1957 results about "Data exchange" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A federated distributed computational system enables secure, privacy-preserving biological data analysis and engineering through interconnected nodes coordinated in a distributed graph architecture. A federation manager allocates resources, manages data flow and lineage, establishes privacy boundaries, and maintains cross-institutional knowledge relationships. Each node contains a processing unit for biological data analysis, privacy preservation protocols for secure multi-party computation, a knowledge graph structure with supporting data stores, and encrypted network connections. The federation manager enforces all computation and data exchange through secure channels while maintaining privacy, security, and contractual boundaries. This architecture enables research institutions to collaborate on complex biological analyses without compromising sensitive data, facilitating breakthrough discoveries through shared computational resources while maintaining strict data privacy and security controls.

The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data. For example, an apparatus may receive a plurality of messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.

The invention relates to the technical field of data exchange, in particular to a data exchange system of a nested tag structure, which comprises a data carrier adaptation module, a structure analysis module, a field mapping module, a logic driving module and a script generation module. According to the method, carrier identifiers are generated through comparison of MIME types, file header features are positioned through pattern matching, the recognition accuracy and adaptability are improved, a nesting relation is calculated through depth-first traversal of file paths, node sequences are generated through analysis in combination with a DOM tree, the label analysis efficiency is optimized, and a mapping table is generated through algorithm matching of field identifiers and database codes; the method comprises the following steps: dynamically constructing a field mapping relation, reducing manual configuration, constructing a decision matrix based on a mapping table, executing logical operation to generate a status bit and triggering a threshold write-in instruction, enhancing strategy dynamic adjustment capability, reducing semantic ambiguity through multi-algorithm collaboration and a dynamic decision mechanism, improving data exchange robustness, and supporting real-time state response. And the transmission timeliness of the industrial Internet of Things is ensured.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

This disclosure relates to a lending system comprising a processor, a memory, and at least one network interface controller configured to enable data exchange with external systems. The memory includes lending management logic configured to execute a personal loan-lending system wherein one or more loans are generated by collecting and preprocessing borrower data from multiple sources, conducting risk assessment and scoring, generating one or more scores based on the conducted risk assessment, and approving loans based on the generated scores. The system leverages machine learning processes to refine scoring accuracy and dynamically adapt to borrower profiles and external conditions. The system may integrate with third-party data sources for enhanced verification and incorporate compliance logic to ensure adherence to regulatory standards. By employing automated, data-driven processes, the system improves efficiency, accuracy, and security in loan origination, servicing, and compliance while enabling dynamic risk management and tailored loan product offerings.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention provides an electronic information data association method and system based on the Internet of Things. The method comprises the steps that a parallel processing architecture is adopted to execute rapid protocol conversion on a high-priority data packet, data recombination is directly carried out through a pre-compiled conversion rule template, a complex format verification link is skipped, and the integrity and format correctness of the converted data packet are determined; response time and error rate statistical information in the data transmission process are obtained, protocol conversion rules and scheduling strategies are optimized through a feedback mechanism, and if the success rate of certain protocol conversion is lower than a standard value, corresponding mapping rules and verification algorithms are updated; and carrying out standardized packaging on all converted data by adopting a unified data exchange standard, adding an equipment identifier, a timestamp and a data type label, establishing a cross-protocol general data format, and carrying out final distribution after judging whether a data packet meets a standard format requirement.

The invention discloses an urban safety risk assessment method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: constructing a multi-source data collection network, and obtaining data from a government department database, Internet of Things equipment, a social media platform and a traffic monitoring system in real time; preprocessing the collected multi-source data, wherein the preprocessing comprises data cleaning, format standardization, unstructured data semantic analysis and sentiment analysis; a cross-department data security sharing mechanism is established, and the traceability and security of data exchange are ensured through a block chain technology; constructing a dynamic risk assessment model, analyzing multi-source data relevance based on a deep learning algorithm, and dynamically adjusting the weight of each risk factor; and generating a visual risk assessment report, and pushing the visual risk assessment report to related departments in real time through an early warning system. According to the invention, through multi-technology fusion and a dynamic optimization mechanism, the accuracy, real-time performance and cooperation efficiency of urban safety risk assessment are significantly improved.

The invention discloses a project-level visualization display method and system for highway multi-source data fusion, and the method comprises the following steps: 1, constructing a multi-source heterogeneous data exchange and integration frame, and carrying out the seamless connection and mapping of a BIM model, GIS space geographic information and an oblique photography three-dimensional point cloud model; 2, formulating a standardized interface specification, and performing specification processing on data from different sources through a data analysis and conversion engine; 3, dynamically and synchronously updating the multi-source engineering data and the project progress in real time; 4, gathering engineering data to construct a digital twinborn model of the expressway project to realize centralized management, multi-dimensional analysis and dynamic presentation in a visual scene; 5, carding and extracting management business logic to construct a flexibly expanded professional application module so as to realize intelligent analysis of the engineering project; and step 6, performing visual immersive display on the multi-source data fusion project by adopting visual man-machine interaction. According to the invention, visual comprehensive management and traceability of each element of the project are realized.

The invention relates to the technical field of distributed file processing, in particular to a distributed file processing method and system, and the method comprises the following steps: based on a task distribution node and a data exchange interface, extracting path characteristics of a task input / output file, analyzing a file exchange sequence of a task path, and calculating a topological structure and length of the path. According to the method, the flexibility and the adaptability of task allocation are realized by combining task state monitoring and real-time adjustment of dependent path weights and task priority marks, resource allocation and a short-time task sequence are optimized by utilizing request frequency and time period records, high-frequency request resource conflicts are effectively relieved, the resource allocation efficiency is improved, and the resource allocation efficiency is improved. Through node historical resource distribution data and time distribution trend analysis, task distribution is adjusted and resource loads are balanced in combination with differences, the stability and balance of the system are enhanced, a file transmission path is optimized based on a load state and a task execution path, and a concurrent task distribution strategy is improved.

The invention provides a whole-process engineering consultation management design method, which comprises the following steps of: constructing a unified data exchange standard and protocol, converting heterogeneous data output by design tools of different professions and different units into a standardized data format, realizing seamless joint and real-time sharing of data through data middleware, eliminating data barriers, and improving the data exchange efficiency. The collaborative design efficiency is improved; according to the method, digital technologies such as BIM are adopted, a digital twin model of an engineering project is constructed in a design stage, a design scheme is deepened through virtual construction simulation and optimization, factors such as a construction process, construction procedures and resource allocation are fully considered, and the construction feasibility and economic rationality of design are improved; a collaborative mechanism of a design stage and a construction stage is established, experienced construction managers and technicians are invited to participate in design review, improvement suggestions and suggestions are provided from the construction perspective for a design scheme, a designer is helped to comprehensively understand construction requirements, and the design scheme is optimized.

The invention discloses a market supervision data exchange platform migration and security enhancement method and system based on a credential environment, and relates to the technical field of network security. In the data migration process, the consistency and privacy protection of the data are ensured through Hash check and a multi-party security computing technology; a domestic commercial cryptographic algorithm and a quantum resistance encryption technology are introduced, so that the protection capability of the platform to existing security threats is effectively improved, and the resistance to future quantum computing threats is enhanced; an intrusion detection system and a virtual honeypot node are integrated, and a defense strategy is monitored and updated in real time in combination with a deep learning model, so that novel attack behaviors can be detected and coped with in real time; according to an intrusion detection alarm and a block chain audit result, a security policy and resource allocation are dynamically adjusted in combination with a machine learning technology, it is ensured that high-risk nodes are monitored in time, resource allocation is automatically optimized, and the resource utilization efficiency and security of the system are improved.

Systems, apparatus, and methods of managing access to private data via tokens are disclosed. The disclosed techniques provide for constructing digital tokens that represent a right-to-access private data where the tokens can be managed as individual objects. Further, such digital access tokens (DATs) can be recorded on a notarized ledger, a blockchain for example. Additionally, the rights-to-access can be embodied as a non-fungible token (NFT), which could further include support for establishing homomorphic encryption workspaces in which private data can be accessed or manipulated without exposing the private data.

Systems, apparatus, and methods of managing access to private data via tokens are disclosed. The disclosed techniques provide for constructing digital tokens that represent a right-to-access private data where the tokens can be managed as individual objects. Further, such digital access tokens (DATs) can be recorded on a notarized ledger, a blockchain for example. Additionally, the rights-to-access can be embodied as a non-fungible token (NFT), which could further include support for establishing homomorphic encryption workspaces in which private data can be accessed or manipulated without exposing the private data.

The invention relates to the technical field of network and information security, in particular to a zero-trust multi-party security data exchange method, which comprises the following steps of: generating a zero-trust token and a capability authorization bill and constructing context description data; authentication encryption is performed on the plaintext fragment, attribute-based encryption is performed on the data one-time key, and a policy bearing ciphertext container is assembled; in the trusted execution environment, authentication decryption is completed through a one-time secret key of data recovery through joint challenge and threshold deblocking, or strategy migration and transfer are achieved through proxy re-encryption; and aggregating the decryption event and the forwarding event by vector commitment, generating a time anchor, and mapping the time anchor into a next round of random base. According to the invention, fine-grained authorization of the key layer, no plaintext transfer, event-level verifiable auditing and rapid revocation are realized.

The invention discloses a transistor high-power electromagnetic heat model optimization system based on deep learning, and relates to the technical field of electromagnetic heat. Comprising an input module, a deep learning module, a global optimization module, an online adaptive module and a real-time simulation module. The input module obtains various key parameters and constructs a high-dimensional input space; the deep learning module constructs a deep learning model by embedding a physical constraint and uncertainty quantization mechanism; the global optimization module is used for carrying out global search on design parameters, rapidly evaluating a target function by utilizing an agent model, and searching a Pareto optimal solution in a design parameter space; the online adaptive module is used for drift detection and updating; the real-time simulation module simulates an electromagnetic field and a thermal field in real time, and data exchange ensures timely synchronization of boundary data through dynamic scheduling and load balancing; all the modules cooperate with one another through data and feedback, and a complete, closed-loop and continuously self-adaptive updating optimization system is achieved.

The invention discloses a data exchange system for performing real-time secure communication with an intranet system, and relates to the technical field of network data security. The system comprises a network environment identification module, a challenge authentication module, a hardware key binding module, a data communication module, an abnormal response module, a strategy updating module and a visual monitoring and log module. According to the invention, by introducing the trust map evaluation module, map modeling is carried out on the characteristics of the equipment in the historical operation process, the rationality of the current operation environment of the system is judged based on the map coincidence evaluation result, the communication module is allowed to operate only when the evaluation result meets the condition, and a judgment mechanism based on the historical trusted behavior is formed; and the prevention and control capability of the system on gradual-change attacks and behavior counterfeit is enhanced.

The invention relates to the technical field of data communication, and discloses a rail transit station-level agent implementation method and system. The method comprises the following steps: constructing a professional body base through an ARM processor, designing a LiteIP security communication bus based on an FPGA, constructing a multi-core CPU cluster to construct an intelligent body center, establishing a real-time and entity database to form a data basis, fusing federated learning and a neural network to generate a decision, and executing edge calculation to realize station-level autonomous control. According to the invention, millisecond-level data exchange and intelligent cooperative control in the station-level system are realized, and the response speed and the processing efficiency of the system in an emergency are remarkably improved.

Various embodiments described herein relate to systems, methods, and non-transitory computer-readable media structured to perform server-to-device secure data exchange using a device access token. In an embodiment, a smart device receives, from a requestor entity provided to the smart device, an account data provisioning request for an account. Based on the account data provisioning request, an account identifier for the account is determined. In some arrangements, the account identifier comprises or is associated with a device access token. Based on the device access token, a data element associated with the account is determined. In some embodiments, the data element is accessible to the requestor entity only if it is not access-restricted based on the device access token. Based on the data element, an executable graphic rendering instruction is generated. The executable graphic rendering instruction is executed, which includes generating and displaying, on a user interface of the smart device, a dynamic account status indicator relating to the account.

A federated learning system (100) for data protection in data sharing and collaboration, consisting of: a module for data acquisition and local preprocessing that is configured to clean, normalize and standardize local data sets at each participating node without transferring raw data externally; a local model training module configured to train a machine learning model on the pre-processed local dataset; a secure model update and encryption module configured to encrypt and secure model parameters or updates before transmission using privacy protection techniques; a federated aggregation and coordination module configured to aggregate encrypted updates from multiple participating nodes into a global model; a module for monitoring and ensuring data protection compliance, configured to enforce data protection budgets and audit protocols and to ensure compliance with data protection regulations; a performance optimization and resource management module configured to optimize communication, computation, and resource utilization across all nodes; and a module for global model delivery and feedback, configured to redistribute the aggregated global model to participants and integrate performance feedback for iterative improvements.

The invention relates to the technical field of edge computing, and discloses an intelligent edge computing co-processing system based on an integrated circuit, which comprises a heterogeneous computing cluster module, a hardware computing unit set, a multi-core control processor based on RISC-V, a programmable pulse tensor computing array and a reconfigurable engine oriented to streaming processing, according to the intelligent edge computing cooperative processing system based on the integrated circuit, zero-delay data exchange is realized through silicon intermediate layer integration of the heterogeneous computing cluster module and a snakelike data channel, and traditional bus arbitration delay is eliminated through real-time operation code analysis and optimal optical communication path mapping of the hardware task routing matrix (TRF) module; the priority path distribution of the task scheduling subsystem is synchronously coordinated with the time-sensitive task, so that the collaborative efficiency of the computing unit is improved in multiple dimensions, the non-blocking transmission of the high-priority task is ensured, and the effect of enhancing the overall processing capability and response speed of the intelligent edge computing is achieved.

The invention discloses a hydraulic fracture monitoring system and method based on an underground micro-seismic sensor network, and belongs to the field of coal mining, and the system comprises a plurality of micro-seismic sensors which are used for collecting micro-seismic signals generated by rock fracture; the plurality of data acquisition substations are used for receiving and processing the micro-seismic signals; the data transmission substation is used for converting the micro-seismic signal processed by the data acquisition substation into an optical signal; the micro-seismic server performs micro-seismic event analysis according to the optical signal; the mining network switch is connected with the data transmission substation and the micro-seismic server and is used for network data exchange; the in-mine ring main unit is connected with the mining network switch and the micro-seismic server to form a communication network of the system; in order to solve the problem that hydraulic fracture detection precision is low due to the fact that micro-seismic signals generated by hydraulic fracture and coal mining activities are difficult to accurately distinguish in a complex underground environment, the hydraulic fracture detection method and device distinguish the micro-seismic signals generated by hydraulic fracture and coal mining activities by dividing a fracture area and a mining area, and the real-time monitoring precision of hydraulic fracture expansion is improved.

The invention relates to the technical field of data processing, and discloses a management method and system for software data assets and a computer readable medium. The method comprises the following steps: scanning transaction software to extract user behaviors and transaction information, and performing risk analysis to generate a data directory; calculating a risk coefficient through a compliance model to form a thermodynamic diagram; executing exception analysis and establishing a hierarchical protection mechanism; monitoring a data timeliness optimization storage strategy to form life cycle management; an anti-fraud algorithm is applied to generate risk control insights; and compliant cross-mechanism data exchange is realized based on insight data. According to the method, full-life-cycle intelligent management and cross-mechanism value collaborative mining of financial software data assets are realized on the premise of ensuring data security and privacy.

Disclosed is a fraud-related gang incident recognition system and method based on intelligence sharing and graph calculation, and a related device. The solution mainly comprises an intelligence sharing platform and an incident model based on graph calculation. The intelligence sharing platform integrates a plurality of trained recognition models, establishes a model intelligence sharing system, and summarizes multivariate information of each model in real time. The incident model based on graph calculation is configured to perform data exchange with the intelligence sharing platform. The incident model based on graph calculation is configured to classify nodes based on a graph neural network, and perform analysis on same on the basis of economic characteristics and social characteristics, to construct a social network graph of the incident, further identify overlapping gang structures on the basis of the social network graph, and transmit the overlapping gang structures to the intelligence sharing platform for recognition processing. In the solution of the present invention, gang organizations in social networks can be effectively discovered and identified by combining a graph calculation method of node attributes and a network topology structure and by using an intelligence sharing platform.

A computer-implemented method of attesting to a sequence of data exchanges between a group, wherein the group comprises a receiving party and one or more sending parties, and wherein the method is performed by the receiving party and comprises: receiving a sequence of two or more respective sets of data items, each respective set being sent by a respective sending party; and for each respective set of data items in the sequence: generating a respective attestation transaction, wherein the respective attestation transaction comprises a respective output locked to a respective Merkle root of a respective Merkle tree, wherein the respective Merkle tree is based on the respective set of data items and each previous respective set of data items in the sequence; and sending the respective attestation transaction to at least the respective sending party that sent the respective set.

A system for managing digital transformation projects, consisting of: • a processing unit configured to execute a variety of instructions in real time during the lifecycle of the digital transformation project to perform risk assessment, quality control, and dynamic compromise optimization; • a storage unit coupled to the processing unit, wherein the storage unit stores project data, risk categories, predefined quality metrics and resilience indicators; • a user interface module configured to display project performance metrics, including risk levels, quality status and resilience indicators, and to allow user interactions for real-time adjustment of project parameters; • A communication module configured to synchronize project data with external systems, databases and cloud-based platforms, ensuring seamless data exchange and real-time reporting between multiple project management tools and stakeholders.

The present application relates to the fields of cloud computing and big data. Disclosed are a system for evaluating a carbon footprint within a full life cycle of a product on the basis of a cloud platform, and an implementation method. The system comprises: a supply chain network cloud platform, which serves as a cloud platform directly connecting with an enterprise user, covers supply chain enterprises corresponding to a product life cycle of raw material acquisition, element manufacture, complete machine assembly, product distribution, product usage and recovery treatment, and consists of a front-end user UI, an external API and a rear-end data processing and analysis unit; a life cycle assessment cloud platform, which implements functions such as data management, calling and loading, and data mining and analysis, performs carbon footprint analysis, measurement and calculation, constructs a product life cycle list database, implements data access, and performs result auditing and arbitration on calculation data; and a cloud platform application enterprise, which enters a user UI interface of the supply chain network cloud platform by means of a cloud platform usage and management interface, and can perform operations such as registration, demand application, data exchange and data storage.

Systems, methods, and instrumentalities associated with inter-connectivity of data flows between various surgical devices and / or systems are disclosed. The surgical devices and / or surgical systems may be interrelated or independent smart surgical devices and / or surgical systems. Data sourced by a first surgical device / system may be communicated to and / or accessible by a second surgical device / system for interactive use and storage. The data exchange may be bi-directional or unidirectional, which, for example, may enable the surgical devices / systems to interface and use each other's data. The data exchange may affect one or multiple surgical device / system's operation.

The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an AI Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises AI-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.