1023 results about "Data storage system" patented technology

The invention relates to the technical field of natural language processing and artificial intelligence, and discloses a silk path knowledge base intelligent question answering system based on a large language model, comprising a silk path data acquisition module used for acquiring multi-source silk path knowledge data; the silk path knowledge base construction module is used for processing the multi-source silk path knowledge data to construct a structured knowledge base and an unstructured knowledge base; the data storage system module is used for storing a structured knowledge base and an unstructured knowledge base and providing a structured retrieval interface and an unstructured retrieval interface; and the man-machine interaction module is used for identifying a user intention according to the user question and outputting an answer corresponding to the user question by adopting a corresponding answer generation mode based on the user intention. According to the method, efficient and accurate silk path knowledge questions and answers are realized by constructing the multi-modal knowledge base and optimizing a retrieval algorithm and a prompt engineering technology.

A method is provided for caching a query submitted to a graph data storage system, including receiving a query from a query caller directed to the graph data storage system, and examining the query, via a cache suitability determination engine, to determine if the query is suitable for caching under a first graph query caching policy based on the query caller being a trusted caller and / or the query scenario being a trusted query scenario. If suitable, generating a query execution plan for the query via a graph query plan generating engine and examining the query plan to determine if the query plan is suitable for caching under the first policy. If not suitable, caching the query under a second or a third graph caching policy based on the geographic location of a memory system on which the query plan is stored, and providing the query execution plan.

The invention relates to a data access prediction scheduling method and system based on a dynamic threshold value, and belongs to the technical field of data processing. The method comprises the following steps of: dynamically adjusting an abnormal detection threshold in an access behavior by utilizing reinforcement learning and an error feedback mechanism, realizing preposition identification of potential high-frequency access data by combining abnormal point classification processing and access trend prediction, and generating a scheduling request to drive cache preheating or interface priority scheduling of the data. According to the method, access behavior modeling, anomaly recognition, trend prediction and strategy feedback mechanisms are combined, interface access optimization requirements in a mass data storage system are met, and the key problems that access behaviors are unpredictable, scheduling response lags and the like are solved.

The invention discloses a bar code and RFID dual-mode fused tamper-proof data storage system, and relates to the field of data security storage, which comprises the following steps: uploading complete data to be stored into a scanning monitoring platform, splitting the data into static data and dynamic data, writing the static data into a bar code module, and writing the dynamic data into the bar code module; randomly distributed initial fluorescent particles are generated on the surface of the bar code data label through a scanning monitoring platform, a unique initial optical fingerprint is generated, if a bar code module is tried to be written or modified, the distribution position of the initial fluorescent particles is modified, an abnormal optical fingerprint is obtained, and an alarm is given; and writing the dynamic data into the RFID module, generating a dynamic key in each communication, and performing cross validation on the hash value based on the static data and the dynamic data. The method has the advantages that a three-dimensional protection system is constructed through a dual tamper-proof mechanism of a physical layer of fluorescent particle displacement and a password layer of a dynamic key in combination with cross validation of a hash tree, and the tamper-proof capability is improved.

A computer-based system and corresponding computer-implemented method process input data queries in a manner enabling advanced functionality for data analytics. The method transforms a query plan tree into a query strategy tree. The query plan tree is constructed from an input data query associated with a computation workload. The method compiles the query strategy tree into dataflow graph(s) (DFG(s)). The method transmits the DFG(s) for execution via a virtual platform. The method monitors execution of the DFG(s). The method outputs, based on a result of the execution monitored, a response to the input data query. The result is received from the virtual platform and represents at least one computational result of processing the computation workload by the virtual platform. The system and method enable rapid and efficient retrieval and analysis of data stored in data lakes and other data storage systems, in response to user queries.

A dynamic marketplace system leveraging store and warehouse mobility features a neuroevolution (NE) engine, an electronic device, and a request handler facilitating communication between the electronic device and the NE engine. The NE engine interfaces with a data storage system and an event handler receiving real-time event data from a public cloud services processor. An intentions handler interprets user intention data to predict user behavior. The NE engine, integrated with a processor, generates a predictive evolutionary model for the marketplace based on request, event, and intention data. An AI agent processor within the NE engine creates a recommendation model for mobile retail vendors, devises route plans, and deploys vendors to strategic locations.

Systems and methods for source chain management with identity-based tokens and enhanced security are disclosed. A processing system receives input that includes a decentralized identifier (DID) uniquely associated with an entity. The processing system invokes a smart contract deployed on a distributed ledger to mint a non-transferable identity-based token permanently linked to the DID. Metadata generated for the token includes cryptographic references to a hierarchical source chain structure stored in a decentralized data storage system, along with cryptographic verification data. Events associated with the DID are memorialized by appending granular event details, including timestamps, data changes, and origin identifiers, into the hierarchical source chain. Authorized access to event details and tokens is provided via an authenticated interface that securely retrieves and reconstructs encrypted data shards distributed across decentralized nodes, enhancing data security, immutability, and verifiability in decentralized environments.

The invention relates to the field of heterogeneous data storage, in particular to a heterogeneous data storage method and system for a civil aviation data platform, and the method comprises the steps: collecting metadata of each subsystem in the heterogeneous data storage system in real time, inputting the metadata into a pre-constructed metadata unified model, and carrying out standardization processing to generate a global metadata view; establishing a data traceability tracking mechanism according to the global metadata view, and mapping dispersed metadata items and data entity operation records according to the data traceability tracking mechanism to generate a traceable metadata change track; and a metadata quality label is embedded in the metadata unified model, a metadata knowledge graph is constructed according to the metadata change track and the metadata quality label, and a resource allocation strategy of the heterogeneous data storage system is dynamically optimized according to an analysis result of the metadata knowledge graph. The problem of low storage management efficiency caused by difficulty in integrating and tracing metadata in a heterogeneous data storage system in civil aviation data is solved.

The invention discloses an electric meter data storage method of a single-phase cost-control electric energy meter, and relates to the technical field of intelligent electric meter data processing, and the storage method comprises the following specific steps: S100, data acquisition and characteristic pro-analysis: acquiring voltage, current and power data of the single-phase cost-control electric energy meter through a voltage sensor and a current sensor, by integrating the voltage sensor and the current sensor, real-time and accurate acquisition of key power utilization data of voltage, current and power of the single-phase charge-control electric energy meter is realized, and by introducing a dynamic time window setting mechanism, the length of a time window is automatically adjusted according to the fluctuation amplitude of the data, so that the accuracy of power utilization is improved. It is ensured that a window is shortened to capture transient changes when data fluctuates violently, the window is prolonged to reduce redundant collection when the data is stable, the flexibility and accuracy of data collection are improved through intelligent adjustment, subsequent data processing can be conducted based on richer and more detailed feature information, and the data collection efficiency is improved. And the intelligent level of the whole data storage system is obviously improved.

Data storage systems are disclosed. In some implementations, a data storage device includes a controller coupled to a plurality of memory devices to control memory operations of the plurality of memory devices, and including M communication lanes, and an interface device configured to include N communication lanes to be connected to a host device that includes N communication lanes and to the M communication lanes of the controller, wherein the interface device includes a connector configured to connect the N communication lanes of the host device to the N communication lanes of the interface device, and a selector configured to, in response to a host port type identification signal received from the host device, selectively activate M communication lanes of the interface device, out of the N communication lanes of the interface device, to transmit or receive the data to or from the M communication lanes of the controller.

Disclosed systems and methods convert user-supplied textual content into animated videos. Textual input is received and processed to identify narrative elements such as characters, settings, and events. These elements are then transformed into visual scene components. Still images generated based on these components are subsequently animated in line with the narrative context. The system can automatically implement storytelling techniques adapted to incorporate neuroscience principles. The system also synthesizes speech for dialogues or narrations using voice synthesis technology that considers emotional markers, tone, and pace. Generated media and metadata are stored in a data storage system that maintains data integrity and enables efficient retrieval. Users can interact with an export interface to choose video resolution, format, and sharing options. A feedback system employing machine learning algorithms collects and analyzes user feedback for real-time adjustments to the generated animated video.

The invention discloses a database data storage system based on multi-level cache, and relates to the technical field of database data storage systems, the database data storage system comprises a basic storage layer, a first-level cache layer, a second-level cache layer, a cache cooperative control module and a data read-write scheduling module, the basic storage layer is used for persistently storing total data of a database; the first-level cache layer is divided into a plurality of hot data partitions; and the second-level cache layer is provided with quasi hot data partitions in one-to-one correspondence with the hot data partitions. According to the database data storage system based on the multi-level cache, the system constructs a'high frequency-secondary high frequency-full quantity 'layered architecture through the first-level cache, the second-level cache and the basic storage layer, dynamic flow of data is achieved in cooperation with a data aging counter and a heat recovery detector, heat reduction data is migrated from the first-level cache to the second-level cache, and the heat reduction data is transferred to the second-level cache. And the heat recovery data returns to the first-level cache from the second-level cache to ensure that the high-frequency data is always kept in a cache layer.

The invention discloses an intelligent RAG enhanced large model auxiliary writing system, and relates to the technical field of artificial intelligence auxiliary writing. In order to overcome the defects of an existing writing auxiliary tool, the adopted scheme comprises an interface and user layer for receiving a writing demand and redisplaying a generation result; the service layer is used for deploying three types of intelligent agents including writing, interactive writing enhancement and checking; the platform layer is used for carrying out industry adaptation analysis, mixed retrieval and RAG enhancement on writing demands and then sending the writing demands to the large model layer; three intelligent agents of writing, interactive writing enhancement and checking are called in sequence based on the output of the large model layer, and then a text is redisplayed to the interface and user layer after fact accuracy verification is completed through a tense knowledge graph; the data layer is used for providing a multi-type data storage system; the large model layer outputs a paragraph-level text and a reference index based on a writing large model, the paragraph-level text is returned to the platform layer, and reference index information is fed back to the interpretability and security layer for tracing; and the infrastructure layer is used for providing bottom layer resource support for each layer.

A computer-based system and corresponding computer-implemented method process computation workloads to enable advanced functionality for data analytics. An execution resource is selected from a set of execution resources of a virtual machine (VM). The resource is for executing a VM instruction. The VM instruction is transformed into machine code for the resource selected. The code is executed via the resource selected. The executing furthers execution by the VM of a dataflow graph (DFG) including a compute node. The compute node has a set of VM instructions including the VM instruction. The DFG corresponds to a portion of a computation workload associated with a user data query. An output of the execution of the DFG represents a result of processing the workload and contributes to a response to the query. The system and method enable rapid and efficient retrieval and analysis of data in data storage systems, based on computation workloads.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention relates to the technical field of data processing, in particular to a distributed data storage system and method based on artificial intelligence, and the method comprises the steps: obtaining multi-source data information of an enterprise, and numbering the multi-source data information of the enterprise; segmenting the multi-source data, creating abstracts for data types, and calculating weight values of the data types; cleaning different types of data, integrating the cleaned data, and distributing a transmission sequence of the data; segmenting to-be-transmitted data into a plurality of data blocks, and after a response instruction of the distributed data node is received, sending the data blocks one by one; the method comprises the following steps: monitoring the operation state of a distributed data node which performs data transmission, constructing an operation state set of the distributed data node, and identifying an abnormal condition in a data transmission process by comparing historical data with a preset threshold value. The data transmission completeness can be ensured through data breakpoint transmission, and the working efficiency of data transmission can be improved.

A method performed by one or more processors comprises displaying code, receiving user selection of a portion of code, determining one or more settable data items, generating a template, displaying the template, receiving a user input value for the settable data items by the template, and executing the code with each of the settable data items set to the received user input value. A data processing pipeline is configured to pass a data item to a first transformer to provide first transformed data, store the first transformed data in a temporary memory, write the first transformed data to the data storage system, and pass the transformed data from the temporary memory to a second transformer.

An example operation may include one or more of detecting a first amount of energy that is received by an energy data storage system during a predetermined period of time, detecting at least one source of the first amount of energy, detecting a second amount of energy that is consumed from the energy data storage system during the predetermined period of time, generating a digital token that includes a value based on the first amount of energy, the second amount of energy, and the at least one source of the first amount of energy, and storing the digital token in a digital wallet.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention relates to the field of log burying point downloading, and discloses a front-end log classified downloading method and device and a storage medium. The method comprises the steps that a log collection system collects a DOM event triggered by a user behavior, and behavior JSON data is generated; the data processing coordinator performs data filtering processing on the behavior JSON data according to a preset filtering rule to generate filtered JSON data; based on a preset classification algorithm, performing label classification processing on the filtered JSON data to obtain classified JSON constraint data; the storage system performs hierarchical encryption of metadata and content on the classified JSON constraint data to obtain encrypted data; and the transmission system receives a log downloading request, queries corresponding log data from the persistent queue based on the log downloading request, and obtains requested log data. In the embodiment of the invention, log collection, data processing and storage are integrated at the front end, so that the conditions of network delay and data loss are reduced, and the classification efficiency of the log data is improved by utilizing the classification labels for marking.

A data storage system is operable to generate operation requirement data based on power usage policy data. An energy utilization-based operation limitation enforcement function is performed upon energy utilization-based operation restriction enforcement input data to generate energy utilization-based operation restriction enforcement data for an operation based on applying the operation requirement data. The operation is executed when the energy utilization-based operation restriction enforcement data indicates execution of the operation is allowed, and execution of the operation is foregone when the energy utilization-based operation restriction enforcement data indicates execution of the operation is disallowed.

In one embodiment, a system comprises a host processor and a storage system. The storage system comprises one or more storage devices, and each storage device comprises a non-volatile memory and a compute offload controller. The non-volatile memory stores data, and the compute offload controller performs compute tasks on the data based on compute offload commands from the host processor.

The invention provides a data storage security monitoring method and system, and belongs to the field of data storage security, and the method comprises the steps: carrying out the multi-dimensional statistical feature extraction of historical operation feature data of a storage system, and constructing a historical security feature vector set; real-time operation feature data of the system is obtained, multi-dimensional semantic feature extraction is carried out, and a real-time security feature vector set is generated; by innovatively adopting a metric learning algorithm, the semantic distance between real-time and historical feature vector sets is calculated, and the abnormal deviation degree of the system is accurately determined. Based on the abnormal deviation degree and a predefined risk classification rule, the method dynamically evaluates the risk level of the storage system. And when the risk level reaches an early warning threshold value, the system automatically generates a safety risk early warning and executes a corresponding grading risk relief operation according to the risk level. According to the method, the security state of the storage system can be monitored in real time, potential risks can be actively identified, and the security protection capability of the data storage system is improved.

A data storage system for managing an operation of a solid-state device (SSD) includes the SSD, an adaptor coupled to the SSD via a first bus protocol and including a non-volatile memory storing a power configuration table. The protocol converter is configured to: couple to the adaptor using the first bus protocol, convert first signals of the first bus protocol to second signals for a second bus protocol, and couple to a host using the second bus protocol. The power configuration table includes power profiles for the SSD each corresponding to an interface type for the second bus protocol. The protocol converter is further configured to: determine an interface type of the second bus protocol, read the power configuration table from the non-volatile memory to determine a power profile corresponding to the determined interface type, and cause the SSD to operate using the determined power profile.

The invention discloses a data storage system and method for artificial intelligence deep learning, and relates to the technical field of data storage, and the system comprises a data obtaining module which is used for recognizing a data source and obtaining data from the data source; the data preprocessing module is used for automatically detecting and cleaning abnormal values in the data; the data storage design module is used for automatically storing the data in different types of storage media based on the access frequency and importance of the data and establishing a dynamic data partition model; the real-time data processing module is used for storing and managing real-time data streams; and the data backup module is used for backing up the data. According to the method, intelligent storage layering is provided, the data can be automatically stored in different types of storage media according to the access frequency and importance of the data, the storage cost is optimized, the access speed is increased, and a data partitioning strategy can be adjusted in real time to cope with the change of the data volume by establishing a dynamic adjustment model and adopting a dynamic data partitioning technology.

The invention discloses a data consistency processing method, a distributed storage system and electronic equipment, and belongs to the field of distributed data storage systems, and the data consistency processing method comprises the following steps: when any storage node in the distributed storage system receives to-be-updated data sent by a client, obtaining a corresponding data updating path by taking the storage node as a first storage node; according to the data updating path, each storage node carries out data persistence on itself after receiving the data to be updated, if persistence succeeds, the data to be updated is transmitted to the next storage node, if persistence fails, data rollback is carried out, and feedback information of path updating failure is sent to the upstream of the data updating path; and after receiving the information, each upstream storage node carries out data rollback on the current updated data. According to the scheme, even if the update message processing flow fails, the consistency of the data between the survival copies / fragments can be ensured in real time.

The invention discloses a bridge monitoring data storage system based on cloud computing, particularly relates to the technical field of bridge monitoring, and comprises an optical fiber sensing network acquisition unit, an edge computing preprocessing unit, a multi-modal data fusion storage module, a dynamic hierarchical compression engine, a fault-data association analysis unit and a distributed cloud storage cluster. According to the invention, a full-link optimization system is constructed, a front end accurately captures key responses, a middle end filters abnormal data to reduce invalid transmission, and a rear end dynamically compresses based on health rating and combines a cloud storage mechanism, so that the storage efficiency is improved by more than 40%, and the storage cost and bandwidth pressure are solved; key data are compared and marked through a knowledge graph, and a triple protection system is constructed in combination with a fault-tolerant strategy; a fusion index is formed by space-time correlation, multi-dimensional analysis is supported, authority control gives consideration to safety and operation and maintenance, storage cost is optimized by cold and hot stratification, and a high-availability and extensible data management platform is provided for bridge monitoring.

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.

Record-level transaction conflicts may be detected at storage nodes of a database back-end storage service. When a prepare statement is received, a storage node can determine whether another prepare statement acting on at least one of the same records has succeeded. If so, then the storage node can indicate to the database access application that there is a conflict, allowing the database access application to wait to try again or abort the transaction.

System-level garbage collection is performed in an SSD-based data storage system employing log-structured writing of data. Respective reclaim unit sizes of the storage devices are identified, wherein each reclaim unit is a unit of programming and erasure used by device-level garbage collection to generate erased reclaim units for allocation for physical data storage. The data is arranged across the storage devices in first and second data units, each first data unit being of a first size U equal to the product of R and a stripe width W, each second data unit including a respective set of first data units which occupy a maximum-size reclaim unit of a storage device among those storing the first data units. The system-level garbage collection is operated with second-data-unit granularity to regularly generate empty second data units for allocation in the log-structured writing of data, reducing write amplification and device wear.