951 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 provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention relates to the technical field of target detection and tracking, in particular to a highway abnormal event detection and tracking method under the view angle of an unmanned aerial vehicle, which comprises the following steps: establishing an aerial photography historical data set of the view angle of the unmanned aerial vehicle; constructing a target detection model and a target tracking model, and training the target detection model and the target tracking model based on the historical data set; acquiring a real-time aerial image of a highway, inputting the real-time aerial image into the trained target detection model, and acquiring position information and environment information of a target vehicle; inputting the detected target vehicle information into the trained target tracking model, and obtaining the motion track information of the target vehicle; according to the position information, the environment information and the movement track information of the target vehicle, analyzing and judging whether the target vehicle has an abnormal event or not; and outputting a detection result of the abnormal event. The technical scheme of the invention can effectively capture the dynamic state of the vehicle and provide effective support for timely discovery and processing of abnormal events, thereby improving the safety and efficiency of road operation.

The embodiment of the invention provides a swimming pool robot positioning and trajectory prediction method and system fusing sonar and vision. The method comprises the following steps: collecting terahertz sonar data for a target area; based on terahertz sonar data, constructing a motion track model of the swimming pool robot; acquiring multi-source image data by adopting a multi-spectrum and event camera collaborative acquisition strategy; a dynamic SLAM algorithm based on deep learning is adopted, dynamic objects in the swimming pool environment are recognized and processed in real time based on the multi-source image data, and the environment image data with the dynamic objects removed are adopted to obtain a swimming pool environment model; performing deep fusion on the motion track model and the swimming pool environment model through a knowledge graph network, and predicting the operation state of the swimming pool robot by adopting a quantum machine learning algorithm to obtain a panoramic model; and creating and updating a digital twinborn model corresponding to the panoramic model in real time. The real-time performance and accuracy of positioning and track prediction of the swimming pool robot are improved, and a user can conveniently maintain and manage the swimming pool robot.

The invention relates to the technical field of image processing, and discloses an AR technology-based precise positioning method and system for minimally invasive surgery of hepatobiliary surgery. The method comprises the steps that blood vessel network data are extracted through a medical image sequence, a three-dimensional structure diagram is generated, operation target points are marked, and a coordinate system is established; collecting dynamic data for mapping matching, and establishing a real-time tracking model; constructing an augmented reality scene and implanting a navigation path; acquiring an instrument position calculation track, and comparing with a navigation map to generate an instruction; constructing a projection interface to display the key area; recording the whole process and evaluating the operation quality. By establishing the real-time tissue deformation tracking model, a multi-source data fusion accurate positioning mechanism and an intelligent operation quality evaluation system, the accuracy and reliability of operation navigation are effectively improved.

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 relates to the technical field of track structure health monitoring, in particular to a track crack recognition and fatigue life prediction method and system based on deep learning and finite elements, and the method comprises the steps: collecting and preprocessing a track surface crack image, and obtaining crack image data; a deep neural network model is trained, and a corresponding track crack recognition algorithm based on deep learning is determined; identifying and extracting positions and geometric dimensions of the cracks; based on the geometrical characteristics and the material attributes of the track, establishing a corresponding track finite element model, and inputting the position and the geometric size of the crack into the track finite element model to generate and obtain a track model containing crack damage; based on wheel-rail rolling contact fatigue finite element simulation, simulating the mechanical response of a track structure, determining the fatigue life of the track in combination with the stress-strain distribution of the track, and predicting the development trends of cracks under different operation conditions; automatic and efficient identification of track cracks is realized, and the accuracy of fatigue life evaluation 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 relates to the technical field of agricultural information, and discloses a visual analysis method and system based on an agricultural big data model. According to the system, crop growth time sequence data are processed by using a multi-scale feature extraction algorithm combining a recurrent neural network and wavelet transform, and key conversion points of a crop growth stage are automatically identified by using a deep learning architecture combining a variational auto-encoder and a hidden Markov model. A standard growth track model and a multi-dimensional deviation measurement index are constructed to evaluate the abnormal growth condition of crops, a dynamic threshold model based on reinforcement learning is applied to realize early warning of abnormal growth, and a multi-dimensional visual interface based on a clock metaphor is designed to visually display crop growth information. The crop growth stage recognition precision and early warning timeliness are improved, and the agricultural production management efficiency is remarkably enhanced.

The invention provides an RGBT target tracking method based on domain adaptation and spatio-temporal information fusion. The problem that an existing target tracking scheme is poor in tracking effect under complex scenes such as target appearance change and shielding is mainly solved. Comprising the following steps: 1) constructing an RGBT data sample set; 2) constructing a dual-branch RGBT target tracking model based on a pre-trained RGB tracker and training the dual-branch RGBT target tracking model; 3) designing an online module updating strategy for dynamically updating the initial template image; 4) taking the initial template image and the search image as the input of the trained model, and taking the dynamic template image as the additional input of the model; and 5) loading the optimal parameter, predicting the motion state of the target in a subsequent frame by using the trained model, and obtaining and outputting a target tracking result. According to the method, the calculation cost is greatly reduced, and the time information of the target can be fully utilized, so that the performance of the feature extraction network is effectively improved, and the accuracy and robustness of target tracking are improved.

The invention relates to the technical field of quantum key distribution, and discloses a communication protocol security verification method based on quantum key distribution, which comprises the following steps of: establishing a quantum communication channel framework, loading protocol parameters to generate an initial security verification model, configuring a security state transition rule, and performing cross-layer data fusion in combination with quantum entity parameters; and obtaining a whole-process safety tracking model. And executing security path deduction through the model, generating a key exchange trajectory chain, constructing security trajectory simulation data, establishing a quantum conflict resolution mechanism, training and generating a security policy optimization model, and outputting security governance parameters. Security increment simulation information is generated based on security governance parameters and the like, a multi-target decision model is constructed in combination with protocol evolution constraint conditions, optimal security path parameters are adjusted and generated, a security verification strategy is verified and updated through a real-time quantum mapping network, and a whole-process security matching target is achieved. According to the method, the comprehensiveness, the accuracy and the dynamic adaptability of quantum communication protocol security verification are effectively improved.

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses a high-precision small unmanned aerial vehicle real-time tracking method which comprises the steps that a multi-modal sensor array is constructed through a millimeter wave radar, a binocular vision module and an infrared thermal imager, and the multi-modal sensor array is used for collecting three-dimensional motion data of a target object in real time; constructing a fusion tracking model based on improved Kalman filtering by using the collected three-dimensional motion data, dynamically distributing the weight of a multi-source sensor through a convolution attention mechanism, and compensating a motion trail prediction error by using an adaptive noise covariance matrix; through cooperative work of the millimeter wave radar, the binocular vision module and the infrared thermal imager, challenges such as illumination change and target shielding can be effectively handled, three-dimensional motion data of a target object are collected in real time, and the target positioning and tracking precision is improved. Meanwhile, the improved Kalman filtering algorithm is combined with a convolution attention mechanism, so that the weight of the sensor can be dynamically adjusted, and a motion track prediction error is compensated.

The invention discloses a single target tracking method suitable for an end side, and belongs to the field of computer vision processing. And dynamic weighted fusion is carried out on the y components of the visible light and the infrared image through the feature fusion network, and target feature representation under weak light is enhanced. The tracking model takes MobileNetV4 as a backbone network and combines a heavy parameter structure, a channel attention mechanism and dynamic multi-scale feature fusion, so that the feature extraction efficiency is improved while the weight is reduced. And designing a multi-threshold decision strategy, and realizing continuous tracking, feature updating or Kalman filtering prediction according to the confidence coefficient. In the training stage, the model performance is optimized through dynamic data enhancement, visible light information compensation and a specific loss function. According to the method, feature robustness is enhanced through multi-modal fusion, network adaptation end-side computing power is lightened, tracking stability is improved through a multi-threshold strategy, the method is suitable for low-power-consumption scenes such as security monitoring and mobile equipment, and efficient and stable single-target tracking in a weak light environment is achieved.

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 is suitable for the technical field of semantic understanding, and provides a dialogue type AI multi-intention understanding and decision-making method for a tourism scene, and the method comprises the steps: obtaining a tourism scene dialogue text which is input by a user and is expressed by a dialect; dialect expression is converted into standard Chinese statements through a dialect translation engine, and translation semantic deviation is corrected through a semantic correction model; performing multi-intention modeling on the converted standard Chinese statements, adopting a pre-training language model to combine with dynamic word segmentation processing, aligning special words of dialects to a standard Chinese word list through a dialect word mapping table, and adjusting semantic representation to adapt to dialect features; generating word list probability distribution, and generating a dialect compatible reply containing a multi-intention decision result in combination with an autoregression model; a dialogue state tracking model is trained according to geographic information, user historical interaction data and a cross-dialect corpus, intention switching and composite requests in multiple rounds of dialogues are analyzed, and dialect compatibility and intention recognition accuracy are effectively improved.

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 intelligent tracking of welding seams, in particular to an intelligent tracking method and system for a free curve welding seam through a mechanical arm. The method comprises the following steps that a laser contourgraph is deployed to scan a welding seam on a mechanical arm, geometric feature data of the welding seam are generated, and surface state mapping is conducted; thirdly, analyzing the distribution trend of pores of the welding seam, estimating the welding compactness loss, and further evaluating the fracture risk of the welding seam; according to the fracture risk data, welding heat output is regulated and controlled so as to optimize the welding quality; and finally, constructing an intelligent welding tracking model based on a random forest algorithm, and uploading the intelligent welding tracking model to a cloud platform to realize intelligent tracking welding control of the mechanical arm on the free curve welding seam. According to the invention, the intelligent tracking technology of the welding seam is optimized, so that the intelligent tracking technology of the welding seam is more perfect.

The invention provides a webpage dynamic element pickup method and system based on RPA, and relates to the technical field of digitization, and the method comprises the steps: firstly building a life cycle tracking model of elements, covering initial loading, state change, failure recovery stages and inter-stage transfer conditions in an element interaction process; then multi-source interaction signals such as page loading events, user operation triggering and element attribute changing of a target webpage are collected, the multi-source interaction signals are analyzed based on the life cycle tracking model, and state evolution modes of elements in different life cycle stages are extracted and comprise loading, changing and recycling triggering conditions; generating a dynamic positioning rule set comprising basic positioning, change response positioning and recovery compensation positioning according to the state evolution mode, judging the current stage of an element through a life cycle tracking model when webpage element pickup operation is executed, calling a corresponding dynamic positioning rule to complete pickup, and feeding back a result to optimize the state evolution mode. And the accuracy and the stability of webpage dynamic element pickup are improved.

The invention provides a method and device for calculating the trafficability of a line under the operation of a heavy haul railway train group, and relates to the technical field of train group analysis, and the method comprises the steps: calculating an interval time parameter under the operation of the train group; drawing a train interval working diagram; constructing a single-line working diagram cycle model based on the train interval working diagram and the interval time parameters; constructing a train group tracking model of the double-track heavy haul railway under train group operation based on the interval time parameters; linearizing and then solving the single-line working diagram cycle model to obtain a minimum limit interval working diagram cycle, and calculating the trafficability of the single-line heavy haul railway line under the operation of the train group based on the minimum limit interval working diagram cycle; and according to the train group tracking model, calculating the trafficability of the double-track heavy haul railway at different stages under the train group operation. The method solves the problem that in an existing line trafficability calculation method, the particularity of a group operation organization method is not considered, and the method is not suitable for capacity calculation under group operation.

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 relates to an airport intelligent bird repelling system based on multi-target detection optimization, and belongs to the technical field of image recognition. The system comprises a detection unit, an identification unit, a repelling unit and an intelligent optimization unit, the detection unit obtains the three-dimensional coordinate information of a whole airspace target, regularly scans the airspace around an airport, continuously tracks and updates the position information of the airport in real time, and then transmits the information to the visual confirmation module. And the visual confirmation module performs visual verification on the bird target found by the radar detection module. And the identification unit performs dynamic identification by using the target tracking model, and performs classification and threat level evaluation on the identified birds according to a preset bird threat level. According to the evaluation result, the repelling unit takes corresponding bird repelling measures; and the intelligent optimization unit optimizes a bird repelling strategy according to the historical bird repelling data and the real-time environment information. The system improves the accuracy and real-time performance of bird detection through a multi-target detection technology.

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 discloses a subway passenger flow dynamic statistics and real-time management and control method based on computer vision. The method comprises the following steps: acquiring passenger flow data to be detected; the to-be-detected passenger flow data are input to a passenger flow dynamic detection model, a passenger flow statistical result is obtained, the passenger flow dynamic detection model is constructed by a YOLO v11 target detection model and an SORT target tracking model, the YOLO v11 target detection model is used for detecting a passenger flow target, and the SORT target tracking model is used for carrying out tracking statistics on the passenger flow target according to the target detection result; the passenger flow statistical result is input into an automatic control model, a passenger flow control result is obtained, and the automatic control model is constructed by a queuing theory model; and performing real-time management and control based on a passenger flow control result. According to the invention, the real-time dynamic data of the passenger flow in the station can be accurately obtained, and the passenger flow is dynamically counted.

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 visual analysis method for traffic intersection collaborative perception, and the method comprises the following steps: S1, employing an intersection rapid calibration algorithm, and obtaining camera images at different positions; s2, for the camera image of each position, inputting a target detection model to obtain a target detection result; s3, inputting the target detection result of each position into a target tracking model, and outputting a target tracking result; s4, converting the target tracking result into a unified global coordinate system based on the affine transformation matrix, and executing global cross-lens ID matching to obtain a cross-camera tracking result; s5, smoothing the cross-camera tracking result to obtain a smooth tracking result; and S6, performing visual perception based on the smooth tracking result to obtain a visual perception result. Compared with the prior art, the method has the advantages of ensuring that the same target can be correctly associated under different camera visual angles, realizing cross-camera consistency identification and the like.

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.