230 results about "Trajectory analysis" patented technology

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

A system and method for real-time team intent modeling using persistent cognitive machines with federated human profiles which processes individual team member behavioral signals through geometric intent analyzers that generate high-dimensional vector representations of individual objectives and preferences. A team intent orchestrator aggregates individual vectors into collective representations within a dynamic geometric manifold that evolves based on team coordination patterns. Federated human profiles enable privacy-preserving knowledge sharing across teams through geometric abstraction techniques that preserve coordination utility while protecting individual privacy. The system implements proactive conflict detection through trajectory analysis that identifies potential coordination issues before performance impact, and provides real-time synchronization mechanisms that maintain team coordination coherence despite individual behavioral changes. Cross-team learning capabilities enable organizational intelligence development through pattern abstraction and context-aware adaptation of successful coordination strategies. The persistent cognitive architecture maintains coordination patterns across sessions and team composition changes, enabling continuous improvement through accumulated team experience.

The invention discloses a collaborative diagnosis method for partial discharge and overheating faults of a high-voltage switch cabinet, and particularly relates to the technical field of fault diagnosis of the high-voltage switch cabinet. Signals are synchronously acquired through ultrahigh frequency, acoustic emission and infrared sensors, and an abnormal candidate sequence is extracted through adaptive baseline segmentation; through laser calibration and time interpolation calibration, uniform event position information and timestamps are mapped; identifying potential coupling pairs of partial discharge and hot spot events based on dynamic threshold and time difference matching; generating a high-risk linkage region through spatio-temporal clustering, trajectory analysis and region fusion; updating the diagnosis model based on the high-frequency data increment, and generating a collaborative diagnosis report; the method breaks through the limitation of a static threshold value and a single sensor, and remarkably improves the diagnosis precision and adaptability of a composite fault under a complex working condition.

The invention provides a safety early warning method and device for abnormal behavior trajectory analysis and a medium, and relates to the technical field of safety early warning, and the method comprises the steps: collecting behavior trajectory data of a target region through a multi-source sensing device, and carrying out the multi-dimensional feature extraction of a behavior trajectory data set; according to the stay frequency characteristics, carrying out abnormity determination on the track space-time distribution characteristics and the speed change characteristics, and constructing an abnormal behavior determination vector; performing wandering mode recognition based on the abnormal behavior judgment vector, determining an abnormal behavior level, and triggering a graded early warning mechanism according to the abnormal behavior level; and generating an abnormal behavior alarm signal according to the grading early warning mechanism, combining the abnormal behavior alarm signal with the electronic fence information of the target area to generate a safety early warning report, and pushing the report to a monitoring terminal. According to the invention, the technical problem of abnormal behavior recognition accuracy in the prior art can be solved, and the technical effect of improving the behavior judgment accuracy is achieved.

The invention relates to the technical field of ship intelligent navigation and trajectory analysis, and discloses a ship trajectory similarity judgment method and system based on multiple dimensions and dynamic weights, and the method comprises the steps: obtaining load data, real-time navigation parameters and environmental parameters, and generating a load state classification result; calculating according to the load state classification result to obtain an acceleration performance index and a turning radius change trend; establishing a mathematical relationship model according to the load state classification result and the turning radius change trend; generating a dynamic incidence matrix according to the mathematical relationship model and the acceleration performance index; according to the dynamic incidence matrix, dynamically adjusting a weight value of a load state to obtain a weight distribution result; and performing calculation according to the weight distribution result and the real-time navigation parameters to obtain a final similarity judgment result. According to the method, the dynamic association of the ship load state and the trajectory characteristics can be realized, and the weight self-adaptive adjustment is realized in the similarity judgment process.

The invention relates to the technical field of motion detection, in particular to an old people falling early warning system based on motion trail analysis, which comprises a gravity center identification module, a trend identification module, an inertia pushing module, a phase judgment module and a grade response module. According to the method, the vertical coordinates of the key points of the waist and the two feet in the image frame are acquired, and the height difference change is continuously tracked, so that the potential falling initial state can be identified from the static posture, the vector included angles among the three groups of bone points of the hip knee, the knee ankle and the shoulder pelvis are subjected to sequence analysis, and whether synchronous disintegration occurs in the coordination action is identified; the identified trend sequence is mapped according to the risk level, a differential response mechanism is applied, a high-density corresponding relation is constructed between the behavior trend and the alarm level, graded response and time sequence triggering of the falling risk are achieved, the accurate identification capability of the falling precursor action is enhanced, the sensitivity and discrimination of the alarm response are also improved, and the safety of the falling risk is improved. And misjudgment and missed judgment are effectively avoided.

The invention discloses a regional termite detection method and system based on multispectral fusion, and the method comprises the steps: carrying a multi-mode spectrum collection device through a movable detection platform, and synchronously collecting a visible light image, a near-infrared hyperspectral image and Raman spectrum data of a to-be-detected region; preprocessing and feature extraction are carried out on the modal data, and the modal data are converted to a frequency domain to obtain frequency features; based on the physical and biochemical characteristics of the termites and the nests thereof, calculating the matching degree and the credibility of each modal feature, and performing adaptive weighted feature fusion according to the matching degree and the credibility to generate comprehensive spectral features; inputting the fusion features into a pre-trained termite identification and risk assessment model to realize precise identification, positioning and threat level assessment of termite individuals, termite paths and nests; and carrying out trajectory analysis on termite activities by combining a multi-target tracking algorithm, and giving out early warning based on identification and tracking results. According to the invention, early-stage, lossless and accurate detection and active early warning of termites are realized, and the detection efficiency and reliability are significantly improved.

The invention discloses a high-precision real-time positioning system and method for positioning personnel based on multi-mode fusion. The system comprises a multi-source data acquisition module, an intelligent data processing module, a fusion positioning model construction module and a positioning result application module, provides visual monitoring, trajectory analysis and scene customization functions, and supports flexible expansion through modular design. According to the method, satellite positioning, Bluetooth beacon and inertial navigation technologies are integrated, the weight is dynamically adjusted in combination with an adaptive weighted fusion algorithm, and a fusion positioning model supports outdoor (satellite dominant + EKF calibration, the precision being 2-5 meters) and indoor (beacon dominant + fingerprint matching, the precision being lt); according to the invention, intelligent switching between three modes (inertial navigation + ZUPT correction) and signal loss (1 meter) is realized, data noise is optimized by using Kalman filtering, and high-precision real-time positioning in indoor and outdoor complex scenes is realized. According to the invention, the problems of insufficient precision, poor environmental adaptability and weak expansibility of a single positioning technology are solved, and the comprehensive performance of the positioning system is significantly improved.

The invention discloses a multi-modal data enhancement method and system based on feature space alignment. The method comprises the following steps: collecting modal original data streams of multiple unmanned aerial vehicles; reversely deducing an internal generation rule from a data final state, and constructing a sparse coding feature representation library through causal pilot signal extraction and singular trajectory analysis; constructing an adaptive weight map according to the feature representation library, and identifying a space alignment path through dynamic calibration processing to generate an alignment imbalance index; performing cross information entropy analysis to establish a collaborative enhancement chain, and identifying a collaborative enhancement mode based on the information coupling degree to generate a complementary enhancement vector; performing multi-dimensional projection reconstruction on the complementary enhancement vector, and determining feature mapping probability distribution through diffusion time inversion to obtain a multi-dimensional projection feature map; carrying out nonlinear enhancement processing to generate an enhancement expansion mode, and identifying an enhancement key node to construct an enhancement regulation and control sequence; and outputting hierarchical enhancement data based on the regulation and control sequence and the projection feature map, and realizing accurate space alignment and collaborative enhancement of multi-sensor data.

The invention relates to the technical field of trajectory analysis, in particular to a trajectory aiming trajectory analysis method which comprises the following steps: acquiring wind speed time-varying data and centroid parameters, calculating wind disturbance stress and screening a maximum value, judging a rotating direction trend, executing pitch angle consistency judgment, and calculating an angle adjustment rate to generate boundary parameters. The method comprises the steps of analyzing a target speed trend to judge delay, calculating a deviation trend difference value, extracting wind disturbance sudden change to mark a high-sensitivity zone, judging that deviation coincides with the high-sensitivity zone, and generating a dynamic correction instruction set. Modeling analysis is carried out on a target delay trend by combining a direction included angle and mode difference change in a target speed trend sequence, and a trajectory correction mapping relation is constructed by combining flight parameters and timestamps, so that the adjustability and striking precision of an overall hit path are effectively improved.

The invention discloses a personnel information security screening method and system based on big data, and relates to the technical field of image processing, and the method comprises the steps: obtaining to-be-processed face data in a region, screening out unregistered personnel, carrying out the matching and correlation of the images of the same unregistered personnel at different image collection points through calculating the feature similarity, and carrying out the recognition of the unregistered personnel. And connecting time points and spatial positions corresponding to the images of the same unregistered person at different image acquisition points into a spatial-temporal trajectory through a trajectory reconstruction algorithm, and pushing multi-modal feature information of the person to an adjacent preset area for the identified abnormal behavior. According to the invention, through a density clustering algorithm based on time and space constraints, feature association is carried out on personnel images collected by different cameras, a cross-region behavior track of the unregistered personnel is constructed, high-risk behaviors such as wandering are detected by using track analysis and an abnormal behavior identification technology, and early warning information is generated and sent to other regions. And the initiative and the coverage range of safety management are improved.

The invention provides an urban population dynamic monitoring and trajectory analysis method and system based on big data. The method comprises the steps of obtaining a multi-modal data set in a city preset range, performing preprocessing and gridding mapping to obtain a multi-modal optimization data set corresponding to a city grid, then performing processing through a preset cross-mechanism federated learning model in combination with a dynamic weight distribution mechanism to generate a cross-mechanism fusion feature matrix, and finally obtaining a multi-modal fusion feature matrix. Coding processing is carried out through a preset LSTM model, processing is carried out through a preset DPC clustering algorithm, and a high-frequency path and a parking high-density area are obtained for visual output; according to the method, the data richness is improved through multi-modal data fusion, the data privacy is protected by utilizing cross-mechanism federated learning, and the real-time performance and accuracy of the data are improved by combining the LSTM model and the DPC clustering algorithm, so that high-precision, high-accuracy and high-timeliness urban population dynamic monitoring and trajectory analysis are realized.

The invention relates to the technical field of data analysis, in particular to a cloud-based teaching platform student learning behavior trajectory analysis method, which comprises the following steps: acquiring a student interaction log, extracting task completion and control operation discontinuity points, identifying continuous learning behaviors and switching types, constructing a candidate period sequence, analyzing time and label changes, and generating a trajectory analysis result. According to the method, the non-interaction time period of the student in task switching can be identified by collecting the time interval between task completion and first control operation, so that the potential attention distraction or task interruption behavior of the student is captured, and the interaction characteristic and the duration of the continuous behavior segment are combined; and active and inactive learning period switching characteristics are further marked, and a task label difference degree and a time interval are introduced in the process of constructing a learning period sequence as a screening basis, so that the analyzed learning paragraph is ensured to have content continuity and behavior pattern difference, and the recognition capability of rule switching behaviors in trajectory analysis is enhanced.

The invention is suitable for the field of smart campuses, and provides a multi-source fusion data-based campus subject trajectory analysis method and system, and the method comprises the following steps: synchronously collecting the original data of a vehicle through an intelligent riding terminal, the original data comprising three-axis acceleration data, tire pressure data and high-precision positioning trajectory data; preprocessing the original data and extracting an acceleration sudden change event, a tire pressure sudden drop event and track deceleration characteristics; performing spatial superposition on the event data obtained through preprocessing and a campus road network GIS layer, and generating a pit damage thermodynamic diagram based on a preset damage judgment rule; and extracting a high-risk road section based on the pit damage thermodynamic diagram, and pushing a high-risk road section warning to the user. According to the invention, a scientific basis is provided for campus road maintenance planning, preventive maintenance is realized, and pavement damage is reduced.

The invention belongs to the field of safety monitoring of wind power equipment, and provides a multi-source data fusion and safety early warning system and method in the hoisting process of a wind turbine generator, and the system comprises a data collection and preprocessing module which collects and preprocesses multi-source sensor data such as a tower inclination angle and a suspension arm inclination angle; the state manifold construction module is used for mapping the preprocessed data to a multi-dimensional space to construct a state manifold and generating an ideal geodesic line; the trajectory analysis and risk assessment module is used for calculating the deviation degree between the real-time state trajectory and the ideal geodesic line and generating a risk score; the dynamic threshold generation module constructs a risk tensor field based on environmental parameters, and extracts a risk threshold curved surface after wind speed adjustment; the early warning decision module is used for comparing the risk score with a threshold curved surface to determine an early warning level and sending information; and the visual display module is used for displaying the state manifold, the track, the tensor field and the early warning information. By introducing a differential geometry theory, state manifold representation and geodesic deviation measurement are constructed, and accurate assessment of the hoisting process risk is realized.

The invention relates to a pressing plate operation behavior analysis and error prevention method and system based on image recognition and machine learning. The method comprises the following steps: acquiring a pressing plate operation area video stream through image acquisition equipment, and segmenting a video into independent operation event segments based on motion detection and trajectory analysis; extracting key point time sequence data of hands of an operator by using a human body posture estimation model, and constructing a dynamic feature sequence fusing a spatial relationship, kinematics and posture semantics; performing multi-level similarity comparison on the dynamic feature sequence and a standard operation template, and judging operation compliance by a machine learning model in combination with a dynamic threshold value; triggering graded early warning and intervention according to a judgment result; and an incremental learning mechanism is adopted, and the template and the threshold value are adaptively optimized based on historical data. According to the invention, accurate and intelligent analysis and active error prevention of the whole operation process of the pressing plate are realized, and the safety level of electric power operation is effectively improved.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention provides a moving target feature extraction method based on a deep convolutional neural network, and the method comprises the steps: continuously obtaining the movement speed change information of a photographed subject and the real-time data of the jitter of a handheld device, obtaining the initial trend value of the movement speed change, carrying out the calculation of the future position of the photographed subject through a preset trajectory analysis model, and obtaining the feature of a moving target. Determining a reference interval of snapshot opportunity prediction; through the adjusted focus locking data, combining with the dynamic weight of the external interference factor to optimize the definition degree of the processed image, and obtaining an optimized image frame sequence; and according to the optimized image frame sequence, analyzing the performance characteristics of the dynamic performance capability, if the dynamic performance capability is lower than a preset threshold value, performing enhancement processing on the image frame sequence, determining dynamic performance output, and if the dynamic performance capability is higher than the preset threshold value, directly outputting the current image frame sequence as the dynamic performance output.

The invention relates to the technical field of trajectory data mining, and particularly discloses a trajectory point state recognition method and device, electronic equipment, a storage medium and a product. The method comprises the following steps: compressing acquired original trajectory data to obtain target trajectory data of different target objects; performing trajectory analysis on the target trajectory data of each target object according to a preset space-time density constraint condition, and generating a trajectory point state recognition result of each target object; wherein the preset space-time density constraint condition comprises a space constraint based on a preset stay radius and a time constraint based on a preset stay duration. According to the scheme, the original trajectory data is compressed, so that high-quality data input can be provided for trajectory analysis; by adopting the space-time density constraint condition combining the space constraint based on the preset staying radius and the time constraint based on the preset staying duration, the staying point and the moving point in the track data can be accurately identified, and the precision and the efficiency of track point state identification are improved.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention discloses a mine drilling group track intelligent comparative analysis method based on multi-source data fusion, and belongs to the technical field of mine engineering digitization. The method comprises the following steps: acquiring a drilling group track and performing track data analysis to obtain a track inclination angle, and numbering the track based on the size of the inclination angle to obtain a design track; actual drilling track data are obtained and preprocessed, the inclination angle average value of local and global actual drilling tracks is obtained, and an inclination angle matching algorithm is used for matching with a design track; and a dynamic coordinate translation mode is used for achieving alignment of the actual drilling track and the design track, and a final result is obtained. Through fusion processing, the method is suitable for analyzing drilling trajectories of different data formats, and the application range of the system is widened; automatic matching of a measurement track and a design track is achieved through the inclination angle, manual alignment errors are avoided, and the comparison precision is improved. Manual operation is reduced, and the automation level of engineering data management is improved.

The invention relates to the technical field of ruggedized computer protection, in particular to a ruggedized computer dust particle prediction and self-adaptive dust prevention method and system. The method comprises the following steps: constructing an air duct airflow state boundary model; motion paths of dust particles with different particle sizes are estimated based on the model, collision detection and stagnation trajectory analysis are executed, and a deposition probability distribution structure is generated; extracting a high deposition risk area, constructing an electrostatic diversion field regulation and control structure, and calculating diversion electrode control parameters; generating an electrode instruction to drive the diversion control module to deflect dust particles; configuring a dust collection area according to the dust particle deflection path and carrying out particle adsorption; and a feedback control vector is generated in combination with the residual dust particle monitoring data and the control parameters, so that self-adaptive updating of electrostatic regulation and control is realized. On the premise that a sealing structure is not needed, dust particle behaviors can be accurately predicted based on CFD and a differential model, the deposition trend of the dust particles is dynamically regulated and controlled, and high-reliability active dustproof control in a complex operation environment is achieved.

The invention relates to the technical field of vehicle trajectory analysis, in particular to a vehicle trajectory anomaly detection method and system based on deep learning, and the method comprises the following steps: obtaining trajectory coordinates and boundary distance to generate symbol offset, constructing a road offset continuous field through interpolation and spatial embedding, calculating the ratio of instantaneous speed to reference speed, and obtaining a vehicle trajectory anomaly detection result. And generating an unbalance degree parameter, executing vector dimension stretching, extracting a trajectory embedding vector and an abnormal score by using a long short-term memory network, executing square summation and difference operation, and generating a trajectory energy gradient value. According to the method, migration characteristics are mapped to a continuous field to achieve track and road geometric constraint association, unbalance degree parameters are used for remarking directional quantity scales to enhance speed dynamic perception, energy gradient modulation scoring is cooperated, a confidence probability curved surface is constructed by using trilinear interpolation, and a curved surface peak value is positioned to output a detection result. Positioning noise is suppressed; and abnormal precision under sparse sampling is improved.

The invention provides an eye movement trajectory analysis method and system based on hybrid clustering and time constraint, and belongs to the technical field of computer vision. Calculating a time difference and a moving speed between continuous original eye movement data points; comparing the moving speed with a speed threshold value, and classifying the moving speed into candidate fixation points and glancing points; extracting spatial features and time features of the candidate fixation points, performing standardization processing, and performing weighted fusion to obtain spatial-temporal feature vectors; clustering the spatio-temporal feature vectors to obtain preliminary clustering labels of the candidate fixation points; performing time constraint processing on each cluster; calculating the duration of the clustering cluster after the time constraint processing, and generating a final clustering label of the candidate fixation point; and outputting a final classification label of each original eye movement data point in combination with the classification result of the glancing points.

According to the traffic incident intelligent identification early warning method and device provided by the embodiment of the invention, a multi-source data fusion mechanism is innovatively constructed, and comprehensive analysis of image, terrestrial magnetism, radar and meteorological data is realized through timestamp alignment and feature extraction. And designing a clustering model based on spatial-temporal characteristics, and establishing an event type identification strategy in combination with density clustering and rule matching. And an influence range evaluation mechanism is introduced, and accurate early warning information pushing is realized through vehicle track analysis and position mapping. According to the method, the defects of the traditional technology in the aspects of data fusion, event recognition, early warning release and the like are effectively overcome, and the accuracy and the real-time performance of traffic event recognition and early warning are remarkably improved.

The invention relates to a stomach tube track monitoring system and device, and the system comprises a track collection module which is used for obtaining the moving track of the insertion end of a stomach tube based on a real-time position; the trajectory analysis module is used for obtaining a real-time deviation between the target trajectory and the moving trajectory, and determining a deviation position and an adjustment angle according to the real-time deviation; the trajectory topological graph construction module is used for constructing a trajectory point cloud set according to the moving trajectory and determining an abnormal point set according to a trajectory topological graph group based on a continuous coherence mechanism; the real-time risk early warning module is used for synchronizing the moving track to a pre-obtained trachea-esophagus model and performing real-time risk early warning according to the position of the abnormal point set in the trachea-esophagus model; and the potential risk early warning module is used for inputting the real-time position into a preset trajectory prediction model to obtain a prediction trajectory at the next moment, and performing potential risk early warning according to the deviation between the prediction trajectory and the target trajectory.

The invention provides a flight path analysis model, a training method, a flight path analysis method and device, and a medium, and belongs to the technical field of artificial intelligence, and the model comprises a flight path coding network which is used for receiving an aircraft flight path sequence and extracting a first flight path hidden feature; the physical consistency constraint network is used for predicting physical state features and performing constraint based on a motion feasible region so as to fuse and generate second track hidden features; the multi-modal adaptation network is used for performing cross-modal fusion on the second track hidden feature and the analysis instruction feature to obtain a multi-modal input feature; and the large language model network is used for receiving the input characteristics, performing semantic reasoning and generating a track analysis result text. According to the method, the physical constraint network ensures that the features conform to the physical motion law, and the cross-modal network is utilized to realize the deep alignment of the track and the language, so that the bottleneck of single-modal analysis is broken through, and the high-order analysis capability of track situation understanding and reasoning in a complex low-altitude scene is remarkably improved.

The invention discloses a user dynamic access control method and system based on multi-dimensional data, and belongs to the technical field of software. The invention aims to solve the problems of user access control policy jitter, security vulnerability and difficulty in explaining compliance caused by static threshold, module independent decision and non-explicit modeling user experience cost and compliance risk cost in the prior art. Therefore, the invention provides a dynamic access control method, which comprises the following steps of: constructing a session-level multi-dimensional access context; dividing risk signal detection according to a visual angle; carrying out risk evidence combination and mode recognition; performing risk trajectory and strategy hysteresis control based on a state machine; unifying action arrangement and conflict resolution of the strategy center; strategy execution and session consistency control are carried out; and effect evaluation and strategy evolution based on multi-dimensional indexes are carried out. Compared with the prior art, multi-view evidence combination, session-level trajectory analysis, state machine and constraint solution and other means are introduced, multi-layer integration from a data layer, a logic layer, a time layer to a control layer is achieved, strategy jitter is effectively relieved, user experience is improved, service continuity is guaranteed, and compliance interpretability is enhanced.

The invention relates to the technical field of basketball judgment, and discloses an infrared time sequence verification basketball goal anti-misjudgment detection method. According to the method, infrared signal sequences of shooting actions are collected in real time through an infrared sensor array on the periphery of a basketball hoop, shooting track fragments are segmented, space-time coupling characteristics are extracted, then a multi-scale track analysis grid is generated, and the grid precision of an infrared signal violent change area is higher than that of a smooth area. Interference feature analysis is conducted on the grid time sequence to generate an infrared signal interference index, the abnormal deviation degree of the basketball track is calculated through a dynamic path verification algorithm in combination with the index, and finally a misjudgment verification conclusion of the shooting action is generated according to the comparison result of the abnormal deviation degree and a preset threshold value. According to the method, the basketball movement track can be comprehensively captured, interference is effectively eliminated, the accuracy and the real-time performance of goal penalty are improved, and the fairness of basketball match penalty is improved.

The present application relates to a kind of based on AIoT gateway's multi-prevention area intelligent linkage alarm method and related equipment, including the following steps, the present application proposes a kind of based on AIoT gateway's multi-prevention area intelligent linkage alarm method, by the Internet of Things sensor environmental data of multiple prevention areas and video image stream are fused, generate prevention area scene three-dimensional reconstruction data, and it is monitored and analyzed, extract environmental characteristic sequence and trajectory analysis sequence.When detecting that multiple prevention area threat level exceeds threshold value, in combination with gateway historical alarm data, cause and effect inference is generated, generates cross-prevention area correlation event chain.Subsequently, matching AIoT gateway's preset collaborative response strategy, and through 4G / 5G network linkage SP voice call module, realize multi-channel alarm push, solve the technical problems that lack depth fusion and collaborative analysis means between video image data and environmental sensor data, it is difficult to form the overall understanding and dynamic modeling of prevention area scene.