108 results about "Identity mapping" patented technology

The invention discloses an explosion-proof equipment full life cycle digital management system based on artificial intelligence, and relates to the technical field of equipment management. The working process of the system comprises the following steps: integrating equipment attributes, operation and maintenance records and environment variable data, calculating a performance attenuation value through a weighting formula, and standardizing the data; correcting an abnormal timestamp by adopting a dynamic time window, realizing cross-system equipment identity mapping in combination with Hash similarity and parameter matching degree, and reconstructing a three-dimensional feature tensor; equipment is divided into three types, and differential weighted pooling processing is executed to generate a classification feature matrix; a reference parameter curve is generated through exponential decay weighting, a normalized deviation score of the fusion environment factors is calculated, and a grading early warning mechanism is triggered; generating an early warning report; and implementing a closed-loop strategy according to the early warning level. The system solves the problems of equipment identity confusion, environment-parameter coupling quantification and the like, realizes full-chain intelligent management from data acquisition to risk disposal, and improves the safety and operation and maintenance efficiency of explosion-proof equipment.

The invention relates to the technical field of data integrity protection, in particular to a property right registration core data security protection method based on a block chain technology, which comprises the following steps: acquiring an anchor point on an identity data generation chain, verifying a historical block to construct a topological structure, and matching a permission mapping field write-in path. And verifying the node permission and generating a data integrity verification report. According to the method, identity anchoring data is generated by binding a registrant identity credential with an address on a chain, structured identity mapping is realized, historical blocks form a logic closed loop through pointer and serial number verification, the integrity of data circulation is enhanced, a field access boundary is established by matching a field set with a node identifier through an authority level, and the accuracy of access control is enhanced. Field contents are written into nodes according to an addressing protocol and a distributed path index is generated, the flexibility and addressability of data positioning are improved, a verification link is fused with a certificate and an authority label to match and screen credible nodes, and integrated verification of path compliance and access security is realized.

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 bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional autoencoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional autoencoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and denoising. The models trained on the MNIST handwritten-digit and CIFAR-10 image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%.

The invention discloses a construction site material RFID tracking and BIM collaborative management system, particularly relates to the field of constructional engineering digital management, and is used for solving the problems of evidence chain breakage, progress labeling forking and acceptance certificate deviation caused by tag and component corresponding failure due to replacement and splitting operations. Time mark correction events are unified through an identity mapping module, tag keys are established to correspond to model keys in a one-to-one mode, an evolution sealing module detects change to generate a new instance and constructs a father-son pedigree redirection event, and a causal chaining module organizes read-write events into a monotonic sequence and promotes a state. And the mutual identification judgment module fuses the identity chain and the space path evidence to calculate a decision coefficient to trigger backtracking correction, and the iterative archiving module summarizes a check result to optimize rules and solidifies snapshots, so that full-link traceability and collaborative consistency of materials from entrance to installation are realized.

The invention provides a user attribution method and system capable of meeting various configurations and conversions, and belongs to the technical field of computer networks, and the method comprises the following steps: according to a unified identity mapping table, ordering behavior records on each device according to a time sequence, integrating the behavior records into a continuous behavior path data set, and determining a full-link view of a user conversion path; carrying out fragmentation storage and parallel analysis by adopting a distributed computing framework through the behavior path data set, and optimizing resource allocation to obtain a real-time processed user conversion path result set; according to a user conversion path result set processed in real time, a cache mechanism is constructed to store high-frequency access path data, and if a new query request is received, data is extracted from a cache preferentially, and a quick-response path analysis result is determined; through a path analysis result, an encryption technology is applied to protect a user identity identifier and behavior data, and in combination with an access control strategy, a safe and compliant attribution analysis data set is obtained. According to the method, the problem of cross-device user behavior tracking is solved, a full-link user conversion path view is provided, the path analysis requirement can be quickly responded, and data support is provided for precise marketing decision making.

The invention provides a voiceprint recognition method and system based on deep voice embedding, and the method comprises the steps: obtaining voice data, and obtaining buffering waveform data based on the voice data; performing resampling based on the buffer waveform data and a preset sampling rate to obtain sampling voice data; performing paragraph segmentation based on the sampled voice data and preset window time to obtain segmented voice data; performing data enhancement processing based on the segmented voice data to obtain enhanced voice data; performing feature extraction on the enhanced voice data based on a pre-training model to obtain a high-dimensional feature vector; calculating frame-level statistical information based on the high-dimensional feature vector, and obtaining a paragraph feature vector based on the attention mechanism and the frame-level statistical information; and feature vector matching is carried out based on the paragraph feature vectors, so that identity mapping is carried out, and voiceprint recognition is realized. According to the voiceprint recognition method and system based on deep voice embedding provided by the invention, the accuracy of voiceprint recognition is ensured, and the efficiency and reliability of voiceprint recognition are greatly improved.

The invention relates to the technical field of cloud computing and identity authentication security, and discloses a cloud environment intelligent identity authentication cross-domain docking method and system.The cloud environment intelligent identity authentication cross-domain docking method comprises the steps that heterogeneous identity data of multiple cloud platforms is collected and preprocessed, and a standardized identity data set is obtained; constructing a unified identity semantic model based on the standardized identity data set and generating a feature embedding vector; performing identity mapping conversion on the feature embedding vector by using a deep neural network to obtain a mapped identity feature vector; performing multi-factor adaptive trust evaluation in combination with the mapped identity feature vector and the real-time behavior data to obtain a dynamic trust evaluation result; generating an adaptive security token based on the dynamic credibility evaluation result; according to the method, the problems of cross-domain isomerism and dynamic trust evaluation of identity authentication in a cloud environment can be solved, and an effective solution is provided for cloud computing security.

The invention relates to the technical field of video behavior analysis, in particular to a post-examination illegal behavior detection method based on cross-mirror tracking and identity authentication, and the method comprises the steps: collecting monitoring videos of all cameras in an examination scene; carrying out identity authentication by adopting Reid identity matching, and forming and maintaining a personnel identity mapping relation; determining a target bounding box, dynamically evaluating and enhancing the quality score of the target bounding box, constructing an optimized trajectory feature, and tracking a trajectory through hierarchical clustering to obtain a corresponding identity ID; a historical feature library is established and dynamically updated, identity conflict detection is executed, and an identity ID is bound or reset; inputting a GAN generator to synthesize an enhanced spatiotemporal feature sequence, inputting the enhanced spatiotemporal feature sequence into a Bi-LSTM discriminator in combination with an image block sequence, executing adversarial discrimination and action classification, and identifying illegal behaviors; when the illegal behavior is identified, real-name system alarm information including an identity ID and an illegal behavior type is generated; therefore, the automatic discovery and real-name traceability of illegal behaviors after examination can be improved.

The invention provides an RDMA network monitoring method and system in a cloud native artificial intelligence system, and belongs to the technical field of cloud native. The method is executed by a network monitoring assembly deployed on each node of a cloud native platform in a containerization mode, and the network monitoring assembly comprises a switch monitoring module and a cluster monitoring module. The method comprises the following steps: respectively acquiring first flow data of each RDMA switch and second flow data on each node of a cloud native platform by using a switch monitoring module and a cluster monitoring module, identifying respective flow identity of the flow data in combination with resource identity information of the cloud native platform, and realizing identity mapping of flow and resources; and converting the traffic data into a data format of a cloud native platform network index, so that the traffic data can be processed and analyzed by using a cloud native platform mode. According to the method, a set of omnibearing and traceable safety observation system penetrating through a cloud native platform and RDMA physical equipment is constructed from the perspective of an AI task, and good compatibility and maintainability are achieved.

The invention relates to the technical field of data processing, and provides a full-chain data closed-loop private domain traffic reaching method, which comprises the following steps of: accessing data of a multi-heterogeneous private domain contact through an edge node, associating dispersed data to a user identifier in combination with an identity mapping mechanism, and constructing a complete private domain data stream; user state vectors are generated through feature fusion of time sequence analysis and machine learning, the problem of data islands is solved, and data support is provided for accurate touch; based on the user state vector, the historical reaching effect feedback data and the real-time scene information, a reaching decision instruction is generated through reinforcement learning, and dynamic matching between a reaching strategy and the user state and between the reaching strategy and the real-time scene is achieved; and meanwhile, through recording whole-chain behavior data after the user reaches, constructing a causal attribution graph, combining anti-fact reasoning to quantify the value contribution degree and the negative feedback attribution weight of the reaching action, and converting the value contribution degree and the negative feedback attribution weight into a reward signal and a penalty factor of reinforcement learning, dynamic updating of reinforcement learning network parameters is realized.

The invention belongs to the technical field of image processing, and particularly relates to an image shadow removal method based on a double-branch network. The method comprises the following steps: acquiring a shadow image to be processed and a corresponding mask image, and preprocessing the shadow image and the mask image to obtain a shadow region image and a non-shadow region image; respectively sending the shadow region image and the non-shadow region image into a shadow removal branch network and an identical mapping branch network for processing to obtain a shadow removal region image and a mapping image; performing pixel-by-pixel addition on the shadow-removed area image and the mapping image to obtain a preliminary shadow-removed image; the image with the shadow removed preliminarily is sent to a color restoration module for color restoration, and a clear image with the shadow removed is obtained; according to the method, shadow removal can be effectively realized, the color shift problem in a shadow removal result can be alleviated, and the method has a good application prospect.

The invention relates to a face identity exchange method based on multi-scale feature transfer, and belongs to the technical field of image-level deep counterfeiting. The method comprises the following steps: acquiring a source face image and a target face image; inputting the two images into a unified multi-scale encoder to obtain multi-scale source face features and multi-scale target face features; the multi-scale source face features pass through an identity mapping network to obtain a multi-scale source face identity vector; a first source face identity vector in the multi-scale source face identity vectors and a first target face feature in the multi-scale target face features are fused through a cross attention fusion module to obtain a preliminary fusion feature; and inputting the multi-scale source face identity vector and the multi-scale target face features into a multi-scale feature transfer decoder to obtain an exchange face image. According to the method, the accuracy of identity information detail transfer can be improved, and a high-fidelity exchange face with a source identity and a target attribute is generated.

The invention relates to a data updating method and device, electronic equipment and a computer program product, relates to the technical field of communication, and can be applied to a dynamic data updating scene of a network function. The method comprises the following steps: receiving a network function dynamic data updating notification sent by a unified data storage function UDR, and updating a local network function group identifier and user identity mapping relation table; the network function dynamic data update notification is generated by a unified data storage function UDR when a change in the dynamic data is detected. According to the invention, after the unified data storage function UDR detects that the dynamic data is changed, the notification is sent to the consumer of the UDR, so that the consumer of the UDR updates the local network function group identifier and user identity mapping relation table, and dynamic data updating is realized.

The invention provides an anti-quantum security-oriented identity-based multivariable ring signature method and system. The system comprises a key distribution module, a signature end module and a verification end module, completing identity mapping according to the user identity information, and generating a private key pair of the user; the signature user constructs a signature ring, generates an anonymous signature and outputs a signature vector; and the verifier verifies the signature and judges whether the signature is valid or not. According to the method, an identity-based signature mechanism and a UOV structure are fused, the signature efficiency and deployment flexibility of the system in a post-quantum environment are improved, the capability of resisting quantum computing threats is achieved, currently known multivariate algebraic attacks can be effectively prevented, and high security guarantee is achieved; according to the method, a ring signature mechanism is introduced, any members in the ring are supported to generate verifiable signatures, identity information of the members is effectively hidden, the method is particularly suitable for application scenes with high requirements for signer privacy, and the method has the advantages of being untraceable for signers, lightweight in public keys, resistant to quantum attacks and the like.

The invention provides a low-illumination image enhancement method based on improved Retinex-Net and an intelligent wearing system, and the method comprises the following steps: collecting a data set with a low-illumination label, and dividing the data set into a training set and a test set; a low-illumination image enhancement model based on improved Retinex-Net is designed and trained and verified, a trained low-illumination image enhancement model is obtained, and the low-illumination image enhancement model is composed of a decomposition network, an adjustment network and a reconstruction network. A sub-network formed by an identical mapping and residual shrinkage construction unit is added into a reflection path of the decomposition network; and enhancing the image by using the trained low-illumination image enhancement model to obtain an enhanced image. According to the low-illumination image enhancement method and the intelligent wearing system provided by the invention, noise generated in an image decomposition process can be effectively suppressed, the definition and quality of the image are improved, and low-illumination image enhancement is realized.

The invention discloses a real scene image defogging method based on large kernel convolution and physical perception fusion, and relates to the field of image defogging, and the method comprises the following steps: building a defogging model which comprises a large kernel strip convolution module and a double-flow physical perception fusion convolution network; collecting a training data set; a multi-scale physical defogging network is trained; defogging the real scene image; a large-kernel stripe convolution module in the convolution network adopts a multi-branch structure design, and the multi-branch structure comprises a local branch, a global branch and an identity mapping branch; the local branches are used for extracting fine-grained textures; the global branch is used for modeling structural features in horizontal and vertical directions; the identity mapping branches are used for transmitting original features. According to the method, multi-branch collaboration and direction perception are set in large-kernel strip convolution, so that the combined modeling capability of the network to local textures and long-distance directivity features is improved, and the adaptability to complex non-uniform haze is enhanced.

The invention relates to a multi-tenant unified storage routing table item compression method and system based on identification mapping. The method comprises the following steps: in a routing table entry insertion stage, dynamically distributing a minimum available reduce ID for each new tenant, and ensuring that an IP prefix space of the new tenant is not overlapped with a tenant using the same reduce ID; in the lookup stage, a message VNI is firstly converted into a reduce ID through a mapping table realized based on an RAM, then the reduce ID and a destination IP are used as a joint key to look up the TCAM, and by constructing a mapping relation from the VNI to the reduce ID, the table item bit width of the TCAM is remarkably reduced on the premise of ensuring the routing correctness. The method is suitable for an IPv4 / IPv6 multi-tenant environment, the hardware area and power consumption can be effectively reduced especially in an FPGA or ASIC high-performance data plane, and meanwhile the line speed forwarding capacity is kept.

The invention relates to the technical field of archive safety management, in particular to an archive global sensing control intelligent protection method and system. The method comprises the following steps: establishing an atomic identity description structure, writing the atomic identity description structure and electronic archive content into an identity mapping table together, and constructing an archive identity anchor point; calling a dynamic identity mapping control algorithm, and constructing a protection strategy mapping table between the entity archive and the electronic archive; based on the derived node information structure, constructing a directed derived chain atlas through a dynamic derived chain tracing algorithm, and generating a dynamic traceable multi-path derived chain; and identifying the conversion derivation node, retrieving the protection strategy mapping table and the protection strategy meta-structure according to the archive identity anchor point corresponding to the conversion derivation node, and constructing a protection strategy inheritance relationship between the conversion derivation node and the upstream derivation node thereof. According to the invention, full-link traceable global intelligent protection in the process of conversion from entities to electronics and multi-path conversion of electronic files is realized.

The invention discloses a multi-target sports classroom exercise density evaluation method and device under a single view angle and a medium. The method comprises the following steps: S110, a video acquisition and preprocessing stage; s120, constructing a data management framework; s210, constructing an electronic fence; s220, performing human body detection and ROI extraction by using a deep learning model; s230, designing a gridding index mechanism; s240, adopting a video stabilization method based on feature points; s310, designing a multi-target state updating mechanism; s320, establishing an identity mapping table and a target identity feature set; s330, constructing a target tracking model; according to the single-view-angle analysis technology, multi-target motion density evaluation based on a single-view-angle video is achieved, multi-angle camera arrangement is not needed, hardware cost and site requirements are remarkably reduced, the system can conduct off-line or real-time analysis through videos collected by portable devices such as a mobile phone and a common camera, and the analysis efficiency is improved. And the practicability and the universality of the system are greatly improved.

Embodiments of the present disclosure provide a key processing method, apparatus, and system based on a unified multi-domain identifier, belonging to the field of information security technology. The system includes: an identity mapping public key architecture and an identity binding public key architecture. Among them, the IMPK architecture includes a root domain key management center and the domain key management centers it manages. The RKMC is used to generate the domain private key of the DKMC and send it to the corresponding DKMC. The IBPK architecture includes the DKMC and all nodes within the domain it manages. Embodiments of the present disclosure are applicable to the key processing process of nodes within the entire domain.

This invention relates to a driving method and system for improving the compatibility of network interface cards (NICs) and optical modules, comprising: acquiring optical module identity data, NIC identity data, and an identity mapping table; the optical module identity data includes an optical module manufacturer's equipment identifier and an optical module manufacturer's name; if the optical module manufacturer's equipment identifier is a first identifier, a first information table is generated based on the NIC identity data and the identity mapping table; new optical module identity data is obtained based on the NIC identity data and the first information table, and the new optical module identity data is sent to the NIC's receive buffer; the NIC chip reads the new optical module identity data from the NIC's receive buffer. This invention generates a first information table, selects new optical module identity data compatible with the NIC from the first information table, and sends the new optical module identity data to the NIC chip. This enables data transmission between the optical module and the NIC when the NIC and optical module cannot connect or data transmission is unstable, thereby improving the compatibility of the NIC and optical module.

The application discloses a conference audio processing method and device and a storage medium thereof, and belongs to the technical field of audio. The conference audio processing method comprises the following steps: acquiring current orientation information in multiple groups of conference audio signals collected by different collection channels; matching the current orientation information with a preset orientation-identity mapping table; and if the matching is successful, taking a target identity corresponding to the current orientation information in the orientation-identity mapping table as a speaker identity of the conference audio signal. The application solves the technical problem that the process of identifying a speaker by using a voiceprint in the prior art is complicated.

The present disclosure is directed to a managed cloud-based FIDO authenticator implemented with a distributed switchboard system. The disclosed switchboard implementation enables proxy automation and management of FIDO-related service, including registration and / or access operations, between FIDO service requesting and relying parties. The described process is based on generation of a single pre-validated and personalized identity token, based on validation, tokenization and identity mapping of an identification record associated with a FIDO service-requesting entity (SRE). The generated FIDO identity token can be used as a validity signature for streamlined resolution of identity for generation of FIDO access credentials. The managed FIDO authenticator further provides a FIDO credential management and recovery features for user and / or client applications reliant upon FIDO-authenticated services. The distributed switchboard implementation further enables implementation of a federated FIDO services for a distributed application whereby various FIDO-reliant application components can perform their own FIDO authentication via the distributed switchboard.

This invention discloses a unified image anomaly detection method based on pseudo-anomalies and contrastive learning. The method constructs a dual-teacher model containing teacher and student networks with identical structures. During training, a twin data flow strategy is employed: on the one hand, the original normal image data flow is preserved; on the other hand, multi-scale simplex noise and external textures are fused to generate pseudo-anomaly images and pixel-level masks, forming a pseudo-anomaly data flow. The network is segmented at the deep feature output end according to the data flow direction, calculating the spatial contrast loss to ensure the fidelity of normal feature reconstruction, the anomaly boundary loss guided by the mask to push away anomalous features, and the global class-aware contrast loss to separate multi-class semantics. This invention overcomes the identity mapping bottleneck of traditional unsupervised networks in multi-class unified scenarios, solves the feature confusion problem, and achieves high-precision multi-class unified anomaly detection without class priors.

The invention relates to the field of data processing, and relates to a multi-party heterogeneous system fusion method, an API gateway and a multi-party heterogeneous system.The method comprises the steps that an original client request carrying a source system token is received, and the target of the request points to the service of a target system; verifying the validity of the token of the source system, and analyzing the identity of a source user; the user identity of the source user in a target system is inquired through an identity mapping module by using the source user identity; generating a legal target system token for the source user according to the inquired target user identity; replacing the source system token in the request header of the original client request with the newly generated target system token so as to obtain a modified client request; and forwarding the modified client request to the target system, and forwarding the response to the client after receiving the response of the target system. By adopting the method provided by the invention, the integration cost and risk of the multi-party heterogeneous system can be effectively reduced.

The application relates to an MRI-CT image synthesis method, device and equipment and a readable storage medium, relates to the medical image synthesis technical field, and comprises the following steps: constructing a training data set comprising MRI image data and CT image data; constructing an improved cycle generative adversarial network model comprising a space domain information maintaining generator and a spectral normalization discriminator and a corresponding target function of the improved cycle generative adversarial network model, wherein the target function comprises an adversarial loss term, a frequency domain consistency loss term and an identity mapping loss term; training the improved cycle generative adversarial network model based on the training data set to obtain an image synthesis model; and converting a to-be-converted MRI image into a synthesized CT image based on the image synthesis model. The application effectively improves image details and overall image synthesis quality through the frequency domain consistency loss, maintains the consistency between a real image and a synthesized image from a space domain level through the space domain information maintaining generator, and improves the stability and reliability of a network training process through the spectral normalization discriminator.

A method of using a computing device executing to interrelate two or more corpuses of dissimilar data that includes receiving input data from each of two or more corpuses of dissimilar data. The computing device computes a pass for each of the input data into two or more encoder-decoder models. The computing device further obtains a prediction of an identity mapping for each of different domains of knowledge from each of the two or more encoder-decoder models. The computing device additionally computes a distribution distance metric as an output from each of a low-dimensional embedding vector representation from each of the two or more encoder-decoder models. The computing device still further computes a function based on each of the predictions from each of the two or more encoder-decoder models and the distribution distance metrics. The computing device additionally updates the two or more encoder-decoder models.

The invention belongs to the technical field of near-infrared spectrum calibration transfer, and particularly relates to a CycleGAN-based non-standard near-infrared spectrum calibration transfer method and a CycleGAN-based non-standard near-infrared spectrum calibration transfer system. Obtaining a non-standard spectrum of a near-infrared spectrometer serving as source equipment, naming the non-standard spectrum as a source domain spectrum, inputting the source domain spectrum into the trained calibration transfer model, and converting the source domain spectrum into a spectrum with target domain spectrum distribution characteristics; the calibration transfer model is constructed based on the CycleGAN, and the identity mapping loss used by a generator in the CycleGAN is optimized for representing the difference between the source domain spectrum and the output of the source domain spectrum input to the generator mapping the target domain spectrum to the source domain. And the difference between the target domain spectrum and the output of the target domain spectrum input to a generator mapping the source domain spectrum to the target domain, and the weight corresponding to the identity mapping loss is a positive number at the initial stage of training and is set to be 0 after the initial stage of training. According to the invention, unnecessary distortion of the spectrum shape structure in the calibration transfer process can be effectively inhibited.