34842 results about "Point cloud" patented technology

Disclosed in the present invention is a three-dimensional scene point cloud data extraction and recognition system based on a power distribution network. The system comprises: a data collection module used for collecting data for multiple times and fusing same to acquire a complete power distribution network scene; a data processing module used for using the least square method to calibrate closest point matching and perform point cloud registration; a data extraction and segmentation module used for using principal component analysis to calculate a normal vector of the points in a neighborhood of each point cloud data point, measuring the curvature change near each point of a curved surface of a three-dimensional scene of a power distribution network by means of Gaussian curvature, and segmenting and classifying point cloud data by means of a random sampling fitting method and a clustering method; a data recognition and monitoring module used for using PointNet to perform local and global feature learning on the features of each point cloud data point, and acquiring global features by means of a maximum pooling operation for coding and recognition; and a data visual analysis module used for connecting to an open-source point cloud data processing library PCL and a hardware device for visual interaction. The efficiency and sustainability of the power supply system are improved.

The invention provides a real scene three-dimensional modeling method and system fusing laser point cloud and images, and belongs to the technical field of urban three-dimensional modeling. According to the method, laser point cloud data and multi-view image data of a target scene are acquired through a synchronous acquisition device, and joint calibration is performed on a laser radar and a camera to generate space-time synchronization parameters. And a dynamic weight registration algorithm is utilized to dynamically adjust the registration weight according to the point cloud density distribution and the image texture complexity so as to realize the multi-level registration of the laser point cloud and the image data. The registered data is used for constructing a three-dimensional geometric model and generating a seamless texture map, meanwhile, semantic-driven hole repairing is carried out on a model missing area, and finally a complete three-dimensional model is output. According to the real-scene three-dimensional modeling method and system fusing the laser point cloud and the image, the limitation of single data source modeling is solved, and real-scene three-dimensional modeling with high precision, high integrity and real-time updating is realized.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention relates to the technical field of industrial robots, in particular to an industrial robot walking control system based on obstacle recognition. Comprising an environment sensing unit; the obstacle analysis and decision-making unit is used for processing the multi-dimensional data output by the environment sensing unit based on a deep reinforcement learning framework, accurately identifying static obstacle and dynamic obstacle types, motion trails and interaction influences, constructing a two-dimensional decision-making model of static obstacle avoidance and dynamic obstacle avoidance, and carrying out obstacle avoidance and obstacle avoidance on the basis of the two-dimensional decision-making model. A differential obstacle avoidance strategy is triggered; the path planning unit is based on a dynamic game path algorithm under space-time constraint; and an instruction transceiving unit. Through the multi-modal fusion sensing technology and the adaptive parameter adjustment module, space-time alignment and feature fusion of multi-source data such as three-dimensional point cloud, texture features and vibration spectrum are realized, a high-dimensional environment state model is constructed, and the problem of insufficient data fusion depth in the prior art is effectively solved.

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 belongs to the technical field of path planning, and discloses an unmanned driving dynamic path planning method and system based on multi-source data fusion, and the method comprises the steps: collecting an ice and snow pavement friction coefficient, a curve curvature and an obstacle point cloud, constructing a sensor confidence coefficient matrix, generating a fused semantic map, and constructing a dynamic environment semantic model. Outputting a real-time friction coefficient field and a risk thermodynamic map layer; the roadside unit broadcasts coordinates of opposite vehicles in a blind area of a curve to a vehicle end, constructs an ice and snow pavement offset crowdsourcing map, and generates a global-local fusion topology; fusing the real-time friction coefficient field and the global-local fusion topology to generate a smooth trajectory set, and further generating a risk optimal path instruction set; a steering angle and torque instruction is decomposed, positioning drift is compensated in real time, and a normal mode for updating the vehicle positioning state and a degradation mode when the millimeter wave radar fails are constructed; and generating execution logs and health state vectors, and aggregating the execution logs and the health state vectors of multiple vehicles to form closed-loop iterative update.

The invention provides a battery assembly process defect real-time detection and classification method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: carrying out the standardization preprocessing of collected battery surface images and three-dimensional point cloud data; a distributed edge computing architecture is adopted for feature extraction, the distributed edge computing architecture comprises a defect propagation prediction module based on a graph neural network, inter-process defect features are constructed into a dynamic graph structure, a defect evolution rule is learned through the graph network, and feature extraction parameters are dynamically adjusted; inputting the extracted features into a deep learning detection model, carrying out feature adaptive mapping and alignment through a multi-modal spatial-temporal feature coding layer, constructing a process association graph through a dynamic reasoning layer based on causal reasoning, and calculating a propagation influence degree; carrying out hierarchical precision detection on different defect evolution probability regions by a prediction perception detection layer with a self-adaptive threshold value, and outputting a defect detection result; and classifying defect detection results based on defect classification standards.

Disclosed in the present invention is an obstacle avoidance method for an industrial robot in a complex environment, comprising the following steps: S1, performing model simplification on an industrial robot in a working space, and simplifying an obstacle into an obstacle point cloud; S2, placing a depth camera outside a motion space of the industrial robot in an "eye-to-hand" arrangement mode; and S3, before the industrial robot moves, using a G-APF-RRT* algorithm to plan a feasible path in the working space. In the present invention, the calculation mode of the distance between the industrial robot and the obstacle is simple, the required time is short, virtual joint interpolation is used and a spherical bounding box is used at a joint to completely envelope the industrial robot so as to simplify a model, and distance calculation can be completed only by calculating the distance between the center of sphere of the limited spherical bounding box and each point in the obstacle point cloud.

The invention discloses a building digital twinning three-dimensional reconstruction method and system based on a large model, and relates to the technical field of building digital twinning and three-dimensional modeling fusion, and the method comprises the steps: laying multi-source equipment in a building, collecting building multi-angle data, and carrying out type distinguishing and structured preprocessing. An adaptive deep network model is constructed and trained to identify building features, and the robustness of the model is improved by adopting a multi-type enhancement strategy. Point cloud splicing and coordinate unification are realized by using a fusion algorithm, and twin model updating and synchronization are realized based on a multi-dimensional threshold. According to the method, high-quality input is established for a depth model; building structure feature recognition has the advantages of robustness and precision; and the twinborn model has dynamic updatable capability. The three parts form a complete closed loop, the technical bottlenecks of a traditional reconstruction method in precision, stability and renewability are finally broken through, and a new building digital twinning three-dimensional modeling path facing a complex scene is achieved.

The present invention relates to a railway anomaly detection method and system based on multi-modal data fusion. The method comprises: separately encoding acquired modal data in a railway environment, and concatenating encoded modal data features, wherein the modal data comprises a one-dimensional vibration signal, two-dimensional image data, and 3D point cloud information; performing automatic weight classification on the concatenated multi-modal data features on the basis of an attention mechanism to obtain a weighted feature vector fused with multi-modal information; and adding a location code to the feature vector and using same as an input of an SAM encoder to obtain a segmentation result, and determining an anomaly condition of a railway on the basis of the segmentation result. The present invention achieves high monitoring accuracy, a real-time response capability, and specificity.

The invention relates to the field of semiconductor packaging material defect detection, in particular to a semiconductor packaging material defect detection method based on deep learning, which comprises the following steps: acquiring a surface image, and extracting a two-dimensional contour and a feature point set; preprocessing the image, and separating a packaging material main body area; constructing a two-dimensional defect identification model based on Transform, and outputting a two-dimensional detection result; scanning suspected and unknown defect areas to obtain three-dimensional point cloud data, and extracting geometric and texture features; fusing two-dimensional and three-dimensional data through a space-time alignment model; utilizing the multi-modal fusion model to output defect positions and types; and evaluating the defect importance based on the material node connectivity and the stress distribution, and generating a visual detection report. According to the invention, high-precision detection of semiconductor packaging material defects is realized, the defect identification rate, the positioning precision and the detection efficiency are improved through multi-modal data fusion and a deep learning model, and a visual report can be generated based on material structure quantification defect importance.

The invention discloses an intelligent geometric reasoning and semantic understanding method based on a three-dimensional large language model, which comprises the following steps of: acquiring point cloud data of a building component through three-dimensional scanning equipment, associating text information, and constructing a multi-modal three-dimensional large language model comprising a geometric perception coding module, a context semantic understanding module and a parameter efficient fine tuning module; a cross-modal contrast loss and task instruction fine tuning strategy is adopted in model training, and finally semantic recognition, attribute completion and historical background analysis results of the building components are output. The method is suitable for building heritage digital protection, intelligent building process monitoring and three-dimensional digital archive management, component function recognition precision and cultural semantic mining capability in a complex scene can be improved, and real-time semantic updating and interactive response of a dynamic construction environment are supported.

The invention provides a reconstruction method of a three-dimensional reconstruction model based on two-dimensional Gaussian splashing, which comprises the following steps: S1, carrying out sparse reconstruction on an input image sequence through a multi-view stereoscopic vision algorithm to generate an initial sparse three-dimensional point cloud and a corresponding camera pose parameter; s2, inputting an improved two-dimensional Gaussian radiation field by using the sparse three-dimensional point cloud and the camera pose as information; s3, dynamically screening a visible anchor point subset based on the current view angle parameter, and generating a rendered image through a differentiable rendering pipeline; s4, calculating a loss function of the rendering image of the training track and the input image to optimize a reconstruction scene; and S5, starting a special visualization tool, and inputting a rendering result. According to the method, by introducing a trimmable anchor point parameterization framework and a multi-scale feature fusion mechanism, light-weight and high-precision three-dimensional scene modeling is achieved, and the problems that traditional 2D Gaussian sputtering is insufficient in multi-view geometric consistency, storage overhead and weak texture region reconstruction and an existing 2D Gaussian splashing method is insufficient in self-adaptive mechanism are solved.

The invention discloses an obstacle avoidance method and device for unmanned aerial vehicle power inspection autonomous flight based on a laser radar and a storage medium, and relates to the technical field of unmanned aerial vehicle obstacle avoidance, and the method comprises the steps: configuring the laser radar and a visual sensor on an unmanned aerial vehicle, and collecting the three-dimensional point cloud data and image data of a flight environment in real time; optimizing the point cloud data to generate a three-dimensional environment model; and based on the three-dimensional environment model, classifying the obstacles into static obstacles and dynamic obstacles, if the obstacles are static obstacles, identifying the types of the obstacles through a CNN model and performing positioning, and if the obstacles are dynamic obstacles, clustering motion tracks through a K-Medoids clustering method. According to the invention, static obstacles can be identified and positioned, and motion trail prediction can be carried out on dynamic obstacles such as flying birds through a dynamic obstacle prediction technology, so that a preliminary flight path is generated, surrounding obstacles are sensed and avoided in real time in the flight process, and it is ensured that the unmanned aerial vehicle can be smoothly executed in an electric power inspection task.

The invention discloses a large-scene three-dimensional reconstruction method based on three-dimensional Gaussian sputtering, and relates to computer graphics. The method comprises the following steps: collecting a multi-view image set of a large scene; obtaining a scene sparse point cloud according to the multi-view image set; performing monocular depth estimation on the multi-view image by using a pre-trained depth prediction network to obtain monocular depth estimation priori; the method comprises the following steps of: performing global training on a scene by utilizing scene sparse point cloud and monocular depth estimation prior to obtain an initial three-dimensional Gaussian model, and performing space grid division on the initial three-dimensional Gaussian model to obtain a plurality of scene blocks with axis alignment bounding boxes; setting image view angle data of each scene block; performing deep supervised training on the Gaussian ellipsoids in the plurality of scene blocks by using a parallel GPU (Graphics Processing Unit); combining the trained scene blocks to obtain a final three-dimensional Gaussian model; in view of low geometric structure reconstruction precision caused by only depending on color information of a multi-view image in large-scene three-dimensional rendering, the method improves the reconstruction precision of large-scene rendering.

The invention belongs to the technical field of positioning and navigation, and discloses a high-reliability positioning and navigation method for an air-ground cooperative unmanned system. According to the method, a negative obstacle is accurately extracted by using an RANSAC algorithm and a region growing method, a two-dimensional grid map is generated through three-dimensional point cloud re-projection, a negative obstacle region is marked on the map, and a prior map is provided for an unmanned vehicle. The unmanned vehicle constructs a local map in combination with a sensor of the unmanned vehicle, and the local map is matched with a prior map through an ICP algorithm to accurately position a global pose. According to a priori map and real-time sensor data, a grid cost map containing static obstacles, dynamic obstacles and negative obstacles is generated, a TSCNN predicts dynamic obstacle tracks according to time sequence data, map cost values are updated, an A * algorithm carries out optimal path planning based on the updated dynamic cost map, and the optimal path planning is carried out based on the updated dynamic cost map. And dynamic obstacles and negative obstacles are avoided in real time. According to the invention, efficient, accurate and safe navigation and obstacle avoidance can be realized in a dynamic environment and a complex terrain.

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

The invention discloses a rescue robot path planning method and system under industrial vision assistance, and relates to the field related to industrial vision, and the method comprises the steps: collecting three-dimensional space data of a rescue environment in real time, generating a dynamic environment point cloud data set, and constructing a three-dimensional semantic map of a rescue area; thermal imaging data updated in real time are called, path analysis is carried out in combination with the three-dimensional semantic map, and a path planning strategy set is obtained; and predicting the motion track of the dynamic obstacle based on the local dynamic obstacle avoidance strategy, optimizing the global path planning strategy according to obstacle prediction track data, and generating a motion control instruction of the rescue robot. The technical problem of poor real-time performance and adaptability of path planning caused by insufficient perception of environment dynamic information in path planning of an existing rescue robot is solved, the strong perception capability depending on industrial vision is achieved, the environment dynamic information is accurately captured in real time, and the real-time performance of path planning is improved. And the real-time response speed of path planning and the adaptability to a dynamic environment are improved.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention relates to the technical field of rail traffic safety detection, in particular to a railway freight train key part online monitoring system based on an unmanned aerial vehicle, which comprises an unmanned aerial vehicle control module, a multi-modal data acquisition module, an edge calculation module, a central processing module and a feedback execution module. A dynamic three-dimensional grid flight path is generated by fusing a train Beidou positioning signal and a millimeter wave radar sensing result, a system is provided with an infrared thermal imager, a laser radar and a high-speed polarization camera, bearing temperature, train body point cloud and a train coupler image sequence are obtained, and temperature rise area identification, structural deformation extraction and coupling state modeling are completed on the edge side. The central processing module outputs a multi-dimensional safety assessment result, and the feedback module generates a compensation control instruction and a graded early warning signal based on the risk fusion index. The method has the advantages of being high in autonomy degree, high in recognition precision and high in response speed, and is suitable for full-time structural intelligent inspection and early warning of the freight train in a high-speed operation environment.

The invention relates to an unmanned aerial vehicle intelligent dynamic path guidance method and system based on deep reinforcement learning. The method comprises the steps of obtaining three-dimensional terrain point cloud and obstacle information of a flight area, constructing an environment topological structure diagram, extracting features through a three-layer diagram convolutional network, and generating high-dimensional environment feature representation. And a deep reinforcement learning state space is constructed, and a multi-target reward function is designed. And initializing the deep reinforcement learning network by using [-0.1, 0.1] uniformly distributed random parameters to generate an initial path scheme. Optimizing through a dynamic planning algorithm, adjusting a node sequence according to a path length and a task dynamic threshold value, and calculating a target function value. And judging whether the current path scheme reaches the optimal balance point, if not, adjusting the weight of the reward function by using a gradient descent method, and regenerating the path scheme until the path scheme is optimal. And finally, converting the scheme into a waypoint coordinate and speed instruction, performing real-time monitoring during flight, and triggering re-planning when an obstacle exceeds a safety threshold, so as to achieve the purposes of safe flight of the unmanned aerial vehicle in a complex environment and the like.

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 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 mechanical arm positioning and grabbing method based on machine vision, and relates to the technical field of machine vision and mechanical arm control, the method comprises the following steps: synchronously acquiring RGB-D images of a target scene through a multi-view camera array, and generating three-dimensional point cloud data through data fusion; an improved LSD algorithm and a PnP algorithm are adopted to calculate the initial pose of the target object, illumination distortion is eliminated in combination with the generative adversarial network, and three-dimensional coordinates are output; a mechanical arm motion error transfer model is constructed based on Monte Carlo simulation, and a candidate grabbing scheme set is generated through reinforcement learning; and an optimal grabbing scheme is screened through a preset priority evaluation rule, and a mechanical arm joint movement track and a control instruction set are generated. Through multi-modal data fusion and a nonlinear optimization algorithm, the technical problems of large target positioning deviation and sensitive illumination interference in a complex environment are solved, and the grabbing precision and robustness of the mechanical arm are improved.

The invention discloses an electromechanical construction method and system based on a BIM technology, and relates to the field of electromechanical construction, and the method comprises the steps: collecting a three-dimensional point cloud of a construction site; a construction site buult BIM model is constructed; constructing an electromechanical BIM model of the construction site; obtaining a logic dependency relationship between electromechanical tasks in the electromechanical BIM model by utilizing a judgment matrix method DSM, and extracting attribute parameters of task nodes; constructing a directed acyclic graph (DAG) for task collaborative optimization according to the logic dependency relationship between the electromechanical tasks and the time sequence and resource parameters of the nodes; obtaining task nodes on the critical path according to a directed acyclic graph (DAG) of task collaborative optimization; establishing a multi-objective optimization model according to task nodes on the critical path, and solving through a multi-objective particle swarm optimization algorithm MOPSO by taking the shortest construction period and the highest resource utilization rate as optimization objectives and taking process logic and resource occupation as constraint conditions to obtain a resource optimization configuration set; and the collaboration of electromechanical construction tasks is improved.

The invention provides an IMU (inertial measurement unit)-assisted depth SLAM (simultaneous localization and mapping) method and system fusing language-vision multi-mode perception, and the system comprises functional modules such as initial calibration and semantic map initialization, pre-integration prediction and key frame judgment, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guidance loopback detection and semantic three-dimensional map incremental updating. IMU motion priori, depth geometric constraint and language model semantic factors are subjected to combined modeling through a graph optimization framework, and high-precision positioning and labeled map construction in a complex dynamic environment are achieved. Compared with the prior art which only depends on geometric or inertial information, the method has the advantages that the loop-back mismatching rate is reduced, the closed-loop convergence efficiency and the long-time relocation robustness are improved, and a semantic interface is provided for upper-layer tasks such as natural language navigation and target retrieval. The method can be widely applied to the fields of service robots, security inspection, intelligent driving, post-disaster search and rescue and the like.

The invention relates to the technical field of intelligent detection, in particular to a container surface damage detection method and device based on machine vision, and the device consists of a multi-modal data acquisition module, a dynamic compensation processing module, a multi-modal data fusion module and a damage classification and positioning module. The multi-modal data acquisition module generates a high-density three-dimensional point cloud through double-laser line scanning, and acquires a multispectral image, an infrared thermogram and a real-time motion state. The dynamic compensation processing module integrates an optical flow method and acceleration data to realize sub-pixel-level motion compensation, and combines adaptive exposure control to optimize the imaging quality under complex illumination. The multi-modal data fusion module strengthens defect feature expression through space-time alignment and a double-branch collaborative attention mechanism, and model parameters are reduced through lightweight network design. And the damage classification and positioning module adopts an improved deep network to realize defect classification and complete millimeter-level three-dimensional positioning. The automatic detection requirement of a port is met, and the problems of poor container detection precision, poor adaptability and the like are solved.

The invention discloses a self-adaptive control unmanned aerial vehicle flight path correction method and system, and relates to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: collecting flight environment data through a sensor group carried by an unmanned aerial vehicle, constructing a three-dimensional environment model, and dividing the three-dimensional environment model into space units, the real-time pose of the unmanned aerial vehicle is determined based on point cloud matching and pose estimation technologies, trajectory offset is predicted according to a preset flight path, flight trajectory deviation parameters are generated, and a triaxial compensation strategy of horizontal plane, vertical direction and lateral offset is analyzed through trend analysis of the deviation parameters. And finally performing dynamic compensation correction on the flight path of the unmanned aerial vehicle according to the three-axis adjustment amount. The technical problem that the real-time accurate correction efficiency of the flight path of an existing unmanned aerial vehicle in a complex environment is low is solved, and the technical effect of shortening the dynamic compensation response time of the flight path deviation of the unmanned aerial vehicle is achieved through an industrial automatic control system device manufacturing technology and an intelligent monitoring device.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to the technical field of intelligent building data processing, and provides a BIM-based bridge collision detection method, which is used for solving the problem of collision early warning lag caused by space-time dislocation of a static model and real-time monitoring data in bridge construction. A comprehensive point cloud data set with a unified space-time reference is generated through multi-source data dynamic fusion and a quantum optimization algorithm, a self-evolutionary dynamic BIM model is constructed, and digital mapping of millimeter-level precision of a construction scene is achieved. A collision risk prediction model is constructed based on causal reasoning and multi-physics field coupling, a multi-dimensional relationship among construction parameters, environment variables and structural response is associated, and a linkage effect caused by mechanical motion is predicted. According to the method, the time dimension limitation of a traditional static model is broken through, full-process closed-loop control of perception-analysis-decision-verification is formed, the real-time performance of collision early warning and the controllability of construction safety are remarkably improved, and the method is suitable for high-precision construction scenes such as complex bridge rotation and cantilever casting.