52011results about "Digital data protection" patented technology

A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention relates to the technical field of data processing, and relates to a blockchain-based supply chain carbon data credible management method, which is characterized in that energy consumption of supplier production equipment is acquired in real time through deploying an edge computing gateway, and supplier production data is acquired in real time through the edge computing gateway deployed on the supplier production equipment; the real-time working condition parameters of the logistics transportation carrier are obtained in combination with the vehicle-mounted terminal, the material disassembly data of the recovery processing link are integrated, and a multi-source data acquisition system of the whole link of the supply chain is constructed. And the block chain network carries out credible processing on the data, a regional carbon emission factor in the industry reference database is called to carry out deviation verification, the intelligent contract engine executes an audit decision based on a preset compliance rule, and when it is detected that the energy consumption deviates from a reference threshold value, a data correction process of multi-party signature confirmation is triggered. According to the method, full-link credible evidence storage and dynamic optimization of the carbon data are realized, and the supply chain carbon emission accounting precision and the cross-border auditing efficiency are improved.

A context-dependent, privileged access control system for dynamic, risk-based authorization, consisting of: a context acquisition subsystem configured to ingest and normalize multimodal contextual data streams from a variety of sources, including endpoint telemetry, geolocation sensors, user authentication metadata, device status metrics, and network traffic descriptors; a behavior profiling processing unit communicatively coupled to the context detection subsystem, the engine configured to maintain per-user behavior baselines using unsupervised learning models and to compute behavioral deviation vectors in real time for each privileged access request; a dynamic risk assessment module operatively connected to the behavioral profile processing unit, the module configured to calculate a multidimensional risk score for each session by applying a weighted aggregate function to behavioral deviation vectors, device risk posture, threat intelligence indicators, and environmental context volatility; a policy decision engine configured to apply programmable access control policies to the calculated risk score using a context-sensitive policy scoring language, wherein the engine is further configured to dynamically determine whether to authorize, deny, elevate, or revoke privileged access according to the current trust thresholds and permission boundaries; A permission enforcement controller unit communicatively connected to one or more endpoints, cloud services, and virtual infrastructure resources. The framework is configured to implement policy decisions by generating ephemeral access tokens, providing just-in-time (JIT) permissions, and initiating permission revocation workflows upon contextual anomalies. a secure hardware appliance consisting of a plurality of tamper-evident modules configured to host the behavioral profile processing unit, the risk assessment module, and the policy decision control unit in a trusted execution environment isolated from general computing resources.

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

The present disclosure relates to systems and methods for facilitating interoperability between blockchain networks to facilitate bridging of non-fungible tokens (NFTs) across chains. In embodiments, a method includes receiving a bridging request at a first bridge smart contract to transfer a source NFT from a first blockchain to a second blockchain. Upon verification, the first bridge smart contract locks the source NFT. An oracle server determines whether a clone NFT associated with the source NFT exists on the second blockchain. If so, the oracle instructs a second bridge smart contract to unlock the clone NFT. If no clone NFT exists, the oracle instructs minting of the clone NFT and assigning ownership of the clone NFT to the user's account on the second blockchain. The clone NFT remains controllable by the user while the source NFT stays locked on the first blockchain, maintaining a one-to-one relationship between the tokens across chains.

The invention discloses an archive data security integration management system, which relates to the technical field of knowledge maps and comprises an archive data acquisition module, a knowledge intelligent analysis module, a knowledge map dynamic construction module, a security situation evaluation module and a security communication center module. The data acquisition module structurally acquires metadata and operation behaviors, the intelligent analysis module dynamically governs data quality, the map construction module constructs a three-dimensional security model, the situation evaluation module quantifies security influence and generates a strategy, and the communication center module realizes dynamic identity authentication and bandwidth allocation. Through the knowledge graph technology, comprehensive integration and safety management of the archive data are achieved, the data quality and utilization efficiency are improved, dynamic evaluation and coping with safety threats are achieved, and the safety and integrity of the archive data are ensured. Meanwhile, through dynamic identity authentication and bandwidth allocation, the safety and efficiency of cross-domain communication are improved.

The invention discloses a multi-dimensional intelligent management method and system for whole-process cost, and the method comprises the steps: generating a time-space associated structured cost data cube according to heterogeneous cost data of the stages of project planning, design, construction and completion; outputting a dynamic cost prediction curve and deviation sensitive nodes based on the structured cost data cube; according to the dynamic cost prediction curve, performing multi-party task allocation optimization by using a block chain enabled BIM / CIM collaboration platform, and generating a collaboration instruction set of smart contract coding; outputting a risk probability matrix and an advanced early warning signal based on the collaborative instruction set and the real-time engineering data flow; and according to the risk probability matrix, adopting a multi-objective optimization algorithm to generate an anti-interference decision scheme set, and outputting an optimal cost control strategy after digital twinborn simulation verification. By using the embodiment of the invention, the cost data of each stage of the project can be efficiently integrated, dynamic cost prediction is realized, and collaborative decision and effective risk management are optimized.

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention relates to the technical field of intelligent decision making, in particular to a simulation system intelligent decision making method and system based on a knowledge graph and federated learning, and the method comprises the steps that each simulation node constructs a knowledge graph sub-graph based on local dynamic data, and multi-modal semantic representation is generated through space-time modeling and event chain reasoning; the federal center initializes a simulation decision model architecture and issues the simulation decision model architecture to each node; each node uses a local knowledge graph to train a time sequence diagram network, extracts an equipment degradation path and abnormal propagation characteristics, and uploads gradient parameters in combination with homomorphic encryption; the federation center fuses the multi-node model through a dynamic weight aggregation algorithm to generate a global decision model; and driving knowledge graph evolution based on real-time data, and performing causal reasoning and decision optimization on an event chain through a federal model. According to the method, the problem of insufficient dynamic decision adaptability in a multi-source data island and privacy sensitive scene in a simulation system is solved.

The invention relates to the technical field of data processing, in particular to an artificial intelligence algorithm-oriented model encryption and privacy protection method, which comprises the following steps of: generating a privacy level mapping table through quantum feature extraction and multi-objective optimization, and dynamically screening node subsets with low delay and complementary data distribution by adopting a quantum annealing algorithm, a federated aggregation formula is improved by combining a weight prediction model of Bayesian optimization, and parameter conflicts caused by non-independent identically distributed data are relieved. For the balance problem of privacy protection and model precision, incremental update parameters are generated through quantum hash comparison, violation data streams are blocked in combination with anomaly detection of a quantum support vector machine, and an auditing chain drives a quantum hybrid optimization framework to synchronously adjust encryption strength and noise rules. And the closed loop iteration mechanism feeds back optimization parameters to the data preprocessing and encryption module to form full-link adaptive cooperation from feature dimension reduction, model training to reasoning output, so that global optimization of privacy protection and model effectiveness in a data heterogeneous scene is realized.

The invention relates to the technical field of judicial data security, and discloses a judicial system confidential data security circulation method based on a block chain technology. Collecting multi-source judicial data, identifying sensitive information through a large language model, and performing differential privacy desensitization processing; constructing an SM4 encryption and TLS 1.3 end-to-end secure channel, generating a data hash fingerprint, and writing the data hash fingerprint into an alliance chain evidence based on a PBFT consensus mechanism; designing a multi-modal classification engine to perform feature extraction and intelligent classification; establishing a hybrid authority model to integrate an XACML strategy and a Kafka queue, and implementing dynamic authority control in combination with an RBAC / ABAC mechanism; a hierarchical encryption storage architecture is constructed, and homomorphic encryption retrieval and erasure code distributed storage are adopted; constructing a judicial knowledge graph based on a BERT model; and deploying a block chain auditing system, and combining LSTM anomaly detection and a DREAD risk assessment model to form a closed-loop risk control system. According to the method, the problems of security risk and privacy disclosure in judicial data cross-department circulation are solved.

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

An example operation may include one or more of generating a dynamically-modifiable image comprising a plurality of pieces of interconnected content via an image generating model and displaying the dynamically-modifiable image via a user interface of a software application, installing a smart contract that includes an identifier of an owner of the dynamically-modifiable image on a blockchain ledger, detecting removal of a piece of content from among the plurality of pieces of interconnected content based on user inputs via the user interface, detecting addition of the removed piece of content to a different dynamically-modifiable image of a different user, and in response to the detected addition, executing an electronic payment transaction to transfer value from the owner of the dynamically-modifiable image to the different user based on the smart contract installed on the blockchain ledger.

Systems and methods for data streaming with attribute-based access control. A client apparatus in a data streaming system receives a message via a data stream, form a data stream provider. The message has a schema version of the schema associated therewith. The client apparatus processes the message to retrieve a record, the record including a data item and a data tag corresponding to the data item. The client apparatus, with reference to the schema version, determine whether an identifier (e.g., client ID) of with the client apparatus has permission to access the data item associated with the data tag. After detecting that the identifier does not have permission to access data associated with the data tag, the client apparatus masks the data item to produce a masked data record. The client apparatus releases the masked data record for further processing by the client apparatus.

The invention relates to the technical field of data processing, in particular to a multi-modal feature fusion software supply chain vulnerability intelligent positioning method, which comprises the following steps: performing encryption aggregation on code structure data of distributed nodes, dependent component metadata and runtime behavior logs through a federated learning framework to generate a cross-node encryption feature set; combining dynamic behavior sequence similarity comparison to output a multi-modal training sample; extracting cross-modal association features of code structure nodes, text entity embedding vectors and dynamic event sequences through a multi-head cross attention mechanism, and generating fusion feature vectors; according to the method, through a collaborative mechanism of multi-modal feature alignment, dependency map risk quantification and reinforcement learning dynamic decision, the limitation of single-modal analysis is broken through, and the probability of vulnerability propagation between components is quantized by using the map attention network, and a high-risk dependency path associated with a code structure is identified. And the accuracy, the cross-ecological generalization ability and the interpretability of vulnerability positioning in a complex supply chain scene are improved.

The invention belongs to the technical field of information security management, and relates to an information security risk assessment whole-process management system. Comprising an omnibearing risk data acquisition module, an intelligent risk assessment engine module, a collaborative rectification execution module, a continuous monitoring and early warning module, a security policy configuration module, a risk knowledge graph construction module and a simulated attack and defense drilling module. According to the information security risk assessment whole-process management system, accurate risk management and control are realized through multi-module collaborative operation, a personalized scheme is accurately generated and strictly tracked by a collaborative rectification execution module according to an assessment conclusion, the rectification efficiency and quality are greatly improved, the risk is timely solved, for example, energy enterprises can quickly repair industrial control system vulnerabilities, and the risk assessment efficiency is improved. And the continuous monitoring and early warning module sensitively captures risk recurrence and new signs by virtue of a real-time architecture and an intelligent algorithm, and carries out multi-channel instant notification and starts a plan so as to ensure that the risk is eliminated in time.

The invention provides an ecological meteorology and satellite remote sensing combined dynamic environment monitoring method and system. Wherein spatio-temporal dynamic concentration data and spectral reflection characteristic data are obtained at a pollutant emission node; generating an associated data block by the spatio-temporal dynamic concentration data and the spectral reflection characteristic data according to a pollution concentration abrupt change event trigger time sequence, and performing chain storage on Hash fingerprints and pollution source geographic coordinate information through a distributed node consensus mechanism; integrating the ecological meteorological observation data to generate a pollutant migration path map; and establishing a pollution influence boundary judgment model according to the pollutant migration path map and the vegetation stress response characteristic data, and monitoring the diffusion range and influence boundary of pollutants in real time through the model to generate an environment monitoring report. According to the technical scheme provided by the invention, the industrial environment pollution dynamic monitoring precision and the decision response efficiency are remarkably improved.

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

In some examples, computer-implemented systems and processes deploy securely de-risked elements of confidential data within a distributed computing environment. For example, an apparatus may obtain configuration data associated with a source data table. The configuration data may specify an identifier of a column of the source data table that includes elements of confidential data, and based on the configuration data, the apparatus perform operations that anonymize the elements of confidential data within the column of the source data table and generate an anonymized column within the source data table. The apparatus may also perform operations that provision an anonymized data table that includes the anonymized column to at least one computing system, which may process the anonymized data table and generate an output data table that includes the anonymized column and maintains a referential integrity between the source data table and the output data table.

The invention relates to the technical field of archive management, in particular to an archive full life cycle management system and method based on cloud computing. The method specifically comprises an intelligent classification module based on a format adaptation degree function and semantic density analysis, a five-stage life cycle management module, and an authority scheduling module fusing RBAC / ABAC authority control and block chain auditing, and based on a matrix decomposition archiving static coding mechanism, document security storage and analysis are realized through an orthogonal matrix and pseudo-inverse operation, and the file security is realized. The data security in a long-term archiving scene is improved; cloud storage resources are dynamically optimized according to the document life cycle state and the access frequency, and the storage cost is reduced in combination with a cold and hot layering strategy; and an AI algorithm is introduced, the archive retention value is automatically evaluated according to laws and regulations and semantic analysis, and a compliant destruction decision is realized. According to the method, through multi-model fusion metadata extraction, dynamic strategy binding and automatic state transition, the whole-process management efficiency and safety are remarkably improved.

The invention discloses an electronic signature security management method and system based on a block chain, and relates to the technical field of information security, and the method comprises the steps: generating a public key and a private key of a user through a quantum resistant encryption algorithm, binding the public key with the identity of the user, recording the public key and the identity of the user on the block chain, and storing the private key through HSM hardware encryption equipment; the method comprises the following steps: standardizing a to-be-signed document to generate a standard document, and calculating the standard document through a Hash algorithm to obtain a document Hash value; the signer signs the document hash value by using a private key and a CRYSTALS-Dillitium signature algorithm, generates an electronic signature and a signature proof, associates the electronic signature, the signature proof, the document hash value and a timestamp with a public key, and records the associated information on the block chain; and the standardized processing rule is written into the smart contract, and after a verifier submits a verification request, document hash value comparison, signature effective state check and signature proof verification are carried out through calling chain data by the smart contract.

Devices, methods, and systems for cons traction resource operations are provided. The method includes receiving a first dataset including one or more first data, entries associated with at least a first construction resource and generating a spatio-temporal sequence for the first construction resource. The spatio-temporal sequence defines one or more operations configured to minimize the construction friction with a project associated with the first construction resource. The method includes outputting at least a portion of the data associated with the spatio-temporal sequence. The method may further include determining an interaction associated with the first construction resource and modifying the spatio-temporal sequence in response to the determined interaction.