

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 invention relates to the technical field of curved surface machining control, and discloses an intelligent curved surface machining method and system based on a CNC cutting machine tool, and the method comprises the steps: carrying out the laser scanning of a to-be-machined workpiece, obtaining the three-dimensional geometric data of the workpiece, activating a target clamping device, and generating an initial clamping scheme; the target clamping device is controlled to execute multi-area pressure gradient clamping operation, and target position data and clamping pressure distribution data are obtained; performing multi-field coupling dynamic analysis to generate a processing optimization parameter set; executing segmented continuous Hamiltonian path planning and self-adaptive lattice point reconstruction according to the machining optimization parameter set, and generating a tool path data set; according to the method, it is ensured that all the surfaces of the polygon prism workpiece are evenly stressed, the problem of machining deviation caused by edge stress concentration in a traditional method is solved, and the overall machining quality of the workpiece is improved.

The invention discloses a point cloud differential defect detection system and detection method based on multi-feature fusion, which are mainly used for high-precision detection of surface defects of a side wallboard of a railway passenger car, and the method comprises the following steps: obtaining datum point cloud data through a designed standard side wallboard model of the railway passenger car, and collecting real-time point cloud data through a laser scanner; preprocessing the obtained point cloud data; calculating the difference between the real-time point cloud and the reference point cloud by adopting a difference method for fusing geometric features such as Euclidean distance, curvature and normal vector, namely calculating the Euclidean distance difference, curvature difference and normal vector difference between the reference point cloud and the real-time point cloud; and a comprehensive abnormal score is generated by further combining a dynamic weight fusion mechanism, and different region characteristics (such as a plane, a curved surface and an edge region) are adapted through dynamic threshold adjustment. According to the method, the defect region is segmented through the clustering algorithm, and the defect area and depth are quantified. The method has the advantages of high detection precision, strong adaptability and good real-time performance, and is suitable for complex surface defect detection in industrial production.

The invention discloses a wharf armor block quality detection method, and belongs to the technical field of concrete structure detection.The method comprises the steps that a surface model is constructed through a movable three-dimensional laser scanning device, and a high-risk defect area is recognized; performing encryption scanning and impact echo detection in the high-risk area by adopting a frequency-adjustable dual-mode flaw detection device to generate a three-dimensional defect distribution diagram; a core sample is drilled in the defect area for compressive strength testing, and the material performance is analyzed in combination with a non-contact infrared spectrum; and establishing a dynamic evaluation model, calculating a safety coefficient by integrating surface defects, internal defects and material discreteness data, and generating a three-dimensional visual report. The problems that a traditional detection method is low in efficiency, insufficient in internal defect positioning precision, one-sided in material performance evaluation and the like are solved, full-dimensional detection from the surface to the interior and from the macroscopic level to the microscopic level is achieved through a multi-source data fusion and closed-loop feedback mechanism, and the reliability of structural safety evaluation and the scientificity of maintenance decision are remarkably improved.

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 provides an intelligent station multi-source heterogeneous data acquisition and remote collaborative analysis method and system, and relates to the technical field of heterogeneous data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the standardization processing, constructing a station real-time adaptive three-dimensional model through a self-calibration laser scanning technology, and carrying out the semantic mapping of the model and a BIM model; constructing a self-evolution feature library based on a digital twinborn model, and carrying out space-time correlation analysis by adopting a graph convolutional neural network; a remote cooperation platform is established to realize real-time cooperation of multiple experts, and a diagnosis process is recorded through a block chain technology. According to the invention, efficient acquisition, analysis and remote collaborative diagnosis of the data of the smart station are realized, and the accuracy and efficiency of fault diagnosis are improved.

Provided is an in-situ high-precision non-destructive testing device for deep-sea parts, the device comprising a bottom plate (1), wherein a linkage base (5) is fixedly mounted on a side surface of the bottom plate (1), a first bearing (6) is provided on the linkage base (5), a rotary rotating shaft (7) is fixedly mounted on the inner side wall of the first bearing (6), the rotary rotating shaft (7) is connected to a driving bevel gear (8), the driving bevel gear (8) meshes with a driven bevel gear (9), a driving threaded shaft (10) is mounted on the driven bevel gear (9), and the end of the driving threaded shaft (10) close to the top surface of a side plate (2) is in transmission connection with the top surface of the bottom plate (1). The driving threaded shaft (10) is rotated to enable a lifting square block (11) in threaded connection with the driving threaded shaft (10) to move upwards, such that a non-destructive testing box is in an inclined position; in addition, by means of a stabilizing assembly, a laser scanner (66) can stably perform a scanning testing operation on parts, thereby avoiding errors in testing results, and improving the accuracy of the testing results. Moreover, the device can simulate a typical marine environment to perform standard testing on deep-sea parts, and can establish a surface damage database for comparison with deep-sea in-situ testing results, thus further improving the reliability of testing results in different marine environments.

The invention discloses a material-based laser scanning system and method, and relates to the technical field of industrial automation, and the system comprises the following components: a laser scanning unit, an environment sensing sensor group, a dynamic compensation and calibration module, a feedback control unit and a visual monitoring interface. According to the invention, the environment change of a scanning site is monitored in real time through the integrated environment sensing sensor group, and the laser scanning data is processed in real time by using an advanced dynamic compensation and calibration algorithm, so that the measurement deviation caused by environmental factors such as vibration, temperature change and dust interference can be effectively reduced; the measurement precision of laser scanning in complex environments such as a coal yard is remarkably improved, meanwhile, the stability of long-term measurement is ensured by continuously optimizing and calibrating model parameters and automatically adjusting laser scanning parameters, and reliable data support is provided for material management.

The invention belongs to the technical field of road deformation monitoring, and discloses a road slope deformation monitoring system and method, and the system comprises a fixed point monitoring mechanism which monitors the displacement, strain, underground water level and inclination parameters of a slope in real time at key points and key positions of the road slope; the route planning module plans the flight route of the unmanned aerial vehicle; the cruise monitoring mechanism is used for monitoring the side slope in all directions; the weather data acquisition module is used for acquiring local weather data; the three-dimensional laser scanner data analysis module is used for identifying abnormal conditions on the side slope; the image analysis module is used for identifying abnormal conditions on the slope; the comprehensive evaluation module is used for comprehensively evaluating the stability of the slope by utilizing a comprehensive evaluation model; and the alarm module comprises an alarm. According to the invention, the fixed-point monitoring mechanism and the cruise monitoring mechanism are combined to monitor the slope in all directions, the comprehensive evaluation module fuses data of various monitoring means, and the comprehensive evaluation model is utilized to comprehensively evaluate the slope stability.

The invention relates to the technical field of spectral analysis, in particular to a fabric fiber component high-resolution spectral rapid analysis method which comprises the following steps: S1, acquiring three-dimensional shape data of a fabric surface through laser scanning, and identifying a fiber arrangement direction and a surface curvature characteristic based on the three-dimensional shape data; s2, dynamically adjusting the incident angle and the focusing depth of a spectrum probe according to the fiber arrangement direction and the surface curvature characteristics identified in S1, and selecting a corresponding spectrum excitation mode; s3, performing feature extraction on the multi-mode spectral data acquired in the S2, and fusing feature vectors of different spectral modes through a deep learning algorithm; s4, analyzing the fused feature vector based on a transfer learning model, and outputting a fiber component type and a proportion thereof; and S5, associating the component data output in the step S4 with space coordinates, and generating a component distribution visualization map. By dynamically adjusting the spectrum probe, fusing multi-mode spectrum data and the like, the analysis precision and speed are remarkably improved.

The invention discloses an intelligent building energy-saving management method and system based on a digital twinning technology, and relates to the field of digital twinning technologies, and the method comprises the steps: building a three-dimensional building model based on a building information model and three-dimensional laser scanning point cloud data, and generating a twinning database through multi-source equipment data fusion; performing energy consumption simulation modeling according to the lamp dimming parameters, the air conditioner performance curve and the elevator operation log in the twin database to generate an energy consumption reference report; acquiring personnel density and environment parameters, and generating a building environment state matrix mapped with the twin database after space-time calibration; and establishing a hierarchical threshold value control strategy library based on the building energy consumption reference report and the building environment state matrix, and generating an optimization control strategy set through the equipment linkage relation matrix. According to the method, by embedding a Pareto optimal solution screening mechanism and equipment mutual exclusion rule verification, the daily average energy consumption is reduced on the premise that the comfort reaching rate of the optimal control parameter packet is ensured.

The invention relates to the technical field of geological disaster prevention and control, in particular to a rock engineering slope stability classification method. According to the technical scheme, the method comprises the following steps: obtaining spatial distribution characteristics of a slope structural plane through combined detection of three-dimensional laser scanning and a geological radar, and establishing a quantitative index system containing occurrence, density and connectivity of the structural plane; the method comprises the following steps: acquiring a fracture signal in a slope in real time by adopting a micro-seismic monitoring system, and extracting a micro-seismic event energy release rate, a dominant frequency offset and a seismic source mechanism parameter through time-frequency analysis; and constructing a geologic structure evolution inversion model, reconstructing a geologic structure evolution process based on the regional geologic database and the field drilling data, and calculating the distribution characteristics of the residual stress field of the structure. According to the method, comprehensive, accurate and real-time monitoring and classified evaluation of the rock engineering slope stability are realized, potential risks can be effectively predicted, a scientific and accurate decision basis is provided for slope support and treatment, and the safety and stability of the rock engineering slope are greatly guaranteed.

The invention relates to a steel structure component virtual pre-assembly method based on three-dimensional laser scanning. Designing a self-adaptive hierarchical scanning strategy, and dynamically adjusting parameters of scanning equipment to obtain data of each region of the component; abnormal points are detected and eliminated by fusing scanning data of different levels; datum points are extracted, coordinate calibration is carried out, data of all levels are spliced, and a full-component three-dimensional point cloud model is formed; classifying the point clouds by using a multi-dimensional data analysis algorithm; establishing a closed-loop data verification system; designing a multi-feature point matching algorithm; performing iterative docking and position calibration through an algorithm; quantitative analysis is carried out on spatial deviation between the components in the assembling process; after the deviation is detected, a fine adjustment scheme is generated, and the position of the component is adjusted; a virtual pre-assembly path is generated according to the shapes, the connection modes and the installation sequence of the components, and the assembly sequence and the layout of spatial positions are controlled; and the assembly state of each component is monitored in real time, and the path is optimized and adjusted through a feedback mechanism.

The invention provides a tree identification method and system based on ground-air fusion three-dimensional laser point cloud, and belongs to the field of environment remote sensing data identification, and the method comprises the steps: obtaining laser radar point cloud data in a set forest region through employing an unmanned plane, and collecting the point cloud data in the set forest region through employing a laser scanning device; preprocessing the two groups of data to obtain preprocessed laser radar point clouds, and performing ground-air data seamless fusion on the preprocessed laser radar point clouds to obtain fused data; generating a canopy height model based on a watershed segmentation algorithm, and performing individual tree segmentation on the fused point cloud data by using the model; extracting a vector boundary after single tree segmentation, distributing a tree ID for each tree, and extracting a single tree point cloud according to the tree ID; the single tree point cloud is further processed to construct a multi-angle point cloud sample data set; and training a deep learning network model by using the multi-angle point cloud sample data set, and detecting the tree species to be detected by using the trained deep learning network model.

The invention relates to the technical field of additive manufacturing, and particularly discloses a real-time defect detection and in-situ repair method and device in the additive manufacturing process, and the method comprises the steps that a multi-mode sensing system is integrated on additive manufacturing equipment, the multi-mode sensing system comprises a high-resolution CCD camera, a laser scanner and a thermal infrared imager, and data acquisition is synchronized; a hierarchical scanning strategy is adopted, two-dimensional surface images, three-dimensional point cloud data and thermal field distribution information are collected before and after each layer is machined, and the data synchronization precision is smaller than 0.1 ms; according to the invention, a multi-modal sensing system and an advanced algorithm are integrated, so that real-time detection and accurate repair of defects are successfully realized; a high-resolution CCD camera, a laser scanner, a thermal infrared imager and other sensors are adopted, a cross-scale RANSAC algorithm and a depth feature fusion network are combined, surface and internal defects are accurately detected, and laser remelting parameters are adjusted in real time for in-situ repair.

The invention relates to the technical field of container models, in particular to a container model construction method based on three-dimensional software. The method comprises the following steps: obtaining point cloud data of a container through three-dimensional laser scanning, carrying out feature subdivision to generate a container feature set, carrying out space positioning on features to obtain a vertex coordinate set, carrying out geometric constraint modeling based on the vertex coordinate set, forming a container skeleton model, obtaining a target cargo image, extracting a basic contour, and carrying out feature extraction; the method comprises the following steps: carrying out three-dimensional reconstruction to generate a cargo model, carrying out space planning on a container skeleton based on the model, calculating gravity center distribution to generate a cargo balance parameter, then carrying out interference simulation and conflict detection to obtain interference conflict data, positioning a conflict focus area, and finally carrying out variant design on the container skeleton according to the conflict area. And generating an adjusted container model. According to the invention, a more comprehensive and more accurate container model construction method is realized.

The invention discloses a tunnel deep-buried drainage ditch rapid blasting excavation method based on vertical drilling, which comprises the following steps: step 1, detecting the geology in front of a tunnel by using vertical drilling coring, geological radar and TSP seismic wave technology, and constructing a three-dimensional model; 2, generating an asymmetric hole net layout based on a geological model and AI, and setting a drilling-free vibration isolation belt; the design of dense inside and sparse outside is adopted, and vibration propagation is blocked; thirdly, a three-stage air spacer is arranged at the bottom of the blast hole, segmented charging is implemented, the explosive energy release time is prolonged, and the single-segment explosive quantity peak value is reduced; fourthly, an electronic detonator is adopted for millisecond detonation, and peak vibration is weakened through the waveform superposition offset effect; 5, arranging fiber grating sensors in the tunnel, collecting data in real time and transmitting the data to the cloud AI platform; dynamically adjusting parameters based on the monitoring data; and 6, evaluating a blasting effect through a three-dimensional laser scanner, generating an evaluation report in combination with data, updating a BIM model database, and continuously optimizing an AI algorithm.

The invention discloses an automobile steering controller fault detection method and system, and relates to the technical field of automobile electronic control. The surface image acquisition module captures an image through a visual sensor, the quality is improved through the image preprocessing module, the convolutional neural network accurately recognizes appearance defects, and the three-dimensional contour detection and thermal imaging module is triggered to be linked to re-check an abnormal area; the three-dimensional contour module confirms patch offset or tombstone standing abnormity by using a laser scanning technology; the thermal imaging analysis module is matched with a machine learning technology to analyze welding spot temperature abnormity; the predictive fault diagnosis module predicts a defect risk by using deep learning and performs early warning in advance; and the collaborative decision optimization module integrates a multi-module data dynamic optimization detection strategy. Through integration of defect detection, re-checking and prediction, the quality detection precision, coverage and efficiency of the automobile steering controller are greatly improved, the high-quality standard and production stability of products are ensured, and the requirement of the automobile industry for efficient detection is met.

The invention discloses an electromechanical engineering construction information operation and maintenance management method and system based on the Internet, and particularly relates to the technical field of data management. Constructing an initial BIM model based on the design drawing, and generating three-dimensional model data containing equipment geometric information and space coordinates; a three-dimensional laser scanning device or a high-precision positioning sensor is used for obtaining the geometrical morphology and actual coordinates of a real object on site; the collected point cloud data is compared with the initial BIM model through a spatial data matching algorithm, deviation is recognized, the BIM model is automatically updated, and model precision dynamic correction is achieved; binding the updated equipment information and an equipment entity with a two-dimensional code, and synchronously binding an actual position coordinate through an indoor positioning label; uploading the corrected model and equipment information to a cloud operation and maintenance platform to realize information centralized management and multi-terminal sharing; the method improves electromechanical engineering operation and maintenance efficiency and information precision, and has good application value.

The invention discloses a municipal road pavement crack multi-modal fusion detection method, which belongs to the technical field of pavement crack detection, and comprises the following steps: S1, obtaining visible light images, infrared thermal imaging and three-dimensional laser scanning data in multi-modal data, generating a time-unified and space-aligned multi-modal data set based on the three-dimensional laser scanning data; s2, extracting a visible light image and an infrared thermal image from the multi-modal data set, and obtaining features corresponding to the visible light image and the infrared thermal image to obtain a multi-modal feature set; and S3, inputting the multi-modal feature set into a deep neural network, and carrying out weighted integration on feature vectors through multi-layer convolution operation to obtain a crack detection result with three-dimensional coordinates. The municipal road pavement crack multi-modal fusion detection method solves the problem that an existing pavement crack detection method is low in accuracy and reliability.

The invention belongs to the technical field of three-dimensional imaging and measurement, and particularly discloses a laser scanning three-dimensional imaging method and device, a medium and equipment, and the method comprises the steps: obtaining laser scanning data of a target object, and the laser scanning data comprises depth data including reflection intensity, a scanning angle, a timestamp and flight time; preprocessing the laser scanning data, and fusing a time sequence and scanning path information to obtain multi-channel input features adaptive to a neural network; constructing a three-dimensional geometric reconstruction model, and training the model; inputting the multi-channel input features into a trained three-dimensional geometric reconstruction model to obtain a dual-channel output tensor containing a dense depth map of the target object and a corresponding uncertainty map; converting the dual-channel output tensor comprising the dense depth map of the target object and the corresponding uncertainty map into a three-dimensional point cloud under world coordinates; and post-processing the three-dimensional point cloud to obtain a three-dimensional image of the target object.

The invention discloses an on-line automatic measurement and compensation method for digital manufacturing, which comprises the following steps of: scanning the surface of a workpiece to be processed in real time through a high-precision laser scanner to obtain a three-dimensional geometric model of the workpiece; environmental parameters in the laser machining process are monitored in real time through multiple sensors; on the basis of the collected real-time geometrical morphology and environmental parameters, the processing deviation of the workpiece is calculated in real time through a data processing and analyzing module, and a compensation model is generated; according to the calculation result of the compensation model, the central control system adjusts a laser processing path and processing parameters in real time; after each time of processing, a feedback signal of each time of error is used as a basis for updating the compensation model so as to ensure that the compensation model is continuously optimized. The method has the following advantages and effects: dynamic correction and long-term optimization of machining errors can be realized by combining real-time data acquisition and an optimization compensation model technology of a deep learning algorithm, so that the machining precision and efficiency are improved.

The present invention relates to the field of dam area deformation early warning technology, and proposes a dam area deformation early warning method and system based on a laser scanner, which includes the following steps: selecting a drone, equipping the drone with a laser scanner and a Beidou positioning system; planning the monitoring flight path of the drone, continuously emitting lasers and receiving reflected signals through the laser scanner, and using the Beidou positioning system to record the scanning position information of the laser scanner in real time to obtain original three-dimensional point cloud data; integrating the three-dimensional point cloud data obtained by multiple scans of the laser scanner into a unified coordinate system through a registration algorithm to obtain a three-dimensional model of the dam area; using a statistical method and a preset threshold to perform a data consistency check on the original deformation data set, screening out abnormal values ​​in the original deformation data set, and obtaining a deformation data set; identifying the deformation data set through a dam area deformation risk assessment model, obtaining a dam area deformation risk level, and taking corresponding early warning measures based on different dam area deformation risk levels. The present invention improves the accuracy of deformation data, realizes accurate judgment of deformation risk levels, and takes corresponding early warning measures according to different risk levels, thereby enhancing the safety of the dam area.

The invention belongs to the technical field of three-dimensional measurement, and discloses a 3D laser line scanning system based on a point cloud positioning algorithm and a control method. The method comprises the following steps: acquiring environment initial point cloud data and scanning target information; carrying out feature extraction, constructing an environment feature description model, carrying out three-dimensional space partitioning processing, carrying out regional reflectivity feature analysis, and constructing a space reflection feature model; adaptive optimization configuration is carried out on the laser scanning parameters, and a scanning strategy is generated; scanning path dynamic planning is carried out on the scanning target information, scanning density distribution calculation is carried out, and a scanning path is generated; performing laser line scanning control by using the scanning path and the scanning strategy, and performing scanning data quality real-time monitoring to obtain scanning quality evaluation data; scanning parameter iterative optimization is carried out on the scanning quality evaluation data, point cloud data real-time fusion reconstruction is carried out, a high-precision three-dimensional model is generated, the modeling precision is improved, and meanwhile the system resource utilization efficiency is optimized.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention relates to the technical field of control and regulation systems, in particular to a photovoltaic module anomaly recognition method based on laser detection, which comprises the following steps: generating a clock signal synchronous with a laser scanning time sequence through a hardware synchronous trigger, and driving a temperature and humidity sensor, a salt mist sensor and an illumination intensity sensor to collect environmental parameters; and constructing a multi-source data set containing the salt crystallization trend and the environmental interference level. And based on a temperature and humidity segmented compensation reflectivity base line, a salt mist mode switching optimization dust retention judgment threshold value, and illumination correlation sensitivity adjustment hot spot detection parameters, dynamic model correction is realized. And a fault database is matched to trigger a grading alarm mechanism, and a decision tree classifier is combined to verify time continuity to generate a diagnosis report. A dynamic detection path is constructed through spiral path planning, a multi-machine contract network protocol and wind disturbance trajectory correction, data feedback is executed to form closed-loop control logic, and the problems of misjudgment and missing detection in high salt mist, temperature change and illumination fluctuation environments are effectively solved.

The invention relates to the technical field of photovoltaic power generation, and discloses a BAPV-based roof photovoltaic intelligent monitoring system and a dynamic tuning method and device. The method comprises the following steps: scanning a preset BAPV assembly through three-dimensional laser, constructing a surface point cloud model, and establishing a three-dimensional coordinate system comprising BAPV assembly installation base points; acquiring a real-time environment parameter set corresponding to the BAPV component; calculating an optimal working angle of the BAPV component according to the real-time environment parameter set based on an improved particle swarm algorithm; simulating an all-weather shadow movement track according to the optimal working angle based on a Monte Carlo method, and generating a component shielding influence weight matrix; establishing a heat balance equation according to the component shielding influence weight matrix so as to calculate a spacing optimization parameter of the BAPV component; a control instruction is generated according to the spacing optimization parameters, and dynamic tuning of the BAPV assembly is completed according to the control instruction; the control instruction comprises a support rotation angle compensation amount, an inverter parameter adjustment amount and an abnormal component isolation instruction. And the pain points of modeling errors, static optimization limitation, thermal runaway risks and the like are solved.

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 relates to the technical field of three-dimensional modeling, in particular to a rural energy facility rapid modeling method based on three-dimensional laser point cloud. The method comprises the following steps: acquiring point cloud data of energy facilities in a rural area by using three-dimensional laser scanning equipment to obtain point cloud data of the rural energy facilities; performing target identification according to the rural energy facility point cloud data to obtain rural energy facility target data; constructing a three-dimensional model according to the rural energy facility target data to obtain a rural energy facility three-dimensional model; and performing refined modeling according to the rural energy facility three-dimensional model to obtain a rural energy facility refined model. According to the method, the energy facility model with high precision, rich details and complete physical attributes is generated by combining geometric refinement, topological optimization and physical attribute simulation, so that the modeling efficiency is remarkably improved, and technical support is provided for various application scenes such as operation and maintenance management, fault detection and simulation analysis of facilities.