231 results about "Hybrid storage" patented technology

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention relates to the field of internet attack detection, in particular to a data security event real-time monitoring method and system, and the method comprises data collection, multi-modal fusion, threat detection, causal reasoning, dynamic response and feedback optimization. Compared with the traditional security analysis which depends on isolated data dimension or simple rule association, is difficult to capture a cross-data-source complex attack mode, and is faced with the limitations of low calculation efficiency, slow link traceability, storage access bottleneck and the like in mass data association analysis, the scheme integrates multi-source heterogeneous data into a dynamic association network through graph structure modeling, so that the security analysis efficiency is improved. Hidden association and behavior patterns among users, equipment and IPs are deeply mined by utilizing a GNN framework, the suspicious degree among entities can be accurately quantified, and hidden attack chains can be identified; and meanwhile, a hybrid storage architecture and a query optimization technology are adopted, so that a security analyst can backtrack a complex attack path in a second level while breaking through the bottleneck of large-scale graph data access performance, and the threat hunting efficiency and the high-level attack traceability are remarkably improved.

The invention discloses a human resource archive filing management system, and relates to the technical field of data management, the system comprises an acquisition module, a processing module and a management module, the acquisition module acquires original archive data of employees, transmits the data to the processing module, classifies the original archive data according to a preset first rule, and stores the classified data to the management module; the first archive data is obtained, controlled and managed, a calling permission and a distribution strategy are generated, the first archive data is recognized through a preset second rule, second archive data is generated, corresponding processing is conducted according to the processing priority, a processing log is reserved, third archive data are obtained and transmitted to the management module, and the management module is used for managing the first archive data. A distributed cloud storage cluster and a local storage node are constructed, an overall storage architecture and a retrieval path of third archive data are optimized, archive management is refined through three-dimensional classification, data integrity is guaranteed through field-level verification and automatic completion, scene-based dynamic authority control safety is achieved, compliance risks are intelligently cleared and avoided, and efficiency is improved through mixed storage. And active management of archive management is promoted.

The invention provides a digital twin hydrological station system which is characterized in that the system comprises a layered architecture system and a multi-dimensional safety protection mechanism, the layered architecture system is composed of a sensing layer, a transmission layer, a data layer, a model layer, an application layer and a display layer from bottom to top, the sensing layer comprises a heterogeneous hydrological monitoring equipment cluster, and the transmission layer comprises a heterogeneous hydrological monitoring equipment cluster. The equipment cluster is provided with a water level sensor array, a Doppler flowmeter, a spectrum water quality analyzer and an environment monitoring terminal, and total element acquisition of hydrological element data, equipment operation state data and environment data is realized; the transmission layer adopts a hybrid communication protocol stack, integrates an ultra-short wave communication module, an NB-IoT narrowband communication module and an optical fiber communication module, and constructs a multi-path redundant transmission channel, and a key data transmission channel is configured with a dynamic link selection algorithm; the data layer comprises a distributed storage architecture, adopts a mixed storage mode of a relational database cluster and an unstructured database, and is internally provided with a data cleaning engine and a spatio-temporal data fusion module.

The invention provides a project cost big data management and analysis system, and relates to the technical field of data management, and the system comprises a data collection and preprocessing module which is used for collecting original cost data from a heterogeneous data source, and carrying out the preprocessing of the original cost data, and obtaining the preprocessed cost data; the semantic feature extraction module is used for converting the preprocessed cost data into a multi-dimensional feature vector based on a multi-level feature extraction system; the similarity calculation module is used for calculating similarities among different cost data based on the multi-dimensional feature vectors to obtain a similarity matrix; and the data storage and management module is used for storing the cost data, the multi-dimensional feature vector and the similarity matrix by adopting a mixed storage architecture, and providing retrieval and recommendation functions of cost projects based on a multi-level feature space index structure. According to the method, the limitation that a traditional method only depends on keyword matching is solved, and the system can recognize the deep incidence relation between the items.

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 relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably 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 discloses an operation and maintenance data management system based on cloud computing, particularly relates to the field of data management, and comprises a distributed data acquisition module, a cloud native data processing module, an intelligent analysis module, a visualization and decision module and a cloud resource management center module. According to the method, heterogeneous data are collected in real time through the lightweight agent, and high efficiency is guaranteed by adopting multi-thread and self-adaptive sampling; data cleaning, standardization and mixed storage are realized by using a stream batch integrated architecture; the intelligent analysis module fuses an improved Robust Z-Score, an LSTM-Attention model and a complex event processing engine, and supports dynamic anomaly detection and fault tracing; the visual assembly builds an instrument panel in a dragging mode, alarm strategies are aggregated in a grading mode, and storm is restrained; the cloud resource management center deeply integrates Kubernetes, predicts demands based on reinforcement learning, automatically expands and shrinks capacity and optimizes cost, and deals with burst traffic in combination with a burst capacity pool.

The invention discloses a spatio-temporal data visualization index management and application method, and belongs to the field of spatio-temporal data statistical analysis processing. Analyzing a time-space visualization service scene, and determining a statistical calculation rule of the time-space index data; a statistical calculation rule of spatio-temporal index data is configured through a visual interface, structured configuration is generated and stored in a standardized configuration table, an ETL process is automatically generated after the system analyzes the configuration to realize data statistics and circulation, and automatic calculation and timing calculation are supported; the method comprises the following steps: designing a hybrid storage architecture of a standardized index data statistical result, and adopting a relational database and data warehouse dual-mode storage strategy: storing original data and statistical results with small data volume by adopting a relational database; for mass data, the mass data are synchronized to a data warehouse through an ETL process, distributed calculation analysis is executed on a warehouse layer based on index configuration, and an analysis result is persistently stored in a column storage format; transparent access to a heterogeneous storage bottom layer is achieved by constructing a unified access interface layer.

The invention discloses an intelligent medical big data information management system, and the system comprises a data collection layer which serves as a sensing neural network, employs an Internet of Things gateway to be compatible with a medical equipment protocol, and deploys an edge calculation node for physiological signal preprocessing; the intelligent preprocessing layer is used for cleaning and standardizing the original data transmitted by the data acquisition layer; the informatization service layer is used for realizing fine-grained authority control based on an RBAC (Role Based Access Control) model, realizing inspection process automation by integrating an LIS (Line Information System) interface through a Hyperdger Fabric block chain evidence storage key operation hash, and constructing a zero-footprint DICOM (Digital Imaging and Communications in Medicine) viewer; the intelligent analysis layer is used for carrying out data life cycle management by adopting a mixed storage architecture, carrying out feature engineering on data, carrying out label matching and classified storage by using efficient neighbor search and similarity / distance measurement, quantifying contribution of each feature or feature combination to a single prediction result through an SHAP value, generating an interpretation result, and outputting the interpretation result; and presenting an interpretation result to a clinician or related personnel through a query interface.

The invention discloses an intelligent elevator information management system based on computer vision. The intelligent elevator information management system comprises a data sensing module, a vision detection module, a strategy network construction module and an information management module. The invention relates to the technical field of elevator intelligent management, in particular to an intelligent elevator information management system based on computer vision, which realizes passenger tracking and accurate counting by adopting a time-space spectrum threshold value guided by optical flow to perform optimal cutting and fusing visual detection confidence and Kalman filtering posterior probability; double attention and multi-scale residual error updating are introduced, and short-time passenger flow high-precision prediction is achieved; based on incremental hierarchical clustering, Poisson load estimation and a strategy network, elevator scheduling is dynamically optimized, and waiting time and energy consumption are reduced; and a relational database and distributed file system mixed storage architecture is constructed through a real-time data display single information management aspect, so that the data consistency, the query access efficiency and the decision support capability are enhanced.

The invention discloses an electronic medical record data structuring standard method, and belongs to the technical field of medical information, the electronic medical record data structuring standard method comprises the following specific steps: 1, data acquisition and format unification: a system receives text data from outpatient records, hospitalized medical records and inspection reports, and sends the text data to a server; the method comprises the following steps of: uniformly converting text data into UTF-8 codes and removing redundant symbols according to time sequence data streams of CT (Computed Tomography), MRI (Magnetic Resonance Imaging) medical images and vital sign monitoring; through the BiLSTM-CRF model and the dynamic term mapping algorithm, the medical entity recognition accuracy is greatly improved, the term standardization rate is remarkably improved, texts, images and time series data are associated through a hierarchical model, cross-modal query is supported, an API interface is constructed based on the HL7 FHIR standard, seamless connection with HIS, PACS and other systems is achieved, the data exchange error rate is greatly reduced, and the data exchange efficiency is greatly improved. The hybrid storage architecture reduces the storage cost, and supports the lateral extension to a million-level medical record scale.

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 discloses a supply chain management system for research and development of electronic materials, and belongs to the technical field of supply chain management systems. Internet-of-things equipment data is acquired through sensors deployed in a raw material production link, a warehouse storage area and a transport vehicle, external interface data is from a supplier system, enterprise internal system data comprises PLM, ERP and MES system data, and the data processing layer performs real-time cleaning, conversion and stream batch fusion processing on the acquired data based on a stream calculation engine and performs data processing on the acquired data. The data storage layer adopts a hybrid storage architecture and comprises a Kafka cluster used for storing real-time data, an InfluxDB used for storing time sequence data, an HBase used for storing unstructured data and a MySQL cluster used for storing dimension data.

The invention relates to the technical field of digital signal processing, and particularly discloses a mixed precision low-resource digital control oscillator implementation method and system based on an FPGA (Field Programmable Gate Array), and the method comprises the steps: dividing a 32-bit phase accumulator into a high-bit 24-bit dominant frequency control section and a low-bit 8-bit dynamic compensation section, and carrying out the phase accumulation in parallel through double carry chains, the high-order section receives high 24 bits of a frequency tuning word FTW to control a dominant frequency, and the low-order section receives low 8 bits of the FTW and overlaps an overflow remainder to dynamically compensate for a phase error. According to the implementation method of the mixed precision low-resource digital control oscillator based on the FPGA disclosed by the embodiment of the invention, through collaborative innovation of mixed precision phase accumulation, hierarchical mixed storage, high-order interpolation reconstruction and dynamic phase compensation, the core problems of a traditional NCO in resource occupation, phase continuity, waveform precision and time sequence performance are systematically solved.

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.

The invention relates to a streaming data and mass batch data storage method, which belongs to the technical field of data storage, and comprises the following steps: classifying mine data into streaming data and batch data; according to the method, a mixed storage architecture is constructed, the mixed storage architecture comprises a data acquisition layer, a batch processing layer, a speed layer, a service layer and an application layer, so that streaming data and batch data are processed and stored by adopting different rules, and the method comprises the following steps of: acquiring the streaming data by adopting an acquisition adaptation gateway and a real-time message queue Kafka; collecting the batch data by adopting a relational database DM8 and a timing scheduling Airflow mode; the batch processing layer is used for dividing the data into cold and hot temperature data and performing classified storage on the data; the speed layer is used for processing and storing the real-time streaming data; the service layer is used for carrying out data integration on the temporary stream processing data and the batch processing result data and providing data query interface service; and the application layer is used for setting a business application module and accessing each database to realize data application.

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 invention relates to an analog-digital hybrid SRAM (Static Random Access Memory) memory audio noise reduction method and system, and the method comprises the following steps: carrying out the differential sampling quantization processing of an input audio signal, and obtaining a multi-bit-width digital audio code stream; inputting the multi-bit-width digital audio code stream into a preset SRAM (Static Random Access Memory) memory chip to carry out mixed mode write-in operation to obtain an analog-digital mixed memory matrix; performing frequency domain feature extraction on the analog-digital hybrid storage matrix to obtain frequency domain extraction features; performing noise identification and suppression on the audio signal based on the frequency domain extraction feature to obtain a time-varying noise suppression coefficient; and performing audio signal recovery processing on the time-varying noise suppression coefficient to obtain a denoised output audio signal, thereby solving the technical problem that power consumption and hardware cost are greatly increased due to the fact that a traditional digital signal processing method generally depends on a high-precision analog-to-digital converter and a high-performance processor.

The invention discloses a model scheduling method, a storage cluster, a device and a medium, and relates to the technical field of computers. The method comprises the following steps: performing hierarchical processing on a model mirror image file in advance to obtain a base layer mirror image file and a model layer mirror image file, storing the base layer mirror image file in a cache space corresponding to a cloud platform, and storing the model layer mirror image file in a network sharing space. The mixed storage of the cache space and the network sharing space of the mirror image file is realized, and the redundant transmission and storage overhead is also reduced. In response to the current scheduling request, after the scheduling of the base layer mirror image file is completed for the first time, the base layer mirror image file does not need to be mounted for the non-first scheduling, so that the multiplexing of the base layer mirror image file is realized. And only the incremental model layer mirror image file is mounted for model iteration, so that the transmission overhead is reduced, the iteration requirement of each model is met, the network bandwidth consumption is reduced, and the deployment efficiency is improved.

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 relates to the technical field of information security, and discloses an anti-quantum multi-key binding digital certificate implementation mode, which comprises a main body public key information field (SubjectPublic Key Info) used for storing a first anti-quantum algorithm public key, the type of the first anti-quantum algorithm public key is determined through an associated algorithm identifier, and the type of the first anti-quantum algorithm public key is stored in the main body public key information field. Comprising an anti-quantum signature public key or an anti-quantum encryption public key, and further comprises a certificate extension for storing a second anti-quantum algorithm public key. According to the anti-quantum multi-key binding digital certificate implementation mode, an anti-quantum signature public key (SubjectPublic Key Info) and an encryption public key (extension item) are separately stored in a single X.509 certificate, signature and encryption dual-function integration is achieved, the dual-certificate requirement is avoided, the extension item adopts an ASN.1 standard and a critical = FALSE mechanism, traditional RSA / ECC public key mixed storage is compatible, transitional smooth migration is supported, and the anti-quantum multi-key binding digital certificate implementation mode is suitable for application and popularization. Through key isolation, anti-quantum hash / KEM verification and unified management, 50% of certificate operation and maintenance cost is reduced, quantum security protection is enhanced, and the method is suitable for high-sensitivity scenes such as the Internet of Things and 5G.

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.