4159 results about "Feature matching" patented technology

The invention discloses an unmanned aerial vehicle routing inspection line adaptive obstacle detection method and system based on a monocular camera. The method comprises the steps of obtaining initial flight parameters, evaluating and optimizing the flight parameters based on scene complexity, collecting an original image sequence and carrying out quality evaluation and exposure compensation. Constructing a multi-resolution feature pyramid, extracting feature descriptors, and performing feature matching and verification; performing multi-constraint pose optimization and depth estimation based on a feature matching result, and reconstructing a three-dimensional point cloud; calculating direction enhancement response to extract a power line candidate area, performing multi-view line segment matching and three-dimensional reconstruction, and analyzing dynamic characteristics of the power line; and based on the power line three-dimensional model and the depth map, carrying out obstacle segmentation, calculating an obstacle distance, evaluating a risk level, and generating early warning information and a monitoring scheme. According to the invention, different scenes can be adaptively processed, the obstacle risk can be accurately identified and evaluated, and the inspection efficiency and reliability are improved.

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention discloses a video super-resolution reconstruction method and system, and relates to the technical field of video restoration processing, and the method comprises the steps: collecting a to-be-reconstructed video frame sequence and a reference frame sequence; extracting a dense optical flow field between the reference frame and the target frame; performing geometric alignment on the target frame to the reference frame according to the dense optical flow field to obtain a motion consistency feature; performing feature extraction on the video frame sequence through a parameterized residual scaling module; inputting the extracted features into a bidirectional propagation module to process forward and backward image sequences respectively, extracting time sequence forward features and time sequence reverse features, and fusing the features to obtain detail features; carrying out adaptive fusion on the motion consistency features and the detail features, and sending the fused features into a reconstruction network to obtain deep visual features; pixel-level adaptive reconstruction is realized according to the deep visual features, and a super-resolution video sequence is obtained; according to the method, the feature space consistency is kept, and meanwhile, the feature matching precision in a complex motion scene is remarkably improved.

A water-turbine generator set voiceprint monitoring and diagnosing method comprises the steps that a multi-channel voiceprint collecting system is adopted, a plurality of voiceprint sensors are arranged at key parts of a water-turbine generator set, and voiceprint signals at different positions are obtained; performing time-frequency analysis on the pre-processed voiceprint signal by using wavelet transform, and extracting voiceprint features to obtain a voiceprint feature vector; if the matching degree of the voiceprint feature vector and the features in the fault feature library exceeds a preset threshold value, determining the fault type and the fault degree; classifying the determined fault type and fault degree by adopting a support vector machine algorithm to obtain a fault diagnosis result; a fault diagnosis result is optimized through a deep learning network, the diagnosis precision is improved, and a final fault diagnosis report is obtained; performing association analysis on the fault diagnosis report and the equipment operation parameters to generate an equipment operation state evaluation report; according to the equipment operation state evaluation report, an equipment maintenance plan is made, and intelligent maintenance of the water-turbine generator set is achieved.

The invention relates to the technical field of agricultural traceability, and discloses an agricultural non-point source pollution intelligent traceability method and system based on a knowledge graph, and the method comprises the steps: achieving the system integration of pollution source features through constructing the knowledge graph fusing multi-dimensional data, and building a sky-ground integrated monitoring network to obtain multi-scale dynamic data. An intelligent traceability mechanism is formed based on deep coupling of a knowledge graph and monitoring data, a high-precision pollution identification model is trained in combination with historical data, and real-time response and dynamic traceability of an over-standard pollution area are achieved. And verifying a traceability result through feature matching and semantic reasoning, quantitatively calculating a pollution contribution rate, and finally generating a reliable traceability conclusion containing the position, the type and the contribution rate. According to the scheme, the bottlenecks of data fragmentation, monitoring simplification, extensive analysis and the like of a traditional method are broken through, and the accuracy, timeliness and credibility of pollution traceability are remarkably improved through a series of full-chain technical systems of traceability identification, analysis and positioning.

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

The invention relates to the technical field of marine monitoring, in particular to a marine target visual servo tracking method, which comprises the steps of multi-modal sensor fusion, a self-adaptive visual tracking algorithm, a servo control and visual collaboration mechanism and a shielding processing and target re-identification strategy. The problem that tracking is unstable under the conditions of illumination change, ship body shaking, target shielding and the like in a traditional method is solved. The IMU, the GNSS and the visual data are fused through extended Kalman filtering, ship body shaking is compensated, and the target state estimation precision is improved; the improved D-Fi ne target detection model is combined with an online feature updating mechanism to dynamically adapt to the appearance change of the target; the prediction and correction control strategy and the double-closed-loop PI D controller cooperate to adjust the camera holder, and the tracking delay is reduced; the multi-clue shielding detection and space-time joint feature matching technology ensures accurate re-identification of the target after shielding is removed. The real-time performance and robustness of the tracking system on an embedded platform are improved, and an efficient and stable target tracking solution is provided.

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

The invention discloses a power transmission line multi-mode warning system and a power transmission line multi-mode expelling method, belongs to the technical field of power transmission line safety monitoring, and aims at solving the problems that power transmission line invasion target monitoring is not accurate, and the warning and expelling effect is poor. An environment image is acquired through an acquisition unit, and redundancy compensation is carried out on a fault unit. In the aspect of image splicing, a database is constructed by using structural feature points of a power transmission line, rapid projection transformation of a fixed area is realized, incremental feature matching is adopted for a dynamic area, and an environment panoramic image is generated. The method comprises the following steps: establishing a background coordinate system based on an environment panoramic image by aligning a fixed structure region, extracting multi-modal features of an intrusion target, constructing a dynamic trajectory parameter set, completing species classification and behavior recognition, constructing a multi-dimensional evaluation index system, dividing threat levels, and generating a thermodynamic diagram. And finally, according to data such as threat levels, a multi-mode grading warning system is constructed, warning equipment is dynamically adjusted, a target track is tracked, a warning effect is evaluated, and accurate and efficient invasion target expelling is realized.

The invention discloses a multi-modal large model assisted laparoscope soft tissue registration surgical navigation method and system. The method comprises the following steps: acquiring a preoperative CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) image and an intraoperative laparoscope image, and converting to obtain a multi-modal feature sequence; inputting the obtained multi-modal feature sequence into a large model for fusion to obtain a cross-modal matching matrix; random sampling is carried out by using an RANSAC random sampling consensus algorithm, an error between corresponding point sets of two data is measured by using an Euclidean distance, an analytical solution is obtained through SVD decomposition, and a feature matching error is introduced to obtain an optimal rigid body transformation pose; performing non-rigid deformation local fitting adjustment on the optimal rigid body transformation pose residual error by adopting a parameterized deformation model in combination with learning prediction deformation, and performing loop execution to obtain a soft tissue organ real-time registration image; automatic, high-timeliness and high-precision real-time registration of soft tissues is realized under the multi-mode and multi-variable conditions, and reliable navigation and positioning support is ensured to be provided in a complex operation environment.

The invention provides a toll vehicle type identification method and system based on expressway passing images, and the method comprises the steps: obtaining an image set of passing vehicles, carrying out the multi-scale brightness compensation processing of a vehicle side contour image, generating a side feature enhancement image, carrying out the contour sharpening processing of a vehicle top contour image, and obtaining a vehicle top contour image; and generating a top feature enhanced image. And performing dual-channel feature fusion on the side feature enhanced image and the top feature enhanced image, generating a vehicle multi-dimensional fusion feature map, inputting the vehicle multi-dimensional fusion feature map into a pre-trained vehicle type matching model, and obtaining a vehicle type feature matching result output by the vehicle type matching model. And performing similarity comparison according to a vehicle model feature matching result and a pre-stored vehicle model feature library, determining a charging vehicle model category corresponding to the passing vehicle, and generating a vehicle model identification mark. According to the method, the robustness of vehicle type recognition in a complex illumination environment can be improved, and meanwhile, the accuracy of vehicle type classification and the charging and passing efficiency are improved by fusing dual-view feature enhancement and a multi-dimensional feature matching mechanism.

The invention discloses an intelligent digital operation and maintenance management system and method for a charging pile, and relates to the technical field of new energy infrastructure operation and maintenance, and the system comprises an information obtaining module which determines a delay information circulation channel, obtains real-time discussion content, processes a discussion text through employing a word segmentation technology, and carries out the operation and maintenance of the charging pile. If the confidence coefficient of the semantic feature vector is higher than a preset threshold value, the semantic feature vector is judged to be a specific question type through state space definition, classified question labels are obtained, the historical matching module obtains features associated with a historical question library, historical question records are matched, and the matching precision is evaluated through reward function design. Determining a historical solution most similar to the current problem; according to the intelligent digital operation and maintenance management system and method for the charging pile, the accuracy of problem classification and solution matching is continuously improved, and intelligentization and high efficiency of operation and maintenance management of the charging pile are achieved.

The invention relates to the technical field of unmanned aerial vehicle remote sensing and artificial intelligence crossing, in particular to an intelligent monitoring system and method based on multi-modal remote sensing data and deep learning, and the system comprises an unmanned aerial vehicle cluster networking subsystem, a mixed feature matching subsystem and a multi-modal fusion and continuous learning subsystem. The method comprises the following steps: constructing an unmanned aerial vehicle cluster carrying a multispectral sensor and a laser radar LiDAR, and carrying out wireless networking among a plurality of unmanned aerial vehicles to realize sharing of acquired images; feature point extraction is carried out on collected images of different time phases, the extracted feature points are input into the generative adversarial network, and the feature points are matched; and receiving the matched collected images, dynamically fusing data of visible light, infrared and other multi-modal images through a space-time attention mechanism, and realizing high-precision target recognition and dynamic environment self-adaption in a small sample scene. According to the method, unmanned aerial vehicle multi-source remote sensing data acquisition, feature fusion and deep reinforcement learning are combined, and the method is used for intelligently monitoring a dynamic environment.

The invention discloses a cradle head tracking method and device based on a binocular camera and a storage medium, and relates to the technical field of computer vision, and the method comprises the steps: processing image data based on a binocular parallax principle, and generating a three-dimensional coordinate of a center point of a tracking target; based on the three-dimensional coordinates of the camera coordinate system and the offset from the optical center of the camera to the rotation center of the holder, generating three-dimensional holder coordinates through coordinate transformation solution; determining a historical track based on the tracking target feature information and a historical target feature matching result, and outputting an identifier and a three-dimensional position observation value through correlation verification of a three-dimensional holder coordinate and the historical track; inputting an observation updating equation correction state through the identifier and the three-dimensional position observation value, and outputting a three-dimensional prediction position; based on the three-dimensional prediction position and a deviation formula, calculating the angle deviation with the camera image center under the holder coordinate system, and driving the holder to center the target in the picture center according to the angle deviation. The problem that the target tracking effect is poor is solved, and the robustness of target tracking in a complex scene is improved.

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention belongs to the technical field of network security, provides a network attack detection method based on dynamic graph coding, and solves the problems of poor dynamic adaptability of an attack path and missing of timing constraint in the prior art. The method comprises the following steps: constructing a dynamic threat map, extracting a triple of heterogeneous threat intelligence by using a RoBERTa model, and adding a timestamp and a confidence attribute; a dynamic graph encoder for time sequence perception is designed, semantic and evolution laws are fused through periodic time coding and a multi-head time sequence attention mechanism, and feature weights are adjusted in combination with a gating residual layer; an event-driven incremental updating strategy is adopted, and node similarity is calculated to achieve local subgraph updating; a time sequence rule base is established, three-dimensional parameter verification attack chain time sequence logic is defined, and abnormity is judged through conflict scores; and finally, integrating a graph updating module, a dynamic coding module and a constraint analysis module to realize multi-source threat feature matching and attack detection. According to the method, the adaptability of attack path evolution is improved through dynamic graph modeling and real-time increment updating.

The invention relates to the technical field of software systems, and particularly discloses a vehicle matching and positioning system and method based on comprehensive characteristics of a vehicle and a container, and the system comprises a multi-mode perception and intelligent identification module, a dynamic task matching and scheduling optimization module, an intelligent decision and automatic execution module, and a user interaction and operation module. According to the system, the states of a container and a container truck are sensed in a fusion mode through multiple sensors, cross-modal data feature learning is carried out through a Transform-VIM self-attention mechanism, and high-precision container recognition in a complex environment is achieved; meanwhile, based on multi-dimensional feature matching and a Hungary optimization algorithm, a dynamic task matching mechanism is constructed, and it is ensured that the container trucks and the containers are in accurate butt joint; the system optimizes a scheduling strategy through reinforcement learning, dynamically adjusts an operation process, and is linked with automatic equipment, so that intelligent and efficient port container transportation management is finally realized, the risks of wrong loading, neglected loading and operation delay are effectively reduced, and the overall throughput and operation efficiency of a port are improved.

The invention discloses an abnormity early warning method and system based on artificial intelligence, and relates to the technical field of abnormity early warning. The method comprises the following components: S1, brain wave data acquisition and preprocessing, S2, emotional state recognition and feature extraction, S3, specific business data acquisition and arrangement, S4, data fusion and abnormal feature construction, and S5, abnormal early warning decision and notification. According to the method, the brain wave emotion features and the specific business data are fused, abnormal feature construction is carried out by adopting a fusion algorithm of multi-scale feature matching and a support vector machine algorithm based on quantum annealing optimization, the abnormal feature rule can be mined more accurately through the comprehensive data processing and analysis mode, and the accuracy of abnormal feature extraction is improved. Therefore, in the real-time data monitoring and analysis process, the accuracy of abnormal early warning is improved, meanwhile, the system can dynamically adjust the monitoring frequency according to the fluctuation characteristics of the data, the timeliness of early warning information is ensured, and related personnel can take countermeasures in time.

The invention provides a digital twin construction method for metal stamping forming and a computer system, and the method comprises the steps: obtaining a multi-source monitoring data set in a metal stamping forming process, carrying out the collaborative feature extraction processing, and generating a material structure feature set and a process dynamic feature set; a mixed feature space is constructed based on the material structure feature set and the process dynamic feature set, multi-dimensional feature matching processing is executed in the mixed feature space, a feature mapping relation network is generated, a dynamic twin model is constructed according to the feature mapping relation network, and real-time state deduction is conducted on the stamping forming process based on the dynamic twin model. And a forming quality prediction result and a process defect positioning result are generated, and a stamping process parameter adjustment scheme is generated and fed back to a stamping control system to trigger parameter optimization operation. According to the method, the early warning capability of process defects and the precision of process parameter adjustment under complex working conditions are improved, and meanwhile, the real-time performance and engineering feasibility of digital twin construction in the large-scale stamping forming process are guaranteed.

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The invention relates to a building prefabricated part quality detection method based on multi-modal vision. According to the method, multi-modal data including 2D image data and 3D point cloud data are obtained, an improved YOLOv8 model is used for performing defect coarse positioning on the 2D image data, defect parameters are calculated, defect areas such as cracks and exposed ribs can be quickly locked, and the parameters of the defect areas can be obtained. And by means of SIFT feature matching and ICP point cloud registration technologies, comparing with a two-dimensional template and three-dimensional geometric parameters of the BIM standard component model to obtain a two-dimensional registration difference chart and a three-dimensional deviation thermodynamic chart. And finally, according to the defect confidence coefficient, the two-dimensional registration difference chart and the three-dimensional deviation thermodynamic diagram, a preset dynamic weighting rule is adopted to carry out joint decision making, and a quality detection result is obtained. According to the method, through the multi-modal data, the improved YOLOv8 model, the point cloud registration technology and the preset dynamic weighting rule, the false detection problem can be effectively solved, the detection reliability and accuracy under the complex working condition are improved, and the quality management level of the building prefabricated part is improved.

The invention discloses an entity alignment method for a multi-modal crop knowledge graph, and belongs to the technical field of knowledge graphs and agricultural intelligent analysis, and the method comprises the steps: obtaining a data feature item set of an agricultural field, and carrying out the time sequence compensation, and generating a growth time sequence feature parameter; performing feature extraction on the data feature item set, performing fusion to generate a multi-modal fusion feature mapping graph, constructing a dynamic feature matching network based on growth time sequence feature parameters, and performing entity node traversal on a reference knowledge graph to generate a candidate alignment set and a corresponding difference unit set; filtering conflict nodes in the candidate alignment set according to a three-dimensional confidence evaluation model to generate an effective alignment chain; and generating a graph updating instruction based on the difference unit set, and reconstructing a knowledge graph topological structure in combination with the effective alignment chain. According to the method, multi-modal feature bridging, dynamic weight optimization of growth stage perception and an incremental conflict resolution mechanism are adopted, so that cross-domain agricultural entity accurate alignment and real-time adaptive updating of the knowledge graph can be realized.

The invention provides an interaction method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: collecting a continuous image sequence, extracting feature points, calculating the position variation and gradient variation of the feature points, constructing a two-dimensional feature vector, and carrying out the clustering through a Gaussian mixture model, thereby obtaining a stable feature class. And weighting the feature points based on a feature weight distribution function, calculating feature similarity and constructing a matching relationship. And calculating a dynamic parallax threshold in combination with the vehicle movement speed, screening reliable matching feature pairs, and solving pose transformation parameters. A feature change sequence is extracted through a feature detection network, a motion feature mapping matrix is constructed, and a compensation depth map is generated in combination with depth information. And scene reconstruction is carried out based on the compensation depth map and the pose transformation parameters, the scene confidence score is calculated, the pose parameters are calibrated, a high-precision pose parameter sequence is obtained, and the robustness of feature matching and the accuracy of pose estimation can be improved.

The invention discloses an educational resource image intelligent recommendation and multi-scale matching system and method based on machine learning, and relates to the technical field of intelligent education, a permeability evaluation model is constructed by collecting interactive behavior data of students and educational resource images to quantify cognitive stability and knowledge mastery parameters; synchronously performing multi-scale analysis on the image to generate a visual feature, a knowledge association feature and a cognitive guide feature; and determining an optimal recommendation level based on dynamic granularity selection processing, generating an enhanced image with a permeability feedback mark, and interactively generating learning evaluation data through the enhanced image. According to the method, cognitive dynamic evaluation and multi-scale feature matching are fused, so that the technical bottleneck of a traditional recommendation system on image granularity selection and cognitive adaptation is solved, and the educational resource recommendation accuracy and learning efficiency are remarkably improved.

The invention relates to the technical field of material verification management, and discloses a government affair material intelligent verification method and system based on machine learning, and the method comprises the steps: obtaining the text information data and verification rules of historical materials; multi-modal feature extraction is carried out; establishing a dynamic verification rule knowledge graph, and verifying the multi-modal features and the target feature points of the text segments to generate a primary verification strategy; performing deep verification processing on the material text fragment to generate a secondary verification strategy; performing third-time verification on the material text fragment subjected to the second-time verification by triggering cross-page feature matching to realize cross-page text fragment association and multi-modal feature fitting so as to generate a third-time verification strategy; performing iterative comparison on the results of the three rounds of verification to generate a credibility score; and the comparison result is adjusted according to the credibility score in combination with the verification risk abnormity assessment, so that a final verification decision strategy can be obtained, and efficient, accurate, dynamic and comprehensive intelligent verification can be realized.

The embodiment of the invention discloses an intelligent surveying and mapping method and system based on AI and BIM fusion. The method comprises the steps that an unmanned aerial vehicle platform carrying a laser radar, an RGB camera and a positioning system is used for scanning ancient building cultural relics and surroundings in a multi-angle flight mode, point cloud data, multi-view image data and position and attitude data are synchronously collected, and the three are associated through timestamps; after the point cloud data and the multi-view image data are preprocessed, cross-modal registration is completed through feature matching and pose estimation in combination with the position and pose data, and a registration data set is obtained; semantic segmentation is carried out on the point cloud data and the image data in the registration data set, and semantic segmentation results are fused based on the incidence relation; classifying and aggregating the original point cloud components according to category labels, constructing a topological relation reasoning assembly relation, calling corresponding BIM template instantiation model components based on the assembly relation, and hooking a segmentation result to generate a semantic enhanced BIM model; and integrating the BIM model and the GIS base map to form a fusion model so as to plot the historic building cultural relics.

The invention relates to an experimental data processing method and device, an AI analysis module and computer equipment, and belongs to the field of data processing.The method comprises the steps that multi-dimensional original data are partitioned according to types, and formats are unified; generating a similarity matrix based on time and space neighborhood information, and marking abnormal fluctuation points; effective signals are separated through a time-frequency feature matching noise library; extracting multi-layer features of basic statistics, time sequence correlation and trend change; and dynamically screening core features to update the tracking type experimental model. The matched AI analysis module integrates hardware circuits of data partitioning, similarity calculation, anomaly marking, noise matching, feature extraction and model updating, and whole-process acceleration is achieved. According to the method, through multi-dimensional data compatibility processing, accurate anomaly detection, multilayer feature fusion and model adaptive optimization, the experimental data processing efficiency and conclusion reliability are remarkably improved, and the method is suitable for real-time analysis of multiple scenes such as scientific research and industry.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

The invention belongs to the technical field of ocean observation, and discloses an ocean wave field reconstruction method and device based on binocular vision. An ocean wave field reconstruction method based on binocular vision comprises the steps that internal parameters of a camera are calibrated through a Bouguet calibration algorithm, a binocular camera provided with an IMU sensor is used for collecting images and pose information, feature points are extracted through an SURF algorithm, feature matching is completed under a game theory framework, and abnormal points are removed through an RANSAC algorithm. Then, camera parameters are calculated based on an eight-point method, and the camera parameters and three-dimensional point coordinates are optimized through an SBA algorithm; thirdly, generating a disparity map by adopting epipolar correction and an SGM algorithm, and performing morphological processing on the disparity map; and the processed image is subjected to triangulation to generate a three-dimensional point cloud, and filtering is carried out. And finally, estimating a sea level position through an RANSAC algorithm, and optimizing a result through a least square method to complete three-dimensional reconstruction of ocean waves.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.