2249 results about "Tracking data" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

The invention relates to the technical field of intelligent control, provides a flow regulating valve servo force control method and system based on a force sensor, and aims to solve the technical problems of response delay, weak overshoot suppression capability and poor long-term operation stability. The method comprises the following steps: acquiring a servo driving current data set and a valve displacement track data set of a target regulating valve; performing pressure feature mapping processing on the servo driving current data set to generate a pressure fluctuation feature set corresponding to the driving current waveform data; the pressure fluctuation characteristic set and the valve displacement track data set are input into a preset force control decision model for dynamic matching processing, and a servo control instruction set is generated; executing multi-stage dynamic adjustment operation on a servo driving unit of the target adjusting valve according to the servo control instruction set, and generating real-time pressure balance state data; and iteratively updating the dynamic matching processing parameters of the force control decision model based on the deviation value of the real-time pressure balance state data and the preset pressure reference value.

The invention provides an aluminum alloy auxiliary frame welding accurate positioning and quality control method and system. The method comprises the steps that S4, welding gun track control parameters are updated through a dynamic adjustment instruction, laser tracking data are fused to optimize the welding path angle in real time, and the accurate movement track of each section of welding seam is determined; s5, acquiring real-time thermal expansion data during welding execution, monitoring temperature field distribution of a welding seam area by adopting an optical measurement technology, and judging whether thermal deformation adjustment exceeds a preset threshold value or not; s7, micron-sized welding spot positioning operation is executed according to the stable positioning state, the dynamic path optimization result is fused to generate a final welding instruction, and consistent welding seam quality data is output; and S8, after weld joint quality data are obtained, weld joint deviation and geometric consistency are detected through a laser tracking technology, whether welding quality meets design requirements or not is judged, and a quality verification report is generated. According to the method, the precision and consistency of intelligent welding of the aluminum alloy auxiliary frame are remarkably improved.

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

A dog training method for intelligently correcting pet behaviors based on multi-modality comprises the steps that multi-modality perception data of a pet is obtained, the multi-modality perception data comprises acoustic signals, motion signals and track data, an acoustic feature tensor, a motion feature tensor and a position feature tensor are constructed, and a unified coupling tensor is formed; inputting the coupling tensor into a pre-trained behavior recognition model, and outputting a behavior tag; the emotion recognition module outputs an emotion label, and updates a character label in a sliding window mode; inputting the behavior label, the emotion label and the character label into an intervention strategy generation model and outputting an adaptive intervention strategy, wherein the intervention strategy comprises a pacifying type strategy and a stimulation type strategy; according to a historical feedback effect record, adjusting a feedback intensity parameter of the intervention strategy, calculating a feedback convergence index and an emotion recovery index, and dynamically correcting a feedback level and a delay strategy of the intervention strategy.

The invention relates to the technical field of data analysis, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a business process visualization early warning and strategy response method, which comprises the following steps: setting a physical interrupt node to collect interrupt operation track data, generating multilevel logic interrupt type structured data based on the operation track data, the method comprises the following steps: acquiring a preset business strategy, mapping the preset business strategy into a visual topological graph, identifying a preset business strategy verification failure type, counting interruption frequency, triggering a grading early warning instruction, and generating a page element weight adjustment parameter and a fault-tolerant threshold set. According to the method, the visual topological graph is constructed and the high-frequency interruption behavior is identified, so that real-time identification and graded early warning of business strategy verification failure are realized, and adaptive adjustment of page elements and strategy thresholds is further driven, so that the user insurance conversion rate is effectively improved and the front-end operation loss risk is reduced.

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.

The invention discloses a track representation enhancement method based on a dynamic subgraph. A historical sign-in track of the user is given, semantic track representation aims at learning low-dimensional vectors capable of revealing user behavior patterns and semantic intentions from user track data, and convenience is provided for subsequent prediction tasks. The method comprises the following steps: firstly, constructing a city knowledge graph based on a space-time semantic tetrad, and explicitly modeling the association between a user track and a space-time scene; secondly, extracting a semantic sub-graph sequence taking a user sign-in track as a core from the user sign-in track by using a relation perception dynamic sub-graph extraction mechanism; and then, learning representation of a sub-graph sequence based on an attention aggregation network of a cross-sub-graph, and further obtaining knowledge-enhanced user track representation. And finally, verifying the characterization quality based on a prediction module, and outputting K sign-in points which the user is most likely to be interested in as results. Through the modeling method of the dynamic subgraph sequence, time-space sensitive trajectory semantics are effectively modeled, and the accuracy of a prediction task is improved.

The invention provides an identity recognition method and system based on unmanned aerial vehicle management and control. The method comprises the following steps: acquiring encrypted identity data based on unmanned aerial vehicle state monitoring, and generating an initial code containing a unique identifier, a position and a state parameter; dynamically adjusting a coding rule according to the acceleration variation, and generating a differential sequence matched with the motion state; generating a composite identifier containing a timestamp and a geo-fence through irreversible conversion; fusing the positioning data to dynamically bind the identifier and the track to generate synchronous track data; when the geofence is triggered, redundancy calibration is activated to generate anti-interference data; the composite identification and the anti-interference data are subjected to fragmentation packaging, and a topology dependence encryption block is constructed through a Hash abstract chain; and recombining the encrypted blocks in sequence based on the Hash chain, and checking the space-time consistency of the encrypted blocks and the geofence during recombining. According to the invention, the identity anti-counterfeiting and cross-region transmission security in the dynamic flight of the unmanned aerial vehicle is improved.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention provides a real-time link anomaly detection and sampling method and device based on edge AI driving, and is applied to the technical field of artificial intelligence. Segmenting and aggregating the original link tracking data in each time window through a dynamic time window division mechanism to generate a statistical feature vector of link topology in the time window, the original link tracking data including a link topology relationship, a service calling level and a performance index; inputting the statistical feature vector into an anomaly detection model obtained by pre-training for analysis to obtain a path anomaly probability score; and dynamically adjusting the sampling strategy of the next time window according to the path anomaly probability score. Through edge AI calculation nodes, dynamic time window division, segmented aggregation and dynamic adjustment of a sampling strategy, the real-time performance of anomaly detection and the reliability and practicability of anomaly detection are improved, adaptive sampling is realized, and the sampling precision and efficiency are ensured.

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

Disclosed herein are systems and methods for energy management. A system, such as a vehicle, includes a plurality of energy storage units that include a supercapacitor and an electrochemical battery. The system includes an energy controller that tracks historical power draw from the plurality of energy storage units over time in power tracking data, and that identifies a power draw based on the power tracking data. The energy controller switches between a first configuration and a second configuration based on the identified power draw. The first configuration is configured for drawing power from the electrochemical battery and disconnecting from the supercapacitor, while wherein the second configuration is configured for drawing power from the supercapacitor and disconnecting from the electrochemical battery.

The invention relates to the technical field of security and protection management, in particular to a campus security and protection management system based on deep learning, which improves the accuracy and robustness of identity recognition by acquiring access control card numbers, face images or fingerprint features and generating standardized identity authentication data. And on the basis of a comparison result of the identity authentication data and the campus database, a behavior chain initialization identifier is generated, and accurate identity binding of the school entering personnel is realized. Furthermore, by collecting multi-camera image stream data, pedestrian re-identification and similarity calculation are executed by using a deep feature matching network, and a cross-camera continuous trajectory data set is generated. And matching the behavior track data set with the conventional path template to generate a behavior offset feature vector. And carrying out joint modeling on the behavior offset characteristics and the identity information through a graph neural network model containing an attention mechanism, and outputting a behavior purpose label and a risk grade score. And a graded security response instruction is generated based on the risk score, so that the missing report rate and the false report rate are effectively reduced.

The invention belongs to the technical field of data analysis, and provides a multi-source operation and maintenance data anomaly analysis method and device, equipment, a medium and a product, the method comprises the steps that multi-source operation and maintenance data is acquired, and the multi-source operation and maintenance data comprises at least two of index time sequence data, application logs, call link tracking data, configuration change records, alarm events and work orders; carrying out joint anomaly modeling on the preprocessed multi-source operation and maintenance data based on a multi-model fusion architecture to identify an abnormal event in the multi-source operation and maintenance data; based on the operation and maintenance knowledge graph and the structured causal model, fault influence path tracing and root cause positioning are carried out on the abnormal event, a root cause analysis result is obtained, and the root cause analysis result is used for indicating a fault root cause node and a fault propagation path in the abnormal event. Therefore, the accuracy of anomaly analysis of the multi-source operation and maintenance data is remarkably improved.

The invention provides a security code automatic generation method and system based on a full information theory and mechanism ambiguity, and the method comprises the steps: S1, carrying out the feature extraction of a grammar verification layer of an input demand, extracting the grammar features of a code through the topological features of an abstract syntax tree, and carrying out the formalized rule verification of the extracted grammar features; s2, constructing a semantic association model of a cross-code snippet through a semantic analysis layer, and forming a propagation path for tracking a data flow and a control flow; s3, in the pragmatic optimization layer, combining pragmatic constraints of the business scene, and generating a security policy through a dynamic utility function; and step S4, optimizing parameters and security policies of the dynamic utility function through knowledge federal evolution and a closed-loop feedback mechanism. According to the method, a deep collaboration mechanism of grammar, semantic and pragmatic three-dimensional information and a dynamic strategy generation model are reconstructed, security logic is converted from external attributes to endogenous cognitive competence, and the security, interpretability and adaptability of automatic code generation are improved.

The invention relates to the technical field of intelligent education, and discloses a personalized historical exercise recommendation and automatic correction system based on a large model, and the system comprises a cross-modal data collection module which is used for collecting character input, time behavior and interaction track data of a student in an answering process, and outputting a collection result to a cognitive map construction module; and the cognitive map construction module receives the acquisition result output by the cross-modal data acquisition module, and is used for generating a cognitive map representing the mastering degree of the students on the historical knowledge points and the relationship thereof based on the acquisition result, and sending the cognitive map to a cognitive drive recommendation module and an automatic correction module. The cross-modal data acquisition module is introduced, so that dominant content answered by the students is acquired, diversified recessive behavior data such as answering duration, editing and modifying tracks, mouse hovering positions and answering pauses of the students are further acquired, and the system can surpass traditional simple answering right and wrong judgment due to the multi-dimensional data capture capability.

The invention relates to the technical field of intermediate frequency furnace sintering temperature control, and discloses a sintering temperature control system of an intermediate frequency furnace. The system comprises a multi-source data acquisition module which is used for acquiring multi-dimensional thermodynamic data, covering temperature distribution, sintering power and heat flow track data in a sintering process in real time; and the temperature field analysis module is used for carrying out dynamic compensation correction processing on the multi-dimensional thermodynamic data, inputting the multi-dimensional thermodynamic data into a sintering analysis processing layer for characteristic modeling, and generating a sintering temperature regulation and control instruction. The sintering analysis processing layer comprises a data preprocessing module and a thermodynamic modeling module, the data preprocessing module performs operation such as temperature interval division and abnormal value elimination on original data, and the thermodynamic modeling module is obtained based on historical data cooperative training. According to the system, through multi-dimensional data acquisition and analysis processing, the sintering temperature can be accurately regulated and controlled, the quality of sintered products and the production efficiency are effectively improved, and the system has good universality and adaptability.

A surgical microscope lighting control system comprising a controller configured to: estimate or determine a lighting need of a surgeon during at least part of a surgical procedure based at least on a viewing status of a region of illumination, wherein the viewing status of the region of illumination is estimated or determined (a) based at least on whether or not the region of illumination is being displayed, or (b) based at least on tracking data of the surgeon or a head-mounted display; determine an adjustment to be made to an illumination provided by a lighting unit of a surgical microscope based on the lighting need; and in response to determining the adjustment, send a first signal to the lighting unit to change the illumination according to the adjustment.

The invention discloses a personnel trajectory drawing method and system based on an AI algorithm. The method comprises the following steps: acquiring a positioning equipment signal and monitoring video data; performing dynamic Kalman filtering denoising processing on the positioning equipment signal to obtain first track data, and performing adaptive illumination compensation enhancement processing on the monitoring video data to obtain first video data; performing trajectory prediction compensation on the signal interruption segment based on an LSTM neural network; personnel motion posture features and scene semantic features are extracted; constructing a space-time correlation graph by adopting a graph convolutional neural network to obtain enhanced trajectory data; performing multi-scale trajectory reconstruction based on an improved Transform architecture to generate final personnel trajectory information; and outputting a visual trajectory report including trajectory precision evaluation, abnormal behavior marking and an electronic fence trigger event. According to the method, deep fusion of multi-source heterogeneous data and accurate reconstruction of the trajectory are realized, and continuity and reliability of personnel trajectory drawing in a complex scene are remarkably improved.

The invention relates to the technical field of biological feature recognition, and discloses a neural reflex motion feature extraction and quantitative evaluation method, which comprises the following steps that: multi-modal behavior data of a premature infant are synchronously acquired through a sensor array, and the multi-modal behavior data comprise a motion inertia signal, optical tracking data and plantar pressure distribution data; preprocessing the multi-modal behavior data, and performing dynamic feature fusion on the preprocessed multi-modal data to generate a joint feature vector; extracting a motion symmetry index and a reflection intensity grade from the joint feature vector; and inputting the joint feature vector, the motion symmetry index and the reflection intensity level into a hierarchical rule engine, and executing risk judgment of a behavior pattern. The invention also provides a neural reflex motion feature extraction and quantitative evaluation system. According to the invention, the accuracy of neural reflex motion feature extraction and quantitative evaluation can be improved.

The invention discloses a personalized intelligent education textbook generation method and device based on a large model, and the method comprises the steps: obtaining a knowledge point set, learning data of a learner, a learning target, and eye movement tracking data; determining a cognitive style vector of the learner according to the learning data and the eye movement tracking data; according to the cognitive style vector and the learning target, a learning path diagram of the learner for the knowledge point set is constructed, and the learning path diagram comprises learning nodes and jump relations among the multiple learning nodes; determining cognitive matching parameters of the knowledge points based on the cognitive style vector and the knowledge point attribute vector of each knowledge point in the knowledge point set, and determining presentation forms of the knowledge points according to the cognitive matching parameters; and based on the learning path diagram and the presentation form, generating a personalized intelligent education textbook through a preset large model. The learning efficiency of students is improved.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention relates to a cargo transportation track real-time tracking method based on the Internet of Things, and the method comprises the steps: carrying out the node collection and multi-source fusion of sensor data (including GPS, RFID, timestamps and the like) of all links of logistics, and employing data standardization and time sequence verification, thereby achieving the consistency processing of different devices and node data. The method is characterized in that standardized trajectory data and breakpoint detection data are stored in a distributed manner by using a block chain non-tampering and multi-party consensus mechanism, third-party multi-source data and a fuzzy reasoning model are fused, and confidence completion is performed on trajectory breakpoints. The system can output a complete and traceable trajectory path, anomaly recognition and responsibility attribution result, and provides a visual and third-party notarization interface. According to the method, the authenticity, the integrity, the space-time consistency and the responsibility traceability of the logistics track data flow are remarkably improved, and the logistics full-link monitoring and risk management capabilities are enhanced.

The embodiment of the invention provides a data tracking circuit, a data tracking processing method, a chip and electronic equipment, the data tracking circuit comprises a selection module and a packaging engine module, tracking source data is received and selected to be output as tracking data through the selection module, the packaging engine module packages the tracking data to generate corresponding data frames, and the corresponding data frames are stored in the storage module. The data frames comprise the identifiers used for identifying the types of the data frames, the larger the number of the data frames of the same type is, the shorter the data bit width of the identifiers of the data frames of the type is, compared with identifiers of fixed lengths, the lengths of most identifiers can be shortened, the data size of the data frames is reduced on the whole, and the data transmission efficiency is improved. And thus, the bandwidth required by the packaged data frame is also reduced.

The invention provides a subjective optometry prediction method combining wavefront aberration and optical signals. According to the method, eyeball wavefront aberration data are measured through a wavefront sensor, cornea, crystal and retina biological parameters are obtained by combining multi-view-field scanning of a galvanometer system, and after high-credibility data points are screened, a gradient refractive index eyeball three-dimensional model is constructed by adopting an NURBS algorithm; using finite element analysis to optimize cornea stress distribution, iteratively correcting the model through a deformation energy function, and dynamically compensating eyeball motion errors based on eye movement tracking data; and generating a multi-field-angle visual chart image through ray tracing simulation, and predicting a subjective optometry result in combination with an image quality evaluation algorithm. According to the method, high-precision modeling, dynamic error suppression and intelligent simulation technologies are fused, the optometry prediction accuracy, the personalized adaptation degree and the dynamic scene adaptability are remarkably improved, and an efficient and reliable solution is provided for refraction correction.

The invention relates to the technical field of risk assessment, in particular to a safe driving assessment method and system based on Beidou navigation. The method comprises the following steps: acquiring corresponding real-time position, speed and driving track data in a vehicle driving process through a Beidou navigation module, and acquiring vehicle state data and environment sensing data to obtain a vehicle multi-dimensional driving state data set; performing vehicle space-time and driving operation feature analysis and driving road matching analysis on the vehicle multi-dimensional driving state data set to obtain vehicle driving road attribute matching data; and carrying out vehicle driving risk estimation and vehicle safety over-rate evaluation based on the vehicle driving road attribute matching data, carrying out safety comparison verification evaluation, generating vehicle personalized driving improvement suggestions, and pushing the vehicle personalized driving improvement suggestions to the vehicle-mounted terminal to execute corresponding vehicle safe driving behavior improvement operation. According to the invention, real-time monitoring, accurate evaluation and active safety intervention of vehicle driving behaviors can be realized.

The invention provides an intelligent business resource scheduling method and system based on big data, and relates to the technical field of data processing, and the method comprises the steps: 1, capturing equipment layer sensor data, business layer order data and logistics layer vehicle track data in real time through distributed data collection nodes, and carrying out the real-time acquisition of the data; a dynamic time window division algorithm is adopted to carry out time sequence alignment processing on the heterogeneous data flow, a ternary state vector matrix with a unified timestamp is generated, the row dimension of the matrix represents a collection time sequence, and the column dimension of the matrix corresponds to equipment layer operation parameters, a service layer task queue state and logistics layer transportation node coordinates respectively. According to the invention, through multi-source data fusion, three-dimensional space analysis and an intelligent algorithm, resource gap accurate identification and cross-level scheduling optimization are realized, and the business processing efficiency and the resource utilization rate are improved.

The invention relates to the technical field of information platform construction, in particular to a construction method and system of a sports information intelligent service platform. The method comprises the following steps: collecting user motion image data and user health basic data; key body node position information in the movement process of the user is extracted; generating a user motion track curve based on the key node position information, and calculating a deviation angle and a deviation distance with a standard track template to obtain motion correctness data; constructing a user fitness risk assessment model according to the action correctness data and the user health basic data, analyzing the difference degree with a preset safety threshold, and generating layered health guidance data; and decomposing the layered health guidance data into quantifiable progress units, and generating progress tracking data. According to the method, the user motion image data and the health basic data are combined, the evaluation model capable of identifying individualized risk factors is constructed, and more accurate safety guidance is realized.

The invention relates to the technical field of computers, and discloses a real-time rendering and interaction method and system for an immersive virtual reality scene. The method comprises the following steps: fusing tuner inertial data and eyeball tracking data, and constructing a prospective state prediction model; generating a predictive focus field in combination with scene visual saliency; synthesizing an anisotropic temporal-spatial resolution graph according to the predicted head angular velocity; gPU variable-rate coloring is driven to realize non-uniform rendering; and re-projection or dynamic fuzzy correction is executed in a self-adaptive manner according to the attitude prediction error before display. According to the technical scheme, the perception delay and the rendering load are remarkably reduced, and the frame rate stability and the visual immersion in a high-dynamic scene are improved.