173 results about "Hybrid storage" patented technology

The invention discloses a network security situation awareness and analysis platform based on AI, and relates to the technical field of network security situation awareness and analysis, and the platform comprises a multi-source data collection module which integrates flow, logs, assets and threat intelligence data, and carries out encryption transmission and standardization; the data preprocessing module purifies and optimizes data, and guarantees data quality and sensitive information security; the AI situation awareness analysis module extracts features through a deep learning model, dynamically evaluates the situation and identifies threats; the threat early warning and decision-making module triggers graded early warning and generates a targeted emergency response scheme; the visual display and interaction module displays information in multiple dimensions and supports query and report generation; and the data storage and tracing module adopts a mixed storage architecture, so that the data security and traceability are ensured. The platform integrates multi-source data and realizes situation accurate perception and intelligent decision by means of an AI technology; the early warning is accurate, the visual interaction is convenient, and the intelligent and efficient level of network security protection is comprehensively improved.

The invention discloses an agricultural heterogeneous multi-core SoC (System on Chip) architecture and equipment integrating vision and environmental perception. According to the architecture, a heterogeneous multi-core design is adopted, a universal CPU and an AI acceleration and preprocessing module are integrated on an on-chip system, an efficient task scheduling mechanism is combined, real-time management and control of multi-source heterogeneous tasks such as image processing, radar resolving and path planning are achieved, and the system response speed and the task execution efficiency are improved. In the aspects of communication and storage, NoC and AXI buses are adopted to cooperatively construct a high-speed data path, DDR4 and Flash mixed storage is supported, and the transmission stability of large-bandwidth data is guaranteed. The power supply and clock management module realizes modularized start and stop and energy consumption optimization through a multi-voltage domain and dynamic power consumption control technology, and is suitable for outdoor long-time operation. The IO and peripheral interface module provides abundant interfaces, and system expansibility and deployment flexibility are improved. The overall framework integrates the advantages of high integration level, high adaptability and low power consumption, and a solid calculation basis is provided for agricultural intelligent equipment.

The invention relates to the technical field of industrial big data storage and management, and discloses an industrial data full life cycle management method and system.The method comprises the steps that original time sequence data of industrial equipment is obtained, Shannon entropy calculation is carried out, and a time sequence importance score sequence is obtained; performing threshold comparison according to the time sequence importance score sequence, and determining a key transient window and a stable normal window; performing lossless differential coding on the key transient window to obtain a high-fidelity data segment; performing feature extraction and sparse processing on the stationary normal window, and performing time domain splicing and index construction to obtain a primary hybrid storage structure; and continuously monitoring the query access path to generate a popularity distribution graph to obtain a hierarchical archiving data set. According to the method, value-aware hierarchical storage and dynamic full-life-cycle management of massive industrial data can be realized.

The invention provides a multi-modal ecological environment monitoring element plug-in integration system and method based on unified objectification modeling. Firstly, an object model and an inheritance system of monitoring equipment, data and an algorithm are constructed; on the basis, a unified logic storage and metadata cataloguing mechanism oriented to heterogeneous databases such as relational databases, non-relational databases and file object databases is designed and realized; the plug-and-play and full-life-cycle management of equipment, data and algorithm models is further realized through a dynamic registration module; common APIs (Application Program Interface) such as object-level addition, deletion, modification and query, space-time association query, concurrent task scheduling and aggregation and the like are externally provided by relying on a unified service bus. And the hybrid storage engine cooperatively manages structured and unstructured data and maintains a topological relation between objects. According to the scheme, object dynamic integration and expansion are completed on the premise that operation is not interrupted, the multi-source heterogeneous data management and cross-modal analysis efficiency is remarkably improved, and the method is suitable for various ecological environment monitoring scenes such as natural reserve, drainage basin pollution supervision and disaster emergency response.

The invention relates to the technical field of medical information and block chain crossing, in particular to a medical image co-processing system and method based on a block chain value network, and the system comprises a block chain network, a privacy calculation gateway, an intelligent contract module, a hybrid storage module and a communication module. The intelligent contract module is provided with a collaborative annotation sub-module, a verifiable federated learning sub-module and a dynamic value distribution sub-module; the method comprises the steps of data preprocessing and encryption uploading, collaborative labeling and quality verification, verifiable federated learning model training, dynamic value distribution, consensus evidence storage and supervision auditing. Medical data islands are broken through by means of the block chain network, data availability and invisibility are achieved in combination with the privacy computing gateway multi-encryption technology, the contradiction between data sharing and privacy security is solved, accurate contribution and return matching is achieved through the multi-dimensional contribution degree evaluation model and the intelligent contract, and the accuracy of data sharing is improved. The traceability and the compliance are ensured by full-process operation logs and data uplink evidence storage.

The invention discloses an RAG knowledge base construction method and device and a medium, and the method comprises the steps: carrying out the analysis of the format, structure and semantic features of input data through a multi-level priority judgment logic, and automatically recognizing the specific data type. And calling a corresponding adaptive segmentation strategy for processing according to the identified data type, and generating data slices with semantic coherence. And asynchronously calling the large language model to generate a keyword set for each data slice, and uniformly storing the data slices, the keyword sets and associated multi-modal resource links by adopting a mixed storage mode to construct an enhanced RAG knowledge base. In the retrieval stage, query keywords are generated for user query according to the same logic, and related results are accurately retrieved from the knowledge base through a keyword matching mechanism. According to the method, the semantic segmentation problem is effectively avoided through intelligent segmentation with adaptive types, multi-modal data access is supported, and unified management and efficient retrieval of heterogeneous resources are realized.

The invention belongs to the technical field of product production cycle management, and particularly relates to an electronic image-text file remote multi-bin collaborative storage method based on a PLM. Comprising the steps that a 1 + N hybrid storage architecture is constructed, one center bin is deployed in an enterprise headquarter data center and stores full document metadata and latest version files, and N area bins are deployed according to geographic areas and store cache copies and incremental update data of local high-frequency access documents; the remote multi-warehouse document increment synchronization is realized through an intelligent synchronization engine, and the intelligent synchronization engine adopts a content awareness increment synchronization algorithm and a dynamic synchronization strategy based on network quality; version management is realized through a version cooperative control module, and the version cooperative control module is used for establishing a global version number mapping mechanism and a conflict resolution decision tree; cross-warehouse process automation is realized through a cross-warehouse workflow engine, and the cross-warehouse workflow engine integrates a BPM process engine and a message queue; the method can adapt to a remote network environment, and the large file synchronization efficiency is improved.

The invention provides an automatic report generation method and system based on multi-source data integration and a medium, and relates to the technical field of data processing, cross-service-end redundancy check integration of a plurality of standardized data streams of a plurality of source service ends is received through a data integration cloud end, a uniform resource pool is output, and the report generation efficiency is improved. Dynamically extracting matched data in the uniform resource pool according to a real-time association label combination output by a user, and generating a temporary data set; and converting the temporary data set into a permission filtering report by taking the consulting permission of the user as a field filtering strategy. The technical problems that in the prior art, the mixed data storage precision is low, data redundancy is caused by lack of a conflict resolution mechanism, and then cross-system data calling response is delayed and the report error rate is high in subsequent practical application are solved. The technical effects of improving the data storage precision of multi-source heterogeneous data mixed storage, and improving the data calling speed and report output accuracy are achieved.

The invention provides a storage device, a storage method and a storage access method based on lake and warehouse integration. The storage device comprises a storage management unit, a unified metadata management unit and a federal query engine unit, the storage management unit is used for realizing hybrid storage and dynamic hierarchical storage; the unified metadata management unit is used for metadata atlas construction, concurrency control and GDPR compliance support; the federal query engine is used for intelligent routing decision, SQL dialect unification and intelligent push-down optimization; based on a lake and warehouse integrated storage device and an access technology thereof, unified management of structured and unstructured data is supported, the problem of data islands is avoided, and cross-engine data visibility is realized through unified metadata management; according to the lake and warehouse integrated storage device and the access method, technologies such as a dynamic hierarchical storage strategy, intelligent routing decision, intelligent push-down optimization and the like are fused, and the storage and access efficiency and the robustness of the system are greatly improved.

The invention relates to a data recovery method and device, computer equipment, a storage medium and a program product. The method comprises the steps that a current repairing task of a hybrid tree is determined, the hybrid tree is a hybrid storage structure of a log merging tree LSM tree and a B tree, the hybrid tree comprises multiple layers, the multiple layers comprise the first layer of the LSM tree and all layers of the B tree, ordered character string tables of the first layer of the LSM tree are merged and sunk into the B tree, and each node in the B tree stores at least one ordered character string table obtained through merging and sinking; under the condition that the current repair task indicates that the repaired damaged data storage object belongs to the B tree, obtaining a B tree index of the damaged data storage object from the current repair task; searching a damaged data storage object from the mixed tree according to the B tree index, wherein the damaged data storage object is an ordered character string table or a node; and under the condition that the damaged data storage object is found, repairing the damaged data storage object. By adopting the method, the data recovery efficiency can be improved.

A compute-in-memory array is provided that implements a filter for a layer in a neural network. The filter multiplies a plurality of activation bits by a plurality of filter weight bits for each channel in a plurality of channels through a charge accumulation from a plurality of capacitors. The accumulated charge is digitized to provide the output of the filter.

The embodiment of the invention provides a big data storage optimization method based on a zero-copy collaboration technology and related equipment, and belongs to the technical field of big data storage. The method comprises a zero-copy data transmission module used for directly writing a data stream into a front-end buffer area of an annular structure through a direct memory access technology, and adopting a zero-copy algorithm based on pointer offset to parallelly separate data of each channel in a multi-thread environment; the intelligent data reduction module is used for deleting duplicated data from the data and then compressing the duplicated data; and the hybrid storage management module is used for managing the hybrid partition storage architecture and dynamically adjusting the distribution of the data in the hybrid partition storage architecture according to the data access mode. The CPU copy frequency is reduced through the zero copy technology, the data transmission efficiency is remarkably improved by combining the data reduction and intelligent layering strategies, the occupied storage space is reduced, and meanwhile the data safety and compliance are guaranteed.

The invention provides a metadata processing acceleration method, electronic equipment and a storage medium, and relates to the technical field of distributed storage. According to the technical scheme, the acceleration card obtains the metadata request, the metadata is cached to the cache module of the hybrid storage architecture based on the dynamic cache management strategy, and the acceleration card directly queries the cache and controls the network card to directly construct the response packet to return when the cache is hit, so that the data processing efficiency is improved. According to the method and the device, the problems of high delay and CPU resource scrambling and the like caused by the fact that a metadata request depends on a CPU to analyze a network protocol and inquire software cache in the prior art can be solved, so that the technical effects of remarkably reducing metadata processing delay, improving metadata throughput, reducing CPU occupancy rate, optimizing hardware resource utilization rate and reducing cross-node synchronous bandwidth occupancy can be achieved.

The invention relates to the technical field of data analysis and fault positioning, in particular to an intelligent analysis and fault positioning system based on aircraft flight data, which comprises a data acquisition module, a self-adaptive data analysis module, a data storage module, an intelligent analysis module, a fault positioning module, a report generation module and an auxiliary module. Data acquisition, self-adaptive analysis, mixed storage, intelligent analysis, accurate positioning and customized report generation are carried out to solve the problems of poor adaptability, insufficient storage performance, insufficient analysis flexibility, priority positioning accuracy and low report practicability in the prior art.

The invention provides a mixed storage and unified query layer data retrieval system based on an AI cloud desktop, which belongs to the technical field of AI intelligent application and comprises a unified query access layer, an SQL optimizer, a vectorization execution engine, a mixed storage engine and a metadata management center. Column storage, inverted index and row storage are organically combined, services are provided for the outside through a unified SQL query layer, and intelligent routing and vectorization computing technologies are introduced. The method is characterized in that an optimal storage engine is automatically selected for execution according to a query mode, the problems that a single inverted index architecture is low in efficiency and high in resource consumption under the scenes of ad-hoc analysis, high-concurrency point search and large-scale aggregation are solved, and better comprehensive query performance, lower data storage cost and higher system expansibility than traditional Elasticsearch are achieved.

The invention discloses an intelligent patient data management method based on the general surgery department, and relates to the technical field of medical information processing, and the method comprises the steps: a multi-source patient data comprehensive collection step: collecting full-process multi-type data through four modes, and dynamically adjusting the collection frequency according to the data type; a data standardization preprocessing step: processing the data by adopting a cleaning algorithm; a structured storage and intelligent index construction step, wherein classified storage is carried out based on a mixed storage architecture; an intelligent data analysis step based on machine learning: constructing a multi-task model; a personalized medical intervention scheme generation step: generating a scheme in combination with an analysis result and a clinical guide; a data dynamic updating and real-time monitoring step: updating data in real time and monitoring key indexes; and a hierarchical authority management and data security guarantee step: distributing authorities according to roles. The method improves the integrity and efficiency of general surgery department patient data management, guarantees the data security and privacy, reduces the medical risk, and improves the rehabilitation effect of the patient.

The invention discloses an R-tree spatial index design method oriented to a DRAM-NVM hybrid storage architecture, relates to the technical field of computer data storage, and provides a write optimization spatial index HR-Tree under the DRAM-NVM hybrid architecture. The core design thought is that internal nodes are deployed on a DRAM, leaf nodes are placed on an NVM, and a targeted data read-write operation process is designed; in the aspect of an index structure and an operation mechanism, a minimum expansion strategy is adopted around a scene of less reading and more writing, a leaf node data structure and an in-situ updating algorithm on the NVM are designed in a targeted manner, and the write request processing efficiency can be improved on the premise of ensuring data consistency. And the multi-character data expansion and efficient fault recovery are provided, so that the actual requirements of the user are better met by allowing more flexible personalized configuration and shortening the service pause time. The method is suitable for the application fields of computer technology, database technology, data storage and the like.

The present application relates to the technical field of data classified storage, in particular to a data integration method and system for multi-classification sports events and a medium. First, the time sequence collection frequency of each professional data is subjected to cluster analysis to identify the multi-modal frequency characteristics thereof. Second, the collaborative index between the frequency cluster of different professional data is calculated to divide the high-correlation data cluster and reduce the mixed storage of low-correlation data and the null value rate of the relational database. Further, the write demand of each cluster is dynamically calculated in combination with the frequency matching strength and storage space occupied in the data cluster, so that the professional data is integrated and stored. In summary, the present application clusters the different types of professional data according to the collaboration of the frequency variation, reduces the null value problem in the storage process of the relational database, analyzes the storage space occupied by the professional data, and reduces the write pressure of the server.

Embodiments of the present application provide a log-merge tree key-value storage system and related methods and related devices, and relate to the technical field of storage. The system is based on a hybrid storage architecture, which includes a host and a persistent storage medium and a non-volatile storage medium connected to the host, wherein the persistent storage medium stores a plurality of ordered string table layers, each ordered string table layer includes a plurality of ordered string table groups, and each ordered string table group includes a plurality of ordered string tables; the log-merge tree key-value storage system adopts a grouping compression mechanism and includes two-level filters corresponding to the ordered string table groups, which include group filters corresponding to the ordered string table groups and table filters corresponding to the ordered string tables. The embodiments optimize the read amplification problem and the write amplification problem of the storage system, and improve the read performance and the write performance of the storage system.

The invention discloses a large model knowledge base operation system based on multi-role cooperation and dynamic optimization. The system comprises a user interaction module used for receiving knowledge requirements triggered by user feedback and identifying knowledge gaps through semantic analysis; the multi-role cooperation module is used for defining roles and permissions thereof, generating knowledge optimization tasks according to a current knowledge gap, and distributing the knowledge optimization tasks to the roles so as to supplement optimized knowledge contents through the roles; the knowledge processing module is used for processing and analyzing the optimized knowledge content; the knowledge storage module is used for carrying out structured and unstructured mixed storage on the knowledge content which is qualified in processing and analysis; and the large model interface module is used for synchronizing the optimized knowledge content to a large model training or reasoning link in real time. According to the method, efficient collaboration of editing and expert opinions can be realized through a large model knowledge base with multi-role collaboration, and the accuracy and timeliness of knowledge contents are ensured.

The present application relates to a big data hybrid storage system and method, and a readable storage medium. The big data hybrid storage system comprises: a first file system, which is used for storing active data; a second file system based on object storage, which is used for storing inactive data; a hybrid storage protocol endpoint, which is used for performing automatic data routing and reading / writing on the first file system and the second file system; and a data synchronization server, which is used for parsing an audit log of the first file system to obtain file usage statistical information, starting a timing task to scan the first file system and the second file system to obtain a corresponding first file list and second file list, and determining, on the basis of the file usage statistical information and the difference between the first file list and the second file list, whether to mirror and synchronize data in the first file system to the second file system. Thus, data storage costs are reduced, and object storage access pressure is alleviated.

The invention discloses a data processing system suitable for thermal power plant fuel whole process management, and belongs to the technical field of electric power industry automation. The system adopts a five-layer architecture and comprises a data acquisition layer used for acquiring real-time data of each link of a fuel whole process through a plurality of data sources; the data storage layer adopts a mixed storage architecture comprising a real-time library, a business library, a history library and a data lake for classified storage; the data processing layer is used for cleaning, integrating, calculating and contrastively analyzing the stored data and constructing a digital twinborn model; the application service layer provides monitoring, optimization, tracing and early warning services; and the display layer is used for data visual display and man-machine interaction. According to the method, the whole-process data chain and the dynamic optimization model are constructed, so that refined and intelligent management of the whole life cycle from fuel purchase to ash disposal is realized, and the fuel management efficiency, economy and safety of the thermal power plant are effectively improved.

The invention belongs to the technical field of supply chain finance, and particularly relates to a warehouse receipt intelligent management method and system based on a block chain, a hierarchical architecture is adopted, the warehouse receipt intelligent management system comprises a front-end interaction layer, a service gateway layer, a micro-service layer and a data storage layer, and the hierarchies work cooperatively through standardized interfaces; the micro-service layer integrates an authority authentication module, a warehouse receipt management module, a block chain evidence storage module and the like, is matched with dual verification and a distributed transaction mechanism, and is combined with an electronic signature containing an identity verification and signing willingness confirmation process and a security component covering JWT authentication and data desensitization functions to realize full-life-cycle operation such as warehouse receipt generation, pledge, circulation and the like; according to the system, warehouse receipt key information hash values are subjected to cochain evidence storage through an alliance chain PBFT consensus, and mixed storage is matched, so that the data non-tampering performance and the legal validity of electronic warehouse receipts are guaranteed, the response efficiency of the system is improved, the trust cost of multi-role cooperation of storage, finance, trade and the like is reduced, and the pain points that traditional warehouse receipts are easy to counterfeit and evidence storage is difficult to trace are solved.

The application relates to the technical field of data analysis and fault positioning, in particular to an intelligent analysis and fault positioning system based on aircraft flight data, which comprises a data acquisition module, an adaptive data analysis module, a data storage module, an intelligent analysis module, a fault positioning module, a report generation module and an auxiliary module, and is used for data acquisition, adaptive analysis, hybrid storage, intelligent analysis, accurate positioning and customized report generation, so as to solve the problems of poor adaptability, insufficient storage performance, insufficient analysis flexibility, low positioning accuracy and low report practicability in the prior art.

This invention belongs to the field of intelligent manufacturing technology and discloses a method and system for process data integration and quality traceability management in intelligent manufacturing production lines for badminton rackets. It unifies data standards and interaction rules by constructing a five-in-one semantic ontology model encompassing products, processes, etc.; it builds an adaptive multimodal acquisition system, integrating three types of data sources: standardized equipment, non-standard processes, and offline testing, and completing edge preprocessing; it achieves chain-based data storage through a consortium blockchain hybrid storage mode, ensuring data immutability and shareability, and achieving efficient storage of all data; it assigns a unique, lifelong identity to each racket, and combined with a full-process digital twin mirror, it visualizes the entire process data from raw materials to the final product and simulates the production process; the consortium blockchain storage ensures the authenticity and immutability of traceability data, and when defects occur in the finished product, it can accurately locate the core influencing processes, parameter deviations, and other root causes through deep correlation graphs.

Embodiments of the present disclosure are directed to a method for storing and processing data. The method includes identifying a database in a memory of a host device having one or more rows and one or more columns. A partition having a partition size is identified, and the one or more rows of the database is identified based on the partition size. The data stored in the one or more rows is converted into a column-based format, and the data is stored in a computational storage device in the column-based format. The computational storage device is configured to retrieve the data stored in the column-based format, in response to a query, and process the query based on the data.

This disclosure proposes a method for synthesizing multimodal data for decision-making attribution explanation in the mining industry, comprising the following steps: First, extracting causal logic from industry texts to construct a structured rule base; second, collecting on-site multimodal data and filtering out data pairs strongly correlated with rules through scenario-based weight modeling and cross-modal association mechanisms; third, using a multimodal generative adversarial network with rule constraint mechanisms and combined with rule mutation technology to synthesize multimodal data with complete logical chains and covering various complex working conditions; finally, ensuring data quality and achieving self-evolution of the knowledge base through a three-level verification and rule feedback mechanism, and storing qualified data in a hybrid storage database. This invention solves the problems of complex causal relationships and scarce high-quality training samples in the mining field, and can automatically and in batches generate logically rigorous and scenario-diverse multimodal data, improving the training efficiency and generalization ability of attribution decision models.

A semiconductor device includes a first interconnection line extending in a first direction; a second interconnection line extending in a second direction; and a memory cell disposed between the first interconnection line and the second interconnection line. The memory cell includes a first electrode; a first memory layer including a ferroelectric layer over the first electrode; a second electrode over the first memory layer; a second memory layer including a high-k dielectric layer over the second electrode; an oxygen reservoir layer over the second memory layer; and a third electrode over the oxygen reservoir layer.

The present application relates to a kind of coal mining operation multi-source time series data attention perception and dynamic memory method, belong to coal mine intelligent technology field.It includes: the three-dimensional feature dynamic extraction and scene information attention fusion of the multi-source time series data of coal mining face, generate scene multi-dimensional feature dataset;Multi-agent driven coal mining scene multi-dimensional dynamic information hybrid storage mode is used to store the multi-source time series data of coal mining face and scene multi-dimensional feature dataset, form data base;When obtaining coal mining scene task instruction, based on data base, in combination with equipment, process, environmental information, the related coal mining scene of it is mapped out, and the content indicated by the coal mining scene task instruction is completed by the cooperation of various types of Agent.The present application introduces Agent intelligent agent architecture and attention mechanism, in combination with dynamic memory management strategy, can provide core technical support for coal mining operation state real-time monitoring, fault intelligent diagnosis and production optimization decision.

The invention relates to a localized database migration and safe operation and maintenance method and system for a thermal power system, and belongs to the technical field of databases. The method comprises the steps that an existing database of the thermal power system is comprehensively evaluated, and a migration plan is made; selecting and building a domestic database platform supporting a hybrid storage engine, wherein the platform respectively processes high-frequency instructions, historical data and equipment metadata by utilizing a memory optimization table, a time sequence database and a relational database; data migration is executed in a planned power failure window or a mirror image test environment, and consistency verification, business function verification and performance pressure test are carried out; executing data governance, including measurement point naming standardization, abnormal data cleaning and data partitioning; after migration is completed, a whole-process security protection system is deployed, transmission is encrypted by adopting a national cryptographic algorithm, and strict access control and two-factor authentication based on roles are implemented. According to the method, the system performance, the data quality and the information security protection level are remarkably improved, and the operation and maintenance cost and complexity are reduced.