2046 results about "Feature matching" patented technology

The invention provides a dam leakage intelligent identification method based on multi-modal fusion and knowledge enhancement, and the method comprises the steps: collecting real-time data of a multi-source sensor disposed at a key part of a dam in a preset monitoring time period, and generating seepage characteristic data; identifying a seepage form entity based on the seepage characteristic data and extracting an instantaneous characteristic entity, and associating the entity into a structured knowledge unit according to a space-time proximity principle; knowledge units are classified according to spatial positions and influence ranges, association rules of the knowledge units are complemented, and knowledge graph construction is achieved; and then, a map inference engine is triggered in a real-time feature matching mode, and graded early warning is implemented. According to the method, physical enhanced seepage characteristics are constructed, seepage forms, dynamic characteristics and inducements are deeply associated by utilizing a knowledge graph technology, accurate diagnosis and reasoning from data abnormity to seepage types, causes and risk levels are realized, and finally, the seepage characteristics are analyzed and analyzed through a dynamic conflict resolution and self-evolution mechanism. And a reliable dam leakage intelligent identification and decision-making system is formed.

The invention provides an underwater target identification method and system based on multi-source sensor data fusion. The method comprises the following steps: firstly, synchronously acquiring an original sound wave reflection signal and original optical image data of a target underwater area, then carrying out acoustic compensation processing on the original sound wave reflection signal to generate a target sound wave reflection signal, and meanwhile, carrying out optical compensation processing on the original optical image data to generate target optical image data; performing dynamic weighted fusion on the reliability measurement of the target sound wave reflection signal and the reliability measurement of the target optical image data through an adaptive fusion module to generate an enhanced multi-modal data set; and finally identifying the specific category of the underwater target through a multi-modal feature matching and decision-making mechanism. According to the technical scheme provided by the invention, the limitation of a single sensing mode is overcome, the quality of collected information is improved, the direct confirmation efficiency of common target recognition is also improved, and high accuracy and data reliability of target category recognition in a complex scene are ensured.

The invention relates to a three-dimensional point cloud registration method and system based on a two-dimensional visual large model. A source point cloud and a target point cloud are decoupled into two-dimensional projection representation; constructing a structure consistency restoration domain, restoring geometric structure fracture and semantic deficiency in the projection image, and generating a dense and continuous restoration image; inputting the repaired image into a pre-trained visual geometry large model, carrying out cross-modal feature matching and pose resolving, and synchronously outputting a pixel-level matching confidence map; and inversely mapping a two-dimensional pose calculation result to a three-dimensional space to complete coarse registration, constructing a high-credibility anchor point set in the three-dimensional space by using a confidence map, and guiding a point cloud fine registration algorithm to converge to global optimum. According to the method, the registration large model for natural image training can be seamlessly migrated to the point cloud data, and retraining is not needed; in a weak texture and partially overlapped point cloud scene, a robust rough registration initial value can still be provided; the convergence speed and the anti-noise performance of fine registration are remarkably improved, and adaptive improvement from coarse to fine is achieved.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.

The invention belongs to the technical field of geometric pretreatment in computer aided engineering (CAE), and particularly relates to a dirty geometry restoration method for a complex three-dimensional geometric model, which comprises a dirty geometry detection and restoration module based on heuristic rules and geometric topology analysis. By means of a multi-model collaborative alignment and bonding mechanism, high-precision assembly between parts is achieved through geometric feature matching and adjacent domain self-adaptive adsorption; acquiring material attributes provided by the simulation platform, and performing accurate cutting and boundary reconstruction on an intersection domain based on an intersection domain intelligent segmentation algorithm of the material attributes to ensure correct mapping of boundary conditions of a physical field; a geometric Boolean processing engine iterative learning mechanism of a general merging algorithm is integrated, and a high-quality model for simulating a grid is generated through multi-round iterative optimization. According to the method, double challenges of internal dirty geometry of a single model and derivative dirty geometry of intersection / adjacent areas among multiple bodies are solved, and key physical attributes and topological continuity of electromagnetic signal links are ensured and are completely reserved during repair.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention discloses a multi-source field-level blood relationship tracking method and system. The method comprises the following steps: extracting features from a multi-source heterogeneous system, matching templates, triggering a corresponding acquisition strategy, and outputting an original blood relationship log; reading an original blood relationship log to generate an abstract syntax tree, extracting a source / target field set to output a field-level mapping relation, further writing the field-level mapping relation into a graph database to form a blood relationship edge, writing metadata into a relational database, establishing a two-level index, and outputting a blood relationship graph capable of being quickly positioned; and taking the blood relationship map as input, returning a target path with the maximum sum of confidence coefficients by adopting a weighted shortest path algorithm, outputting a link-level traceability result, and displaying the link-level traceability result. According to the method, complete tracking of the field-level blood relationship is realized, the query response time is shortened to a millisecond level, rapid access to multiple types of data sources is supported, and the problems of insufficient blood relationship analysis precision, low query efficiency and weak multi-source adaptability in the prior art are solved.

The invention discloses a volume cloud rendering method and system based on three-dimensional Gaussian splash, and the method comprises the steps: carrying out the feature matching and structure reconstruction of a volume cloud image through an SfM algorithm, obtaining a sparse point cloud in a scene, and representing the sparse point cloud as an anisotropic Gaussian ellipsoid; decomposing the illumination transmission process of the volume cloud into two parts of forward single scattering and internal multiple scattering based on a radiation transmission equation to obtain an illumination modeling formula embedded volume cloud rendering process; determining the contribution of each three-dimensional Gaussian ellipsoid to a rendering result according to the opacity index of the three-dimensional Gaussian ellipsoid, and dynamically cutting the low-contribution three-dimensional Gaussian ellipsoid by adopting a delay deletion strategy to reduce redundancy; a comprehensive loss function is constructed, so that the three-dimensional Gaussian model is focused on the volume cloud region and the structure detail reduction capability is enhanced; and outputting the optimized volume cloud rendering result and the three-dimensional Gaussian model. According to the method, the volume cloud rendering efficiency and quality are effectively improved on the premise of ensuring real-time rendering.

The invention discloses a mutual cooperative door lock complementary acquisition method and system, and the method comprises the steps: extracting the structural feature data of a target person according to an event that a self door lock recognizes a non-white list person, generating an assistance request containing a target ID and the feature data, and transmitting the assistance request to an adjacent door lock; according to the assistance request received by the adjacent door lock, target feature matching is carried out, and after matching succeeds, a target video stream is collected and selectively coded; according to the time points when the target appears and disappears, generating an association starting pointer and an association ending pointer, and synchronously storing the pointers between the door locks; and analyzing the association pointer according to the playback request, and automatically starting stream playing of the association video streams of the plurality of door locks to realize multi-view synchronous display. By utilizing the embodiment of the invention, continuous automatic tracking and multi-view synchronous playback of cross-equipment target activities can be realized through active cooperation and data association between door locks, and the continuity and efficiency of security monitoring are improved.

The invention relates to a six-eye camera panoramic video real-time splicing method and system and computer equipment, and the method comprises the steps: synchronously carrying out the image collection through a six-eye camera array, and adding a timestamp to each video frame image; performing real-time feature extraction on the video frame image, and establishing a feature matching relationship in a view field overlapping region of adjacent cameras; based on the feature matching relationship, mapping each video frame image to a unified splicing coordinate system through projection transformation; performing splicing processing on the video frame images after projection transformation through multi-band fusion, and eliminating splicing traces at the boundary of the field of view; and reconstructing the panoramic frame images obtained after splicing processing according to a time sequence, and outputting a continuous panoramic video stream so as to realize real-time and seamless splicing and correction of six paths of high-definition video streams and finally output a global panoramic video stream with no visual fracture and consistent time and space.

The invention provides a multi-step automatic testing method based on machine vision, and the method comprises the steps: capturing an original image sequence of a test scene through a camera, extracting the contour features of a test object in an image through an edge detection algorithm, and obtaining a preliminary positioning coordinate; if the dynamic position change trend exceeds a preset threshold value, adjusting an image enhancement parameter to suppress background noise, and obtaining an enhanced target image; feature matching is carried out through the enhanced target image, a robust identifier such as a texture mode is extracted, and an accurate three-dimensional position coordinate is obtained; according to the accurate three-dimensional position coordinates, calculating an execution deviation value of the current step, and judging whether the deviation value is within an allowable range or not; if the deviation value is within the allowable range, a control instruction sequence is generated and transmitted to the mechanical arm, and a step execution confirmation signal is obtained; comparing a confirmation signal with a next image sequence through the steps, updating positioning model parameters, and determining a continuous adjustment scheme of the whole test process.

The invention relates to the technical field of computer vision, in particular to a method for generating a blind person navigation obstacle avoidance map. The method comprises the following steps: driving a blind guiding robot to a preset inspection point, and collecting a surrounding environment image in real time; generating a three-dimensional point cloud according to the environment image, and constructing an environment map; extracting feature points based on the surrounding environment image, performing feature matching, and outputting a feature matching result; evaluating an inter-frame motion state by using a feature matching result; determining pose features of the blind guiding robot according to the inter-frame motion state; semantic segmentation is carried out by using the pose features, and an obstacle area is determined; acquiring an obstacle image of the obstacle area, and fusing the obstacle image with the surrounding environment image to generate an obstacle map; and constructing a dynamic occupancy grid based on the obstacle map. According to the invention, real-time updating and intelligent obstacle avoidance functions of the blind navigation map are realized based on the computer vision technology, and the navigation safety and the path planning accuracy are improved.

The invention belongs to the field of visual tracking, and relates to a multi-target visual tracking method based on cooperation of a dynamic neural field and a neural network, which takes a cross-modal cooperation architecture as a core and comprises a dynamic neural field module based on multi-target trajectory maintenance and shielding matching and an improved MoESDQ neural network module. Meanwhile, a collaborative decision-making mechanism is designed, when the activation peak value of the dynamic neural field is attenuated to a preset threshold value, neural network feature matching is triggered, and disappearance target reproduction correlation is achieved based on cosine similarity. The objective of the invention is to solve the visual tracking capability under the condition of scene and target motion change in a monitoring range, for example, under an intelligent traffic intersection scene. The problems of high ID switching rate, multi-target misassociation and low tracking precision under a real-time tracking background caused by scene change or frequent shielding of vehicles and pedestrians, similar target appearances, transient disappearance and reproduction of the targets and sudden illumination change are solved.

The invention provides a multi-source information fusion unmanned aerial vehicle marine target tracking method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining image data, and extracting the features of a target region; unmanned aerial vehicle attitude data, radar position detection data and AIS identification data are obtained, attitude disturbance correction is executed, and time axis alignment is performed on the data; calculating the visual feature matching offset of the adjacent frames, and comparing the visual feature matching offset with the radar azimuth change trend; constructing a candidate target spatial position set, and executing distance convergence screening and spatial overlapping constraint processing of a geometric projection range on the candidate target spatial position set; motion direction continuity comparison is executed, and target position data is generated; generating a track control instruction for controlling the flight direction and speed of the unmanned aerial vehicle according to the target position data, and adjusting the subsequent flight path of the unmanned aerial vehicle according to the track control instruction; according to the invention, autonomy and accuracy of unmanned aerial vehicle marine target tracking are improved.

The invention discloses a PDF contract file identification method and system, a medium and a program product, and relates to the technical field of information identification, and the method comprises the steps: carrying out the image analysis of an obtained PDF contract file through employing a deep learning model, generating an editable text, and synchronously identifying the page layout of the document, outputting structured data including page numbers, text paragraphs and coordinate information; performing multi-dimensional feature matching on the text paragraphs according to an adaptive semantic analysis algorithm, a preset contract template library and a dynamic keyword library, and positioning contract core element information; performing entity relationship verification on the content of the contract core element information, correcting an extraction error through dependency syntactic analysis and semantic role labeling, and establishing a contract element data set containing a confidence coefficient weight; and performing serialized packaging on the verified contract element data set to generate a contract element list conforming to electronic signature authentication. According to the invention, the processing efficiency and recognition precision of the PDF contract file are improved.

The invention discloses an underground pipeline intelligent detection and mapping method based on image recognition, and the method comprises the following steps: 1, obtaining continuous video images, associating feature matching pairs of adjacent key frames, and forming a pose parameter set; 2, inputting the key frame into an improved YOLO-World detection network, and outputting a detection result set; 3, obtaining a geometric consistency matching set according to the detection result set; 4, performing multi-view triangularization on the geometric consistent matching set to form a weight factor; 5, introducing a weight factor, and executing incremental beam adjustment optimization on the pose parameter set and the three-dimensional sparse point set to obtain a sparse semantic point cloud; and step 6, outputting an underground pipe network topology map. According to the invention, high-precision and high-robustness intelligent identification and topological mapping in a complex underground pipeline environment are realized.

The invention provides a complex heterogeneous molded surface three-dimensional vision measurement method and system, and the method comprises the following steps: (1), extracting part design model features through a semantic segmentation and intelligent recognition method, and building a to-be-measured feature set; (2) a three-dimensional measurement viewpoint planning method based on reinforcement learning is adopted to obtain a viewpoint sequence and a motion path; (3) when the local measurement system reaches a preset viewpoint, a self-adaptive single-pixel imaging feature matching method is adopted for mutual reflection and semitransparent molded surfaces; for other molded surfaces, a parameter adaptive fringe projection three-dimensional measurement method is adopted to obtain a single-viewpoint three-dimensional point cloud; (4) converting the single-viewpoint three-dimensional point cloud into a global coordinate system to realize three-dimensional point cloud splicing; (5) traversing each viewpoint of the viewpoint sequence, and executing the steps (3) and (4) to obtain a multi-viewpoint spliced three-dimensional point cloud; and (6) based on the point cloud error estimation, fusing the multi-view three-dimensional point cloud. The problem of accurate acquisition of the three-dimensional point cloud of the complex heterogeneous molded surface is solved.

The invention relates to the technical field of fingerprint identification, and discloses a small-area fingerprint identification method and device, and a medium. The method comprises the following steps: acquiring a to-be-identified fingerprint image; performing ridge line feature point extraction on the to-be-identified fingerprint image to obtain a feature point set; the feature point set comprises a first feature point and / or a second feature point, the first feature point is a bifurcation point and / or an end point of a fingerprint ridge line, and the second feature point is a maximum curvature point of the fingerprint ridge line; based on feature points in the feature point set, performing image feature extraction on the to-be-identified fingerprint image to obtain a plurality of corresponding local fingerprint image features; and performing feature matching on the local fingerprint image features and preset target fingerprint image features, and generating a fingerprint identification result of the to-be-identified fingerprint image based on a feature matching result. According to the embodiment of the invention, the recognition precision of small-area fingerprint recognition can be improved.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.

The invention relates to the technical field of computer vision and deep learning, in particular to an image feature matching method and system based on self-supervised learning, and provides an image feature matching method and system based on self-supervised learning. Features are extracted by means of an online network model and a momentum encoder, and image features are matched by adopting a multi-order sequence comparison algorithm after the model is trained through a mixed comparison loss function. Multiple innovations of asymmetric data enhancement, a mixed loss function, an attention mechanism and a multi-order sequence comparison algorithm are fused, optimal balance of matching accuracy, training efficiency, robustness and calculation overhead is achieved, and the method is suitable for scenes of intelligent traffic fee evasion detection, security monitoring, medical image analysis, industrial quality inspection and the like.

The invention relates to the technical field of spectrum detection, in particular to a Raman spectrum-based rice oil detection method, which comprises the following steps: extracting main and auxiliary peaks to construct a peak group structure, performing sliding comparison to output a conformity region, identifying jump to remove an abnormal signal, calibrating a peak position to generate a correction structure, and performing homing numbering to complete a detection scheme. According to the method, the feature matching rule is constructed by combining the peak position spacing, the intensity ratio and the wavenumber index, the recognition precision of the multi-peak structure in the spectrum is enhanced, non-continuous response signals are recognized and eliminated through derivative change trend and proportion deviation constraint, and the accuracy of stable response extraction is improved. Dynamic calibration and position adjustment of peak positions are realized by utilizing an offset trend sequence, the consistency of a peak group structure in an overall direction and the continuity of a local structure are ensured, a numbering system and structure attribution division is completed after homing sorting, and the integrity of feature expression in a complex sample and the orderliness and reliability of spectral data processing are improved.

The invention relates to the field of tunnel engineering, in particular to a tunnel surrounding rock stability intelligent evaluation system based on multi-source image fusion, which comprises an image acquisition module, an image processing module, an image recognition module, a time sequence analysis module, a risk monitoring module and a surrounding rock evaluation module, the image processing module adopts a pyramid feature matching method and a feature similarity algorithm to realize registration and multi-scale superposition fusion of different image source features; the image recognition module recognizes surrounding rock abnormal features by using a deep learning network model; the time sequence analysis module maps the multi-time-sequence surrounding rock state characteristics to a high-dimensional manifold based on a Riemannian manifold model, and calculates a deformation rate and an acceleration through a geodesic theory and curvature analysis; the risk monitoring module calculates a real-time monitoring index and sends out an early warning signal; the surrounding rock evaluation module evaluates the surrounding rock grade through the surrounding rock stability index and analyzes the support safety risk.

The invention relates to the technical field of energy storage system monitoring, and discloses an energy storage equipment health state assessment method and system in a multi-mode grid-connected scene. The method comprises the following steps: acquiring historical multi-source heterogeneous data and real-time operation parameters of an energy storage unit; according to the historical multi-source heterogeneous data, carrying out working condition identification, auto-encoder training and clustering analysis to obtain an ideal health state vector center library of a sub-scene; performing feature mapping and distance calculation according to the real-time operation parameters and the ideal health state vector center library to obtain a state deviation degree; and performing time sequence trend fitting and multi-dimensional feature matching according to the state deviation degree to obtain a final health level. According to the method, accurate evaluation and early decline early warning of the health state of the energy storage equipment under complex working conditions can be realized.

The invention discloses an intelligent item selection recommendation system of a live broadcast room based on AI digital people, relates to the technical field of intelligent item selection recommendation, and solves the technical problems of generating a dynamic and high-confidence matching scheme based on highly matched commodities and adjusting a combination strategy in combination with a real-time scene and user feedback. Through the multi-dimensional weighted calculation of the feature matching score, the crowd matching score and the scene association score, the hidden integrating degree of the commodity and the user demand is quantified, the highly matched commodity is screened out, the invalid recommendation is reduced, the comprehensive confidence degree of the commodity combination is calculated based on the association rule algorithm, the personalized matching scheme conforming to the real-time scene is generated, and the user experience is improved. A high-potential scheme is screened through a self-adaptive threshold value, the matching rationality and transformation potential are improved, a comprehensive feedback processing module is introduced, a user is supported to customize combined commodities, and optimization suggestions are provided through system evaluation; and for user change feedback, generating hidden collocation based on a user portrait, and realizing real-time iteration of a recommendation scheme.

The invention discloses an unmanned aerial vehicle high-altitude cleaning path precise planning method and system based on visual navigation, and the method comprises the following steps: constructing an initial three-dimensional grid model of a to-be-cleaned building, dividing cleaning regions according to the initial three-dimensional grid model, and setting priorities; the unmanned aerial vehicle collects visual data of a cleaning area through a multi-mode visual sensor, wherein the visual data comprise surface texture, depth information and a stain distribution thermodynamic diagram; performing local correction on the initial three-dimensional grid model based on the real-time visual data to generate a high-precision dynamic three-dimensional grid model; generating a cleaning path by adopting an adaptive grid traversal algorithm according to the priority of the cleaning area and the coverage range of the unmanned aerial vehicle cleaning head; obstacles in the path are detected in real time through a visual feature matching algorithm, and when the obstacles are detected, path re-planning is carried out to avoid the obstacles until all the areas are cleaned. According to the method, the modeling precision and the dynamic adaptability are improved, and the intelligence and the pertinence of path planning are realized.

The invention discloses a soil layer interface recognition model construction system based on intelligent image recognition, and relates to the field of soil layer recognition, and the system comprises a data collection module which obtains soil layer depth image data through a sensing collection device, synchronously collects probe detection resistance, side wall friction resistance and pore water pressure signals of corresponding depths, and sends the data to a data processing module; time and space registration is carried out on all the collected data according to depth identification; the feature matching module is used for extracting soil features from images and probe signal features from signal data and accurately judging whether sudden change points or intervals represent a real soil layer interface or not, so that the false detection rate and the omission ratio are greatly reduced, breakthrough of soil layer interface recognition accuracy is achieved, local interference is merged or maintained, and the recognition accuracy of the soil layer interface is improved. High robustness is maintained in a complex stratum environment, a foundation bearing capacity attenuation coefficient of a specified period in the future is output based on environment load history and future load prediction, and a high threshold feature and a soil layer identifier which have the maximum influence are identified.

The invention relates to the technical field of knowledge questions and answers, in particular to a large language model knowledge question and answer method and system fused with a multi-modal knowledge graph. The method comprises the following steps: acquiring structured and semi-structured data and unstructured multi-modal data in an enterprise business scene; capturing data change through a real-time synchronization technology and executing targeted preprocessing to ensure the consistency of data quality and modal characteristics; constructing a dynamically updated multi-modal knowledge graph based on the preprocessed data, and integrating a multi-modal embedded model, a reordering model and a fine-tuned generative large model to obtain a knowledge storage-feature matching-semantic generation integrated architecture; according to the method, the defect of knowledge lag of a traditional system is overcome, and seamless fusion of the AI capability and the enterprise core business process is realized.

The invention discloses a water conservancy equipment life prediction and fault monitoring method. The method comprises the following steps: multi-modal data acquisition and dynamic preprocessing: constructing an acquisition frequency adaptive model; multi-domain fusion feature extraction and adversarial dimension reduction: multi-domain feature extraction is carried out, then a generator of a Wasserstein GAN architecture is constructed to reconstruct original features, a discriminator calculates a Wasserstein distance, the generator is forced to reconstruct interlayer features of the discriminator through feature matching loss, and high-correlation features are screened in combination with a maximum information coefficient and a Pearson's correlation coefficient; constructing and training a gated attention fusion network; and constructing a fault monitoring model based on a Copula theory: capturing a normal operation mode of equipment, then estimating feature edge distribution through an empirical distribution function corrected by KDE, constructing a D-vine Copula structure to hierarchically capture nonlinear dependency, calculating an AnmallyScore index integrating edge anomaly and correlation sudden change penalty, and triggering an alarm when the AnmallyScore index is greater than a threshold value.

The invention relates to the technical field of intelligent education and Internet of Things fusion, in particular to a multi-modal interaction control method and system of a multifunctional teaching-assistant robot and the robot. According to the system, a tablet AI processor runs an Android system as a core, a display module, a man-machine interaction module, an audio input module, an audio output module, an image acquisition module, a temperature and humidity sensor module, a WIFI or Bluetooth module, an Ethernet module and an Internet of Things module are connected, and the Internet of Things module supports RS485 wired access and Zigbee wireless access at the same time; the tablet AI processor executes voice interaction, video call, face recognition, environment monitoring, network interaction and equipment linkage, and provides a face recognition alignment and depth feature matching algorithm and an annular microphone array beam forming and sound source direction estimation algorithm, so that multi-modal interaction and multi-equipment linkage in a teaching scene are realized. The convenience and the safety are improved; and the equipment access cost is reduced.

The invention discloses an underwater scene three-dimensional reconstruction system and method based on 3D Gaussian sputtering, and belongs to the technical field of computer vision and three-dimensional reconstruction. The invention provides a feature extraction and enhancement method based on an AWDesc descriptor and a lightweight FeatureBooster network, and supports detection and description of key points of a fuzzy and weak texture area in a multi-view underwater image. According to the method, the self-enhancement and cross-enhancement strategies are combined, the discriminability and context consistency of descriptors are improved, and the mutual nearest neighbor and RANSAC algorithm are utilized to construct stable feature matching pairs, so that the mismatching rate is effectively reduced, and the accuracy of camera attitude estimation and sparse point cloud construction is improved. According to the method, the image enhancement network based on the cross Transform and the mixed contrast learning mechanism is adopted, and the ubiquitous problems of color distortion, fuzzy degradation and insufficient contrast of underwater images are effectively relieved. According to the method, a covariance correction mechanism and an underwater perception loss function are introduced into a 3D Gaussian sputtering three-dimensional modeling framework, and adaptive adjustment of Gaussian kernel scale, density and structural directivity is realized.