22805 results about "Data store" patented technology

A system is disclosed for continuous and intermittent monitoring of physiological parameters. The system comprises at least one sensor coupled to a wearable device configured to measure physiological parameters of a user; a non-transitory data store storing data collected from the at least one sensor and computer-executable instructions; and a processor in communication with the at least one sensor and the non-transitory data store, wherein the computer-executable instructions, when executed by the processor, configure the processor to: continuously measure the physiological parameters by sampling the at least one sensor at a sampling rate to obtain repeated measurements at series of intervals configured to capture important physiological events; compare measurements of the physiological parameters to a threshold to determine a likelihood of a physiological event; and alert the user when an occurrence of the physiological event is likely.

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 technology is generally directed to a method for controlling a satellite system comprising at least one satellite. The method may include receiving and processing a set of control parameters associated with an orientation of the at least one satellite via a classic control model to generate a set of actions to control the orientation of the at least one satellite and storing the set of actions as data in a buffer. The processing and storing are iteratively repeated until the data stored in the buffer exceeds a threshold. When the data stored in the buffer exceeds the threshold, the method may further include iteratively implementing, based on each of the set of control parameters and the data stored in the buffer, the machine learning model to control the orientation of the at least one satellite to stabilize the at least one satellite.

Method and system for analyzing software or firmware of computing systems to assess security properties includes loading predicate device input data including characteristics about predicate devices; translating predicate device input data into predicate device model data describing characteristics or dependencies of the predicate device input data relevant to the analysis; determining digital twin configuration data used to configure digital twin; loading the digital twin configuration data onto the digital twin; storing configuration data in the memory; instructing the digital twin to configure itself to implement the loaded digital twin configuration data; determining security analysis to be carried out on the digital twin; simulating the predicate device; executing security analysis on the digital twin; generating output data describing the result of execution of the security analysis; storing output data pertaining to the result; and determining if the result satisfies a predetermined condition, and if so, executing action corresponding to the result.

The present disclosure provides a method and system for orchestrating automated data processing and transformation. A centralized orchestrator receives a request to process a client dataset and initiates a data ingestion process to obtain sample data. A semantic analysis module analyzes the sample data to determine semantic types of data fields. A transformation module generates data transformation instructions based on the determined semantic types. The orchestrator deploys a data processing pipeline to a client-controlled environment and configures privacy preservation parameters to identify and obfuscate potential personally identifiable information. The pipeline applies the transformation instructions and privacy parameters to the dataset. A configuration module determines data storage configurations for the transformed dataset. The transformed dataset is stored according to the configurations in a client-controlled or cloud environment. A machine learning module generates a model based on the transformed dataset, which is stored in a model repository accessible to the client.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention discloses an agent long-term memory modeling method based on a memory network, and relates to the field of agents, and the method comprises the steps: storing vector data to a vector database, and storing structured metadata to a relational database; receiving an input request of a user, and executing vector similarity retrieval in the vector database to obtain semantic similar fragments; executing structured data query in the relational database to obtain structured metadata; the memory abstract is retrieved; forming a candidate data set; taking a filtered result as memory information, inputting the memory information into a large language model through a cue word project, and generating reply content; the input request of the user and the generated reply content are combined to form a new interaction record; processing the new interaction record and historical interaction records stored in a vector database and a relational database through a large language model to generate a memory abstract; aiming at the insufficient long-term memory emotion interaction coherence of the intelligent agent, the emotion interaction coherence is improved.

The invention provides a data all-flash storage optimization method and system based on cloud computing, and the method comprises the steps: receiving a storage operation request stream of a user terminal through collecting a multi-dimensional performance data set of each node of an all-flash storage cluster in real time, wherein the multi-dimensional performance data set comprises a storage space fragmentation rate, an input / output request queue depth and a network link bandwidth utilization rate; analyzing a to-be-stored data block identifier set and an operation mode label, traversing a historical access record library based on to-be-stored data block identifiers, extracting historical access time sequence characteristics and physical storage position proximity of associated data blocks, and combining the multi-dimensional performance data and the historical access time sequence characteristics to obtain a data block identifier set; according to the method, a dynamic access popularity prediction model in a preset time period is constructed, and finally, a cross-node fragmentation storage topological graph and a cache preloading strategy matrix are generated according to the proximity of the dynamic access popularity prediction model and a physical storage position, so that the optimization of all-flash storage is realized, and the data storage performance in a cloud computing environment is improved.

The invention relates to the technical field of data storage and processing, in particular to an RAID card static cache management method and device based on data popularity, and the method comprises the steps that the physical position of a data block needing to be accessed in an SSD array is acquired according to a read-write request; historical data access information of the RAID card is collected, a historical data set is generated, and a neural network model is trained by using the historical data set to obtain a cache management model; predicting and outputting data blocks which are possibly accessed in the future and the access probability of the data blocks through the cache management model; the RAID controller dynamically adjusts a cache strategy in combination with an LRU strategy according to a prediction result output by the cache management model and a read-write request of a file system, and optimizes a storage position and an updating mechanism of a data block in a cache space; and when the hit rate or the data access efficiency does not reach the preset value, adjusting the parameters of the cache strategy and updating the cache strategy. According to the method, cache resources can be more effectively distributed, the cache hit rate is improved, the cache replacement overhead is reduced, and therefore the performance of a storage system is optimized.

A system and method for providing a digital financial advisor using fine-tuned large language models is disclosed. The system includes a data advisor application, data fusion suite advisor engine, human advising engine, a knowledge base and large language model (LLM) fine-tuning engine to create specialized language models (SLMs) that mimic specific human financial advisors. Multiple digital avatars embodying the appearance and communication style of human advisors are generated. The system processes user and client profile data, along with advisor-specific information, to provide personalized financial advice. It handles client queries, escalating complex issues to human advisors when necessary. The digital advisor system communicates with profile datastores, user devices, and external data sources for comprehensive financial analysis. Continuous learning capabilities allow the system to improve its performance based on interactions and feedback, combining AI efficiency with personalized human-like advisory.

The invention provides an augmented reality cooperation system for a cloud desktop, and the system comprises a cloud service module which generates a virtual desktop image, and carries out the resource monitoring and data storage; the AR equipment is used for collecting data and interacting with virtual reality; the collaborative rendering module is used for fusing AR rendering and virtual reality; the interaction module is used for a multi-modal interaction mode and personalized recommendation; the intelligent scheduling module is used for dynamic resource allocation, resource prediction and optimal management; the network optimization module is used for designing a layered edge architecture and introducing a QUIC protocol to carry out optimized transmission of data streams; the security module is used for carrying out encryption transmission and verifying the identity and access of the user; and the compatible module is used for defining standardized interfaces and protocols and realizing multi-platform support. Functions and resources can be deeply fused, the problems of network dependence, interaction precision, resource allocation and the like are solved, optimal AR presentation and experience effects are provided, and various application scenes are supported.

The invention relates to the technical field of system engineering modeling, and particularly discloses an MBSE optimization method based on a large language model, which realizes MBSE whole process automation and intelligentization by constructing a'demand-knowledge-model 'dynamic closed-loop framework and fusing RAG and LLM. The method specifically comprises the following steps: constructing a domain knowledge enhancement library, and integrating LLM to construct a demand analysis engine and a dynamic modeling optimization system; a domain expert inputs a demand through a natural language interaction interface, and the demand is analyzed into structured data through LLM; generating a parameterized model conforming to the MBSE specification by combining the RAG technology with the knowledge in the library; after the model runs through a simulation tool chain, the LLM adjusts parameters according to a simulation result to generate an iteration scheme; and after the modeler passes verification, storing the model and data into a database to form a knowledge source. According to the method, domain knowledge dual-drive modeling and cross-role collaboration are achieved, the knowledge base self-evolution capacity is achieved, the problems that traditional MBSE is high in manual dependence and insufficient in semantic fault and knowledge fusion are effectively solved, and the modeling efficiency and reliability are remarkably improved.

The invention belongs to the technical field of data storage, and discloses a multi-source marine environment data storage method and system based on HDF5. According to the method, a hierarchical storage framework is constructed, a domain-type-source three-level directory architecture is constructed, a space-time dimension dynamic extension mechanism is executed, and space-time composite coding is performed; designing an HDF5 nested packet storage architecture, wherein grid topological structure definition, dynamic resolution self-adaptive storage, multi-source data processing and metadata management are carried out; constructing distributed data association and indexes, including data standardization preprocessing and cross-source data association; the storage performance is optimized and expanded, wherein density sensitive blocking and layered compression are carried out. According to the method, redundant storage of a high-latitude region is greatly reduced, and dynamic adaptation of a local encryption grid is supported. According to the invention, open circulation and collaborative utilization of interdisciplinary data resources are facilitated.

The invention discloses an e-government affair platform management method and system based on cloud data, and relates to the technical field of data processing, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, extracting keywords, carrying out the multi-scale time coding and multi-scale space coding of the data, and storing the data in a database; constructing a government affair knowledge graph based on the database, mining association relationships of department services and analyzing affair handling habits of users; generating a government affair examination and approval path based on the knowledge graph, optimizing the government affair examination and approval path through reinforcement learning, dynamically adjusting the examination and approval process, performing a simulation test by using a digital twinning technology, and verifying the user satisfaction after the examination and approval process is optimized; and storing the government affair data in a database and performing authority management. According to the method, the electronic government affair data processing capability can be improved, the examination and approval efficiency is improved, the verifiability of an optimization strategy is enhanced, and technical support is provided for digital and intelligent transformation of government affair services.

The invention relates to the technical field of synchronization in data communication, and provides a PTP protocol-based chip-level hardware timestamp generation method and system, and the method comprises the steps: 1, receiving a synchronization message and a delay request message from a main clock, carrying out the evaluation of a physical layer signal, and recording a first hardware timestamp; 2, responding to the received delay request message, generating and sending a delay response message, acquiring bidirectional transmission delay data by exchanging the delay request and the delay response message, and calculating clock skew in combination with the first hardware timestamp and the bidirectional transmission delay data; 3, adjusting the local clock frequency according to the clock skew; 4, after the synchronization is completed, associating the second hardware timestamp with the corresponding data message, and storing the associated data in a buffer area in the chip; and 5, generating standardized timestamp information, and outputting the standardized timestamp information through a chip interface. And high-precision time synchronization and timestamp generation are realized.

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

Systems and methods for monitoring and evaluating time-series real-time environment data to create a high-resolution, high-fidelity actual (e.g., nowcast) and predicted (e.g., forecast) representation of an environment of interest. In some aspects, the system comprises instructions to obtain a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period, identify one or more precursory signals within the set of real-time environment measurements, determine at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold, generate a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements, and display, at a user interface, the generated time-series representation of the actual environment state.

To provide an information processing device, an information processing method and a program that can visualize resident's needs to move and actual service conditions of public transportation.SOLUTION: An information processing device 100 comprises: a traffic data storage unit 132 which stores information on stops of public transportation; a movement data storage unit 133 which stores movement data including places of departure, routes and destinations; a reception unit 124 which receives visualization conditions; a totaling unit 125 which totals, for each of mesh sections of a map, departure / arrival counts of the movement data extracted from the movement data storage unit by the use of visualization conditions; and an output unit 126 which outputs a visualized map including stops, and outputs one or more mesh sections in different display modes according to departure / arrival counts.SELECTED DRAWING: Figure 1

The invention discloses an operation and maintenance management method of an integrated management system, and belongs to the technical field of operation and maintenance of systems. The invention discloses an operation and maintenance management method of an integrated management system, and aims to solve the problems of data islands, slow fault positioning, experience dependence on strategies and the like in traditional operation and maintenance. The method comprises the following nine core processes: dynamically accessing multi-source heterogeneous data and carrying out standardization processing; constructing a hierarchical time series data storage structure; generating a modeling dependency and fault path of the equipment knowledge graph; adopting a three-layer anomaly detection model to identify anomaly; fault root causes are positioned through causal reasoning and a Bayesian network; generating an energy efficiency strategy based on reinforcement learning and multi-objective optimization; triggering the self-healing workflow to execute operation; testing the robustness of the system in a sandbox environment; and iteratively updating the knowledge graph and the AI model to form a closed loop. According to the method, automation and intelligentization of the whole operation and maintenance process are realized, and the system availability and the energy efficiency management level are improved.

A disclosed data platform may be configured to access a custom policy developed by a particular client entity that uses the data platform. The custom policy may invoke a query that targets a target dataset ingested from a cloud environment and stored in a data store. The data platform may determine that the custom policy is likely to be of value to one or more other client entities, besides the particular client entity, that also use the data platform. In response to the determining that the custom policy is likely to be of value to the one or more other client entities, the data platform may generate a community policy based on the custom policy. The community policy may be available for use by the one or more other client entities. Corresponding methods, systems, and products are also disclosed.

The invention provides an industrial robot control system and method. The system comprises a trajectory planning module, a dynamic compensation module, a feedback control module, an execution module, a sensor module and a data storage module. The trajectory planning module generates an expected trajectory of an end effector of the robot; the dynamic compensation module calculates a dynamic compensation torque based on the expected trajectory and a robot dynamics model; the feedback control module generates feedback control torque according to the deviation between the real-time pose data acquired by the sensor module and the expected trajectory; the execution module drives robot joints to move according to the synthesized moment of the dynamic compensation moment and the feedback control moment; the sensor module collects robot joint angle, angular velocity and torque data in real time; the data storage module stores robot dynamic model parameters, control algorithm parameters and historical motion data. High-precision and smooth robot motion control can be achieved, the dynamic response and stability of the system are improved, and energy consumption and motion errors are reduced.

The invention provides a block chain-based clinical test data integrity system, and relates to the technical field of data processing, and the system comprises the following components: a multi-source heterogeneous data acquisition and preprocessing module, a multi-level encryption and under-chain data storage module, an alliance block chain network, and an alliance block chain network maintenance and verification shared distributed account book. The distributed account book only stores all digitally signed clinical test data fingerprints, metadata and operation logs, does not store original data, and is linked with the intelligent contract automatic management module, so that the clinical test data cannot be tampered in the whole life cycle from the source to archiving. Hash anchoring, digital signature and decentralized verification mechanisms of the block chain technically eradicate the possibility of unauthorized tampering of data, and ensure scientificity and reliability of clinical test results.

The invention relates to the technical field of big data processing, and provides a mass data storage optimization method which comprises the following steps: analyzing target data according to access frequency and / or access time of the data, and dividing the target data into hot data, warm data and cold data; aiming at the hot data, the temperature data and the cold data, performing compression processing by adopting corresponding compression algorithms; before the target data is stored, performing duplicate removal on the target data according to the fingerprint of the target data; monitoring the use condition of the memory, the access frequency of the target data and the performance of the compression algorithm in real time, and deploying system resources according to a monitoring result; and setting a multi-level cache, and caching different types of target data to the multi-level cache according to the divided types of the target data. According to the technical scheme, mass data can be efficiently stored and rapidly accessed, and the storage space utilization rate and the overall system performance are remarkably improved.

The invention relates to the technical field of data storage, can be applied to business scenes such as medical health and financial science and technology, and discloses a multi-Pool storage resource adaptive writing method which comprises the following steps: monitoring available storage space, read-write input / output rate, average response time and data access frequency of each Pool; dynamically adjusting the current write-in weight of the Pool according to the monitoring result; converting the current write-in weights of all Pool into probability values, and constructing a discrete probability distribution table; selecting a target Pool through a pseudo-random number generator according to the probability distribution table; and writing the to-be-stored data into the target Pool. According to the method, the running state of each Pool is continuously monitored, and the writing weight is adjusted in real time, so that the problem of resource allocation rigidity under a fixed weight strategy is avoided; in combination with a discrete probability distribution table constructed based on normalized weight and a pseudo-random selection mechanism, selection of the target Pool has certain load balancing elasticity, and data can be more effectively guided to be written into the Pool with light load and excellent performance.

The invention discloses a manufacturing enterprise data center system and method based on a large model, and the system comprises a data collection layer which is used for constructing a multi-source heterogeneous data collection system; the data storage layer is used for constructing a data warehouse system comprising multiple layers and realizing data lake storage; the data processing layer and the service layer are used for deploying a model and integrating an anomaly detection module, a multi-modal fusion engine and a prediction model; the service layer is used for an interface group, integrating a Drive / ClickHouse query engine and a Redis cache, and supporting multi-condition combined query and millisecond response; deploying a visual service module and an RBAC / ABAC authority control system to realize data grading sharing; and the application layer is used for developing a production optimization application, a supply chain management application and a CRM application. Through a uniform data acquisition interface and an efficient data acquisition tool, mass data inside and outside an enterprise can be integrated, data islands are broken, centralized management and sharing of the data are realized, and availability and value of the data are improved.

Apparatuses, systems, and methods for bounded fault compliant metadata storage. Memory devices include a first data terminal and a second data terminal. As part of an access operation a first set of data and a first set of metadata may be sent / received across the first terminal and a second set of data and a second set of metadata may be sent / received across the second terminal. The first set of metadata may be stored in a first location and the second set of metadata may be stored in a second location in the memory array, such as a first and second column plane. The two locations may be remote from each other.

A computer system may have one or computers and one or more data storage devices storing instructions, which when executed by the one or more computers implements a characterization manager, comprising: a test data database storing a plurality of test data sets; the characterization manager configured for selecting one or more test data sets from the test data database and apply the selected test data sets to a Generative AI system to derive from the Generative AI system an output; an output analyzer for processing the output to generate characterization data describing one or more facets of the output.

The invention relates to a photovoltaic power station real-time monitoring management system and method based on a cloud platform, and belongs to the field of photovoltaic power station intelligent monitoring. The system adopts a three-layer architecture of an application layer, a platform layer and an edge layer deployed at a cloud end, the edge layer is composed of distributed edge nodes, data are acquired through a data acquisition interface, and local preprocessing is executed; the platform layer configures a data storage and machine learning model, and performs generating capacity prediction and equipment health degree evaluation; and the application layer provides a visual monitoring interface and an operation and maintenance management terminal, and supports multi-dimensional data display and alarm information pushing. And the edge layer and the platform layer adopt a calculation task dynamic allocation mechanism, so that the cloud load is reduced while the real-time performance is ensured. The full-life-cycle monitoring of the photovoltaic power station is realized through a pure software architecture, the system has the advantages of elastic expansion, cross-platform compatibility and remote maintenance, and the deployment efficiency and the operation and maintenance response speed of the monitoring system are remarkably improved.