3544 results about "Data storing" patented technology

The invention relates to a matrix storage operator optimization method and device, computer equipment and a readable storage medium. The method comprises the following steps: allocating memory resources for target data; determining a logic structure corresponding to the source data, establishing a first mapping relation between each logic index in the logic structure and a register address of the source data, and establishing a second mapping relation between each logic index in the logic structure and a memory address of the target data; and determining a corresponding relationship between a memory address of the target data and a register address of the source data based on the first mapping relationship and the second mapping relationship, and storing the source data stored in a register into a memory resource corresponding to the target data based on the corresponding relationship. By adopting the method, the generalization ability of the matrix storage operator can be improved.

An illustrative method for querying multiple datasets may include generating, based on models each associated with and defining attributes of a different datasets stored in a plurality of data stores, a semantic layer defining relationships between the models and that provides a centralized application programming interface (API) for exposing the datasets by way of a common query language, receiving, by way of the centralized API, a query request for information that depends on data included in multiple datasets included in the plurality of datasets, querying, based on the query request and the relationships between the models defined by the semantic layer, the multiple datasets, and presenting, based on the querying, a query result representative of the information that depends on the data included in the multiple datasets.

A Retrieval-Augmented Generation (“RAG”) framework may include an RAG vector data store with information about vector embeddings. An RAG pre-processing platform may access unstructured data and perform vector embedding to generate a vector embedding for unstructured data to be stored in the RAG vector data store. The RAG pre-processing platform may also access structured data from the knowledge base and create a summary and metadata about the structured data to be stored in the RAG vector data store. An RAG retriever platform may receive a user prompt from a user, perform vector embedding, and retrieve context-relevant information for unstructured and structured data by searching for similar embeddings in the RAG vector data store. An RAG reader platform may combine the context-relevant information with the user prompt and an RAG prompt to create a LLM prompt. A context-aware response is then output to the user.

A store employee assistance platform is provided that is enabled with generative artificial intelligence to deliver role-specific, context-aware responses to natural language questions submitted by users, including store employees. The platform includes a store employee assistance application with a chat interface through which a user may submit a question. The platform identifies the user's role, store location, and access level, and retrieves relevant enterprise content from a vector database and historical data store. A prompt engine constructs a contextualized prompt incorporating the user's identity and the retrieved information, and submits the prompt to one or more generative AI models. The resulting response is tailored to the user's responsibilities and delivered through the chat interface, providing real-time operational guidance specific to the user's role within the retail environment.

Generative media content (e.g., generative audio) can be dynamically generated based on various inputs, which can include blockchain data. A playback device accesses blockchain data stored via a distributed ledger and generates media content based at least in part on the blockchain data. The playback device can access a library of pre-existing media segments and arrange a selection of pre-existing media segments from the library for playback according to a generative media content model and based at least in part on the blockchain data. The generated media content can then be played back via the playback device.

A robotic fleet resource provisioning system includes a computer-readable storage system storing a fleet resources data store and resource provisioning rules. The fleet resources data store maintains a fleet resource inventory indicating fleet resources, each with features, configuration requirements, and a status. The resource provisioning rules are accessible to an intelligence layer to ensure that provisioned resources comply with the resource provisioning rules. The system receives a request for a robotic fleet to perform a job and determine a job definition data structure. The definition data structure defines a set of tasks that are to be performed in performance of the job. The system determines a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory. The system determines a respective provisioning configuration for each respective fleet resource. The system deploys the robotic fleet to perform the job.

A snapshot of metadata of a file system on a source storage system is stored in one or more of storage devices of a target storage system. A request to access data stored in the file system is received. A portion of the metadata including identification information of the data is identified. The data stored in the file system is retrieved by providing the source storage system with the identification information of the data.

The invention relates to the technical field of Internet of Things data management, in particular to an intelligent terminal identity authentication and data security management method, system and device based on a block chain and a medium, and the method comprises the steps: generating a unique identity label and a public and private key pair of an intelligent terminal device, a fog node and a user, packaging a digital certificate, and writing the digital certificate into a block chain network; the intelligent terminal equipment initiates a registration request, and the blockchain network distributes a hosting fog node to the intelligent terminal equipment after verification is passed; the intelligent terminal device generates data, constructs a data fingerprint, sends the data fingerprint to the block chain network for storage, encrypts the data, and sends the data to the hosting fog node for storage; a user initiates an authentication request, and a temporary access token is generated after verification is passed; and establishing a secure communication channel by using the temporary access token, obtaining the required encrypted data stored by the fog node, decrypting to obtain the required data, and verifying the integrity of the required data by using the data fingerprint. According to the invention, the overall security and reliability of data management of the Internet of Things system can be significantly improved.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects (and core objects) for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

Described are examples for creating a graph model for data obtained from multiple data sources and stored in a common data format. A selection of columns of data stored in the common data format that are to be associated with one another in the graph model can be obtained. Based on the selection, a graph model can be created including metadata that defines a list of vertices corresponding to at least a portion of the columns and a list of edges between two or more of the vertices identifying a relationship between corresponding columns. A graph query or graph algorithm can be performed over the graph model of the data as stored in the common data format without requiring copying of the data to a separate graph database.

The invention provides a power grid energy efficiency optimization and load prediction management method and device, relates to the field of energy and power, and solves the technical problem of data storage and processing delay caused by poor database expansibility and low algorithm efficiency. The method comprises the following steps: integrating various sensor data and intelligent sensing terminal data to form multi-dimensional data; and sending the multi-dimensional data to an edge computing node for preprocessing, and storing the multi-dimensional data in a distributed storage cluster. Load data and multi-dimensional influence factors in the multi-dimensional data stored in the historical distributed storage cluster are acquired, and a prediction model is constructed. And analyzing the multi-dimensional data stored in the distributed storage cluster through an artificial intelligence analysis engine, capturing nonlinear and non-stationary change characteristics of the load, and obtaining real-time prediction data. And acquiring real-time operation data of each link of the power grid from the distributed storage cluster, and optimizing a scheduling strategy in combination with a dynamic loss characteristic model of the equipment. The method and the device are used in the power dispatching process.

The Asynchronous Crypto Asset Transfer and Social Aggregating, Fractionally Efficient Transfer Guidance, Conditional Triggered Transaction, Datastructures, Apparatuses, Methods and Systems (“SOCOACT”) transforms transfer of assets (TOA) initiation request inputs via SOCOACT components into TOA confirmation outputs. A crypto asset transfer notification is sent to a first entity from a second entity. Account verification data generated by the second entity is determined. A set of account data stored by the first entity is retrieved and used to generate a hash. It is verified that the determined account verification data matches the generated hash. A requested asset is determined. An API call to generate a crypto token asset for the requested asset in a socially aggregated blockchain datastructure is made. An address of an aggregated crypto transaction trigger entry is determined. Oracle data that satisfies the crypto smart rule is provided.

A power system includes a central unit, a zone unit capable of communicating with the central unit, and a downstream electric wire configured to electrically connect a predetermined downstream unit to the zone unit. A central processor of the central unit executes processing including transmitting electric wire characteristic data stored in a central storage device to the zone unit at a predetermined timing. A zone processor of the zone unit executes processing including writing the electric wire characteristic data transmitted from the central unit into a zone storage device in response to reception of the electric wire characteristic data, and determining whether to turn a semiconductor switch coupled to the downstream electric wire to an OFF state, based on the electric wire characteristic data written into the zone storage device and the current detected by a current sensor.

The invention relates to a distributed system multi-copy distribution method based on RDMA (Remote Direct Memory Access), belongs to the technical field of distributed systems, and solves the problem of relatively high write-in time delay of distributed system multi-copy distribution in the prior art. The method comprises the following steps: a client writes data corresponding to a plurality of I / O requests into a predetermined memory slot position of a master node and predetermined memory slot positions of slave nodes; the client sends the global metadata to the main node; the master node sends the local metadata aiming at the master node to the master node, and sends the local metadata aiming at each slave node to each slave node; each slave node persists data stored in a predetermined memory slot of the slave node to a nonvolatile storage medium of the slave node; and the master node persists data stored in a predetermined memory slot of the master node to a non-volatile storage medium of the master node, and the data persisted to the non-volatile storage medium of the master node and the data persisted to the non-volatile storage medium of each slave node are copies of each other. And the write-in time delay is reduced.

A processing device performing driving environmental data related processing related to a local driving environmental data provided with data in an autonomous driving mode of a host vehicle is provided. The processing device includes a processor and a storage medium. The processor is configured to provide update to the local driving environmental data of a planned driving area where the host vehicle is planned to travel, based on probe information recognized through sensing by the host vehicle, among the local driving environmental data stored in the storage medium; and adjust a control level for each type of driving task, which is controlled according to a driving scene of the host vehicle in the autonomous driving mode, to an individual correlation level that correlates with an update count of the local driving environmental data of the planned driving area.

A computer-implemented method of training an encoder neural network of an autoencoder, comprising: receiving a data stream at the autoencoder, the autoencoder comprising an encoder neural network, a decoder neural network, a first memory layer, and a second memory layer; and incrementally training the encoder neural network. The incremental training comprises: performing an encoding process on the input data by the encoder neural network to obtain a latent representation of the input data; processing the encoded input data and encoded input data stored in the first memory layer from previous iterations of the training steps to create a memory representation; performing a decoding process on the latent representation; processing the decoded input data and the updated memory representation to refine the updated memory representation; and outputting the refined memory representation to the encoder neural network for use in a next training step.

Systems and methods are described herein for authentication and password security. The system may detect involuntary and voluntary brain signals of a user and measure the characteristics of those signals. The signals may be detected and analyzed using a wearable device comprising a plurality of sensors. The system may authenticate the identity of the user by triggering the user to imagine content or react to presented content. The content may comprise an image or movement, and brain signals of the user may indicate signals that are consistent for the user. The system may authenticate the user based on the brain signals based on data stored for the user or profile for the user. This determination may be performed by a machine learning model trained to classify users based on the brain signal data.

The invention provides a meteorological-based online question and answer service agent construction method and device, and relates to the field of agents, and the method comprises the steps: constructing a multi-dimensional knowledge base based on meteorological and industry knowledge data, and carrying out the real-time retrieval of a retrieval enhancement generation model RAG, and dynamically updating the knowledge data stored in the multi-dimensional knowledge base; constructing a standardized tool library based on weather and industry tools; obtaining user dialogue data, identifying a user demand real intention of a current problem in the user dialogue data, performing semantic screening on tools in the standardized tool library by utilizing an Embedding model according to the user demand real intention, and determining a target tool matched with the user demand real intention in the standardized tool library according to a semantic screening result; and based on the knowledge data, utilizing a TODOLIST planning mechanism to perform dynamic answer path planning for a current question in the user dialogue data through a to-be-called target tool to obtain an answer planning path.

The invention provides a data transmission method and device, equipment and a storage medium. The data transmission method is applied to the controller, at least two buffer areas are arranged in the controller, and the data transmission method comprises the steps that in response to starting of a first timer, CAN data sent by external equipment through a CAN controller local area network bus is stored in the buffer area in an idle state in the at least two buffer areas; in the process of storing the CAN data through the buffer area in the idle state, forwarding the CAN data stored in the buffer area in the occupied state in the at least two buffer areas through a serial peripheral interface (SPI) bus; wherein the data transmission rate of the CAN bus is smaller than that of the SPI bus.

The invention discloses a large language model training-oriented FP8 gradient adaptive optimization method and device, and relates to the technical field of computer machine learning. The method comprises the steps that in the learning iteration process of a large language model for data input by a user, a processor obtains an original high-precision gradient tensor of a current iteration step and historical information which is stored in a storage and contains historical gradients, and a scaling factor and a gradient value are quantized according to a target FP8 format to obtain quantized input data; after the quantized input data stored in the memory is filtered and denoised, the state of the optimizer is updated and stored in the memory, and the processor obtains the update quantity of the system parameters by utilizing a learning mechanism corresponding to the optimizer according to the basic learning rate and the system parameters. And optimizing the large language model according to the system parameter update quantity and a preset evaluation strategy to obtain a trained large language model. By adopting the method, the optimal balance of computer storage precision, efficiency and system resource consumption under large language model low-precision training is realized.

A database system may include a machine learning model which may be used to perform various data analytics for data stored in the database system. In response to a request to invoke the machine learning model to generate a prediction from data stored in the database system, the database system may evaluate the request to determine whether to perform the request at a primary query engine or assign the request to another computing resource distinct from the primary query engine. The database system may provision the other computing resource as needed to create a secondary query engine to use the machine learning model to generate the prediction as requested.

In some examples, a compute platform includes an electronic module having a memory storing platform data, and an attribute certificate containing a platform data measurement value based on portions of the platform data stored in respective memory regions of the memory in the electronic module. A processor performs validated access of the electronic module based on performing an initialization exchange with the electronic module, obtaining a platform data measurement value based on the platform data in the electronic module, and authenticating the platform data in the electronic module based on the obtained platform data measurement value and the platform data measurement value contained in the attribute certificate.

A blockchain-based data processing method, performed by a consensus node on a second blockchain, includes: obtaining a cross-chain message submitted by a forwarding service device, and obtaining a blockchain identification carried in the cross-chain message associated with cross-chain transaction data stored in a first blockchain; obtaining, in a second blockchain corresponding to the blockchain identification, block Merkle information corresponding to the first blockchain, and obtaining, in a block Merkle tree represented by the block Merkle information, a Merkle proof set corresponding to the cross-chain message, the Merkle proof set including node information of one or more nodes in the block Merkle tree,; verifying validity of the cross-chain message according to the Merkle proof set and the block Merkle information; and obtaining a transaction execution result corresponding to the cross-chain message in response to verification succeeds, and storing the transaction execution result into the second blockchain.

The invention discloses a data access control method and device, a storage medium and electronic equipment, and relates to the field of computers.The data access control method comprises the steps that a target reading request is received, and the target reading request is used for requesting to read data corresponding to target time from a storage space in a memory according to target address information; in response to the target reading request, positioning a target cache region corresponding to the target address information in a cache space in a memory; and extracting the target data of the target snapshot version corresponding to the target time from the reference snapshot version and the reference data which are stored in the target cache region and have the corresponding relationship, thereby solving the technical problems of relatively low data access efficiency and the like caused by the fact that historical snapshot data reading needs to depend on a rear-end disk. The technical effects of cache acceleration of historical snapshot data reading and improvement of data access efficiency are achieved.

The present disclosure provides a system and method for generating text summary and data visualizations for executive summaries of annual product quality reports based on an input query from a user. The method for generating text summary for annual product quality reports (APQR) of an enterprise, comprising: providing one or more indexes corresponding to one or more categories of data stored in a database, receiving a query from one or more users, wherein the query is a text and based on the at least one of the indexes of the one or more categories of data, extracting one or more relevant textual content from the query using natural language processing, querying the database for retrieving the one or more indexes of the relevant textual content, providing one or more few shot prompts for generating an output in a predetermined format based on the query received from the one or more users, inputting, the one or more few shot prompts and the retrieved one or more indexes to a large language model (LLM), generating a text summary based on the input to the LLM and rendering the generated text summary at a user device.

The invention relates to the technical field of computers, can be applied to the field of science and technology finance, and discloses a multi-system micro-front-end dynamic integration method and device, electronic equipment and a storage medium. The method comprises the following steps: deploying a base application, configuring a sandbox mechanism and a front-end plug-in of the base application, initializing a cache strategy of the base application, acquiring a target system as a sub-application, and opening a state refreshing interface of the sub-application; when a user accesses the sub-application after logging in the base application, calling the unified authentication service to generate a user token; the access token is obtained from the unified authentication service through the sub-application according to the user information and the key pair, the validity of the access token and the user token is verified through the unified authentication service, the user information is user data stored after successful login, and the key pair is obtained from the unified authentication service in advance; and if the access token is valid, loading the sub-application on the base application by using a sandbox mechanism and a front-end plug-in. According to the method, efficient integration and stable operation of the multi-system micro front end are realized.

A server receives and rebroadcasts live streaming video content from a video capture device, such as a mobile phone or unmanned surveillance vehicle. The server includes a media server configured to stream selected video content to a client device, a video analysis system configured to analyze the live video content and generate object detection data, a storage system configured to store the generated object detection data and an identifier of the associated live video content, and a search engine configured to receive a text-based search request, search the object detection data stored in the storage system for relevant search results, and generate a list of live and stored video content associated with the relevant search results.

The utility model discloses an anti-tamper detection circuit and communication equipment, the communication equipment comprises a power supply module, a processing module and an anti-tamper detection module, the anti-tamper detection module comprises an anti-tamper detection circuit, and the anti-tamper detection circuit comprises an anti-tamper switch, a first switch and a signal trigger module; the processing module is connected with the power supply module, the anti-disassembly detection module is connected with the power supply module and the processing module, and the anti-disassembly detection module is configured to send a power supply signal and a disassembly signal when detecting that the communication equipment is disassembled; wherein the power supply module is configured to power on the processing module in response to a power supply signal and control the processing module to start; and after being started, the processing module responds to the disassembly signal and destroys data stored in the communication equipment. Through the mode, the communication equipment can be automatically powered on and started up when illegal disassembly is detected in the shutdown state, self-destruction is immediately realized, a large amount of encrypted data is deleted, and the safety of the communication equipment is improved.

Provided is a method for writing data to a Solid State Disk (SSD), an apparatus and the SSD. According to the method, a Logical Block Address (LBA) of each of a plurality of pieces of written data stored into a target cache module is acquired to obtain a plurality of Logical Block Addresses (LBAs); the plurality of pieces of written data in the buffer is stored into a solid-state storage unit of the SSD; whether the plurality of LBAs are consecutive is determined to obtain a determination result. In a case where the determination result indicates that the plurality of LBAs are consecutive, a Physics Block Address (PBA) of each of the plurality of pieces of written data in the solid-state storage unit is acquired to obtain a plurality of PBAs and a target mapping table according to the plurality of LBAs and the plurality of PBAs is generated.

A memory device includes a memory cell array configured to store data, a page buffer circuit configured to store data in the memory cell array or read data stored in the memory cell array, and a scan register circuit configured to receive a pass / fail result of data from the page buffer circuit and store the pass / fail result in a plurality of scan registers. The scan register circuit obtains information about the scan register where the fail result is stored through a scan operation, and uses the information about the scan register where the fail result is stored to determine the number of fail bits and / or a position index indicating the position of the fail bits according to the operation mode.