1529 results about "Optical flow" patented technology

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The invention discloses a dynamic scene three-dimensional reconstruction method and device based on hydrogen energy unmanned aerial vehicle survey, and the method comprises the steps: obtaining dense time sequence multi-view image data of a target region through a hydrogen energy unmanned aerial vehicle platform, and carrying out the preprocessing of radiation correction and geometric correction; carrying out optical flow analysis and deformation rate clustering on the preprocessed image, identifying a pseudo-static anchor point and constructing a dynamic reference field; introducing a dynamic reference field as a soft constraint in a binding adjustment process, and optimizing a camera pose to generate a three-dimensional point cloud with consistent time and space; and finally, mapping the point cloud to a space-time voxel grid, constructing a surface evolution model by using a graph neural network or an anisotropic diffusion algorithm, and calculating a surface deformation vector to realize continuous and high-precision three-dimensional reconstruction of the disaster scene surface deformation process.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention discloses an intelligent control and detection system for precision coating uniformity, and relates to the technical field of non-contact thickness measurement, the system comprises a spectrum confocal chromatography detection module, a non-contact multi-wavelength full-breadth detection module, an optical flow field dynamic measurement module and the like, a data processing unit is combined with a model to generate an evaluation model, and the evaluation model is used for evaluating the coating uniformity. The system has the advantages that multi-layer coating structure data are obtained in a full-breadth mode through a linear array sensor of the spectrum confocal chromatography detection module, a slurry full-breadth flow velocity vector field is analyzed in combination with the optical flow field dynamic measurement module, and the flow velocity measurement accuracy is improved. And the data processing unit fuses the multi-modal data to generate an evaluation model, and the regulation and control strategy generation module outputs an instruction containing edge air pressure regulation, so that accurate control over coating uniformity is achieved, damage to a base material is avoided, material waste and manual debugging time consumption are reduced, and the production efficiency and the product yield are remarkably improved.

The invention discloses a video monitoring abnormal behavior real-time detection method based on a graph neural network, and the method comprises the following steps: collecting a video frame sequence, extracting a detection frame, a key point and an optical flow feature, generating a node feature matrix, and constructing a dynamic graph structure; establishing a dynamic graph neural network model based on EvolveGCN, and updating a convolution weight by using a gating circulation unit; calculating event intensity and change rate according to the motion abrupt change signal, generating a time delay parameter and adjusting a weight modeling step length; performing low-rank decomposition and spectral radius projection on the convolution weight matrix, and adjusting a spectral constraint threshold according to an abnormal score; inputting a weight matrix to generate graph branch and hypergraph branch embedded representation; exchanging topology correction information based on a mutual generation mechanism and updating model parameters; and inputting the dynamic graph structure and the node feature matrix in real-time reasoning, calculating an abnormal score and outputting a detection result. According to the invention, adaptive evolution and high-precision anomaly detection of dynamic graph modeling are realized.

The invention provides a bridge structure damage detection method based on image processing and CNN-LSTM, relates to the field of graphic image processing, and solves the problems that bridge vibration monitoring in the prior art is time-consuming, labor-consuming, high in cost and limited in precision, and adopts the technical scheme that the method comprises the steps of obtaining a recorded vibration video of a to-be-detected bridge structure; extracting the vibration video frame by frame to obtain bridge image data; detecting angular points by using a Harris algorithm, screening image data, and calculating vertical displacement of each feature point one by one; using a Lucas-Kanade optical flow algorithm to calculate the motion vectors of the feature angular points of the adjacent frame images; calculating the actual vibration displacement of the bridge; and carrying out standardization processing on the actual vibration displacement to obtain a two-dimensional matrix, inputting the two-dimensional matrix into the trained CNN-LSTM model, and outputting a final identification result of each type of damage. According to the scheme of the invention, high-precision bridge vibration displacement can be calculated, and efficient, accurate and high-precision bridge damage detection can be realized through the CNN-LSTM damage identification model.

The invention discloses a space-time speckle projection three-dimensional imaging method based on multi-frame optical flow alignment. Firstly, a projector based on DLP is used for projecting a space-time speckle pattern to a measured scene, and a binocular camera synchronously collects a three-dimensional space-time speckle image. The calibration parameters of the binocular camera are used to carry out stereo correction on an acquired original speckle image, and a parallax image is generated frame by frame in combination with a coarse-to-fine single-frame speckle matching strategy. And estimating a two-dimensional inter-frame displacement field between continuous disparity maps by using an optical flow method by taking an intermediate frame disparity map as a reference, compensating motion artifacts in a space-time speckle image, and ensuring strict space-time registration of a dynamic target. And based on the speckle image after motion correction, a speckle matching strategy is expanded to a time-space domain, and high-precision multi-frame three-dimensional measurement of a complex dynamic scene is realized. The method is suitable for performing rapid and high-precision three-dimensional modeling on a moving target in an unstructured environment, and can perform accurate three-dimensional measurement on a high-speed dynamic target undergoing any translation or rotation motion.

The invention provides a multi-mode intelligent control method and device for a robot body and electronic equipment. The multi-mode intelligent control method comprises the following steps: acquiring multi-view images and task texts of cameras at multiple parts of the robot; generating two-dimensional track supervision information and key point supervision information based on the two, and converting the two-dimensional track supervision information and the key point supervision information into a visual supervision graph; key point features are generated through the supervision graph, and track features are generated in combination with the multi-view image; extracting a depth image, optical flow information, a matched segmentation mask, an execution mechanism attitude and a motion frequency, and generating corresponding features; inputting each feature into a multi-modal fusion reasoning model, generating a relative action prediction feature and converting the relative action prediction feature into an action control instruction; and controlling an execution mechanism to complete the task. According to the method, track supervision, key point supervision and multi-modal prior are introduced under the vision-language condition, and perception integrity, robustness and execution precision are remarkably improved.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The invention provides an airborne image target feature enhancement and three-dimensional reconstruction joint optimization method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting an image sequence through a binocular camera, carrying out the epipolar correction and alignment, calculating an optical flow field to obtain space-time alignment feature points, generating a disparity map based on the matching cost, and converting the disparity map into a scene point cloud, performing graph structure segmentation to obtain a target point cloud, and finally extracting boundary points to generate a triangular mesh and mapping a texture image to reconstruct a three-dimensional model. According to the invention, effective enhancement of image target features is realized, and the target identification accuracy and the three-dimensional reconstruction precision are improved.

The invention provides a micro-expression recognition method and device based on a multi-scale optical flow guiding attention mechanism, and the method comprises the following steps: constructing a pre-trained micro-expression recognition model which comprises a preprocessing module, a dynamic motion branch and a space-time appearance branch which are arranged in parallel, a cross-modal fusion module, and a classification head; and obtaining a to-be-recognized micro-expression video, and inputting the to-be-recognized micro-expression video into the micro-expression recognition model to obtain an emotion category According to the scheme, the attention sensitivity of the model to micro-motion of key areas such as orbiculus oculi muscle and zygomatic lesser muscle is remarkably improved under the guidance of the optical flow dynamic information, so that the accuracy of emotion category prediction is improved.

The invention discloses a satellite video multi-target tracking method based on an optical flow and a state space model. The satellite video multi-target tracking method comprises a network model training part and a video multi-target tracking part, the network model training part comprises the steps of preprocessing a training data set, constructing and training a target detection network fused with optical flow feature enhancement, and constructing and training a multi-target sequence association network based on a state space model; the video multi-target tracking part comprises the steps of inputting an unstable image satellite video stream, detecting the position of an interested target in each frame by using a target detection network, carrying out inter-frame target association by using a multi-target sequence association network, and generating a continuous multi-target tracking trajectory. Through multi-scale optical flow enhancement, residual mask fusion and autoregression prediction, unification of high precision, real-time performance and track continuity is realized, and the method is suitable for complex and dynamic satellite video scenes.

The invention relates to a micro-expression recognition method based on double-flow feature fusion, and belongs to the field of computer vision. The method comprises the steps of obtaining N frames of images of a micro-expression video from a start frame to a vertex frame, and calculating an optical flow field between adjacent frames by adopting an optical flow algorithm so as to effectively extract optical flow features of micro-expressions; the method comprises the following steps: acquiring N frames of images of a micro-expression video from a start frame to a vertex frame, converting the N frames of RGB face images into a CIE Lab color space, calculating a pixel difference between two frames of converted Lab images, and extracting pixel stream features of micro-expressions; and inputting the optical flow features and the pixel flow features into a constructed double-flow three-dimensional convolutional network for feature extraction and fusion, and classifying micro expressions. According to the method, the dynamic and subtle changes of the micro-expression are effectively captured by combining the optical flow and the pixel difference characteristics. By integrating spatial and temporal information, richer feature representations are provided. The improved attention mechanism further focuses on fine facial changes, and the accuracy of micro-expression classification is improved.

The invention relates to the field of sealing control, in particular to a multifunctional sealing control method based on visual identification and a sealing machine. The method comprises the following steps: collecting a multispectral visual image of a material to be sealed, performing material depth visual identification and intelligent sealing parameter matching one by one, and generating personalized sealing process parameters; performing dynamic optical flow tracking according to the multispectral visual image, and calculating an accurate position coordinate and a time point when the material arrives at the position of the sealing machine; intelligent sealing action planning is carried out according to the precise position coordinates and the time point, and an intelligent sealing planning instruction is generated; carrying out conveyor belt rhythm analysis based on the multispectral visual image, and carrying out conveyor belt synchronous adjustment based on the intelligent sealing planning instruction to generate a synchronous adjustment signal; and synchronous transmission and sealing driving are carried out based on the personalized sealing process parameters and the synchronous adjusting signals. Sealing process parameters are adjusted in real time, and the overall production rhythm and efficiency of the sealing machine are improved.

The invention discloses a dynamic scene robust vision SLAM (Simultaneous Localization and Mapping) method based on multi-feature collaborative optimization, which comprises the following steps of: acquiring an image sequence, carrying out dynamic target detection and segmentation through an instance segmentation network, generating a segmentation mask containing a dynamic region mark, and identifying and separating a dynamic object and a static background; removing feature points corresponding to the dynamic object based on the segmentation mask to obtain static feature points; carrying out pose estimation based on the static feature points, and for the key frame, carrying out feature matching with the previous key frame by minimizing a re-projection error, and solving to obtain the camera pose of each key frame; for non-key frames, performing camera pose tracking and data association on the previous frame by adopting an optical flow algorithm, and accumulating solving results to obtain pose tracks of all the non-key frames; the key frames and the non-key frames are subjected to differential processing by fusing feature matching and an optical flow algorithm, so that the calculation efficiency is remarkably improved while the positioning precision is ensured, and the real-time performance is improved.

The invention discloses an emergency rescue real-time human body detection method and device based on time sequence motion feature enhancement, and the method comprises the steps: obtaining visible light and infrared image sequences of a rescue region and environment parameters (including smoke concentration and illumination intensity) in real time, and carrying out the spatial registration preprocessing; respectively carrying out frame difference processing on the two types of image sequences, generating a binary motion mask, calculating an optical flow amplitude, and carrying out adaptive fusion according to smoke concentration to obtain a multi-scale motion energy field; human body micro-motion frequency band energy is extracted through time-frequency transformation, a frequency domain dynamic attention mask is generated, and a saliency motion target area is extracted in combination with a multi-scale motion energy field; constructing a double-branch neural network, extracting time sequence motion features and multi-modal appearance features, dynamically distributing weights and fusing the weights, and outputting a human body bounding box and detection confidence; and calculating environment complexity according to the environment parameters, determining a dynamic confidence threshold, verifying a detection result by combining the average temperature of the human body bounding box region, and generating alarm information if a condition is met. The method aims at solving the problems that a traditional method is high in omission ratio and unstable in recognition in a complex environment.

The invention discloses a deep-sea mining area sediment plume dynamic monitoring system and a multi-module fusion analysis method thereof, and belongs to the technical field of deep-sea environment monitoring and ocean engineering. The system comprises a fixed monitoring unit and a movable monitoring unit, the fixed monitoring unit is composed of an in-situ optical and three-dimensional sonar observation base station, a sediment capturer array and a multi-parameter sensor array and is arranged in a mining area; and the mobile unit comprises an AUV (Autonomous Underwater Vehicle) carrying a multispectral laser radar and an ROV (Remote Operated Vehicle) carrying a binocular vision system and a micro sampler. According to the analysis method, a plume motion field is extracted through multi-source data fusion, dark channel defogging and a pyramid optical flow method, a physical-data dual-drive model is constructed to simulate the plume diffusion and settlement process, a three-dimensional dynamic thermodynamic diagram is generated in real time, and an intelligent early warning threshold value is set based on the diffusion radius and the settlement flux. According to the invention, plume multi-scale, high-precision, real-time and dynamic monitoring and evaluation are realized, and deep-sea mining operation optimization and environment compliance supervision are effectively supported.

Methods for motion-controllable video diffusion include extracting optical flow fields from an input video and computing warped noise by iteratively warping noise between consecutive frames using the optical flow fields. The iteratively warping includes (i) re-Gaussianizing expanded pixel regions by sampling fresh Gaussian noise, and (ii) aggregating contracted pixel regions by merging noise particles and renormalizing variance to preserve spatial Gaussianity. An output video is generated by initializing a diffusion process with the warped noise and iteratively denoising to produce temporally coherent output frames. Various other methods, systems, and computer-readable media are also disclosed.

The invention relates to a three-dimensional data processing method and device based on Beidou differential positioning. The method comprises the steps that multiple pieces of original point cloud data collected by different scanning stations at different time points for a construction area are acquired; performing de-noising processing on each piece of original point cloud data to obtain de-noised point cloud data corresponding to each piece of original point cloud data; according to the multiple pieces of denoised point cloud data, identifying and removing point cloud data corresponding to a moving object existing in the construction area in combination with time sequence analysis or an optical flow method to obtain target point cloud data; performing Poisson reconstruction according to the target point cloud data to generate a triangular mesh surface model; and performing texture enhancement processing on each triangular patch in the triangular mesh surface model to obtain a three-dimensional model corresponding to the construction area. By means of the method, the three-dimensional model with centimeter-level geometric accuracy can be obtained.

The invention discloses an event-image dual-mode fusion video turbulence correction method, medium and system, and belongs to the field of digital image processing, and the method comprises the steps: synchronously obtaining an event voxel of a target and a to-be-corrected image sequence; inputting the event voxels into a pre-selected coding and decoding structure to obtain features, projecting the features along the optical flow direction, and performing three-dimensional reconstruction to obtain target motion features; extracting background scene representation; fusing the target motion feature and the background scene representation in a channel dimension to obtain an edge guide feature; inputting the image sequence and the image sequence into a pre-trained video restoration network to obtain a turbulence-corrected video; the training loss of the coding and decoding structure comprises an optical flow field estimated according to an image sequence without turbulence disturbance and a target motion field obtained by projecting the feature along the optical flow direction. The method can accelerate the recovery process and improve the recovery quality.

The invention provides an automatic equipment anomaly monitoring method and system based on image processing, and relates to the technical field of automatic equipment anomaly monitoring, and the method comprises the steps: converting an equipment operation video into a multi-dimensional physical field feature: in a motion field dimension, based on optical flow field analysis, extracting a full-period displacement statistical histogram and a space thermodynamic diagram, the motion instability characteristic of the mechanical transmission system is accurately quantified; in a vibration field dimension, an energy spectrum and a vibration thermodynamic diagram are generated innovatively through time-frequency transformation of a displacement signal of a frequency spectrum monitoring point, and frequency domain feature visualization of a hidden vibration fault is achieved; in the dimension of a structure field, edge gradient analysis and texture feature extraction technologies are fused, a time sequence structure thermodynamic diagram sequence is constructed to capture a progressive damage evolution rule, a three-field abnormal index dynamic weighting fusion mechanism overcomes the limitation of traditional single-point monitoring, connected domain analysis of a fused thermodynamic diagram is combined with an LBP texture and morphological feature decision tree, and the defect of the prior art is overcome. Automatic equipment abnormity monitoring based on image processing is realized.

The invention provides a river flow velocity intelligent monitoring method and system based on multi-scale optical tracking velocity measurement. A plurality of detectors with different characteristics are organically combined, and dynamic grid density adjustment based on flow velocity gradient is realized through adaptive grid adjustment; establishing a multi-dimensional track quality evaluation system through intelligent track evaluation; the real-time performance optimization realizes the performance optimization of multi-thread parallel processing, integrates an intelligent grid optical flow detection and dynamic density adjustment mechanism, and optimizes the detection precision and performance in real time according to the flow velocity gradient and the flow field complexity; a multi-track quality evaluation system is utilized, tracks are screened and fused from three dimensions of motion consistency, physical constraint and time stability, and the accuracy and reliability of data are remarkably improved. The method overcomes the problems that a traditional speed measurement method is complex in operation, limited in environment, insufficient in precision and the like, supports real-time processing, can be widely applied to the fields of water conservancy projects, environment monitoring, flood prevention early warning, scientific research and education and the like, and has remarkable advancement and practical value.

The invention discloses a bill recognition method and system based on artificial intelligence image enhancement, and relates to the field of image recognition. The method comprises the following steps: S1, extracting multi-dimensional quality features based on an original image of a bill and calculating a scene consistency factor; s2, adjusting a global enhancement weight according to a scene consistency factor, adjusting a local gain in combination with a detail fidelity factor, and performing adaptive enhancement on the original image to generate an enhanced image; s3, establishing an optical flow field model to analyze geometric deformation of the enhanced image, and performing adaptive correction in combination with local deformation rigidity to generate a corrected image; and S4, analyzing gradient features and character confidence of the corrected image, extracting a candidate character region, and performing context recognition by adopting a sequence model to obtain a text field set. Scene complexity is quantified through multi-dimensional quality features, and detail fidelity self-adaptive enhancement and optical flow deformation correction of high-frequency character distinguishing are combined, so that bill character definition and recognition accuracy are remarkably improved.

The invention belongs to the technical field of gait recognition, and particularly relates to a human gait recognition method and system in a complex scene. And sequentially performing illumination correction, human body posture angle alignment and shielding processing on the extracted human body gait data, and performing human body gait recognition based on the gait data obtained after illumination correction, human body posture angle alignment and shielding processing. According to the occlusion processing, a comprehensive and reliable occlusion area judgment standard is set based on the connection relation between skeleton nodes, the human anatomy reference distance, kinematics constraint and the time continuity requirement, and then a missing human body area is reconstructed through optical flow compensation or bilinear interpolation based on information of visible pixels around the occlusion area. The accuracy of gait recognition in a complex scene is improved, the problem of adaptability of the complex scene is solved in a breakthrough mode, high-precision gait identity recognition is achieved, and the security and protection monitoring requirement of a real scene is met.

The invention provides a vehicle throwing object detection and positioning method and system based on multi-channel spatial-temporal feature fusion, and the method comprises the steps: obtaining a continuous time sequence image frame sequence of a road scene, and generating an optical flow image sequence through an optical flow algorithm; outputting a detection frame of each vehicle in each standardized image through a deep neural network target detection model, and generating a region of interest according to the detection frames; a fusion feature vector is generated for each region of interest, a space-time fusion feature sequence is constructed, and a thrown object classification result and a positioning result of each region of interest are generated based on the space-time fusion feature sequence and the multi-branch full-connection network; and based on the classification result and the positioning result of the thrown object, inputting the obtained coordinates of the suspected area of the thrown object into a spherical camera for tracking. According to the method, the thrown object can be efficiently and accurately detected and positioned automatically, the accuracy and real-time performance of thrown object detection are improved, the false alarm rate is reduced, and the precision degree of positioning the position of the thrown object is improved.

The invention discloses an old people abnormal expression recognition method based on gradient covariance analysis. The method comprises the steps of old people expression data set construction, time sequence optical flow feature-based facial expression region screening, abnormal expression enhancement loss estimation, gradient covariance-based facial expression region feature enhancement, micro-expression model training and micro-expression model testing. Aiming at the problems that the expression movement of the elderly is not obvious and the abnormal expression is easy to neglect, the facial expression key region is positioned by using the time sequence optical flow feature, the abnormal expression is introduced to enhance the loss so as to punish the leak detection condition, the back propagation gradient of the loss and the covariance thereof are calculated, and the region with larger covariance has more obvious feature change, so that the detection accuracy is improved. According to the method, facial expression region features based on gradient covariance are fully considered, key region analysis is enhanced, and the accuracy of abnormal expression recognition of the old people is effectively improved.

The invention discloses a three-dimensional semantic scene completion method based on camera enhancement, a medium and equipment, and the method comprises the steps: obtaining a depth map and an optical flow graph through a left image and a right image, extracting a two-dimensional feature from the left image, and extracting a two-dimensional feature from the depth map; mapping the left image to obtain context features; performing depth optimization based on the two-dimensional features, the two-dimensional features and the depth map to obtain depth estimation distribution; obtaining and generating an expanded three-dimensional feature map through context feature operation expansion, and sequentially executing three-dimensional deformable cross attention and deformable self-attention operations on the three-dimensional feature map to output updated three-dimensional features; and geometric semantic enhancement is carried out on the updated three-dimensional features, and finally three-dimensional semantic scene completion is completed. The depth prediction precision is improved, depth estimation optimization is carried out, finally the geometric structure and detail prediction capability of the model is enhanced, and three-dimensional semantic scene completion is completed.

The invention relates to the technical field of equipment state monitoring, in particular to an emergency cut-off valve bolt looseness detection method and system based on multi-modal fusion. The method specifically comprises the following steps of multi-source data synchronous acquisition, accelerometer installation and data processing, environmental adaptability preprocessing, bolt area optical flow feature extraction, Kalman filtering multi-source fusion, bolt looseness detection and loose bolt positioning. According to the emergency cut-off valve bolt looseness detection method based on multi-modal fusion, online, automatic and real-time accurate monitoring and early warning are achieved, the reliability and the anti-jamming capability of the system are remarkably improved, a high-precision and quantifiable objective diagnosis result is provided, good economical efficiency and large-scale deployment feasibility are achieved, and the method is suitable for popularization and application. The industrial pain point that a traditional contact type sensor scheme is difficult to apply to an industrial site with a large number of bolts in a large scale due to too high cost and technical complexity is effectively solved.

The invention discloses an artificial intelligence-based equipment security and protection monitoring method, equipment and a medium, and relates to the technical field of equipment security and protection monitoring, and the method comprises the steps: constructing a deep learning optical flow estimation model based on image frame sequences, carrying out the optical flow motion vector calculation of adjacent image frames through employing the model, and extracting the motion characteristics of smoke diffusion; textural features are extracted from the image frames, normalization and time alignment are carried out on the textural features and smoke diffusion characterization features, and a space-time joint feature vector sequence is constructed; performing smoke event classification prediction on the sequence through a time sequence classification model containing an attention mechanism to obtain an event type and confidence; and triggering a security response according to the smoke event type and the confidence coefficient. According to the invention, the dynamic characteristics of smoke diffusion are effectively extracted, and the detection precision is improved; the capturing capability of time sequence continuity and space consistency in the smoke diffusion process is enhanced through spatio-temporal joint modeling, and missing report and false report are reduced.

The invention relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps of: acquiring data according to an acquisition template, extracting a pulse time sequence by self-adaptive threshold peak detection, calculating an instantaneous phase according to linear interpolation, calculating a statistical magnitude, comparing a quantitative index with a preset threshold value, and calculating a pulse time sequence according to the statistical magnitude. Judging a steady state by combining a peak loss rate and an abrupt change detection rule, and calculating phase consistency between channels for verification; the method comprises the following steps of: splitting acquired data according to a concept entity to form a data relation model, implementing rapid global rigid estimation and applying affine transformation, estimating a pixel-level displacement field by adopting a pyramid dense optical flow network, applying the displacement field to an original pixel, and performing time domain fusion by taking optical flow confidence and a registration residual error as weights; and cutting the short-time image stabilization sequence after registration compensation, and outputting a pixel-level risk thermodynamic diagram, a candidate focus list and each output confidence interval by taking a hybrid network of a convolution front end and a space-time Transform backbone as a prediction model.