2210 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.

Cloud units in cloud storage include containers including data containers storing file segments, segment tree containers storing upper-level segments of segment trees representing the files, and cloud containers storing headers from the data and segment tree containers. A header for a data container includes fingerprints identifying the file segments. A header for a segment tree container includes fingerprints identifying the upper-level segments. A storage appliance stores a first level of metadata for each cloud unit in a read-write state, a second level of metadata for each cloud unit in a read-only state, and a third level of metadata for each cloud unit in an offline state. The third level requires less storage than the first and second levels. The second level requires less storage than the first level. The limited metadata space of the appliance is managed by maintaining at least a subset of cloud units in the read-only state.

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 present invention relates to the technical field of medical triage, and discloses an emergency patient triage system based on artificial intelligence, including: a data acquisition module: used to obtain the patient's identity and basic information by performing face recognition and fingerprint recognition on the patient, and receive health data uploaded by a wearable device, and transmit it to the patient's corresponding historical pathology library; a medical interaction module: used to provide an interactive platform for patients to remotely input pathological conditions based on 5G wireless communication technology; a multimodal data fusion module; a pathological feature matching module; a triage matching module; and a system integration and display module. Through the convolutional neural network model combined with pathological feature matching, based on the patient's question reply content, the condition and the judgment of the severity, a rapid judgment of the possibility of the disease, the generation of consultation content and the optimization of triage results are achieved, thereby improving the accuracy and efficiency of triage and reducing misdiagnosis and missed diagnosis.

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.

Systems and methods for generating fixed function shared accelerators (FFSAs) implement and / or comprise operations of receiving source code, the source code indicating a plurality of workloads to be performed by an electronic circuit; generating a plurality of abstract syntax trees (ASTs) based on the source code, wherein respective ones of the plurality of ASTs include a plurality of nodes corresponding to function instructions; generating a plurality of fingerprinting vectors corresponding to the plurality of ASTs, wherein respective ones of the plurality of fingerprinting vectors encode at least one of a number of nodes, a number of edges, a density, a computation intensity, an operands percentage, a control, or a data dependency; and providing the plurality of fingerprinting vectors to a machine learning (ML) model, wherein the ML model is configured to predict similarities between different ones of the plurality of workloads and to output at least one candidate FFSA.

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 relates to the technical field of data processing, and discloses a radio frequency fingerprint identification method based on a deep optimization adaptive attention mechanism, and the method comprises the steps: receiving a target equipment radio frequency signal, and carrying out the down-conversion, filtering and digital sampling of the target equipment radio frequency signal, and obtaining an original IQ data sample; preprocessing the original IQ sample to obtain a training data set; inputting the training data set into a convolutional neural network for pre-training to obtain a pre-training model; performing fine tuning on the pre-training model to obtain a downstream task model; and using the downstream task model to classify and identify unknown equipment data. Through time window segmentation and feature enhancement, the definition and consistency of radio frequency signals are improved, and meanwhile, the influence of noise and interference is reduced; and the effect of improving the capturing capability of the model on multi-scale and key features in a complex environment is achieved through the differentiated attention mechanism module.

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.

A system and methods for cybersecurity rating using active and passive external reconnaissance, comprising a web crawler that send message prompts to external hosts and receives responses from external hosts, a time-series data store that produces time-series data from the message responses, and a directed computational graph module that probes, scans, and fingerprints devices within a cyber-physical graph and analyzes the results as time-series data to produce a weighted score representing the overall cybersecurity state of an organization.

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.

The invention relates to the technical field of biological recognition, and discloses an identity authentication system based on a biological recognition fingerprint technology, and the system comprises a fingerprint data collection module which is used for collecting fingerprint two-dimensional image data and three-dimensional point cloud data through a multi-mode technology; the fingerprint feature extraction module is used for extracting feature vectors from the collected two-dimensional image data and three-dimensional point cloud data; the forgery detection module is used for analyzing dynamic pressing behaviors recorded in the collection process and detecting forgery fingerprints; the data privacy and security storage module is used for encrypting and storing the fingerprint feature template and executing feature matching in an encryption domain; and the identity authentication module is used for fusing the fingerprint features and other biological features. According to the method, the identity authentication precision and robustness are improved through the multi-modal dynamic feature fusion technology, the Paillier homomorphic encryption and dynamic behavior detection technologies are combined, data privacy protection is achieved, and the fake fingerprint recognition capability and the system security are remarkably enhanced.

The invention relates to an unmanned aerial vehicle target detection method based on a space-time synchronization association mechanism, and relates to the technical field of wireless detection, and the method comprises the following steps: S1, building a unified time reference system, and ensuring that precise timestamp alignment is carried out on IQ data streams collected by two technologies, and the precision reaches a microsecond level; s2, acquiring unmanned aerial vehicle signal data obtained by protocol reverse analysis and a radio frequency fingerprint identification technology; s3, constructing a three-dimensional incidence matrix containing a time window, a frequency domain position and a space arrival angle, and performing space-time matching on the detection events in the synchronous window; and S4, analyzing a matching result to identify a real unmanned aerial vehicle target. According to the method, the unmanned aerial vehicle signal can be effectively and accurately analyzed, the accuracy and reliability of unmanned aerial vehicle target detection are improved, and the possibility of multi-target false alarm is reduced.

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 ultrasonic detection device, a matching layer in the ultrasonic detection device comprises at least two acoustic impedance groups for realizing at least two acoustic impedance gradients, and the thickness of each acoustic impedance group is smaller than or equal to a first threshold value. The first threshold value is the thickness of the acoustic impedance group when the acoustic transmission coefficient of the acoustic impedance group is greater than or equal to a preset acoustic transmission coefficient, and the first threshold value is smaller than 1 / 4 of the wavelength corresponding to the acoustic impedance group. The matching layer is configured to be at least two acoustic impedance groups with at least two acoustic impedance gradients, and the thickness of each acoustic impedance group is set to be far less than 1 / 4 of the thickness of the wavelength corresponding to the acoustic impedance group under the condition of ensuring that the matching layer has enough acoustic impedance coefficient, so that the thickness of the acoustic impedance group is less than 1 / 4 of the thickness of the wavelength corresponding to the acoustic impedance group. On the basis of the matching layer formed by the at least two acoustic impedance groups, acoustic impedance gradient matching can be achieved, and the thickness can be small under the condition that the transmissivity is guaranteed. Therefore, the thickness of the ultrasonic detection device is reduced, attenuation of transmitted sound waves is reduced, and the sensitivity of ultrasonic detection is improved.

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.