7001 results about "Laser scanning" patented technology

The invention discloses an early recognition method and system for a dam crest crack of a high-core-wall rockfill dam, and the method comprises the following steps: marking InSAR data, unmanned plane oblique photography data and three-dimensional laser scanning data through a spatial-temporal index structure, carrying out the abnormal value cleaning through a dynamic multi-dimensional threshold system, and carrying out the early recognition of the crack at the crest of the high-core-wall rockfill dam. A registration data set is obtained through processing of a segmented adaptive standardization processing unit and a space-time registration unit; extracting local features by utilizing probability tensor decomposition, constructing a global feature matrix by combining map entropy calculation, and obtaining a fusion feature set through multi-dimensional probability combination; constructing a multi-level early warning index based on hierarchical sparse representation, identifying a critical state by adopting a probability migration matrix, and determining an early warning level through an adaptive threshold network and probability combination discrimination; and carrying out multi-dimensional verification by adopting a combined verification matrix, and realizing system optimization and updating through a multi-objective optimization network and an incremental learning matrix. According to the method, the accuracy and reliability of early crack recognition are 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 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 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 relates to the technical field of mold detection, and discloses a precision plastic mold detection method, device and equipment and a storage medium, the method comprises the following steps: carrying out multispectral imaging and three-dimensional laser scanning on the surface of a precision plastic mold to obtain multispectral image data and three-dimensional point cloud data; performing multi-scale adaptive threshold segmentation to obtain a first three-dimensional model; performing tensor voting surface reconstruction and texture recovery to obtain a second three-dimensional model; performing global optimization to obtain a target three-dimensional model; extracting multi-view features and carrying out defect classification and positioning to obtain defect category and distribution information; according to the defect category and distribution information and the geometrical characteristics of the precise plastic mold, an optimal scanning detection path is calculated by using a sequence convex optimization algorithm guided by ray tracing, and a self-adaptive mold detection strategy is generated, so that the coverage rate and the precision of detection are improved, the detection time and the resource consumption are balanced, and the detection efficiency is improved. And efficient, comprehensive and accurate mold quality control is realized.

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 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.

A method and apparatus are disclosed for a clutter tidying robot utilizing floor segmentation for its mapping and navigation system, whereby a perception module and navigation module transform lidar and image data from lidar sensors and cameras of a robot sensing system using segmentation and pseudo-laserscan or point cloud transformations to generate global and local maps. The robot pose and maps are transmitted to a robot brain that directs an action module to produce robot action commands controlling the operation of a clutter tidying robot using the pose and map data. In this manner multi-stage planning and sophisticated obstacle avoidance techniques may be incorporated into autonomous robot operations.

The invention provides an electromagnetic field composite powder feeding type plasma arc additive manufacturing formability regulation and control method and system, and relates to the technical field of plasma arc additive manufacturing. The flowing direction and speed of liquid metal in a molten pool in the deposition process are regulated and controlled through the target composite electromagnetic field, and the morphology and the internal organization structure of a deposition layer are controlled; in the process of regulating and controlling the liquid metal in the molten pool, process parameters and molten pool dynamic image data in the deposition process are collected in real time, and a pre-trained state recognition model is used for conducting self-adaptive adjustment on the process parameters of a subsequent deposition layer; and after deposition of each layer is completed, a laser scanning system is adopted for measuring the actual geometric size and surface appearance of the deposition layer, deposition manufacturing of the next layer is executed based on the optimized composite electromagnetic field control parameters, the corrected layered slicing data and the process path, and the execution is executed circularly till manufacturing of the whole workpiece is completed.

A point cloud matching-based welded seam size measurement method, comprising: acquiring first point cloud data of a region to be welded before welding acquired by means of a three-dimensional laser scanner and second point cloud data of a welded region after welding acquired by means of the three-dimensional laser scanner (10); on the basis of the first point cloud data and the region to be welded, determining point cloud data of a region of interest to be welded (20); on the basis of the second point cloud data and the welded region, determining point cloud data of a welded region of interest (30); matching the point cloud data of the welding region of interest and the point cloud data of the welded region of interest, to obtain a matching result (40); according to the matching result, extracting a welded seam point cloud set (50); on the basis of a point cloud principal component analysis method, analyzing the welded seam point cloud set, to obtain welded seam sizes (60). The efficiency and accuracy of rebar welded seam measurement can be improved. Further disclosed are a point cloud matching-based welded seam size detection system, a computer-readable storage medium, and an electronic device.

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 discloses a real estate three-dimensional surveying and mapping method and platform driven by three-dimensional laser scanning, and belongs to the field of geographic information surveying and mapping, and the method comprises the steps: obtaining a real estate engineering drawing, and controlling equipment to collect image data and establish an initial data set; constructing a three-dimensional coordinate system based on the drawing, and calculating surveying and mapping complexity; planning an acquisition path according to an acquisition result and complexity, and configuring an acquisition density identifier and a pre-control point; performing multi-angle scanning according to a path to establish point cloud data; aligning the point cloud data in the coordinate system and performing coarse registration by using the pre-control point; and calculating the correlation degree of adjacent points between the point clouds, and carrying out feature point matching to complete fine registration fusion so as to obtain a three-dimensional surveying and mapping result. The technical problems of low field data acquisition efficiency and insufficient surveying and mapping data accuracy in existing real estate surveying and mapping are solved, and the technical effects of improving the field data acquisition efficiency of real estate surveying and mapping and enhancing the surveying and mapping data accuracy by adopting three-dimensional laser scanning are achieved.

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 provides a tunnel disease detection system and method based on three-dimensional laser scanning. The method comprises the following steps: inputting tunnel lining point cloud data obtained by laser three-dimensional scanning; performing noise reduction processing on the tunnel lining point cloud data; according to the tunnel lining point cloud data after noise reduction processing, lining peeling, lining cracks and lining water leakage diseases are identified through image feature processing; misjudgment processing and removal are carried out on lining stripping, lining cracks and lining water leakage diseases; and distinguishing and marking disease positions and / or areas in the three-dimensional point cloud model by using different modes. According to the invention, the position, size, shape and other information of the tunnel lining disease can be extracted and analyzed, so that the tunnel lining disease can be quickly and accurately identified and positioned, targeted prevention and repair can be carried out, potential safety hazards can be eliminated, and the driving safety in the tunnel can be ensured.

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 provides a multi-region independent temperature control intelligent frequency conversion refrigeration and preservation method and system, and relates to the technical field of refrigeration and preservation. A region division module, a temperature control module, an environment monitoring module, a data analysis module, an intelligent frequency conversion control module and a user interface module work cooperatively; fine temperature control management of different areas in the refrigeration equipment is realized, the system adopts three-dimensional laser scanning and multispectral image acquisition technologies to automatically divide temperature areas, a quantum-level precision thermistor array and an intelligent fuzzy PID controller ensure accurate temperature control, and meanwhile, humidity and air flow are monitored to optimize a storage environment, so that the temperature control efficiency is improved. The data analysis module is combined with deep learning and an association rule mining algorithm to provide decision support for frequency conversion control and realize energy consumption optimization, the user interface module provides visual operation experience, and the whole scheme improves the intelligence, accuracy and energy efficiency of refrigeration and preservation, and is suitable for various cold chain scenes.

The invention relates to the technical field of positioning calibration, and provides a water jet scalpel robot autonomous operation three-dimensional detection and positioning calibration method, which comprises the following steps: S1, manually controlling the tail end of a mechanical arm to carry out coarse scanning, and searching a calibration target; s2, fine scanning path planning of the line laser scanner is carried out, and the scanning directions X and Y of the line laser scanner are determined; s3, multiple times of scanning are carried out in the X direction and the Y direction, and multiple pieces of point cloud data are obtained through scanning; s4, recording and processing the point cloud data, and reserving the point cloud data of the calibration target; s5, performing error feature estimation and calibration on the reserved point cloud data of the calibration target; s6, manually controlling the calibration of the tail end of the water jet cutter, and correcting the space offset of the tail end of the cutter head of the water jet cutter; s7, filtering the point cloud data obtained during normal operation to obtain global error compensation; and S8, performing dynamic positioning correction on the operation target. The method is used for scanning positioning error compensation and correction of a hand-eye system of the water jet scalpel robot and dynamic error correction in the operation process.

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.