643 results about "Tracking model" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses an intelligent building energy-saving optimization platform and method based on carbon footprint tracking, and relates to the technical field of building energy saving and carbon emission management. The method is used for solving the problems of extensive carbon emission evaluation, rigid quota distribution and insufficient energy-carbon collaboration. A three-dimensional carbon density map is constructed by collecting people flow, equipment energy consumption and environment data in real time, and carbon emission hotspots are dynamically identified. And analyzing the association between the power grid and the renewable energy source through a carbon flow tracking model, and correcting a weight output contribution matrix. The characteristics of equipment energy efficiency, building material hidden carbon emission and the like are fused to construct a carbon emission gene entropy, a quota migration strategy is generated in combination with a game algorithm, and oriented transfer from high carbon to low carbon buildings is promoted. A double-ring collaborative framework is constructed, an inner ring chaos search optimization device starts and stops to suppress carbon density fluctuation, an outer ring carbon price mapping adjusts energy storage scheduling, accurate carbon emission tracing, quota dynamic allocation and energy-carbon deep collaboration are achieved, building low-carbon transformation is supported, and the building cluster carbon emission reduction efficiency is improved.

The invention discloses a micro-grid intelligent economic regulation and control system and method based on carbon emission optimization, and relates to the technical field of economic regulation and control. The method comprises the following steps: deploying an Internet of Things sensor to collect photovoltaic generating capacity, energy storage SOC, load demand and power grid carbon intensity data in real time, and transmitting the data to an edge computing node through a 5G / optical fiber hybrid communication network; preprocessing the data by adopting wavelet transform, and establishing a time sequence prediction model of photovoltaic output / load demand; constructing a dynamic carbon flow tracking matrix; performing decision optimization according to the output dynamic carbon emission spectrum and the power carbon flow traceability data; and the edge node issues a control instruction through a Modbus-TCP protocol to carry out economic regulation and control. According to the method, multi-source data are collected in real time, a minute-level carbon emission equation is established based on a dynamic carbon flow tracking model, dynamically-changed energy carbon emission factors and line transmission loss are fused, and the micro-grid carbon footprint is accurately quantified.

The invention discloses an active power distribution network interactive carbon reduction decision-making agent construction method based on carbon flow distribution, and relates to the technical field of active power distribution network low-carbon scheduling. According to the method, a carbon flow distribution dynamic tracking model is constructed, node-level carbon emission intensity is quantified, an intelligent agent framework based on a Markov decision process is designed, a state space including real-time carbon flow, new energy output and load fluctuation and action spaces including unit adjustment, an energy storage strategy and the like are defined, and the real-time carbon flow, new energy output and load fluctuation are determined. And optimizing a carbon emission minimization target through a reward function, training an intelligent agent by adopting a reinforcement learning algorithm fusing physical constraints and historical data, and finally generating a multi-target collaborative optimization decision by combining offline pre-training and online fine tuning strategies, so as to synchronously optimize new energy consumption, line loss and economy. According to the invention, the carbon emission intensity of the system is effectively reduced, and a technical support is provided for constructing a novel low-carbon and high-elasticity power system.

The invention relates to the technical field of educational resource recommendation, and discloses an educational resource intelligent recommendation method and system based on big data driving, and the system comprises a data fusion processing module, a portrait modeling module, a resource feature engine module, an intelligent recommendation core module and a closed-loop feedback module. By fusing knowledge state, cognitive ability and interest preference three-dimensional portraits, capturing user learning ability evolution in real time, dynamically updating knowledge mastery by adopting a knowledge tracking model, and perceiving interest migration in combination with an attention mechanism, the problem of learning cold start in a new field is solved, recommendation coverage range and accuracy are improved, and user experience is improved. The cognitive load sensitive ant colony optimization algorithm is designed, the learning path continuity is guaranteed through a heuristic function, the learning path structure reasonability is optimized, the cognitive burden of a user is reduced, a personalized knowledge attenuation model is constructed by fusing an Ebbinghaus forgetting curve, the knowledge long-term retention rate is increased, and the review resource release accuracy is enhanced.

The invention provides a target tracking method, electronic equipment and a readable storage medium. According to the target tracking method provided by the invention, by combining generation and updating of the static template and the dynamic template, the problems of time-varying characteristics and background noise in infrared small target tracking can be effectively solved. Firstly, a generated static target image template is cut by multiple scales, so that the adaptability to different scales of a target is improved; and the generation of the dynamic target image template is combined with the dynamic template of the previous frame and the temporary template of the current frame, and updating is carried out through a fusion strategy, so that the morphological change and the thermal radiation fluctuation of the target can be reflected in time, and the problem of response lag caused by the time-varying characteristic of the target is solved. And finally, inputting the static and dynamic templates into a pre-trained target tracking model for processing, thereby effectively suppressing interference of complex background noise, and accurately positioning the target.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention relates to the technical field of satellite altimetry, in particular to a height measurement method based on a feature-enhanced multi-base integrated re-tracking model, which is characterized in that the feature-enhanced multi-base integrated re-tracking model FMERM is constructed to optimize the position of a reflection waveform re-tracking point, and the FMERM model executes the following processing processes: 1, extracting input variable features by using PCA, and extracting the input variable features by using PCA; combining with an original input variable to form an enhanced feature set; secondly, optimizing the hyper-parameters of the XGBoost model by using a grid search method, so that the hyper-parameters of the XGBoost model are optimal; thirdly, utilizing the trained model to accurately calculate a re-tracking point normalization power value of the reflected signal waveform; fourthly, the sea surface height is inverted through the geometrical relationship and direct reflection signal time delay and atmospheric correction; an effective means is provided for solving the problem that a reflection waveform re-tracking method is inaccurate, and powerful support is provided for high-precision satellite-borne GNSS-R sea surface height measurement.

The invention discloses a numerical control machine tool machining track real-time correction method based on multi-source visual perception, and the method comprises the following steps: S1, synchronously collecting a multi-source real-time image sequence of a machined workpiece in a machining process through a plurality of visual sensors disposed at different visual angles of a machining region of a numerical control machine tool; s2, performing preprocessing and feature extraction on the multi-source real-time image sequence, and fusing to obtain real-time three-dimensional coordinate information of key feature points, corresponding to the theoretical processing track model, on a processing workpiece; s3, the real-time three-dimensional coordinates of the key feature points are compared with theoretical coordinates of corresponding points in a pre-stored numerical control machine tool theoretical machining track model, and the track deviation between the actual machining track and the theoretical machining track is obtained through calculation; and S4, based on the trajectory deviation, generating a trajectory correction value through a preset correction algorithm. The method can better correct the machining track of the numerical control machine tool in real time.

The invention provides a multi-mode remote sensing target tracking and positioning and intention discrimination method and device. The method comprises the following steps: acquiring a plurality of visible light image frames and a plurality of infrared light image frames, and carrying out frame alignment operation on each visible light image frame and each infrared light image frame to obtain a plurality of groups of effective image frame pairs; for each group of effective image frame pairs, determining tracking identification information of each detection object in the effective image frame pairs based on the effective image frame pairs and a pre-trained multi-modal detection tracking model; for each detection object, determining longitude and latitude tracks of the detection object based on the tracking identification information and a back projection mapping function; and determining the behavior intention of each detection object based on a behavior recognition model and the longitude and latitude tracks of each detection object. The accuracy of target tracking and behavior intention recognition in the remote sensing video can be improved.

The invention belongs to the technical field of computer vision and artificial intelligence, and particularly discloses a video target tracking method and device, a medium and equipment, and the method comprises the steps: synchronously obtaining an infrared image and a visible light image which comprise a to-be-detected target; the infrared image and the visible light image are preprocessed; constructing a to-be-detected target tracking model, and training the model; and inputting the preprocessed infrared image and visible light image into a trained to-be-detected target tracking model so as to carry out identification and position tracking on a to-be-detected target. According to the invention, high-precision, high-robustness and high-continuity position tracking of the to-be-measured target in a complex environment can be realized.

The invention relates to the technical field of logistics tracking, in particular to a shipping logistics tracking method. Comprising the following steps: detecting a signal interruption time point and duration to obtain a signal interruption interval; according to the signal interruption interval, the navigation speed, direction and position which are finally recorded before interruption of the ship are extracted from a historical navigation trajectory database, and an initial state vector is generated; predicting a future transportation path of the ship through the corrected position sequence, and generating a predicted path set; according to the prediction path set, constructing a ship motion track in the three-dimensional virtual scene, and generating a visual track model; obtaining a decision support view by combining the confidence coefficient of the reckoning position sequence; and updating a port scheduling plan in real time through a decision support view, and generating an optimized loading and unloading time sequence. According to the method, the problem that the calculation precision of the position of the ship is not high in a complex scene of signal interruption is solved, and the ship position precision and the prediction precision of a future transportation path are improved.

The invention relates to a big language model enhanced knowledge adaptive learning planning system, and belongs to the field of intelligent education. The system comprises a knowledge center module, a learner portrait module, a path planning module and an intelligent learning guiding module, and the knowledge center module extracts entities and relationships from a multi-modal data source by using a large language model to construct a knowledge graph; the learner portrait module collects multi-dimensional learning data of the user and maps the multi-dimensional learning data to corresponding nodes of a knowledge graph, and dynamically deduces a learner portrait through a Bayesian knowledge tracking model; the path planning module generates an initial learning path based on the knowledge graph and the learner portrait, establishes a collaborative filtering analysis model, predicts and optimizes the expected effect of the current learner following the initial learning path in combination with a Bayesian knowledge tracking model, and finally generates a target learning path; and the intelligent learning guiding module generates a standardized knowledge card for each knowledge node on the target learning path through a security retrieval enhancement generation technology.

The invention discloses an RGBL tracking method based on target prior autoregression query. The RGBL tracking method comprises the steps that RGBL data sets are collected and aligned, and a training set and a test set are constructed; the method comprises the following steps: on the basis of an RGB tracking network AQATrack of autoregression query, constructing an RGBL tracking model based on target prior autoregression query; by introducing language and visual semantic tokens, learning target features of each mode; designing a language semantic token enhancement module to enhance the target features of the language semantic token, and designing a target feature extraction module and a visual semantic token feature enhancement module to improve the target features of the visual semantic token; and fusing language and visual semantic token features in a decoder, outputting a multi-modal semantic token feature, and using the multi-modal semantic token feature as target prior for query with an initialization value of zero, and capturing spatio-temporal information in an autoregression learning mode. According to the invention, by introducing the target prior, the target features in the spatio-temporal information can be captured more effectively in the initial stage, so that the target positioning and tracking process of the tracker is accelerated.

The invention relates to the technical field of archive management, and discloses an archive life cycle full-process digital supervision method, which comprises the following steps: constructing an archive digital twinborn framework, loading a metadata standard, and generating an initial holographic archive mapping model; a life cycle state transition rule is configured, and a whole-process dynamic tracking model is obtained by combining entity associated parameter fusion data; executing compliance deduction to generate a trajectory chain and simulation data; a metadata conflict resolution mechanism is established, a supervision strategy optimization model is trained and generated, and governance parameters including entity association degree weights and the like are output; generating file increment simulation information; constructing a multi-target decision model to iteratively adjust the supervision framework to obtain an optimal supervision path parameter; and verifying and updating the supervision strategy through the real-time holographic mapping network. According to the method, full-process dynamic supervision of archives is realized, and the management efficiency and the intelligent level are improved.

The invention aims to provide a SiamFC-pytorch tracking model pre-processing and post-processing acceleration method and a SiamFC-pytorch tracking model pre-processing and post-processing acceleration system based on an NPU (Network Processing Unit). The system is realized based on a Yulong810SOC, and comprises a video input module (1), a hardware decoding module (2), a detection module (3), a tracking module (4) and a turntable control module (5), the method comprises the following steps: a, a video input module receives a video stream, and the video stream is decoded into an original image in a YUV format through a hardware decoding module; b, the detection module runs a YOLOv5 model through a neural network engine of the NPU; c, an NPU pre-processing and post-processing unit of the tracking module executes template branch pre-processing, search branch pre-processing and search branch post-processing on the YUV image and the target initial bounding box; and d, the turntable control module adjusts the posture of the camera turntable according to the real-time coordinates of the target. The method is applied to the technical field of computer vision and artificial intelligence hardware acceleration.

The invention belongs to the field of explosion load and response prediction, and particularly relates to an impact load and response intelligent prediction system and method based on machine vision. An impact load and response intelligent prediction system based on machine vision comprises an image data preparation module, an image recognition and segmentation module, a numerical calculation module and a load and response prediction module, and the image data preparation module obtains, marks and enhances an image data set required by a target detection and tracking model; the image recognition and segmentation module recognizes and segments splashing fragments and dynamically tracks the splashing fragments; the numerical calculation module generates a sample database required for training the machine learning model; and the load and response prediction module outputs a forward prediction model, a reverse prediction model and a prediction result. According to the prediction system and method, computer vision, a numerical calculation method and machine learning are fused together, and the problems that a traditional method is low in efficiency and cannot conduct reverse prediction according to fragment distribution after impact explosion are solved.

The invention discloses a spraying control method of a waveform guardrail spraying manipulator, which relates to the technical field of industrial automation and robot control, and comprises the following steps: acquiring relative position change data of the manipulator and the surface of a waveform guardrail in a rotation process through a pre-established manipulator rotation track model; and for each time node, recording the spraying angle change and distance dynamic adjustment information, establishing a dynamic space mapping relationship under a manipulator rotation state for spraying control, and triggering a secondary spraying supplementing control instruction according to the coating quality consistency feedback, so that the reliability and stability of the spraying supplementing effect can be further ensured, and the spraying efficiency is improved. Therefore, the equipment utilization rate of the manipulator and the automation and intelligence level of spraying operation are remarkably improved while the uniformity of the coating and the consistency of spraying quality are improved.

The invention relates to a multi-mode interactive indoor unmanned aerial vehicle target detection and tracking method and system. The system comprises a target detection and tracking module, a laser radar inertial odometer module, a video display and interaction module and a flight control module. The method comprises the steps that an unmanned aerial vehicle end receives and processes RGB images, laser radar point cloud and IMU data; and optimizing a YOLOv8 target detection model and an OSTrack target tracking model through a TensorRT inference engine. By fusing LiDAR and IMU data, accurate state estimation and map construction are provided. And the ground station end displays a video stream and generates a control command through the video display and interaction module. And calculating a flight speed and a yaw angle rate by estimating an error between the target image position and the current position of the unmanned aerial vehicle, and generating a flight control instruction. The problems that an indoor unmanned aerial vehicle system often faces low target detection precision, poor tracking stability and difficult user interaction experience are solved, and the method and the system are more efficient, more accurate and higher in interactivity.

The invention relates to the technical field of artificial intelligence, and discloses a Transformer training method for paint surface AI defect identification, which comprises the following steps: synchronously acquiring RGB images and three-dimensional point cloud data through a six-axis mechanical arm to form a multi-modal data set; generating synthetic defect data by using fluid dynamics and a ray tracing model to expand a sample set; realizing feature alignment by adopting cross-modal contrast learning; aggregating defect structure capsules through a dynamic routing attention network; performing knowledge retrieval and fusion in combination with the prototype memory matrix; and finally, the sorting mechanical arm is driven to execute sorting or rechecking operation based on the multi-task output and the uncertainty score. According to the method, the problems of insufficient structural representation of complex defects, weak generalization ability of rare defects and low reliability of sorting decision are solved, and high-precision and high-reliability automatic defect detection and sorting are realized.

The invention discloses a link tracking monitoring method, system and device and a computer readable storage medium, and relates to the technical field of communication, the method tracks a link needing to be tracked through a preset link tracking model, unnecessary byte code injection, interception and excessive tracking data are prevented from being generated, and the link tracking monitoring efficiency is improved. The influence on a network, a CPU (Central Processing Unit), a memory and the like caused by transmission, analysis and storage of the tracking data is avoided, the relation among the invasion degree of the existing code, the link tracking granularity and the performance overhead is balanced, and the influence on the existing service performance and the concurrent processing capability is avoided; a byte code enhancement technology is adopted to enhance original service processing logic, enforcement coding and decoding byte codes are injected, execution calling information of a specified link (function / method) is intercepted, enhanced, recorded and sent, zero invasion and zero development are carried out on existing service codes, and tracking of the full-link calling process of an application layer data protocol HTTP / RPC and a transmission layer protocol UDP is achieved.

The invention discloses a robot dog target following method based on computer vision. The robot dog target following method comprises the following steps: S10, preparing human body data, human body weight identification data and a single target tracking data set; s20, designing a lightweight human body detection network architecture and a detection human body frame, and performing model training based on the human body data; s30, constructing a lightweight single-target tracking model, selecting a detection frame of a to-be-tracked target as initialization input, receiving an initial frame transmitted by a robot dog camera as a template image, and performing specified template tracking and following; s40, constructing a lightweight full-scale model, starting a relocation mechanism after a lost target is tracked by a single target, and controlling the robot dog to turn around in situ, globally search a specified target and relocate a disappearing target; and S50, based on the tracking result of the single target, converting the target position on the image to an actual three-dimensional coordinate, and controlling the motion displacement of the robot dog to complete the following operation.

The invention relates to a knowledge tracking model research method integrating difficulty perception and memory enhancement. According to the method, a graph attention network coding exercise-knowledge point topological relation is constructed, and a difficulty embedding layer and a gating fusion mechanism are combined to realize joint characterization of difficulty features and a topological structure. The difficulty coefficient is innovatively introduced as a dynamic regulation factor of attention weight in memory network updating, the memory intensity of high-difficulty exercises is enhanced, and the discrimination and topological consistency of knowledge representation are improved through a multi-task optimization framework integrating graph structure loss and contrast loss. According to the method, challenges of exercise difficulty perception and knowledge point topological relation modeling are effectively solved, experimental results show that the AUC of the model reaches 0.93 (improved by 7.8% compared with traditional BKT) on an ASSIST2009 data set, the AUC of the model reaches 0.90 (improved by 0.21 compared with KSGAN) on an EdNet data set, objective indexes and teaching scene verification show that the method has remarkable advantages in the aspects of knowledge point correlation modeling and knowledge tracking, and the method is suitable for popularization and application. The method provides an innovative solution for personalized education, and has technical breakthrough and industrial application values.

The embodiment of the invention discloses an image target labeling method and device, electronic equipment and a storage medium. According to the embodiment of the invention, a to-be-labeled video frame sequence of a road camera can be acquired; detecting any current frame in the video frame sequence based on a preset target detection model, and generating a detection frame for the target detection object; obtaining a prediction frame in the current frame, wherein the prediction frame is generated by a preset target tracking model according to the motion state of the target tracking object in the previous frame or multiple frames and the position of the detection frame; and matching the detection frame in the current frame with the prediction frame, if matching succeeds, allocating the historical identity identifier of the target tracking object corresponding to the prediction frame to the target detection object corresponding to the detection frame, and if matching fails, allocating a new identity identifier to the target detection object corresponding to the detection frame. Therefore, based on the time-space coherence of the video, the target is tracked and labeled efficiently and accurately.

The invention discloses a target tracking model based on an enhanced target detection algorithm, and relates to the technical field of real-time target tracking. A CD-YOLO detector is constructed; a robust multi-target tracker is constructed; constructing a collaborative detection-tracking optimization mechanism; evaluating and optimizing the performance; according to the method, a deformable convolution and coordinate attention mechanism is introduced into YOLOv10, so that the perceptual ability of the model to geometric deformation and spatial information is enhanced; the CD-YOLO and the enhanced StrongSORT tracker are combined, so that the detection precision and the tracking stability of the system in complex scenes such as dense crowds, serious shielding and variable visual angles are remarkably improved; a collaborative optimization mechanism between detection and tracking is established, so that a detection result and tracking feedback can be mutually enhanced, a closed-loop robust tracking process is formed, and the overall precision and stability are remarkably improved on the premise that the real-time performance of the system is not affected.

The invention relates to the technical field of vehicle intelligent driving and active safety control, and discloses an active obstacle avoidance method and system considering multi-source uncertainty and fault positioning, and the method comprises the following steps: S1, predicting the tracks of a normal vehicle and a sideslip vehicle, and obtaining the predicted position and covariance of the normal vehicle and the sideslip vehicle; s2, constructing an extended state equation containing a positioning fault, and estimating and compensating a positioning deviation; s3, establishing a linear error tracking model, unifying various uncertainties into equivalent disturbance, and solving a disturbance invariant set; s4, generating a candidate obstacle avoidance track based on the compensated state; s5, track comfort is evaluated, hard constraints are checked, and infeasible tracks are removed; s6, transversely expanding the contour of the vehicle by using a disturbance invariant set, and carrying out longitudinal and transverse separation expansion on the contour of the target vehicle based on the predicted covariance; s7, performing robust collision detection by using the disconnect axis theorem; and S8, carrying out robust tracking control by adopting Tube-MPC. According to the invention, safe and stable obstacle avoidance control can be realized under complex disturbance and fault positioning conditions.

The invention relates to a knowledge tracking model fusing dynamic edge weight and forgetting gating, and aims to solve the problems that an existing knowledge tracking model cannot effectively model dynamic association of knowledge points and neglects a memory recession effect. According to the method, by constructing a time sequence diagram neural network, based on a learnable time attenuation factor and an attention mechanism, the edge weight between knowledge points is dynamically adjusted, and self-adaptive modeling of the incidence relation evolving along with time and learning behaviors is achieved. Furthermore, according to the Ebbinghaus forgetting curve theory, personalized forgetting rate parameters are defined, and the generated memory intensity is embedded into the LSTM unit to serve as a gating signal, so that the influence of knowledge decline on the cognitive state of the student is explicitly simulated. The invention also provides a hierarchical fusion architecture, which combines the time sequence behavior sequence and the map structure information, and improves the prediction precision and generalization ability of the model. According to the method, chain state degradation caused by knowledge forgetting can be accurately identified, and a theoretical basis and decision support are provided for intelligent recommendation of personalized review paths in an adaptive learning system.

The invention discloses an intelligent obstacle recognition method for an unmanned loader, and aims to solve the problems of low obstacle recognition precision, poor real-time performance and insufficient coordination of obstacle avoidance and operation in a complex operation scene. The method comprises the following steps: establishing a multi-source sensing system such as a laser radar and a camera, and realizing data time sequence synchronization, space unification and noise optimization through FPGA hardware; obstacle space and category features are extracted through double-branch parallel detection, and a modal weight fusion result is dynamically distributed in combination with a scene; an improved Hungary algorithm and a classification tracking model are adopted to realize obstacle ID stable association and trajectory tracking, and trajectory prediction is completed based on LSTM and Kalman filtering; and a dynamic avoidance area is constructed, and an obstacle avoidance path is generated through global-local bilevel programming and a safety potential field algorithm and is corrected in real time. The obstacle recognition robustness and real-time performance in a complex environment are improved, and safe and efficient operation of the unmanned loader is guaranteed.

The invention relates to the technical field of aerial target identification, and discloses an aerial dynamic target accurate identification method and system. The method comprises the following steps: acquiring multi-dimensional spectral data of a target through a multi-spectral imaging device, extracting spatial distribution characteristics, establishing a three-dimensional motion track model according to the spatial distribution characteristics, and calculating an instantaneous velocity vector; a motion deviation index set is generated in combination with preset reference motion parameters, abnormal behavior categories are divided through clustering analysis, and a key frame sequence is marked; reconstructing a local motion characteristic spectrum based on the key frame sequence, extracting morphological change parameters, and inputting the morphological change parameters into a pre-trained target recognition network to obtain a type recognition result and a confidence score; when the confidence coefficient is lower than a threshold value, activating a supplementary recognition process, acquiring high-resolution texture data, and fusing the high-resolution texture data with the initial recognition result to generate a final recognition tag; and updating the feature database and adjusting the network weight according to the final identification tag.

The invention relates to the technical field of knowledge tracking, in particular to a knowledge tracking method based on hybrid convolution, and the method comprises the steps: obtaining the interaction information of a learner and education content; the interaction information is input into a preset knowledge tracking model, the answer correct probability of a target question is predicted, the knowledge tracking model is used for constructing an input sequence based on the interaction information, behavior characteristics in different time ranges in the sequence are extracted through multi-scale causal convolution, and the answer correct probability of the target question is predicted; modeling is carried out in combination with an attention mechanism with a distance penalty term, and finally knowledge tracking is completed in combination with question answering correctness probability prediction of the target question. According to the invention, knowledge tracking is carried out in combination with multi-scale causal convolution and a distance decay attention mechanism, and the ability of modeling the cognitive state of a learner is improved.