307 results about "DBSCAN" patented technology

The invention provides an unmanned aerial vehicle optimal route planning method and system fusing image recognition and M-RRT and APF algorithms. The point cloud and visual data of a fan are obtained in real time through a multi-mode sensor, key parts of blades are recognized based on deep learning, a geometric mapping model is constructed, the blade tip points are corrected in a clustering mode in combination with DBSCAN, and the shutdown posture is predicted. A global path adopts an M-RRT algorithm improved by a direction heuristic factor to guide and search a key area of a blade; a local track is optimized through a dynamic weight APF algorithm, and a repulsive force field is adjusted in real time to cope with attitude changes. An online re-planning mechanism is introduced, NSGA-III multi-target optimization is triggered when the environment suddenly changes or the tracking error exceeds a threshold value, and the optimal track is generated by integrating energy consumption, time and safety. The system integrates high-precision sensing, self-adaptive planning and dynamic optimization, and the inspection coverage rate and the track safety under the complex shutdown attitude are remarkably improved.

The invention provides a full-layout defect rapid detection system based on pattern matching and classification, which relates to the technical field of semiconductor manufacturing and comprises a data acquisition and multi-source fusion module, a self-adaptive preprocessing module, a dynamic pattern matching module, a multi-source feature fusion module and a closed-loop optimization and output module, the dynamic mode matching module dynamically generates a defect template from real-time data through a clustering algorithm (such as DBSCAN) and a generative adversarial network (GAN), the speed and accuracy of defect detection are remarkably improved in combination with reinforcement learning filtering and region focusing technologies of the self-adaptive preprocessing module, a detection strategy can be adaptively adjusted according to the real-time data, and the defect detection accuracy is improved. According to the method, a high-risk area is preferentially matched, and GPU parallel computing acceleration processing is performed, so that the detection time is shortened to 50% or below of that of a traditional method, deformation or fuzzy defects can be effectively identified, the false alarm rate is reduced by about 30%, and high-quality input is provided for subsequent matching and classification.

The invention relates to the technical field of chemical safety and intelligent control, in particular to an explosion-proof intelligent temperature control method and system for a hydrogen peroxide storage tank based on multi-modal monitoring, and the method comprises the steps: collecting multi-dimensional data such as temperature, pressure and H2O2 concentration through a sensor array, carrying out the Kalman filtering and wavelet transform denoising, recognizing local abnormal points through an improved WMDS-DBSCAN algorithm, and obtaining the explosion-proof intelligent temperature control of the hydrogen peroxide storage tank based on the WMDS-DBSCAN algorithm. Predicting a hot spot diffusion trend in combination with an unsteady heat conduction equation and an RF-ADI algorithm; weighted Delaunay triangulation is utilized to construct a self-adaptive monitoring grid, risk area probability distribution is output through an SAM-LSTM-CNN hybrid neural network, and a dynamic amplitude limiting fuzzy PID controller adjusts the opening degree of a nozzle to achieve temperature control. According to the method, multi-modal data and an intelligent algorithm are fused, abnormal accurate detection, risk prediction and self-adaptive temperature control are realized, a closed-loop optimization mechanism is formed, and the operation safety and reliability of the hydrogen peroxide storage tank are effectively improved.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention belongs to the technical field of smart cities, and discloses a point cloud-based urban building roof wireframe reconstruction method, which comprises the following steps of: fusing a fast point feature histogram and a multi-scale roof geometric descriptor, generating robust point-by-point features, screening candidate angular point clusters in combination with a classification head, and adaptively segmenting the candidate point clusters based on density parameters by utilizing DBSCAN (Density-Based Spatial Clustering of Applications with Noise), so as to reconstruct a point cloud-based urban building roof wireframe. Initial inflection points are extracted through unsupervised clustering, noise is effectively suppressed, and irregular distribution is adapted; then multi-scale geometric information of an initial inflection point neighborhood is aggregated through an inflection point correction network, offset is learned to correct position deviation, and inflection point positioning precision is improved; and finally, the edge classification network automatically deduces the topological connection of the roof wireframe based on the geometrical relationship and feature relevance of prediction inflection points, so that error accumulation caused by dependence on manual rules in a traditional method is avoided, the generalization ability of a complex roof structure is enhanced, the correction network learns offset through a multi-scale context, and the inflection point positioning precision is remarkably improved.

The invention discloses an unknown radar radiation source sorting method based on time sequence feature clustering. The unknown radar radiation source sorting method comprises the following steps: acquiring radar signal pulse description words of pulse signals; according to the radar signal pulse description word, performing spatial clustering on the pulse signal to obtain a spatial clustering result; for each spatial clustering result, according to the radar signal pulse description word of the pulse signal in each spatial clustering result, performing time feature clustering on the pulse signal in each spatial clustering result to obtain a time sequence clustering result; and analyzing a time parallel relationship and a time continuous relationship of the time sequence clustering results, and performing pulse group sequence blending on the time sequence clustering results to obtain a sorting result. On the basis of the existing clustering algorithm, the information of the pulse signal in the time dimension is introduced, the time sequence feature clustering of the pulse signal is realized by using the ST-DBSCAN algorithm thought and introducing the TOA-PA constraint interval, and the method has strong robustness for the complex electromagnetic environment.

The invention belongs to the technical field of public safety, and particularly relates to a map-based focused person management and control large model visualization system and method, and the system comprises a multi-source data collection layer which is used for constructing a multi-modal data set comprising identities, tracks and biological characteristics; the large model analysis layer comprises a space-time trajectory prediction model, a risk integral calculation engine, a group event early warning model and data processing; the visual interaction layer dynamically renders personnel tracks, risk thermodynamic diagrams and police resource distribution based on a three-dimensional map engine, and supports hierarchical early warning display and emergency plan linkage; the monitoring and tracking layer is matched with the tracking and early warning layer, frequently-going / non-frequently-going areas are divided through an improved space-time ST-DBSCAN algorithm, dangerous behaviors of management and control personnel are monitored and tracked, and a differential early warning mechanism is triggered. The multi-modal data fusion technology is combined, real-time loading of ten thousand people-level track points is achieved, data collaborative training of public security organizations in multiple places is supported, and dangerous behaviors of management and control personnel can be monitored and tracked.

The invention relates to the technical field of fan blade fault diagnosis, and discloses a fan blade fault diagnosis system and method based on HHT and DBSCAN. Synchronously acquiring vibration signals acquired by a three-axis acceleration sensor and wind speed and rotating speed working condition data acquired by an SCADA (Supervisory Control and Data Acquisition) system; eMD empirical mode decomposition is carried out on the collected vibration signals, the vibration signals are decomposed into a limited number of IMF signals, and effective IMF signals are screened; generating a Hilbert spectrum through HHT, extracting multi-scale frequency band energy features, and fusing SCADA data to carry out working condition adaptive normalization on the features; and then, processing the normalized features by adopting a self-adaptive DBSCAN clustering algorithm based on K-distance map parameters, and realizing fault diagnosis by identifying an abnormal cluster with a sample proportion of less than 5% and combining with a frequency band energy deviation threshold. According to the method, the problems that a traditional method is not thorough in decomposition of non-stationary signals, depends on labeled samples and is insufficient in clustering robustness are solved, and unsupervised and high-precision blade state monitoring is achieved.

The invention discloses a method for detecting and tracking a dynamic obstacle in a weak light environment. The method comprises the following steps: 1) acquiring an image; (2) a U-depth detector and a DBSCAN detector are used for conducting rapid detection on a near obstacle, and after weak light images of a far obstacle are enhanced through a lightweight image enhancement network LPF, a lightweight FastDet model is input to conduct deep learning auxiliary detection; 3) calculating the intersection-to-union ratio of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, fusing the intersection-to-union ratio results of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, and generating a set bounding box; 4) performing data association on the detected obstacles through a feature association method, and performing continuous tracking by using Kalman filtering; and 5) identifying a dynamic obstacle from all detected obstacles, and outputting the size of a three-dimensional bounding box of the dynamic obstacle and the center position coordinates of the dynamic obstacle in the unmanned aerial vehicle coordinate system.The method provides a high-precision and low-delay solution for obstacle perception of the unmanned aerial vehicle in a weak light environment, and is suitable for different scene requirements.

The invention relates to the field of gas leakage identification, and discloses a gas leakage identification method and system based on sound localization, a medium and equipment, and the method comprises the steps: collecting a leakage sound wave signal through a microphone array, and extracting acoustic features in a complex background; simulating a diffusion process of gas in a turbulence environment after gas leakage through an established leakage gas convection-diffusion model, and combining gas concentration distribution in the simulated diffusion process with acoustic characteristics to predict sound source localization at a leakage position; modeling a sound source localization search process as POMDP, and iteratively updating a confidence state of a sound source position through a particle filter; spatial distribution features are extracted from the confidence state through DBSCAN clustering to serve as input of the LSTM-DQN network, spatial and temporal features are fused through the constructed LSTM-DQN network, cross-scene generalization is achieved through transfer learning, and sound source coordinates are dynamically optimized and output through the Q network. According to the invention, the positioning accuracy and real-time performance in a complex environment are improved.

The invention belongs to the technical field of integrated circuits, and particularly relates to a time mismatch extraction method based on parallel windowing autocorrelation. According to the method, firstly, clustering processing is carried out on signal points; thirdly, preliminarily screening out legal signals based on the time occupancy rate of the signals, and constructing an initial legal signal library; a similarity score between the signals is further calculated through an IDK algorithm, and adjacent frequency bands are divided through a Pettitt method; and finally, continuation of legal signals is realized by means of a DBSCAN clustering algorithm, and automatic construction of a legal signal library is completed. The method is not only suitable for scenes without prior information, but also can be applied under the condition with partial prior information, and can be dynamically updated according to actual requirements. The method is based on radio frequency spectrum monitoring big data, can construct a legal signal library with high reliability and strong generalization ability, provides effective support for subsequent illegal signal monitoring, and has important application value.

The invention relates to the technical field of echelon utilization of decommissioned lithium ion batteries, in particular to a decommissioned power lithium battery sorting method and system based on DBSCAN and WOA-FCM. The method comprises the steps that static characteristics of decommissioned power lithium batteries are determined; based on the static characteristics, first-stage sorting is carried out on the retired power lithium batteries through a density-based noise application space clustering algorithm, so that abnormal batteries are eliminated, and the clustering number of normal batteries is determined; and for the remaining normal batteries after the abnormal batteries are removed, dynamic characteristics are extracted based on a battery discharge voltage curve, second-stage sorting is carried out through a whale optimization-based fuzzy C-means clustering algorithm, and a battery sorting result is obtained. Therefore, through two-stage collaborative optimization, the sorting accuracy and efficiency are improved, and the high consistency of the recombined battery packs is ensured.

The invention discloses a road advertisement putting method and system based on intelligent travel data, and relates to the technical field of intelligent traffic and precise advertisement putting, and the method comprises the steps: collecting intelligent travel data; the intelligent travel data comprises vehicle real-time trajectory data, vehicle attributes, a road network topological structure and environment data; inputting the standard trajectory data set into an improved ST-DBSCAN spatial clustering algorithm, performing spatial-temporal density clustering analysis to identify a high-frequency passage path and an associated vehicle set, and outputting a high-frequency path list; associating the high-frequency path list with an advertisement exposure log, and performing strong rule mining through an FP-Growth algorithm to generate an advertisement putting rule base; and based on the high-frequency path list and the advertisement putting rule base, combining the real-time track data of the vehicle, and generating a dynamic advertisement putting instruction. According to the invention, the accuracy, the automation level and the resource utilization efficiency of advertisement putting are integrally improved.

The invention discloses a water supply pipe network monitoring point arrangement method based on a risk assessment and optimization algorithm, and relates to the technical field of intelligent pipe network monitoring, and the method comprises the steps: collecting water supply pipe network data, carrying out the preprocessing, and storing the data in an InfluxDB time sequence database; constructing a multi-dimensional risk assessment model, and generating risk scores of the pipe sections; a greedy algorithm and genetic algorithm hybrid optimization strategy is adopted to generate a water supply network monitoring point layout scheme; the method comprises the following steps: deploying an edge computing gateway, distributing water supply network data to the edge computing gateway through an MQTT protocol, generating a pipe section risk probability by using a pipe section risk probability model, setting a four-level judgment threshold, and triggering a hierarchical alarm mechanism; a DBSCAN algorithm is used for identifying a water supply network risk high-incidence area, water supply network monitoring points are increased, and the monitoring radius of the water supply network monitoring points is dynamically adjusted. According to the method, through a greedy-genetic hybrid optimization strategy, a global optimal solution and rapid convergence of monitoring point layout are realized.

The invention discloses a shallow sea area water depth inversion method based on a satellite-borne laser radar, and particularly relates to the technical field of single-photon water depth information extraction. The denoising method comprises the following steps: S1, data preprocessing: reading data, carrying out visual presentation, dividing photons into offshore photons, sea surface photons and undersea photons, and intercepting an effective photon region; s2, coarse denoising: denoising the area intercepted in the step S1 by adopting a DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm; s3, fine denoising: screening effective signals of sea surface photons by adopting a Gaussian fitting algorithm, removing sea photons, separating subsea photons from the sea surface photons, performing density analysis on the subsea photons, extracting a density peak point, and constructing a density trend curve of the subsea photons by adopting a cubic spline interpolation method, eliminating residual noise deviating from normal distribution according to the trend curve; and S4, performing water depth inversion: performing refraction correction on the subsea photons subjected to fine denoising, fusing the corrected subsea photons and the sea surface photons subjected to fine denoising, and generating a water depth distribution diagram of the shallow sea area.

The invention discloses a multi-level water supply and drainage balance monitoring method and system for the water affair industry, and relates to the technical field of water affair monitoring. The method comprises the following steps: deploying a multi-level monitoring network; the method comprises the following steps: collecting real-time data of a multi-level monitoring network, executing space-time alignment operation, and preprocessing the data; constructing a quarterly three-dimensional water balance equation, and dynamically estimating through Kalman filtering; dBSCAN clustering is implemented based on pipe network topology, and leakage positioning is carried out to generate a leakage probability thermodynamic diagram; and establishing a hierarchical response mechanism to carry out multi-level early warning linkage, and rendering a multi-layer perspective view in a GIS (Geographic Information System) engine. According to the method, a quarterly three-dimensional water balance equation is constructed through a city-region-enterprise three-level dynamic weight model and a hierarchical penetration algorithm, and dynamic estimation is performed through Kalman filtering; dBSCAN clustering is implemented based on the pipe network topology, leakage positioning is performed, a leakage probability thermodynamic diagram is generated, the water affair monitoring efficiency is improved, and the manual troubleshooting workload is reduced.

The invention provides an electrical load release method based on clustering analysis, and relates to the technical field of electrical load adjustment. The electrical load release method based on clustering analysis comprises the steps of data collection, collection of historical data of electrical loads in a target area through a power grid load power monitoring system, data preprocessing, elimination of abnormal values in the collected electrical data in the target area, and extraction of static characteristics and dynamic characteristics of power data. And the data features are standardized. According to the method, the K-means clustering algorithm and the DBSCAN clustering algorithm are adopted, the convergence speed of the algorithms and the stability of the clustering result are effectively improved by combining the characteristics of the two algorithms, and the load release strategy which is high in pertinence and feasible is formulated based on different electrical load categories obtained through clustering analysis.

The invention relates to the technical field of data processing, and discloses a trajectory tracking method and system for intelligently positioning a child schoolbag. The method comprises the following steps: carrying out data acquisition processing on a GPS (Global Positioning System), Bluetooth, Wi-Fi (Wireless Fidelity) and an acceleration sensor arranged in the children schoolbag to obtain multi-source positioning original data; inputting the original data into an improved Kalman filtering algorithm for fusion processing to obtain position information; constructing a spatio-temporal trajectory sequence for the position information to form a historical activity trajectory data set; and executing a DBSCAN clustering algorithm based on the data set to generate child activity route data. According to the positioning jitter elimination method based on the environment adaptive moving average algorithm, the problem of positioning fluctuation of children in places such as schools and parks is effectively solved. Through a route extraction technology combining DBSCAN clustering and iterative projection fitting, a conventional activity track and a time rule of a child are accurately identified, and efficient and reliable abnormal behavior detection is realized.

The invention discloses a ship trajectory clustering method, which comprises the following steps of: cleaning AIS data to obtain a plurality of navigation trajectories of a ship in a set time period; the Hausdorff distance between any two navigation tracks is calculated to serve as the calculation basis of a DBSCAN algorithm; a core point prediction model is constructed, high-probability data points in the navigation trajectory are screened out to serve as candidate core points, and neighborhood query and clustering expansion are performed on the candidate core points through a DBSCAN algorithm; a Bayesian optimization algorithm is improved through an adaptive acquisition function, and hyper-parameters of a DBSCAN algorithm are optimized based on the improved Bayesian optimization algorithm. The DBSCAN algorithm has the beneficial effects that the DBSCAN algorithm is improved aiming at the problem of relatively low operation efficiency of the density clustering algorithm under the condition of large data volume, and the operation efficiency of the algorithm is improved while the calculation precision is ensured. The Bayesian optimization algorithm is introduced to automatically optimize hyper-parameters of the DBSCAN algorithm, and the effect and stability of clustering analysis are improved.

The invention provides a gas field reservoir lithology earthquake prediction method based on CCIDI cleaning and TransMSAE, and the method comprises the steps: constructing a CCIDI cleaning module, and employing a well-divided lithology-divided strategy: employing IQR to remove statistical anomalies, employing DBSCAN to remove boundary noise, employing Isolation Forest to remove high-dimensional residual noise, and forming a high-quality logging data set; the method comprises the following steps: constructing a TransMSAE model: pre-standardizing a Transform encoder to capture long-range dependence, and providing robust time sequence representation; multi-scale convolution is introduced into the MSCCAM to extract a local-global mode, and cross attention is fused to improve complex deposition dynamic perception; aMFEM gating residual self-adaptively enhances minority class features, and minority class recognition is remarkably improved; designing NAFL-ProtoLoss mixed loss: NAFL focus inhibition easy-to-classify samples, ecological niche punishment over-confidence prevention, ProtoLoss zoom-in class inward pushing-out between classes, and optimization of unbalanced classification; finally, training and reasoning are carried out based on the model, and high-precision lithology prediction is achieved. The method provided by the invention has high generalization and accuracy, and has excellent reservoir prediction performance in a complex geological environment.

The invention relates to a manual driving vehicle overspeed behavior identification and intervention method based on a detection carrier, and the method comprises the steps: building a detection carrier system, collecting adjacent HV point cloud data through a laser radar, carrying out the median average filtering, K-Means and DBSCAN combined clustering preprocessing, and achieving the multi-target dynamic tracking through a Point Pill + DeepSORT model; and calculating the real average speed of the HV by using a 1-3s self-adaptive speed measurement window, and comparing the road speed limit to complete the overspeed identification. And when overspeed is judged, dynamically triggering a corresponding high-speed camera to capture, extracting license plate information, fusing speed and GPS data to generate illegal evidence. The method comprises the following steps: quantifying the influence of a detection carrier on an HV individual overspeed behavior, training an HV swarm agent based on VAE-WGAN data enhancement and a double-branch deep Q network, simulating multiple traffic scenes in SUMO, and determining an optimal deployment scheme of the detection carrier by using a dynamic Gaussian mixture Bayesian network and a double-layer double-target optimization model. The method breaks through the limitation of traditional speed measurement, improves the recognition precision and deterrence effect, and balances the traffic safety and efficiency.

The invention provides a target vehicle pose detection method and system based on a single-line laser radar point cloud, and relates to the technical field of vehicle positioning, and the method comprises the following steps: S1, carrying out the preprocessing of point cloud data collected by a laser radar based on a DBSCAN algorithm, screening out noise points, and generating an effective cluster; s2, performing L-shaped feature fitting on each cluster, and calculating a tire center point coordinate; s3, calculating vehicle pose data according to the at least three tire center points, wherein the vehicle pose data comprises a wheelbase, a vehicle center coordinate and a parking angle; and S4, transmitting the vehicle pose data to a parking AGV control system through a communication protocol. The method has the beneficial effects that by adopting the positioning method integrating the laser radar and AGV four-wheel positioning, the vehicle navigation and positioning precision is further improved; a laser radar point cloud is combined with an L-shaped feature fitting algorithm, the pose of a target vehicle is obtained in the walking process of the parking AGV, the pose detection efficiency of the parking AGV is improved, and the walking fluency of the parking AGV is improved.

The invention discloses an analysis method and system based on AI video recognition behavior monitoring, and relates to the technical field of video recognition, and the method comprises the steps: collecting video data, calculating the overall pixel gradient change of adjacent frames, marking preliminary candidate frames, calculating the edge saliency intensity of each frame, and analyzing a salient region point set; clustering and calculating the geometric center point of each cluster as an anchor point value, and forming an anchor point set through the center points of each cluster; according to the method, concentrated extraction of salient points is realized through a density-based clustering algorithm DBSCAN, so that an extraction result has logic uniqueness and local integrity of a salient target, a local feature map is extracted by using a shallow convolutional network, target texture and edge information is effectively captured, bounding box optimization is realized by rotating an IoU loss function, and the robustness of the target is improved. And the fitting precision of the boundary of the salient region is improved.

The invention relates to the field of data processing, in particular to an archive information management method and system based on artificial intelligence, and the method comprises the steps: according to the analysis of classified historical archives, extracting the features of word vectors, sentence vectors, time vectors, place vectors and the like, and calculating the feature classification accuracy and information entropy to determine the attention weight. And calculating the density by using the cosine similarity of the feature vectors of the archives to be classified, fusing the feature vectors to obtain a target vector and the comprehensive density, and adaptively adjusting the initial neighborhood radius of the DBSCAN clustering algorithm. And finally, based on the adjustment radius and the Euclidean distance, clustering classification and archiving are carried out on the archives, and the classification accuracy and the system intelligence level are improved. According to the method, multiple features are comprehensively utilized, and the clustering radius is adaptively adjusted, so that the accuracy and adaptability of archive classification are improved, the performance of a clustering algorithm is optimized, and the local structure of data is reflected more accurately.

The invention discloses a drought and flood sudden change event identification method, device and system based on a point cloud and a DBSCAN algorithm, and the method comprises the steps: calculating a meteorological drought index standardized rainfall evapotranspiration index corresponding to each grid point based on obtained grid point meteorological data; comparing the meteorological drought index standardized rainfall evapotranspiration index with a preset drought threshold value and a flood threshold value, identifying drought grid points and flood grid points, and converting the drought grid points and the flood grid points into corresponding three-dimensional point cloud data sets; based on the three-dimensional point cloud data set, utilizing a DBSCAN algorithm to respectively cluster and extract drought events and flood events; and for the drought event and the flood event, based on a preset time threshold value and a space overlapping threshold value of the drought event and the flood event, combining to generate a drought and flood sudden turning event. According to the invention, accurate identification of drought and flood sudden turning events can be realized.

The invention discloses a gallium oxide crystal storage environment monitoring system and method based on artificial intelligence, and relates to the technical field of intelligent industrial monitoring, and the method comprises the steps: inputting a multi-source coupling data set into a space-time hypergraph model, carrying out the multi-source data coupling of a hypergraph construction layer, and carrying out the spatial dependence capture and time sequence feature extraction of a space-time convolution layer, the method comprises the steps of forming an environment parameter prediction matrix, performing damage quantification and probability mapping on a gallium oxide crystal damage image, obtaining a reference damage distribution diagram, performing coupling association on the environment prediction matrix and the reference damage distribution diagram, outputting an environment-damage weight matrix, and performing defect positioning on the reference damage distribution diagram according to the environment-damage weight matrix. And obtaining a damage sensitive area. According to the invention, through the space-time hypergraph model, the DBSCAN clustering analysis and the risk grade division strategy, the accuracy and the real-time performance of the monitoring scheme are enhanced.

The invention discloses a robot sensing and autonomous navigation method and system for a three-dimensional topological map, and the method comprises the steps: obtaining the three-dimensional point cloud data of a surrounding environment, the acceleration and angular velocity data of a robot, and predicting the pose of the robot; identifying a free space and an obstacle area, determining an occupation state of each point cloud voxel, and generating a three-dimensional occupation map; dividing a free space into a plurality of convex clusters through neighborhood search based on a DBSCAN algorithm, and constructing a three-dimensional topological graph; identifying a vertical channel, and setting corresponding edges to be connected with portal nodes to form a global three-dimensional topological graph; and according to the global three-dimensional topological graph, determining a global path and a local obstacle avoidance path, and combining the global path and the local obstacle avoidance path to obtain a global optimization path. The technical bottlenecks that a two-dimensional map lacks a vertical dimension and a traditional voxel method is insufficient in real-time performance are broken through on the whole, and an efficient, safe and coherent solution is provided for autonomous navigation of a robot in a complex three-dimensional environment.

The invention discloses a dynamic multi-dimensional fast track association method and device based on a millimeter wave traffic radar. The method comprises the following steps: acquiring point cloud data acquired by a millimeter wave traffic radar; clustering the point cloud data through a DBSCAN clustering algorithm to obtain a point cloud clustering result; adopting a centroid algorithm to complete the condensation of trace points in the point cloud clustering result, and obtaining the condensed trace points; and carrying out association calculation on the condensed trace points and the currently managed track or historical trace points based on an association algorithm of an optimal association coefficient to obtain matched track information.

The invention relates to the technical field of construction progress monitoring, in particular to a construction progress multi-dimensional dynamic monitoring method based on the Internet of Things and BIM, and the method comprises the following steps: obtaining a component number and a planned time period through the BIM, collecting an action label, a three-dimensional coordinate and space projection matching time, and generating a component behavior set through DBSCAN clustering; the method comprises the following steps: slicing an action sequence to calculate frequency and coverage proportion; classifying component states to generate progress deviation; matching the deviation with a Hungary algorithm to calculate a coupling map; extracting a mechanical track and path included angle, normalizing and correcting the included angle to generate a deviation sequence, and grading track segments to generate a dynamic monitoring index set. According to the method, time-space association is established based on component numbers and coordinate projection, a classification model is constructed by using time slice coverage to identify progress deviation, task chain coupling is analyzed through bidirectional matching, a deviation sequence is generated through included angle correction, a delay amount and coverage rate evaluation system is established, and progress deviation tracing and abnormal early warning are achieved.

The invention relates to an ultrasonic detection and damage evaluation method and system for a curved-surface composite component, and the method comprises the steps: collecting three-dimensional point cloud data of a surface damage region of the curved-surface composite component, and constructing a detection path point sequence; the industrial robot and the ultrasonic phased array are controlled to move and scan synchronously according to time, and ultrasonic A scanning signals and robot tail end pose information are collected; three-dimensional space coordinates of all reflection points in the curved surface composite material component are calculated in combination with pose derivation, and an initial three-dimensional damage point cloud is constructed; setting a segmentation threshold to construct an initial segmentation damage point cloud; a traditional DBSCAN clustering algorithm is improved for clustering segmentation, and point clouds of all independent damage areas are obtained; and voxelizing the point cloud of the independent damage area into a three-dimensional voxel model to realize quantitative assessment of the damage. According to the method, automatic and high-precision three-dimensional damage detection and evaluation of the complex curved surface component are realized, and the problems that a traditional ultrasonic detection method depends on manual operation and is poor in adaptability, the detection result is not visual and the like are effectively solved.