1651 results about "Fingerprint recognition" patented technology

The invention relates to the technical field of intelligent control of test equipment, and discloses a semiconductor device test equipment control system and method based on industrial data processing, and the method comprises the steps: collecting multi-source heterogeneous data of semiconductor device test equipment, and carrying out the preprocessing; constructing a structural causal model, and performing root cause identification through anti-fact reasoning by using the structural causal model; constructing an abnormal test fingerprint and a knowledge base; generating an intervention scheme, evaluating the generated intervention scheme, and selecting an optimal intervention scheme; processing the detected anomaly, and generating and implementing a preventive control strategy based on historical anomaly data and a causal analysis result; according to the invention, by introducing innovative technologies such as causal inference, anti-factual inference, abnormal test fingerprint identification and Monte Carlo tree search, intelligent control of semiconductor test equipment is realized.

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

The invention relates to the technical field of computer security, and discloses a computer remote login identification device based on artificial intelligence, which comprises a user side device module used for collecting biological identification data and input behavior data of a user; the authentication and verification module is used for executing multi-mode identity authentication and dynamically adjusting an authentication strategy; the behavior analysis and risk assessment module is used for analyzing a behavior mode and a login environment of the user; the database and storage module is used for storing user identity information, behavior characteristic data and authentication logs; and the security monitoring and alarming module is used for monitoring the login activity of the user in real time and detecting potential security threats. According to the invention, the design based on multi-modal authentication greatly improves the security of remote login. By combining a plurality of authentication means such as face recognition, fingerprint recognition and voice recognition, the system can effectively prevent security vulnerabilities such as password stealing, fingerprint counterfeiting or facial image attack.

A method is provided for dynamically generating a digital watermark for a data file. The method includes receiving a request to access the data file from a user; dynamically generating an encryption key based on at least one parameter selected from the group consisting of the identity of the user, the time of access, and the mode of access; embedding a digital watermark into the data file using the dynamically generated encryption key, wherein the digital watermark is unique to the request; providing access to the data file with the embedded digital watermark to the user; and storing information related to the encryption key and the parameters used for its generation in a secure database.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

The embodiment of the invention provides a multi-platform e-commerce order management method and system based on cloud data analysis, and the method comprises the steps: carrying out the analysis and standardization processing of order data through employing a dynamic format conversion channel, constructing a dynamic priority evaluation matrix in combination with real-time logistics load and historical aging data, and carrying out the analysis and standardization of the order data. A geographic position weight model is constructed based on equipment fingerprint identification and address similarity calculation, an order topology aggregation scheme is generated through a spatial incidence matrix, a distributed decision tree is constructed according to inventory fluctuation prediction and supplier response rate, a picking path is optimized, and finally logistics node data is integrated to construct a visual tracking interface. According to the technical scheme, abnormal order real-time early warning and compensation path planning are achieved through a self-repairing mechanism, logistics state holographic projection is generated, the flexibility and efficiency of the multi-platform e-commerce order management system are improved, and the problems of high complexity, poor expansibility, performance bottleneck and the like existing in a traditional system are effectively solved.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

An Automated AI-Driven Lifestyle Orchestration System proactively manages a user's daily life by intelligently populating calendars with relevant, time-sensitive marketing objects. This is achieved through sophisticated AI-driven analysis of user directives, preferences, and real-time location data obtained via computing devices like smartphones. The system leverages advanced media identification modules employing fingerprinting and watermarking to accurately match user-captured media (via a Percipient Sample Pack (PSP)) to specific content and associated products. It facilitates dynamic commerce through hierarchical linked lists for purchasing authentic or similar items, supported by an affiliate program. This inventive solution significantly improves personal organization and digital commerce by transforming passive interaction into a proactive, intelligent, and monetizable user experience.

Integrated intelligent system for beverage production, including distilled spirits and various brewed or fermented products, combining traditional methods with advanced technologies. System incorporates controlled fermentation unit, advanced distillation apparatus, and accelerated aging unit using novel methods like ultrasonic waves and thermal cycling. Real-time analysis system employs multiple sensors for continuous chemical composition monitoring throughout production process. Intelligent control mechanism optimizes processes and adjusts parameters dynamically. Chemical fingerprinting system enables precise quality control and authenticity verification. Flexible interface allows for customization of beverage profiles based on desired flavor characteristics and market demands. Specialized unit creates complex non- alcoholic alternatives. System adapts to different production scales and beverage types. Represents significant advancement in beverage production technology, offering unprecedented control, consistency, efficiency, and rapid product development across various beverage categories.

The invention provides a network information security adaptive threat intelligence analysis and response method and system. The method comprises the following steps: acquiring an encrypted traffic load byte stream, and segmenting the encrypted traffic load byte stream into a time sequence traffic matrix according to a time window; and analyzing the communication metadata, matching the features of the threat intelligence library, and generating a dynamic threat fingerprint based on protocol compliance and behavior abnormality. And performing multi-stage wavelet packet decomposition on the matrix, extracting the energy spectrum density, the information entropy and the time-frequency variable coefficient of the high-frequency component, and performing weighted fusion to generate a frequency-domain composite abnormal index representing heartbeat signal characteristics. And identifying hidden heartbeat periodicity and protocol violation modes through a multi-mode fusion mechanism in combination with the frequency domain index and the threat fingerprint, and outputting a threat score. And if the attack exceeds the threshold value, intercepting the flow, verifying a new attack feature, and then reversely updating the threat intelligence library to form a defense self-evolution closed loop. According to the method, automatic interception and closed-loop updating of the threat intelligence library are realized, and the defense adaptability is remarkably improved.

The invention relates to the technical field of Internet of Things, and discloses a multi-factor dynamic authentication Internet of Things network security access platform, which adopts a multi-factor authentication mechanism, combines biological characteristics, behavior characteristics and environment characteristics of equipment, performs identity verification through biological recognition, equipment fingerprints and behavior analysis, and utilizes a dynamic authentication strategy. Detecting an abnormal IP behavior and triggering a security policy by adopting a dynamic IP binding technology, combining with equipment fingerprint identification and learning and analyzing an equipment network behavior mode; a dynamic key management mechanism is adopted, and keys are automatically generated, distributed and updated according to different devices, application scenes and communication requirements; machine learning and artificial intelligence technologies are utilized to comprehensively analyze historical access data, abnormal behavior modes, environment security states and the like of equipment, and the security risk of equipment access is automatically evaluated if suspicious equipment is detected. The method has the advantage of improving the security of the Internet of Things.

A non-invasive liquid integrity monitoring system using dielectric fingerprinting and machine learning to detect and identify contamination in sealed containers is described. The system may employ externally-mounted sensors that measure dielectric properties through electromagnetic interrogation, comparing measurements against baseline signatures to detect deviations indicating contamination, tampering, or degradation. Industry-specific ML models enable identification of specific contaminants with confidence, providing alerts without breaching container integrity.

The invention provides an intelligent data security exposed surface risk assessment method, system and device and a medium, and relates to the technical field of information security, and the method comprises the steps: constructing an exposed surface asset map through an asset fingerprint recognition engine, and calculating an asset exposure index; constructing a vulnerability knowledge base, and identifying potential vulnerabilities; based on the standard vulnerability score, the vulnerability utilization tool activeness and the attack event frequency in the set time period, calculating the triggering probability of the potential vulnerability as a dynamic vulnerability score; malicious IP access frequency and leakage record matching degree are obtained through firewall logs and dark web monitoring data, and threat intelligence factors are obtained through calculation; calculating an exposed surface risk value in combination with the asset exposure index, the dynamic vulnerability score and the threat intelligence factor; and based on a time decay model, according to the exposed surface risk value and the business influence factor, calculating a residual risk value, and obtaining an exposed surface risk assessment result. The accuracy of risk assessment is further improved through multi-dimensional data fusion.

The invention relates to a radio frequency fingerprint identification method and system based on a convolution-attention mechanism and multi-packet reasoning, and belongs to the technical field of communication networks and artificial intelligence. Comprising the following steps: step 1, capturing an equipment transmission signal, and preprocessing the transmission signal to obtain a spectrogram; step 2, constructing a radio frequency fingerprint identification model, and inputting the obtained spectrogram into the radio frequency fingerprint identification model for training to obtain a trained radio frequency fingerprint identification model; and step 3, performing prediction by using the trained radio frequency fingerprint identification model to obtain a final prediction result. Aiming at the problems of high calculation complexity and redundant module interaction existing in a traditional model fusion mechanism, a module separation strategy is adopted, so that a convolution module and a Transform module are independently optimized, the calculation complexity is reduced, and the stability of the model is improved.

The invention discloses a cross-website domain small sample website fingerprint identification method oriented to an onion network. The method comprises a pre-training strategy based on feature alignment and domain adversarial training and a fine tuning strategy based on a double attention module. In the pre-training strategy, a multi-channel matrix traffic representation mode is designed, and rich traffic features are provided; constructing a feature extractor based on a multi-scale convolution structure and a double-attention mechanism, and supporting learning and adaptive weight distribution of different granularity features; and designing a composite loss function fusing classification loss, feature distribution difference loss and domain adversarial loss, and realizing effective feature alignment between the source domain and the target domain. In a fine tuning strategy, a fine tuning strategy based on a double-attention module is adopted, and the rapid adaptive capacity of the model to a new task is improved while the stability of pre-training features is kept. According to the method, even in the face of complex website domain changes, rapid adaptation to a new target domain data set can be achieved by depending on a very small number of samples, and an effective technical basis is provided for anonymous communication network governance practice.

The invention provides an Internet of Vehicles broadcast frame radio frequency fingerprint identification method based on LMMSE channel estimation. The method comprises the following steps: obtaining subcarrier data in a resource grid based on an obtained signal of a physical side link broadcast channel; performing root-mean-square delay expansion and channel autocorrelation matrix construction based on the subcarrier data, and combining priori signal-to-noise ratio regularization and time domain windowing operation to obtain a channel estimation value; initial radio frequency fingerprint features are obtained through the channel equalization and the channel estimation value, and the improved neural network and the initial radio frequency fingerprint features are utilized to carry out classification identification on the Internet of Vehicles equipment. Influences of noise and channels on fingerprints are effectively considered, and the purpose of extracting fingerprints of different devices in a complex environment more accurately is achieved.

The invention provides an automatic perception model method and system for illegal access in a network security isolation area, and belongs to the technical field of computer systems based on specific calculation models.The method comprises the steps that firstly, a network topological graph matrix of the security isolation area is constructed, an equipment asset list is established, and then distributed flow collection nodes are deployed to obtain real-time network data; a deep packet detection technology is used for extracting features to establish an equipment behavior baseline library, a multi-target risk assessment function is used for carrying out risk grade division on equipment, a multi-layer perceptron and a time sequence anomaly detection algorithm are used for identifying abnormal communication, and an equipment fingerprint identification mechanism based on physical layer characteristics is established to verify the legality of the identity of the equipment. A security isolation intelligent sensing network model is utilized to analyze network behaviors, a multi-dimensional abnormal scoring system is constructed to calculate risk scores, a response mechanism based on a rule engine is realized, a federal learning technology can be selectively adopted to optimize the model, and an all-dimensional and multi-level illegal access automatic sensing protection system is formed.

The invention discloses a lightweight real-time radio frequency fingerprint identification method based on streaming jump connection, and belongs to the field of communication signal processing. The implementation method comprises the following steps: adopting a WiSig data set as a training set and a test set of a neural network; and replacing a two-dimensional convolutional layer in the ResNet network with a one-dimensional convolutional layer. The ADC information sampling throughput in the edge device is greater than the reasoning throughput of the radio frequency fingerprint model, a plurality of input branches are added at different depths of the radio frequency fingerprint model, and the multi-input branch structure enables the generation time of the feature patterns needing to be fused to be consistent, thereby avoiding the distribution of the storage space. And receiver distortion features and channel noise features are removed from the same kind of radio frequency fingerprint information in different time periods within the preset time. Fusion feature fingerprint identification information is used as input of a classifier to obtain a more accurate prediction vector, a vector output by a full connection layer is mapped into a probability value through a Softmax function, a neural network is trained through a cross entropy loss function and an SGD optimizer, and radio frequency fingerprints are identified through the trained neural network.

The invention belongs to the technical field of Internet of Things information security, and particularly relates to a radio frequency fingerprint identification method based on multi-image feature fusion. According to the method, multiple images are extracted from original IQ data, the limitation of single image features is made up, different basic networks are selected according to different types of image data, the basic networks are optimized, SA-D-ResNet18 and SA-ConvNeXt-T networks are provided, a cross attention mechanism module is introduced between the networks, and image feature information is exchanged. In the final stage of feature extraction, a Concat mode is used to splice and fuse the features of a plurality of images. Therefore, the method can still maintain high recognition accuracy in a more complex environment. According to the invention, a common cross attention mechanism in multiple modes is applied to radio frequency image feature fusion, an image-based classification technology is also applied to the field of radio frequency fingerprint identification, and the radio frequency fingerprint identification method based on multi-image feature fusion is researched.

A non-invasive liquid integrity monitoring system using dielectric fingerprinting and machine learning to detect and identify contamination in sealed containers is described. The system may employ externally-mounted sensors that measure dielectric properties through electromagnetic interrogation, comparing measurements against baseline signatures to detect deviations indicating contamination, tampering, or degradation. Industry-specific ML models enable identification of specific contaminants with confidence, providing alerts without breaching container integrity.

The invention discloses an invoice automatic auditing and intelligent settlement system for automobile road rescue, and relates to the technical field of intelligent settlement systems, a correction module integrates a machine learning model to analyze historical bills, automatically corrects unreasonable settlement amount and generates a correction suggestion report, a prompt module builds an intelligent priority matrix, and the intelligent priority matrix is sent to a server. The reminding frequency is dynamically adjusted according to parameters such as the historical settlement scale and the service life of a supplier, the settlement module recognizes an abnormal invoicing terminal by combining an invoicing equipment fingerprint recognition technology, a payment process is automatically triggered after verification is passed, a supplier credit portrait system is established, and a dynamic settlement scheme is supported. The settlement system establishes an intelligent priority matrix to realize important supplier high-frequency reminding and low-risk project low-frequency intervention, so that the whole process timeliness is guaranteed, the defects of a traditional rule engine in coping with complex scenes are effectively overcome, and the response capability and the autonomous decision-making capability of the system to abnormal behaviors are improved.

The invention discloses a network flow confrontation defense method based on heuristic ant colony optimization, and belongs to the technical field of network security and privacy protection. According to the method, a self-adaptive disturbance generation strategy is designed according to spatial and temporal features of a network traffic trajectory, a disturbance injection rule meeting real-time constraint and direction consistency is established, an integrated substitution model is constructed, a feature fusion mechanism of spatial convolution, time sequence circulation and noise reduction coding is used, and a traffic fingerprint identification model with strong generalization ability is generated. An improved ant colony optimization algorithm is adopted to improve the disturbance search capability, and a batch processing method and a universal patch scoring mechanism are integrated to improve the defense universality of samples among different network flows. The method can be directly applied to a black box scene of real-time communication, gradient information or white box access permission of a target model is not needed, the attack recognition suppression rate exceeding 90% is achieved when only 20% of bandwidth overhead is increased, defense universality and deployment feasibility are remarkably improved, and meanwhile disturbance overhead is reduced.

A modular system designed for privacy-preserving content recognition and supplemental content delivery across web and mobile environments. The system employs lightweight character sampling and vision-based recognition to generate unique content fingerprints without storing or replicating original data. It features a hybrid processing architecture, using local computing resources for intensive tasks while optimizing performance on resource-constrained devices. Core functionalities include multi-method content fingerprinting, real-time monitoring with adaptive sampling, and secure supplemental content association. Operating entirely on the client-side, it complies with website terms of service and privacy regulations. Advanced features include AI-driven content recognition, blockchain-based verification, and granular content targeting through resizable selection interfaces. This technology enables seamless delivery of supplemental content while preserving privacy, reducing resource usage, and ensuring scalability across browsers, mobile applications, and edge devices. It is particularly applicable in industries such as education, retail, and secure data sharing.

This disclosure describes a technique to fingerprint TLS connection information to facilitate bot detection. The notion is referred to herein as “TLS fingerprinting.” Preferably, TLS fingerprinting herein comprises combining different parameters from the initial “Hello” packet send by the client. In one embodiment, the different parameters from the Hello packet that are to create the fingerprint (the “TLS signature”) are: record layer version, client version, ordered TLS extensions, ordered cipher list, ordered elliptic curve list, and ordered signature algorithms list. Preferably, the edge server persists the TLS signature for the duration of a session.

Provided is an electronic device. The electronic device may include a display, a fingerprint sensor provided in an area of the display, and at least one processor. The at least one processor may receive a user input for fingerprint registration, based on the user input, control the display to display a first user interface displaying information to guide a position for a touch input based on a position of the fingerprint sensor at a first position on the display, and based on receiving a first touch on the first user interface, control the display to display a second user interface indicating a degree of a fingerprint recognition related to the first touch.

A station (STA) in a wireless network. The STA is configured to cause receiving, from a target STA, information associated with a first anchor point (AP) and model training; transmitting, to the first AP, a first request that first AP perform a first ranging with two or more other APs; receiving, from first AP, first ranging data that include location information for first AP; determining a location of first AP based on the first ranging data; training a first model based on the information associated with model training and the location of first AP; transmitting, to first AP, a second request that first AP perform a second ranging with target STA; receiving, from first AP, second ranging data including movement information of target STA; and determining a first predicted position for target STA based on the second ranging data and the trained first model.

A method of double-contrast magnetic resonance fingerprinting including: optimizing, using Cramér-Rao lower bound (CRLB) via a computer system, radiofrequency (RF) pulse parameters in an MRF sequence; loading, via the computer system, the RF pulse parameters optimized into an MRI scanner; and capturing raw k-space data by selecting different signal contrast modules for dual-contrast encoding at varying repetition times (TR); reconstructing, via the computer system, at least one image including a plurality of pixels; defining, via the computer system, a dynamic range and step size for tissue parameters; and creating, via the computer system, a dictionary based on Bloch equations, the dynamic range, and the step size; and comparing, via the computer system, signal evolution for each of the plurality of pixels in the at least one image to the dictionary to determine quantitative parameters for each of the plurality of pixels, and generating a quantitative parameter map.

The invention designs a cross-Tor version domain small sample website fingerprint identification method based on comparative learning. The method comprises three parts of a pre-training strategy based on comparative learning, a fine tuning strategy based on double-branch confidence alignment and a flow classification strategy driven by a prototype. The method comprises the following steps: firstly, constructing a discriminative representation space through a trunk convolution feature extractor and a traffic attention module based on a pre-training strategy of comparative learning, and introducing supervised comparative learning to obtain feature representation with a cross-domain migration capability; secondly, a double-branch confidence alignment fine tuning strategy is adopted, the stability of a model structure is kept in combination with a partial freezing mechanism, and rapid adaptation to a target domain is achieved through joint optimization of cross entropy loss and weighted comparison loss; and finally, introducing a prototype-driven classification strategy, and on the basis of a semantic structure formed in a fine tuning stage, realizing accurate identification of Tor traffic in closed world and open world scenes by using a category prototype, so that the cross-domain generalization ability of small sample website fingerprint identification is remarkably improved.

The invention discloses a radio frequency fingerprint identification method and system based on a double-path cross attention fusion network, and is suitable for an application scene under a low signal-to-noise ratio condition. In an Internet of Things system, prevention of security threats such as illegal monitoring, data tampering and equipment pretending is a basis for guaranteeing user communication security. The radio frequency fingerprint identification technology based on physical layer feature authentication is generated at the right time, has the characteristics of passive authentication, no need of a secret key and the like, and is widely used for enhancing the identity authentication capability of a wireless network. However, in the prior art, the recognition performance is limited in a low signal-to-noise ratio environment, so that how to improve the recognition accuracy in the scene becomes a key problem to be broken through urgently. According to the radio frequency fingerprint identification method based on feature fusion provided by the invention, the one-dimensional radio frequency signal and the two-dimensional time-frequency graph are subjected to multi-modal fusion through the double-path cross attention fusion network, and useful complementary features are further extracted from noise, so that the influence of the noise is reduced, and the identification accuracy of unknown equipment is improved.

The invention relates to the technical field of dynamic modeling and digital twinning, and discloses a digital twinning dynamic modeling method for new material research and development, which comprises the following steps: constructing a low-order linear reference model, calculating a residual signal flow, and extracting residual non-statistical topological characteristics by using a sliding window to retrieve a system matrix correction increment; constructing a parallel shadow verification model to execute virtual deduction, and screening and executing parameter hopping according to a convergence divergence ratio; according to the method, through a closed-loop mechanism of residual form fingerprint identification and shadow deduction verification, the problem of capturing discontinuous physical phase change under low computing power is solved, and real-time high-fidelity synchronization of the model on complex microscopic evolution is realized while environmental noise interference is suppressed.