105 results about "Behavioral analysis" patented technology

The application discloses a kind of intelligent analysis methods and systems for driving behavior video frame of vehicle abnormal state of Internet of Vehicles terminal vehicle, it is related to Internet of Vehicles technical field, including: response vehicle abnormal state trigger instruction, obtain driving behavior video sequence and vehicle motion disturbance parameter;Determine image domain vibration mapping relationship based on the vehicle motion disturbance parameter, determine the physical shaking component in the driving behavior video sequence;From the motion feature of the driving behavior video sequence, the physical shaking component is eliminated, and the driver real action feature is obtained, and according to the behavior analysis credibility state of residual noise after elimination;Based on the behavior analysis credibility state, determine the effective state of the driver real action feature, generate analysis method evaluation result.The application realizes the active control of data quality, improves the reliability of Internet of Vehicles terminal vehicle abnormal state determination.

This invention relates to the field of equipment collaborative control technology and discloses a server-based equipment collaborative control method. The method involves a server continuously receiving multi-source sensor data and surrounding environmental information, performing consistency verification and data fusion on the data and information to generate a comprehensive feature dataset. This comprehensive feature dataset is then submitted to a collaborative requirement assessment module to calculate preliminary collaborative requirement parameters. Based on these parameters, the historical interaction records of the equipment group are accessed, and multiple parameter verification units process these records in parallel to obtain confirmed collaborative requirement parameters. Based on these confirmed collaborative requirement parameters, a behavior trend prediction module is activated to output behavior prediction parameters, while simultaneously acquiring a real-time operational snapshot of the equipment group. This snapshot is then analyzed by a behavior analysis unit to extract actual behavior parameters. Finally, the confirmed collaborative requirement parameters and the actual behavior parameters are logically integrated to output a collaborative control decision for the equipment group.

The application relates to the field of computer-aided technology and specifically discloses a vision-impaired drawing assistance system based on physiological signals and behavior analysis, aiming to solve the problems of mechanical assistance process and poor interactive experience in the prior art. The system comprises a physiological signal acquisition module for acquiring user concentration and emotion, a drawing behavior perception module for capturing brush pressure and trajectory, a multi-modal data fusion and intention analysis module for fusing data and analyzing creation intention, an adaptive guidance strategy generation module for generating personalized guidance strategies, and a multi-channel feedback execution module for providing voice, tactile and sound feedback. Through the cooperative work of the above modules, the system can realize real-time perception of user state and intention, dynamically adjust multi-sensory assistance guidance, and thus improve the naturalness of interaction and the assistance effect.

This invention discloses a data security access control method based on an IoT platform, relating to the field of secure access technology. It includes a dynamic threat assessment step, which determines data risk by analyzing the behavioral sequence of the data parsing process. Specifically, for a specified data type, based on the parsing behavior of historical harmless data samples, a baseline of normal behavior sequence is constructed and its corresponding normal behavior ratio range is determined. Then, the current data to be assessed is parsed, and the real-time behavioral sequence generated during the parsing process is monitored and recorded. This invention overcomes the limitation of traditional access control that only manages who can access the data. By introducing a dynamic threat assessment engine into the access link, it performs in-depth behavioral analysis on the accessed data content itself, constructing a subject-object two-way security system that can effectively defend against data reverse intrusion attacks launched using legitimate data channels.

This invention discloses an individual motion behavior analysis system based on dynamic modal configuration and rule-guided fusion, belonging to the fields of intelligent perception, sports science, and human-computer interaction. It includes a data input module, a dynamic modal organization and configuration module, a multimodal feature modeling and fusion module, an indicator-level evaluation and behavior state prediction module, a statistical rule-guided analysis and result correction module, and a structured result generation and output module. These modules work together to form a complete closed loop for motion behavior analysis technology. This invention aims to address the problems in existing motion behavior analysis technologies, such as limited dimensionality of single-modal information, rigid multimodal fusion methods, insufficient stability and interpretability of analysis results, and strong dependence on large-scale manually labeled data.

System and methods of a mobile application to remotely monitor residents with device-free sensing of human motion over Wi-Fi is provided. A detailed behavioral analysis of various functions related to the sleep and activity of the subject has been provided. The Remote Care Application (RCA) is a mobile application on several platforms that displays the results of all the analysis and behavioral patterns obtained from the motion from the subject. The system relies on existing wireless communication signals and machine learning techniques in order to automatically detect the motion of the subject, sleep analysis of the subject, activity analysis of the subject.

This invention provides an AI behavior analysis method and system for routers, gateways, and cameras in the field of video surveillance technology. The method includes: Step S1, acquiring surveillance video streams through cameras; Step S2, calling a multi-task deep learning model to infer from the surveillance video streams to obtain behavior recognition results carrying abnormal behaviors; Step S3, generating structured behavior event logs based on the behavior recognition results and sending them to the router or gateway; Step S4, the router or gateway receiving behavior event logs from one or more cameras and performing cross-device correlation analysis to obtain correlation analysis results; Step S5, generating alarm commands at the router or gateway based on preset rules and correlation analysis results, distributing the alarm commands to trigger the target device to execute alarm response actions. The advantages of this invention are: greatly improving the accuracy, real-time performance, multi-behavior collaboration capabilities, and resource efficiency of edge AI behavior analysis.

The application discloses a kind of intranet penetration tracing methods of multi-scale heterogeneous log behavior analysis, it is related to network security technical field, the method includes: the original log stream of heterogeneous log source is standardized, and uniform semantic event stream is generated;Unified semantic event stream is divided into multiple scale time windows, the event density characteristic index of each window is calculated, and short-time dense subsequence is identified based on event density characteristic index;The short-time dense subsequence identified is clustered based on semantic weighted entropy analysis, and attack dense section and normal high load section are distinguished;Based on the semantic correlation of event, long-time sparse clues are extracted from uniform semantic event stream;With long-time sparse clues as time anchor point, adjacent attack dense section in its extension time range is positioned, and intermediate interpolation event sequence is generated by conditional time sequence model, to reconstruct complete attack chain.

The present application belongs to the technical field of intelligent video analysis, and particularly relates to a non-employee stay and loitering intelligent detection system and method for a financial service area. The system comprises a video acquisition layer, a feature extraction layer, an identity recognition layer, a behavior analysis layer, and a decision output layer. The video acquisition layer comprises a multi-camera video stream acquisition module, a video preprocessing module, and a space-time synchronization module. The feature extraction layer comprises an appearance feature extraction module, a behavior feature extraction module, a trajectory feature extraction module, and a scene feature extraction module. The identity recognition layer comprises a multi-feature fusion employee recognition module, an employee feature library management module, and an identity confidence evaluation module. The behavior analysis layer comprises a stay behavior detection module, a loitering behavior detection module, a space-time behavior analysis module, and a clearing time period detection module. The present application realizes accurate detection and real-time alarm of non-employee abnormal stay and loitering behavior in a financial service area, reduces the false alarm rate, and improves the detection accuracy and real-time performance.

The application relates to an artificial intelligence technology and discloses a product recommendation method based on behavior analysis, which comprises the following steps: generating a standard user portrait according to basic information of a user; adding specific behavior information of the user into the standard user portrait and inquiring N users with the specific behavior information as associated users; obtaining associated user portraits of the associated users, calculating distance values between the standard user portrait and the associated user portraits, and determining associated users with the associated user portraits with distance values less than a preset threshold as target users; obtaining specific behavior information of the target users, statistically analyzing preference probabilities of the users to different information according to the specific behavior information of the target users; and generating an information recommendation list in the order of the preference probabilities and outputting the information recommendation list to the user. In addition, the application also relates to a blockchain technology, and the basic information can be stored in a node of the blockchain. The application further provides a product recommendation device, equipment and medium based on behavior analysis. The application can improve information recommendation accuracy.

The application relates to the technical field of user behavior analysis, and discloses a hearing aid use behavior analysis system based on multi-dimensional time sequence characteristics, which comprises a time alignment module, parameter channel data, environment channel data and behavior channel data are acquired, the data are sorted according to server time alignment to form multi-channel time sequence data, asynchronous acquisition is continuously represented on the same time axis, a feature statistical module is used for calculating short-term features of statistical quantities in a preset time window, long-term features of wearing time and average hearing aid power are statistically calculated according to natural days, the features are combined to form a multi-dimensional time sequence feature sequence, scene and operation changes are stably described, a channel modeling module is driven by the sequence to form a multi-channel time sequence model, user behavior feature representation and hearing loss trend prediction results are output, the short-term fluctuation and the long-term evolution are connected, a behavior clustering module is used for clustering to form a user behavior portrait according to the representation, and individualized test and fitting suggestions are generated in combination with the hearing loss trend prediction results and the parameter channel data.

The application discloses a network security risk behavior intention recognition method based on controllable perturbation, relates to the technical field of computer network security and terminal behavior analysis, and actively injects controllable perturbation signals into a resource layer of a terminal system, synchronously collects the running states of each resource layer of the terminal system based on a unified time reference, and generates system state time sequences with time alignment relations; a cross-layer perturbation response dynamic model is constructed, and multi-dimensional dynamic characteristics are extracted; user behaviors are evaluated based on characteristic quantization, a paradigm shift from passive observation to active detection and from application layer semantics to system bottom layer response is realized, the recognition capability of unknown behaviors and confrontation behaviors is significantly improved, the perturbation does not affect normal use of the user, and a brand-new technical framework is provided for terminal security protection in complex scenes.

The system according to this embodiment aims to achieve crime prevention while protecting privacy. [Solution] The system according to the embodiment comprises an avatar display unit, a behavior analysis unit, a data integration unit, and an information disclosure unit. The avatar display unit represents a person as an avatar. The behavior analysis unit analyzes the behavior of the avatar displayed by the avatar display unit. The data integration unit integrates the data analyzed by the behavior analysis unit with statistical data. The information disclosure unit discloses information based on the data integrated by the data integration unit.

The application relates to the technical field of video analysis, and discloses a linkage alarm method, device and equipment based on video analysis and a storage medium, the method comprising the following steps: performing behavior semantic identification on a video frame collected by a doorbell to obtain a visitor behavior mode, and encoding the video frame and the visitor behavior mode to obtain a semantic vector; constructing a three-dimensional semantic map in front of the door according to the video frame and tracking a visitor to obtain visitor trajectory data; decoding the semantic vector and the visitor trajectory data to obtain a behavior analysis result; selecting a linkage strategy from a preset action space and generating an alarm instruction according to the behavior analysis result and the visitor trajectory data, and executing the alarm instruction through the doorbell; the application realizes end-to-end real-time response, meets the strict requirements of an actual doorbell application scene on response speed, and further improves the accuracy of doorbell video analysis and linkage alarm.

This invention proposes a geospatial data security risk monitoring method based on user entity behavior analysis. The method includes: S1, multi-source heterogeneous data acquisition and semantic preprocessing; S2, multi-dimensional behavioral feature extraction and vectorized representation; S3, adaptive behavioral baseline modeling and dynamic updating; S4, multi-level anomaly detection and risk quantification; and S5, adaptive response and dynamic handling: executing response measures according to risk scores. This invention's risk-score-based hierarchical response mechanism effectively reduces the cost of handling security incidents while ensuring business continuity.

This invention discloses an AI video behavior real-time analysis system for police law enforcement recorders, relating to the field of video behavior analysis. It includes: a topology construction module S101, used to collect spatial location information of key human body points from real-time video and construct an initial human body topology map using the spatial location information; a topology reconstruction module S102, used to dynamically reconstruct the topology of the initial human body topology map using a graph convolutional network with sparse constraints on topological connections, obtaining a dynamic topology feature map; and a feature extraction module S103, used to extract local motion pattern features of human behavior based on the dynamic topology feature map and use the local motion pattern features to determine potential areas of behavioral abnormalities. This invention achieves accurate identification and real-time positioning of abnormal behaviors in police law enforcement recorder videos by constructing a human key point map that integrates dynamic topology reconstruction and local motion pattern analysis.

This invention relates to the field of passenger behavior analysis technology and discloses a passenger behavior analysis system based on image recognition and trajectory tracking. The system includes: an image acquisition unit that acquires carriage image data; a trajectory evaluation unit that identifies passenger targets in the carriage image data, constructs the current state vector and error covariance matrix, and generates prior state estimates and prior error covariance estimates; a trajectory tracking unit that acquires the passenger position coordinates and passenger motion state at the next moment, generates a trajectory correlation matrix, selects target observations, determines gain coefficients, and generates posterior state estimates and posterior error covariance estimates; a sequence generation unit that updates the state vector and error covariance matrix at the next moment to form a continuous trajectory sequence; and a behavior analysis unit that identifies whether abnormal behavior exists. This application achieves continuous tracking of passenger trajectories and improves the accuracy of abnormal behavior identification.

The application provides a device maintenance assistance method and device, an electronic device and a storage medium, and belongs to the technical field of device maintenance. The method comprises the following steps: determining current maintenance node information of a target device; performing maintenance object matching according to the current maintenance node information to obtain maintenance object information of the target device; collecting maintenance object operation video data corresponding to the maintenance object information by using a target virtual reality device; performing operation behavior analysis according to the maintenance object operation video data to obtain operation behavior information corresponding to the maintenance object information; performing maintenance compliance judgment according to the operation behavior information to obtain a maintenance compliance judgment result; and in response to the maintenance compliance judgment result not satisfying a preset maintenance compliance condition, outputting maintenance assistance guide information corresponding to the current maintenance node information by using the target virtual reality device. The application can effectively assist the device maintenance process in a real operation scene and reduce the maintenance assistance cost.

The application relates to the technical field of rescue dispatch, and discloses a rescue dispatch method and system based on driving behavior analysis, which comprises the following steps: determining whether to execute standby resource dispatch based on a driving risk index, extracting the occupation states of rescue vehicles, rescue personnel and rescue equipment, determining rescue dispatch load, obtaining the resource occupation rate, task queuing volume and communication time delay of an edge node, and determining a node load rate; judging an execution node of a dispatch calculation task according to the rescue dispatch load and the node load rate; determining the delay rate of a predicted arrival time and a planned arrival time based on the execution node, and completing rescue dispatch based on the execution node and the delay rate. The application ensures the dynamic unloading capacity of the edge node and guarantees the reliability of the rescue dispatch.

The application relates to the technical field of intelligent agent cooperation and discloses a practical training behavior analysis method based on multi-intelligent agent cooperation, which comprises the following steps: first, performing time sequence alignment and aggregation on multi-source practical training data to generate a time sequence sequence containing scene states and operation instructions; using a behavior recognition intelligent agent to establish a scene evolution model, performing multi-step deduction on a virtual environment state based on the sequence; in view of a preset reference instruction set, evaluating the reachability of the instruction set relative to risk constraints and task targets in the deduced state, and calculating a decay index reflecting the degree of recoverable space contraction; then, using a target matching intelligent agent to calculate task completion degree which is deeply coupled with the decay index, and combining resource consumption data of the practical training process to generate a comprehensive score; finally, mapping the comprehensive score to a qualified or unqualified evaluation result based on an offline calibrated qualified benchmark value. The application can accurately identify high-risk operation trends and realize the standardization of practical training evaluation.

This invention discloses an intelligent early warning method and system for trusted data spaces based on multi-source behavioral analysis, belonging to the field of data processing technology. The method includes: acquiring multi-source behavioral data generated by a target account entity within a trusted data space and extracting multi-dimensional behavioral features; configuring a series of stepped analysis windows with stepped time lengths, using the current time node as the analysis starting point, and combining entropy weight analysis to determine the dynamic feature weights under different stepped analysis windows, forming a stepped feature weight matrix; based on the sliding window method and a preset sliding step size, iteratively performing entropy weight analysis on the multi-dimensional behavioral features with time backtracking as the sliding direction, forming a backtracking stepped feature weight matrix series; combining the stepped feature weight matrix and the backtracking stepped feature weight matrix series to verify the behavioral consistency of the target account entity, and issuing an account warning based on the verification results. This invention achieves early and accurate early warning for trusted data spaces.

This application relates to the field of educational informatization technology and discloses an artificial intelligence-based virtual simulation teaching system for medical physiology, comprising: a system for collecting, processing, and encrypting and storing multi-source teaching data, including job requirement data, student operation data, voice interaction data, and clinical case data; a virtual simulation teaching module for constructing virtual diagnosis and treatment scenarios and conducting simulation teaching; a behavior analysis and demand adaptation module for correlating and analyzing job requirement data and learning behavior data, and outputting a behavior analysis report; a moral education evaluation module for quantifying medical ethics elements into constraint indicators and generating a moral education evaluation report in conjunction with the behavior analysis report; and an industry-education collaboration module for establishing a data transmission channel, accessing external data to update the simulation scenario, and enabling two-way interaction of teaching and evaluation-related data. This application improves the adaptability and evaluation accuracy of medical virtual simulation teaching.

The application discloses a human posture estimation and behavior analysis method, comprising the following steps: S1, collecting visible light images and thermal imaging images, performing feature extraction and fusion, and generating fused semantic features; S2, obtaining a refined key point heat map and a partial affinity field according to the fused semantic features; S3, generating a key point set according to the refined key point heat map and the partial affinity field; and S4, determining an abnormal behavior according to the key point set. The application effectively overcomes the shortcomings of large pixel-level fusion calculation overhead and difficult alignment of shallow feature fusion modalities, and significantly improves the robustness and precision of human posture estimation under complex lighting conditions such as day-night alternation and backlight.

The application discloses an Nginx server security protection method and device based on an agent and a storage medium, which are applied to an agent system and comprise a traffic perception agent, a behavior analysis agent, a content detection agent, a context association agent and a decision agent, and the method comprises the following steps: obtaining request information of an incoming Nginx server request through the traffic perception agent; inputting the request information into the behavior analysis agent, the content detection agent and the context association agent, and performing corresponding and parallel behavior analysis, content analysis and context association analysis on the request through the behavior analysis agent, the content detection agent and the context association agent; inputting the behavior analysis result, the content analysis result and the context association analysis result into the decision agent, determining a security protection strategy, and performing security protection on the Nginx server based on the security protection strategy.

The application discloses a user behavior analysis method based on E-SIM, and particularly relates to the technical field of user behavior analysis; the method comprises the following steps: collecting E-SIM configuration switching logs, network policy parameters and user behavior event sequences, and constructing a unified time sequence dataset; determining a configuration switching trigger window based on the unified time sequence dataset, analyzing the correlation degree between network environment migration and user behavior change, and obtaining a candidate drift segment; respectively performing factor deconstruction on the device side and the network side, and outputting device side and network side pseudo migration scores; comprehensively judging identity recognition drift based on the pseudo migration scores, calculating a behavior correction vector; reconstructing the user behavior sequence by using the behavior correction vector, correcting an abnormal behavior label, and outputting a corrected user behavior analysis result. The accuracy and reliability of user behavior analysis are improved.

The application discloses a kind of teaching behavior analysis method and system based on knowledge base teaching feature fusion and modeling, and the application relates to data analysis technical field, including the following steps: collecting multi-source heterogeneous ideological and political education data and being associated to specific student and time point, form original material, structured data is stored to data warehouse, unstructured data and language content are stored to data lake, construct lake warehouse integrated data pedestal, establish the mapping relationship of query language and structure data field, and the semantic vectorization of unstructured data, generate consciousness tendency score and behavior quantization index, to calculate ideological and political development deviation degree, when exceeding adaptive threshold, trigger early warning, and extract relevant evidence fragments, timely targeted education intervention, to effectively reduce the risk of student's ideological development deviation.

This invention discloses a method and system for cross-domain educational interaction behavior analysis based on semantic alignment and bias matching, relating to the fields of computer data processing and artificial intelligence application technology. The method includes: identifying and removing formatting noise (non-natural social language) and text content within citation tags from text data to generate a candidate analysis corpus; performing joint topic modeling and homogeneity screening on the candidate analysis corpus to generate a semantically aligned dataset, extracting the structural feature vector of each text data in the dataset, performing bias structure matching on the dataset based on the structural feature vector, generating feature-balanced pairwise analysis samples to establish a balanced dataset; performing parallel computation on the balanced dataset, and fusing the parallel computation results to generate a multi-dimensional educational interaction behavior analysis report. This invention ensures that the comparative analysis is strictly limited to the intersection of similar cognitive tasks, avoiding spurious correlations caused by different task types.