266 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 relates to the technical field of geographic information systems, in particular to a map tile data efficient access method and system based on hybrid storage and intelligent layering, and the method comprises the following steps: constructing a multi-level storage architecture, carrying out dynamic heat analysis, carrying out intelligent layering scheduling, preloading and space prediction, and carrying out transmission and access optimization. The method has the beneficial effects that the multi-level storage pool is constructed by combining the advantages of object storage and a distributed file system; adopting a dynamic tile popularity analysis model to realize hot data cache acceleration and cold data hierarchical archiving; and meanwhile, a tile request preloading mechanism is introduced, and an access path is optimized in combination with a spatial locality prediction algorithm. According to the method, the storage cost can be remarkably reduced, the tile request delay is reduced, quick response in a high-concurrency scene is supported, and the method is suitable for application scenes such as Internet map services and smart cities.

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 coal preparation plant production data full-process management system and method, and the method comprises the steps: a data collection subsystem collects equipment data and production data of different data sources of each plant through a preset data collection interface; the data transmission subsystem encrypts and transmits equipment data and production data acquired by the data acquisition subsystem to the data cleaning subsystem for cleaning, and the data storage subsystem stores the data cleaned by the data cleaning subsystem to a plurality of databases of different types in a mixed storage mode. And the data application subsystem obtains the target data from the data storage subsystem and performs production plan optimization, equipment fault early warning and quality control and optimization based on the target data and preset data. By the adoption of the method, efficient processing and deep application of the whole process of production data of the coal preparation plant are achieved, powerful support is provided for intelligent and refined production management of the coal preparation plant, and the overall operation level and economic benefits of the coal preparation plant can be effectively improved.

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 technical field of nuclear power equipment quality management and control, in particular to a nuclear power unit quality defect source analysis and total value chain tracing method, which comprises the following steps: S01, collecting total value chain multi-modal data including structured data, logs, texts and sensor time sequence data, and carrying out BIO labeling, BERT coding, BiGRU-Attention feature extraction and CRF sequence labeling; constructing a mixed storage data resource pool; s02, mining association rules by adopting a dynamic pruning improved Apriori algorithm, processing sample imbalance in combination with chi-square test and an NSGAII algorithm, and analyzing defect causes; s03, a dynamic knowledge graph containing multiple types of nodes, relations and threshold attributes is constructed, and rapid defect tracing is achieved through entity linking and probability path searching; the objective of the invention is to solve the problems of difficulty in defect source calibration, low tracing efficiency and the like in nuclear power equipment quality control.

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 FPGA (Field Programmable Gate Array)-based high-performance large language model accelerator and a reasoning method, which adopt a structure of combining a plurality of computing units (CU) and a matrix processing unit (MPE), and can efficiently allocate computing tasks and realize parallel processing by virtue of the parallel computing capability of the FPGA, thereby greatly improving the computing efficiency. Through parallel processing of a plurality of tasks, the reasoning speed is obviously improved, and the calculation bottleneck in a traditional scheme is avoided. And secondly, in the aspect of memory management, through a hybrid storage strategy of a high-bandwidth memory (HBM) and an off-chip memory (such as DDR), utilization of memory bandwidth is optimized, efficient flowing of data among a plurality of computing units is realized, delay in data transmission is reduced, and efficient data access in the computing process is ensured.

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 present invention discloses a multimodal hybrid database storage and interaction method based on deep learning. The present invention realizes the hybrid storage and real-time retrieval interaction function of different modal data (images, videos, voices and texts) on the basis of the existing separate storage of multimodal data and the use of single-modal hybrid label interaction technology, further increasing the understanding of fused data and improving the interactive performance. On the technical level, the present invention uses a deep learning unified general model to extract and fuse features of images, videos, voices and texts respectively; the present invention adopts the universal storage of multimodal fused data; the present invention adopts the matching calculation of user data and fused feature data; the present invention performs multimodal conversion and returns according to the matching information and the universal unified model.

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 industrial Ethernet switch based on an FPGA. The industrial Ethernet switch comprises an input network interface, an FPGA core processing unit, an output network interface, a monitoring management interface, a clock generation module, a power module and a data storage module. According to the switch, the FPGA assembly line architecture is applied, analysis, switching and forwarding of industrial network frames are achieved, the end-to-end transmission delay of the industrial network frames is greatly reduced by means of the parallel processing characteristic of hardware, and the strict requirement of real-time control frames for low delay can be met. The advantages of parallel processing of FPGA hardware are fully exerted, transmission delay is remarkably reduced, the reliability in an industrial environment is enhanced through a redundant power supply and hybrid storage design, real-time monitoring of network data is supported, the state of an industrial network is conveniently mastered in time, and the method is suitable for an industrial control network with extremely high requirements for real-time performance and stability.

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 spatio-temporal data hybrid storage system and device based on a smart city and a storage medium, and relates to the technical field of data processing, the spatio-temporal data hybrid storage system based on the smart city comprises a processing module and a storage module, and the storage module comprises a file system and a database cluster. Data interaction is carried out between the file system and the database cluster, the file system comprises a local file system and a distributed file system, the database cluster comprises a relational database, a non-relational database and a search database, and data interaction is carried out between the relational database and the non-relational database. And data interaction is performed between the non-relational database and the search database. The problems that spatio-temporal data storage and management are disordered and are not easy to manage, and spatio-temporal data read-write analysis and calculation efficiency in the field of smart cities is low are solved, and the use performance and efficiency of the spatio-temporal data in the field of smart cities are improved.

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 discloses an alarm event processing method, a transmission system, a processing device, a medium and a product, and relates to the technical field of servers. A management controller is divided into a volatile storage area and a non-volatile storage area which belong to the management controller in advance, so that an efficient caching strategy of a mixed storage area is realized, and the loss of an alarm event when a network is interrupted is completely eradicated. And when the network is recovered, obtaining the target alarm event from the memory area, and transmitting the target alarm event to a remote server. In the process, a returned alarm event fingerprint list needs to be received, so that repeated transmission is avoided through recording of a unique fingerprint identifier. If the target alarm event does not exist in the sending queue, the target alarm event needs to be sent again, so that missing sending of the target alarm event is prevented, recovery transmission of the alarm event under the condition of network recovery is achieved, it is guaranteed that the remote server can completely receive the alarm event, and the fault diagnosis capacity of the remote server is improved.

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.