5286 results about "Feature matching" patented technology

The invention provides a building health monitoring and evaluation method and system based on a physical neural network, and the method comprises the steps: collecting the monitoring data of vibration, strain, displacement and the like of a building through a sensor network, and carrying out the data preprocessing based on wavelet transformation and a neural network; inputting the processed data into a physical constraint encoder, mapping the processed data to a physical space by adopting a Hamilton quantity encoding method, and constructing constraint conditions in combination with physical model parameters; performing feature matching by using a physical constraint neural network, and extracting key features; adopting a principal component analysis method to decompose early warning indexes, and constructing parameter mapping through a deep neural network; generating a hierarchical decision rule based on a decision tree algorithm; and carrying out constraint check and cost calculation on the candidate schemes, and finishing scheme sorting through an analytic hierarchy process. Physical knowledge is embedded into a deep learning framework, and the accuracy and reliability of monitoring evaluation are improved.

The invention discloses a decision-making method and model for off-line reinforcement learning and continuous on-line fine tuning, and the method comprises the steps: obtaining original text-image pair data and historical track data, and obtaining an enhanced state sequence through feature matching verification and standardization processing; performing sequence prediction by using a multi-level attention mechanism, and obtaining pre-training model parameters through knowledge distillation and task adaptability enhancement; on the basis of stability evaluation and plasticity control, online knowledge migration is carried out on the model, and optimized model parameters are obtained by combining experience playback optimization; and finally, performing multi-dimensional performance evaluation and resource optimization. According to the method, through multi-modal feature fusion, dynamic knowledge migration and an adaptive optimization mechanism, the problems of insufficient feature representation, disastrous forgetting, low optimization efficiency and the like are effectively solved, and the learning efficiency and generalization ability of the model are improved.

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 present invention provides a three-dimensional reconstruction method integrating a laser radar and oblique photography, comprising: S1, acquiring an oblique image and image data and preprocessing same to obtain a first oblique image, wherein the image data comprises POS data; S2, acquiring coordinates of a ground control point and high-precision POS data; S3, correcting the oblique image to obtain a second oblique image; S4, performing aerotriangulation on the second oblique image, and obtaining an effective matching point pair by means of improved SIFT; S5, performing combined adjustment on the basis of the effective matching point pair and performing dense matching to obtain a dense point cloud; S6, acquiring a laser point cloud and preprocessing same; S7, unifying the coordinates of the laser point cloud and the dense point cloud, and using improved ICP to obtain a fused point cloud; and S8, constructing a triangulated irregular network and performing texture mapping to generate a three-dimensional model. In the present invention, on the basis of the advantages of high resolution and strong anti-interference capability of a laser radar, defects in an oblique photography three-dimensional model are mitigated, and meanwhile, an oblique image is corrected and an algorithm used in feature matching is improved, thereby improving the precision, integrity and efficiency of the three-dimensional model.

The invention discloses a concrete bridge crack detection method based on computer vision, and relates to bridge detection, and the method comprises the steps: collecting a crack image, GPS data and a depth image of a concrete bridge; according to the GPS data, establishing a mapping relation between the pixel coordinates and the fracture physical space coordinates; based on feature matching of ORB features and a registration optimization algorithm based on mutual information, obtaining alignment of the crack image and the depth image at a pixel level, and obtaining a color crack image and a depth crack image; a MaskR-CNN model is adopted to extract a pixel-level crack segmentation mask of the crack region; according to the crack segmentation mask and the registered depth crack image, depth information of a crack region is estimated through a depth completion algorithm based on a conditional generative adversarial network (CGAN), and a crack depth distribution map is obtained; according to the crack depth distribution map, geometric parameters of the crack are extracted through a morphological algorithm and a central axis transformation algorithm. Aiming at the low concrete bridge crack detection precision in the prior art, the detection precision is improved.

The invention discloses an unmanned aerial vehicle autonomous routing inspection conductor detection method and system based on Bezier curve modeling. The method comprises the steps that a binocular image sequence, GPS positioning and IMU attitude data are collected through an unmanned aerial vehicle, synchronous alignment processing is carried out on multi-source data, a scene dynamic evaluation value is calculated, adaptive image enhancement is executed, and space-time features are extracted and fused. Constructing a multi-scale feature pyramid, extracting wire topological features, and generating enhanced features in combination with environment context information; and generating a control point candidate set based on the enhanced features, optimizing Bezier curve parameters, and performing time sequence consistency optimization. According to the invention, high-precision lead detection and robust tracking in a complex scene are realized, and the reliability of autonomous inspection of the unmanned aerial vehicle is improved; the key technical problems of scale change, feature matching, dynamic modeling, data fusion, parameter optimization and the like in the wire detection process can be solved.

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 provides a laser enhanced vision three-dimensional reconstruction method and system based on Gaussian splashing, and belongs to the field of computer vision. The method is divided into two parts of pose calculation and scene reconstruction: during pose calculation, relative rotation is solved by using image feature matching, and the relative pose with a real scale is further solved according to the pose of the relative rotation initialization point cloud ICP; the pose is further optimized by combining the re-projection error of the image matching feature and the matching error of the point cloud through a least square method, so that the high-precision pose is obtained; during scene reconstruction, a large number of three-dimensional Gaussian balls are used for representing a scene, an image with a high-precision pose and a depth map generated by point cloud projection are used for supervising a composite image capable of being micro-rendered, and a gradient descent method is used for optimizing parameters of the three-dimensional Gaussian balls to reconstruct and optimize the three-dimensional scene. Therefore, dense reconstruction of the scene and high-quality new view angle image synthesis are realized.

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 image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention relates to a data access method and system of an intelligent Internet of Things gateway. The method comprises the following steps: performing feature matching and protocol identification on a plurality of sensors and terminal equipment connected to the intelligent Internet of Things gateway by using an equipment feature library to obtain an equipment protocol type; analyzing the original data to obtain standardized data; performing encryption processing to obtain an encrypted security data packet and an encrypted session key; performing multi-factor transmission channel selection to obtain an optimal transmission channel combination; and based on the optimal transmission channel combination, sending the encrypted security data packet to the Internet of Things platform, verifying the received data confirmation information based on a consensus algorithm, and outputting a data access state report through a data visual interface and an AI voice assistant. The processing strategy can be dynamically adjusted according to the equipment characteristics, the data characteristics and the network environment, and the flexibility and adaptability of the system are improved.

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 provides an AI-based automatic identity card image cutting and OCR recognition method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a multi-angle depth image and a visible light image, reconstructing a three-dimensional model of a certificate through a three-dimensional point cloud registration network, and achieving the perspective correction of an image through the combination of attention posture estimation and projection matrix optimization; a double-flow memory neural network is adopted to carry out information area positioning, a feature extraction backbone network constructs a multi-scale feature pyramid, and a dynamic memory updating network carries out feature matching and dictionary updating based on a time sequence attention module and comparative learning; and performing character recognition on the segmented information sub-images through a multi-branch feature enhancement network, improving the recognition precision in combination with a self-attention enhancement unit and a context reasoning unit, and dynamically optimizing network parameters by using a reinforcement learning controller until a preset recognition credibility threshold value is reached.

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 relates to the technical field of video stitching, and discloses a monitoring video stitching method, device and equipment and a storage medium, and the method comprises the steps: carrying out the video framing of a monitoring video collected by a monitoring terminal, and obtaining a plurality of target video frames; performing human shape detection to obtain a human shape detection result; aligning and cutting a face region in the human shape detection result to obtain a standardized face image, and extracting a face feature vector; performing motion prediction and trajectory extrapolation to obtain a multi-target human shape tracking result; inputting the multi-target human shape tracking result into a distributed computing framework for task allocation and cross-node feature matching to obtain a global human shape tracking result; and performing double-flow network processing on the plurality of target video frames according to the global human shape tracking result, calculating to obtain video stitching parameters, and performing stitching operation on the plurality of target video frames to obtain a stitched video, thereby effectively improving the quality and visual effect of the stitched video.

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.

A pressure sensor calibration method based on environmental interference compensation relates to the technical field of pressure sensor calibration, and comprises the following steps: acquiring an output value and environmental data of a target pressure sensor, marking acquisition time, and setting an acquisition period; performing statistical analysis on the environmental data of the target sensor in each historical acquisition period to obtain key values of each type of environmental parameters; a feature database is constructed, a simulation experiment is carried out based on the environment feature sets in the feature database, and pressure deviation values under different environment feature set conditions are obtained; and constructing a deviation output model, inputting the environmental data of the current acquisition period into the feature database for feature matching, and performing compensation adjustment on the target pressure sensor based on the deviation output model according to a feature matching result, or performing flexible compensation adjustment operation on the target pressure sensor. Therefore, the measurement precision, the stability and the adaptability of the pressure sensor are obviously improved.