314 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.

The invention relates to a multi-defense-area intelligent linkage alarm method based on an AIoT gateway and related equipment, and the method comprises the following steps: fusing the environment data of an Internet of Things sensor of a plurality of defense areas with a video image stream to generate three-dimensional reconstruction data of a defense area scene; and monitoring and analyzing the data, and extracting an environment feature sequence and a trajectory analysis sequence. And when it is detected that the threat levels of the multiple defense areas exceed a threshold value, causal inference is carried out in combination with gateway historical alarm data, and a cross-defense-area association event chain is generated. According to the method and the system, the AIoT gateway is used as a core, then a cooperative response strategy preset by the AIoT gateway is matched, and the SP voice call module is linked through a 4G / 5G network, so that multi-channel alarm pushing is realized, and the technical problem that comprehensive understanding and dynamic modeling of a defense area scene are difficult to form due to the lack of deep fusion and cooperative analysis means between video image data and environment sensor data is solved.

The invention discloses a security risk cognitive training system and method based on behavior trajectory analysis. The system comprises a multi-source data acquisition module, a behavior trajectory analysis module, a dynamic training module and a real-time feedback and evaluation module. The multi-source data acquisition module comprises a sensor network unit and an environment data unit. Physiological, behavior and environmental data are integrated through the multi-source data acquisition module, multi-modal fusion analysis is realized in combination with a Transform architecture, and the one-sidedness problem of traditional single data is solved; an LSTM network and a Prophet model are used to identify an abnormal behavior mode in real time and predict a risk trend, and real-time capture and dynamic early warning of risks are realized; a customized training task is generated by means of PPO reinforcement learning, a dynamic virtual scene and self-adaptive difficulty adjustment are combined, and the problems of fixed training content and lack of personalization are solved; through a multi-modal feedback and quantitative evaluation system, the risk perception capability and the training effect are improved, and closed-loop optimization of data acquisition-analysis-training-evaluation is formed.

The invention provides an accurate flight path tracking control method, device and equipment for an unmanned aerial vehicle, and the method comprises the steps: generating fusion state data through the fusion of GPS flight path data and flight state information, and constructing a flight path-state phase space model through employing a phase space reconstruction technology; phase trajectory feature parameters are extracted based on dynamic trajectory analysis, and dynamic attractor features are determined through attractor recognition processing; analyzing and identifying the center position of an attractor and the position of a topological singular point by using phase space topology, and constructing composite potential field intensity distribution formed by overlapping a conventional gravitational potential field and a repulsion-attraction bipolar potential field; gradient calculation is carried out on the composite potential field, and adaptive modulation is carried out based on the relative relation between the current position and the attractor center; in a local coordinate system taking a singular point as an original point, decomposing a modulation gradient vector into radial and circumferential components, and forming a spiral potential field through selective rotation enhancement; and finally, generating a tracking control instruction and performing signal modulation output, thereby realizing accurate tracking control of the unmanned aerial vehicle on the preset route.

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 relates to the technical field of physical education, and discloses an artificial intelligence-based physical education evaluation system, which comprises a data acquisition module, a multi-target detection module, a key point modeling module, an action trajectory analysis module, a multi-modal fusion module and a dynamic feedback module. The data acquisition module receives a video stream of the camera device and motion physiological data of the wearable sensor; the multi-target detection module locates a target based on a YOLOv8 algorithm; the key point modeling module constructs a three-dimensional action attitude model by using an OpenPose algorithm; the action trajectory analysis module evaluates action standard; the multi-modal fusion module integrates the data to construct a multi-dimensional feature matrix; the dynamic feedback module generates a real-time score and a personalized correction instruction through the optimized LSTM network. In addition, the system further comprises a model self-adaption module, an abnormal action recognition module and a distributed calculation module. According to the system, precise evaluation of physical education is realized, the sports safety is guaranteed, personalized teaching is promoted, and the physical education quality is effectively improved.

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 discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

The invention discloses a vehicle trajectory similarity calculation method based on semantic fusion and enhanced comparative learning, comprising the following steps: acquiring and preprocessing original trajectory data of a vehicle, and constructing a POI semantic region; constructing a track heterogeneous graph fusing time, space, weather and POI semantics; generating track embedding by using an embedding network fused by multidimensional features of time, space, weather and POI semantics; a meta-path is generated through reinforcement learning to capture a high-order semantic relationship, and final embedding is generated through dynamic weight fusion; optimizing an embedding space by utilizing a comparative learning framework; calculating the similarity between trajectory embedding vectors by adopting cosine similarity; according to the method provided by the invention, weather is used as an independent dimension to be fused into trajectory coding, four-dimensional joint characterization is realized, key semantic association paths are mined through reinforcement learning, the problem of scarcity of annotation data is relieved by utilizing comparative learning, the accuracy of trajectory similarity calculation is improved, and the high-precision requirement of fields such as intelligent transportation on trajectory analysis can be met.

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 relates to the technical field of engineering management monitoring, and discloses a vertical shaft tunneling intelligent construction comprehensive monitoring system which comprises a state sensing module, a three-dimensional modeling module, a risk early warning module, a decision scheduling module and a closed-loop control module. A multi-source sensing network is constructed to collect equipment operation states, environmental parameters and personnel positioning data in real time, and digital twin-driven three-dimensional total-factor virtual mapping is established, so that the problem of lagging identification of risks such as abnormal vibration of a cutterhead and sudden change of concentration of harmful gas in traditional manual monitoring is solved; millisecond-level sensing and intelligent early warning of abnormal working conditions in the construction process are achieved, the safety management and control capacity of vertical shaft construction is improved, accurate pre-judgment and self-adaptive regulation and control strategy generation of a fault path are achieved by fusing dynamic evolution trajectory analysis of a geologic model and real-time construction data, and the non-planned shutdown frequency and equipment idling energy consumption are reduced.

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 relates to an aircraft trajectory online prediction method based on Time-GAN and PINN-SR, belongs to the technical field of aircraft trajectory analysis and intelligent prediction, solves the problem of insufficient model generalization ability in the prior art, and comprises the following steps: S1, obtaining multi-dimensional time sequence data of a historical flight trajectory of an aircraft; s2, establishing a Time-GAN model for aircraft trajectory data enhancement, and performing adversarial training and joint optimization; s3, the Time-GAN model generates a specified number of synthetic trajectory data to form an aircraft trajectory data set; s4, establishing a PINN-SR model for aircraft trajectory prediction, and performing training; s5, acquiring a real-time trajectory multi-dimensional time sequence of the aircraft to be predicted, and inputting the current PINN-SR model to obtain a trajectory prediction result; s6, evaluating a trajectory prediction result, and selecting to return to the step S5 or carry out the next step according to the evaluation result; step S7, correcting the current PINN-SR model according to the real-time track data; and S8, according to the received signal, judging whether the flow is ended or not.

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 discloses a sparse trajectory pattern learning method based on mutual learning semantic distillation, and particularly relates to the field of trajectory analysis. Comprising the following steps: acquiring spatial characteristics of a road section to which a track belongs and spatial-temporal characteristics of each track point, and determining an embedding vector of each track point according to the spatial-temporal characteristics and spatial characteristics of the track points; constructing track features based on semantics and semantic features based on tracks; determining a track-based semantic vector according to the semantic-based track features, and determining a semantic enhanced track vector according to the track-based semantic features; fusing and interacting the semantic vector based on the track and the semantic enhanced track vector to obtain an embedded vector of the track; and inputting the embedding vector of the track, the initial occurrence time, the position of the interest point and the cue word into a pre-trained LLM text encoder to obtain a track mode. On the basis of the method, mode missing detection caused by data sparsity can be avoided, and then the accuracy of trajectory analysis is improved.

A system is provided for implementing trajectory analysis entry controls. A trajectory analysis entry control system is implemented in a vehicle, and supports remote and automatic entry control functions for the vehicle. For example the system triggers various automatic entry controls functions, such as remotely unlocking doors and / or a trunk of a vehicle, by tracking and analyzing a user's trajectory of movement while they are approaching the vehicle. A system includes a user identification device to identify whether a user is authorized to access the vehicle, such as a driver. The system also includes a trajectory tracking device that determines a trajectory of the user with respect to the vehicle. As the user's trajectory is determined to be approaching the vehicle, the trajectory tracking device further determines which entry point of the vehicle the user is approaching and automatically engages an automatic unlocking of that access point of the vehicle.

The invention relates to the technical field of image recognition, in particular to a smart home scene interaction method based on an image recognition technology, which comprises the following steps of: capturing real-time image data of a resident based on a camera, and extracting pixel points and color depth information in an image to obtain initial posture data; and based on the initial attitude data, an OpenPose algorithm is applied to detect a joint position, the attitude of the resident is identified, and a joint position analysis result is generated. According to the invention, based on the motion trail analysis of the time sequence, the system can analyze the conversion process of different postures, and the stability of behavior classification is improved. And joint motion analysis is adopted, so that classification of behavior states is more detailed, different subtle motion changes under the same category can be recognized, and misjudgment is reduced. In combination with an environment parameter dynamic adjustment strategy, the system can perform personalized adjustment according to a user state instead of depending on a fixed rule to perform equipment control.

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 relates to the technical field of intelligent shipping, and discloses a ship intelligent track analysis system and method, and the system comprises a data collection module, a track modeling module, a track anomaly detection module, a track optimization module, a global track reconstruction module and a feedback and decision support module. The data acquisition module is used for acquiring operation basic data, environment information and multi-source fusion data of a ship. By adopting the technical scheme of multi-module cooperation, comprehensive monitoring and intelligent analysis of the ship moving trajectory are realized, and the problem of weak data integration capability in the prior art is solved; by introducing the recurrent neural network and the long and short term memory network, the accuracy of trajectory modeling and prediction is improved; a global track reconstruction module is used for generating a multi-ship cooperative optimal path, and the defect of dispersed route planning is overcome; meanwhile, a visual and flexible feedback and decision support module is provided, the path adjustment response speed is optimized, and the practicability and the intelligent level of the system are remarkably 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 relates to a method for evaluating and debugging a sterile pipeline for food and beverage processing, which comprises the following steps of: introducing standardized sterile gas into a closed pipeline system to establish a micro-positive pressure environment; detecting a pressure change curve of each node of the system in real time by using a micro-differential pressure sensing module; calculating the position and the size of a trace leakage point; recording the motion trail of the particles in the pipeline by using a dynamic particle imaging system PIV; flow dead angles are identified through trajectory analysis, and the potential pollution accumulation area of the retention area marks the pollution accumulation probability of each section of pipeline in a quantitative mode; preparing simulation liquid, and adding biomarker particles to enable the viscosity and the fluidity of the target beverage to be close to those of the target beverage; according to the identified potential pollution accumulation area, local directional pouring is carried out; full-process automatic data acquisition, model judgment and debugging instruction issuing can be realized, manual intervention is greatly reduced, the system stability is improved, and the system is suitable for various food and beverage production scenes.

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.