3384 results about "Identity recognition" patented technology

The invention provides a visitor identification and multi-dimensional user portrait routing distribution method based on private domain traffic, and relates to the technical field of data analysis, and the method comprises the steps: collecting multi-source data information of a visitor from a private domain traffic pool, constructing a multi-dimensional feature vector, generating a visitor identity fingerprint feature, and building a visitor identity identification model based on the visitor identity fingerprint feature; constructing a multi-level label system based on a recognition result of the visitor identity recognition model; performing feature extraction on historical behavior track data and historical content interaction data of visitors by adopting a transfer learning algorithm, classifying the extracted features based on the multi-level label system to generate user portrait features, dynamically updating the user portrait features in an incremental learning mode, constructing a multi-dimensional user portrait model, and displaying the multi-dimensional user portrait model. And distributing different visitor requests to the corresponding service nodes by adopting a routing distribution strategy in combination with the real-time load states of the service nodes.

The invention provides a remote identity recognition method and system based on unmanned aerial vehicle management and control requirements. Wherein the preparation state of the unmanned aerial vehicle is determined by generating an inquiry code, sending the inquiry code to all online unmanned aerial vehicles in a target airspace, and receiving an identity response signal comprising a unique identifier, a task brief description and a flight path. A multi-dimensional data fusion algorithm is combined with historical flight data, meteorological conditions and geographic information to predict an unmanned aerial vehicle behavior mode, and a behavior prediction report is formed. The block chain technology is used for cross verification, and the real identity and authorization state of the unmanned aerial vehicle are verified through the dual key encryption technology and the consensus algorithm. And according to a verification result, using machine learning to construct a temporary virtual fence and plan an optimized route, and automatically adjusting an air corridor to form a traffic management scheme. And finally, analyzing actual flight parameters by applying a self-adaptive control algorithm, and further optimizing route setting. According to the technical scheme provided by the embodiment of the invention, the safety and efficiency of air traffic management of the unmanned aerial vehicle are improved.

Various examples described herein support or provide generation and management operations of personalized images using machine learning technologies, including receiving portrait images of an entity; using machine learning models to generate an identity that represents the entity; identifying a template that comprises text descriptions of a scene and conditions; and using a machine learning models to generate a personalized image based on the identity and the template.

To solve the problems of fractured identity across multiple blockchains, these methods and systems allow for a single unified identity to be managed and resolved across multiple chains within an interop network. By leveraging message passing, identity records can exist securely across different chains. The system incorporates counterfactual blockchains, enabling trust-minimized name registration that reduces costs while maintaining decentralization and security. ENS name resolution is supported across both onchain and offchain environments, with verifiable proofs ensuring efficient resolution. By structuring message passing and utilizing decentralized indexers, these methods and systems provide a scalable and reliable framework for cross-chain identity.

The invention relates to the technical field of audio and video identity recognition, in particular to an audio and video identity recognition system based on multimode clue driving. The audio and video identity recognition system comprises an audio feature extraction module, a video feature extraction module, a multimode clue fusion module, a living body detection module and an identity recognition and verification module. The audio feature extraction module is used for capturing a voice signal of a user and extracting key voiceprint features, the video feature extraction module is used for acquiring facial features or limb features of the user and extracting related features, and the multi-mode clue fusion module is used for performing intelligent weighted fusion on the features of audio and video modes. The method comprises the following steps: firstly, using a living body detection module to comprehensively analyze the dynamic characteristics of audio and video, judging whether a user is a real individual, and finally, completing the final verification of the identity of the user through an identity recognition and verification module. The problem of feature extraction and modal fusion in the prior art is effectively solved.

The invention relates to the technical field of transformer substation monitoring and early warning, in particular to a monitoring and early warning method for an intelligent transformer substation and a control system of the monitoring and early warning method. And comprehensive coverage of the equipment state and the environment data is realized. Time sequence modeling is carried out through the long short-term memory network (LSTM), the dynamic trend of operation parameters is effectively captured, potential anomalies are recognized, the accuracy of equipment anomaly judgment is remarkably improved by combining the semantic segmentation technology and equipment type feature extraction, and the false alarm rate and the missing report rate are reduced. Through combination of a rotatable camera and a convolutional neural network (CNN) and multi-modal data fusion, accurate personnel identity recognition and behavior path tracking are realized, non-staff and abnormal behaviors are intelligently detected, and potential security threats are early warned in time.

The invention provides an unmanned aerial vehicle identity recognition method and system, and the method comprises the steps: receiving a flight behavior data flow from an unmanned aerial vehicle; processing the flight behavior data flow by using time sequence analysis and a Bayesian network to obtain a multi-dimensional behavior trajectory model; comparing the multi-dimensional behavior track model with a preset identity behavior library through a graph matching algorithm so as to identify a behavior identifier matched with the multi-dimensional behavior track model; and determining the identity of the unmanned aerial vehicle by using a space-time consistency verification mechanism and a random forest algorithm according to the relevance between the cross validation behavior identifier and pre-stored unmanned aerial vehicle identity information. According to the invention, the accuracy and efficiency of unmanned aerial vehicle identity recognition are 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 the technical field of face recognition, in particular to a face recognition method and system for a dynamic environment, and the method comprises the steps: collecting a face video stream through a multispectral imaging device, and carrying out the preprocessing of dynamic noise reduction, distortion correction and the like; constructing a multi-scale space-time fusion feature extraction network to extract dynamic space-time features and fuse cross-modal features; establishing an environment disturbance simulation generation model to generate a virtual sample, and performing domain adaptive alignment; designing an online incremental feature updating mechanism to optimize parameters of the feature encoder; deploying a heterogeneous graph neural network to carry out multi-modal decision fusion; and a hierarchical verification architecture is adopted to complete identity recognition. The system comprises a data acquisition and preprocessing module, a multi-scale space-time fusion feature extraction module and the like. According to the method, the problem of face recognition in a dynamic environment is effectively solved, the recognition accuracy, robustness, real-time performance and reliability can be remarkably improved in the scenes of complex illumination, posture expression change, shielding, background noise and the like, and the method has a wide application prospect.

The invention relates to the technical field of intelligent lock recognition, in particular to an intelligent lock image feature recognition method and system based on deep learning. The recognition method comprises the following steps: constructing a multi-branch deep neural network model, wherein the multi-branch deep neural network model comprises a backbone network, a feature extraction branch and a feature fusion branch; a framework training model is used to learn identity recognition, attitude estimation and environment perception tasks; and identifying the comprehensive feature representation by using a trained framework training model, and generating a control instruction of the intelligent lock according to an identification result. According to the intelligent lock image feature recognition method based on deep learning provided by the invention, comprehensive analysis of multi-dimensional information such as identity, posture and environment is realized by constructing the multi-branch deep neural network model.

The invention discloses a physical fitness evaluation method based on human skeleton trajectory tracking, which comprises the following steps: S1, acquiring whole-process video data of a physical fitness test through video acquisition equipment, and generating a video frame according to an acquisition frequency; s2, processing the video frame by using a skeleton dynamic analysis algorithm, and extracting human skeleton key points; s3, extracting identity features of the testee through a motion map identity recognition network, and tracking and confirming the identity features; s4, performing action recognition, fragment segmentation and compliance judgment on the skeleton key point time sequence track; s5, aiming at the abnormal skeleton key points, performing complementation and time sequence smoothing by adopting an inverse kinematics inference method; s6, inputting the complete skeleton key point time sequence data into the human body action evaluation model for processing, and quantizing and outputting a physical ability evaluation index; and S7, generating a physical ability evaluation report and giving action feedback and optimization suggestions. According to the invention, objectivity, accuracy and intelligent level of physical ability evaluation are effectively improved.

The invention discloses an electrocardiosignal open-set identity recognition method based on a hybrid network and a cross attention mechanism, and belongs to the technical field of identity recognition. The electrocardiosignal open-set identity recognition method comprises the steps that electrocardiosignals are preprocessed to obtain standardized input; constructing a hybrid network model to extract a long-time dependency relationship between multi-scale local space information and a time sequence; fusing different types of information by using a cross attention mechanism; and establishing an Openmax classification layer to realize identity tag identification in an open set environment. By means of the method, the known individual identity can be effectively recognized, unfamiliar persons are rejected, and the accuracy of electrocardiosignal identity recognition and the applicability in the practical application scene are improved.

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 provides an intelligent exhibition hall control method and system based on Internet of all things. The method belongs to the technical field of the Internet of Things, and comprises the following steps: constructing an intelligent exhibition hall network architecture based on the Internet of Things, and wirelessly connecting to a central control platform to form a network environment of Internet of all things; acquiring user identity information through an identity recognition device, and constructing a user identity database; and analyzing user preferences through an intelligent algorithm according to the user identity information in combination with user historical behavior data. According to the method, the identity information and the historical behavior data of the user are analyzed through an intelligent algorithm, personalized exhibition recommendation and exhibition routes can be provided for the user, the user can find interested exhibits more quickly, and therefore the visiting experience is improved.

The invention provides a multi-mode authority management and record tracing method and system for an intelligent lock. The method comprises the following steps: step 1, obtaining the intelligent lock, performing initialization setting on the intelligent lock, connecting the intelligent lock to a home local area network, and establishing secure communication connection with a cloud server; according to the invention, multiple identity recognition modes are supported, multiple permission types are provided, and a permission type combination strategy can be formulated, so that the flexibility of permission management is greatly improved, and diversified use requirements of modern families are met; according to the method, unlocking information of each time is automatically recorded, and a user behavior model is constructed in combination with a big data analysis technology, so that potential safety risks and abnormal behavior modes can be quickly and accurately identified, and the efficiency and accuracy of troubleshooting are improved; the intelligent lock can be quickly and automatically switched to the emergency mode when facing a complex environment or a special condition, so that the malicious attack resisting capability of the system is effectively improved, and the safety and the stability of the intelligent lock in the complex environment are enhanced.

The invention provides an intelligent tool warehouse management system, which belongs to the field of tool management and comprises a multi-mode sensing module, an intelligent access control module, an edge calculation module, an anomaly analysis module and a visualization module. The multi-mode sensing module realizes identity recognition, real-time positioning and integrity verification of the tool through RFID and UWB dual-mode positioning, visual recognition and sensor fusion technologies; the intelligent access control module is based on dynamic authority control and gravity sensing goods shelves, and tool storing and taking compliance is ensured; the edge computing module adopts localized data processing and low-power-consumption communication, and supports network disconnection disaster recovery; the abnormity analysis module identifies violation operation through an LSTM model and triggers grading alarm; the visualization module displays the tool state and the warehouse thermodynamic diagram in real time based on the digital twinning technology. The problems that traditional warehouse management depends on manpower, efficiency is low and errors are prone to occurring are solved, and high-precision tracking, intelligent early warning and optimal scheduling of the tool in the whole life cycle are achieved.

The invention discloses an automatic fare collection method and system based on multi-modal identity recognition, and relates to the technical field of rail transit automatic fare collection systems and multi-modal biological recognition fusion, and the method comprises the following steps: 1, enabling a user to approach a recognition area, carrying out the multi-modal data collection, and carrying out the quality detection and preprocessing; 2, feature extraction and credibility preliminary calculation are carried out; 3, environment data are collected and subjected to standardization processing; step 4, adaptively predicting a weight through machine learning, and constructing weight smoothing and time domain constraints; step 5, carrying out D-S evidence step-by-step fusion, and calculating comprehensive credibility; and step 6, comparing the comprehensive credibility with a preset threshold value, if the comprehensive credibility is greater than or equal to the preset threshold value, determining that the identification is successful and the gate is opened for passing, otherwise, determining that the credibility is insufficient, and performing alternative / manual processing. The system comprises a front-end identification terminal, a read-write industrial control all-in-one machine and a cloud management platform.

A system executes an authorization process for initiating a session with a computing entity. Executing the authorization process includes determining an identity associated with the computing entity, identifying a current set of access policies associated with the identity, and determining, based on the current set of access policies, a first set of actions that the computing entity is authorized to perform. While executing the session, the system executes a first action in accordance with the current set of access policies. Subsequent to executing the first action, the set of access policies is modified. The system detects an occurrence of a trigger condition, and in response, re-executes the authorization process for the session, including determining, based on the modified set of access policies, a second set of actions the computing entity is authorized to perform that differs from the first set of actions.

The present invention relates to the field of intelligent marketing technology, specifically to a SAAS platform based on mobile digital marketing and its method, wherein the platform includes a behavior sequence module, an identity matching module, a conversion willingness evaluation module, a marketing link optimization module, and a reach strategy adjustment module. In the present invention, through the timestamp sorting and behavior pattern analysis of access data, it is possible to accurately identify the access stability between devices, effectively eliminate random interference, ensure the accuracy of identity recognition, classify unpaid behaviors, optimize marketing strategies, screen user interest preferences, match suitable marketing reach strategies in an automated manner, realize personalized push, improve user interaction rate, adjust push content and frequency, make marketing information more targeted, reduce user interference, improve click-through rate and stay time, improve user conversion rate, and optimize marketing links through a closed data loop to form a more efficient marketing strategy execution system.

The invention provides a PPG big data driven biological feature recognition method, which comprises the following steps: acquiring PPG signal data of trainees, and performing high-pass filtering and low-pass filtering preprocessing on the PPG signal data to obtain preprocessed PPG signals; framing is carried out on the preprocessed PPG signal, a Mel frequency cepstral coefficient feature and a Gammatone frequency cepstral coefficient feature are extracted respectively, the Mel frequency cepstral coefficient feature and the Gammatone frequency cepstral coefficient feature are fused, then principal component analysis dimension reduction is carried out, and a fusion feature is obtained; a universal background model UBM of a Gaussian mixture model is constructed based on the fusion features, zero-order, first-order and second-order Baum-Welch statistics are calculated, a global difference space matrix is estimated from the Baum-Welch statistics, and i-vector identity authentication vectors are extracted; and inputting the i-vector identity authentication vector into a long short-term memory network for training and classification to obtain an identity recognition result of the trainees. According to the invention, the influence of motion artifacts can be eliminated, the overall variability of PPG signals is captured, and an identity authentication scheme is provided for wearable equipment.

The invention discloses a non-inductive identity recognition and real-time tracking method based on personnel in a video. The method comprises the following steps: S1, acquiring video stream data and extracting a key frame image; s2, key point coordinates are extracted, and a time sequence skeleton sequence is constructed; s3, decomposing gait, action and attitude features to generate standardized features; s4, inputting the standardized features into an improved multi-scale image convolutional neural network, extracting identity representation features, and generating a target identity code; s5, matching identity codes based on similarity measurement, calculating a consistency score, and establishing an identity tracking trajectory; s6, tracking is carried out in combination with identity codes and consistency scores, identity mapping is dynamically updated, and non-inductive recognition is achieved; and S7, updating a track in real time, adjusting a tracking state, and ensuring identity stability. According to the method, the improved graph convolutional network and the consistency measurement technology are combined, the identity recognition precision and tracking stability are improved, and cross-scene non-inductive identity recognition is achieved.

A method and system for fish identification based on body surface texture features and geometric features are provided. The method employs an improved Resnet network and a deep learning Yolov8 network to extract body surface texture features and geometric features of a fish on the basis of considering influences of fish tail swing and an oxygen concentration on a fish body form based on a small sample learning framework, and then realizes identity recognition of a fish individual by coupled analysis of the body surface texture features and the geometric features. The method can realize high-accuracy fish identification with few training samples of a fish to be identified from the perspective of actual application, provides theoretical basis and technical support for accurate fish stock assessment and accurate estimation of industrially farmed fish biomass, and meets the development requirements of modern agriculture.

The invention relates to the technical field of unmanned aerial vehicle intrusion detection, in particular to an unmanned aerial vehicle intrusion detection system based on personnel intrusion alarm, which comprises a user authentication module, an area setting module, an unmanned aerial vehicle control module, a shooting control module, an intrusion trend judgment module, an intrusion identification module, a display terminal, a server and a cloud database. According to the invention, a user can flexibly set a monitoring area after authentication, generates an unmanned aerial vehicle control signal, dispatches an unmanned aerial vehicle in a target aircraft nest base station, accurately determines a dispatchable unmanned aerial vehicle, inputs task data information to the dispatchable unmanned aerial vehicle, photographs personnel in the monitoring area through the dispatchable unmanned aerial vehicle, generates an image set, and sends the image set to the target aircraft nest base station. And then an intrusion trend value is calculated according to the image sets to determine whether a face recognition instruction is triggered or not, and identity recognition judgment is performed according to the face recognition instruction to determine whether an alarm signal is generated or not, so that omnibearing and high-efficiency monitoring of a monitoring area is realized, and the accuracy and reliability of safety monitoring are remarkably improved.

The invention provides a family blood pressure measurement and identity recognition system and method based on oscillatory wave characteristics, and relates to the technical field of household medical equipment. The method comprises the following steps: firstly, acquiring original oscillatory wave signals during blood pressure measurement, judging whether the signals are available or not, further fusing time domain and frequency domain features to perform quality evaluation on available oscillatory wave signals, and screening high-quality signals; filtering and noise reduction preprocessing is carried out on the oscillatory wave signals with good quality; then respectively extracting time domain, frequency domain and time-frequency joint features from the preprocessed oscillatory wave signal to obtain multi-index features of the oscillatory wave signal; carrying out standardization processing and unsupervised feature fusion on the multi-index feature data; finally, non-invasive blood pressure measurement and identity recognition are completed. According to the method, the oscillation wave signals are subjected to fusion analysis according to the multi-index characteristics, non-invasive blood pressure measurement is achieved, the accuracy and calculation efficiency of identity recognition are enhanced, historical blood pressure measurement results of fixed members are conveniently stored, and a family blood pressure monitoring system is constructed.

The invention relates to the field of fan operation safety, and discloses a double-fan operation authority control system and method based on face recognition, and the system comprises an identity recognition module, a verification control module, a state feedback module, an authority management module and an abnormity monitoring module. The identity recognition module is used for collecting information of operating personnel and completing two-person in-place confirmation; the verification control module dynamically manages the double-person verification progress and adjusts an operation strategy; the state feedback module monitors the verification state in real time and generates a deviation signal for dynamically adjusting the verification strategy; the authority management module issues fan door authority and tool cabinet authority after verification is completed, and records an authority operation log; the abnormity monitoring module detects abnormal behaviors in the verification and permission using process, and when abnormity is detected, an alarm is triggered, and permission distribution is paused. According to the invention, the safety, verification efficiency and system reliability of fan operation are improved, and dynamic authority control and abnormity tracing functions are realized.

The invention discloses a meta universe-based conference interaction method, and relates to the technical field of virtual conferences, and the method comprises the steps: extracting the facial features of participants, comparing the facial features with a pre-established facial feature library, obtaining the unique identity of each participant, and recognizing the identities of the participants; after the identities of the participants are determined, position changes of key body joint points of the participants are detected in real time, and specific actions of the participants are judged by analyzing relative positions and movement tracks between the joint points; the method comprises the following steps: receiving the acoustic characteristics of the participant language, converting the voice signal of the participant into a text, and analyzing the semantics and emotion of the text in combination with natural language processing to obtain the language expression intention of the participant. According to the method and the device, the optimal interaction mode can be dynamically selected from the preset library according to different interaction intentions, and the audio and video parameters and the interface layout are adjusted in real time, so that intelligent interaction in a multi-user scene is realized, the interaction experience of a conference is optimized, and the interaction effect can also be improved.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent rationing system for pig house feed supply, which comprises the following steps: performing identity recognition on pigs in a feeding area through radio frequency identification tags, and extracting weight, growth cycle and behavior characteristics; based on historical ingestion records, health data and environmental factors, a dynamic nutritional requirement model of the pigs is constructed, and the current feed requirement is predicted in combination with a multi-factor regression algorithm; according to the feed demand result, the feeding amount and the feeding frequency of the corresponding trough are adjusted in real time; dynamically adjusting the power of a conveying motor and the pressure difference of a pipeline section by utilizing a variable-frequency driving control technology; and collecting the ingestion response and the feeding execution state of the pigs, and outputting an updated pig allocation result in combination with online feedback and historical data differences. The feeding device has the advantage that the feeding stability is improved.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention provides an identity recognition method and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: acquiring identity information of a target user, travel information of the target user, a two-dimensional code encryption processing rule of the target user and identity verification information of terminal equipment; processing the identity information of the target user to generate an initial two-dimensional code image; processing the travel information of the target user to generate identification information of the target user; processing the initial two-dimensional code image based on the identification information of the target user and a two-dimensional code encryption processing rule of the target user to generate a target two-dimensional code image; obtaining an initial electronic certificate identification model matched with the identification information of the target user; and processing the initial electronic certificate identification model based on the training set and the verification set to generate a target electronic certificate identification model, and processing the target two-dimensional code image and the identity verification information of the terminal device to generate an identification result of the target electronic certificate.

The invention relates to the technical field of indoor old people safety monitoring, and discloses an indoor old people tumble detection system based on a hybrid mode, which comprises a video acquisition module, an audio acquisition module, an identity recognition module, a tumble detection module, an emotion recognition module and an alarm module. According to the method of the indoor old people tumble detection system based on the mixed mode, video and audio data are creatively fused, multi-dimensional monitoring of the state of the old people, capturing of action postures of the old people by the video acquisition module and obtaining of voice and environment sound information by the audio acquisition module are achieved, and the two complements each other; abnormal sound in the audio, such as collision sound of falling and shouting sound, can assist in judgment, the multi-dimensional data acquisition and analysis mode greatly reduces misjudgment and missed judgment caused by single data, the accuracy and reliability of detection are remarkably improved, and the safety of old people is comprehensively guaranteed.