1441results about How to "Improve storage efficiency" patented technology

The invention provides a block chain data management method and system, and relates to the technical field of block chains, and the method comprises the steps: receiving and preprocessing an electronic file; encrypting the archive by using a public key and calculating a hash value; packaging the encrypted file and the hash value into a block chain transaction and broadcasting the block chain transaction; acquiring block information and safely storing the private key; verifying the integrity of the electronic file; recording a full life cycle operation log; the log is stored in an on-chain and off-chain combined mode; auditing the electronic file; and realizing access authority automatic management based on the intelligent contract. According to the method, the security, traceability and management efficiency of the electronic archives are improved.

The invention discloses a multi-layer semantic understanding large model agent construction and application method. The method comprises the following steps: S1, preprocessing input data; s2, analyzing the preprocessed data by using a large language model and dialogue history, identifying user intentions and extracting deep semantic features; s3, key information is extracted from data intending to be recognized, structured representation is carried out, and model configuration is adjusted through dynamic hyper-parameter optimization; s4, querying the knowledge graph, associating and organizing the extracted key information with the existing data, and generating a structured knowledge representation; s5, vectorizing the structured knowledge representation, and storing the vectorized structured knowledge representation in a vector database to support rapid similarity search; and S6, in combination with the generated unified semantic representation and the large model, performing deep analysis on user input, generating coherent and logical personalized answers, and returning the personalized answers to the user. The method can support various application scenes such as intelligent question and answer, content generation, sentiment analysis and multi-modal search.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention discloses a live-action three-dimensional model construction method, system and device based on a digital city, and the method comprises the steps: obtaining the multi-source sensing data of a city, and carrying out the data preprocessing, and the multi-source sensing data comprises city image data, point cloud data and GIS data; converting the preprocessed multi-source sensing data in different formats into a preset standard format, and performing registration and fusion of the multi-source sensing data to obtain fused multi-source data; performing data optimization on the fused multi-source data, and constructing a city-level three-dimensional real scene model according to the optimized fused multi-source data and a preset three-dimensional space analysis tool; the preset three-dimensional space analysis tool provides space analysis and three-dimensional space visualization functions; according to the invention, efficient and accurate real-scene three-dimensional data of a city is provided, and three-dimensional and visual space information is provided.

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention discloses a laser SLAM (Simultaneous Localization and Mapping) method in a dynamic environment of a well mine, which relates to data processing and comprises the following steps of: performing mapping by utilizing a Cartograph SLAM algorithm; segmenting the PCD point cloud map by adopting an octree-based segmentation method according to a preset movement or deformation frequency threshold value of an object in each region in the well mining environment; adjusting the map resolution of each area in the PCD point cloud map according to the segmentation result; a change detection algorithm is used to carry out change detection on an area with object movement or deformation frequency exceeding a threshold value in the segmented PCD point cloud map, and new point cloud data of the change area is updated to the original PCD point cloud map in an increment mode; carrying out registration and fusion on the PCD point cloud map after local updating and an original PCD point cloud map; the global consistency is improved by aiming at the poor global consistency existing in the laser SLAM mapping of the well industry and mining in the prior art.

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

The invention relates to the technical field of industrial internet data acquisition, in particular to a data acquisition method and system based on an industrial internet operating system, and the method comprises the following steps: S1, detecting and recognizing data sources of a plurality of industrial devices and sensors; s2, collecting original data from a data source in real time, and preprocessing the original data; s3, distributing part of calculation tasks to a plurality of edge nodes; s4, storing the data in a distributed database or cloud storage in a classified manner; s5, performing fragmentation processing on the data in the cache; s6, performing data synchronization and consistency verification; and S7, converting the data into a corresponding format. According to the invention, by implementing efficient data fragmentation processing, a dynamic storage strategy and secure data consistency verification, the processing speed and storage efficiency of the industrial internet data acquisition system and the security and integrity of data are remarkably improved, and the requirements of high efficiency and high reliability of modern industrial production are effectively supported.

The invention discloses a surveying and mapping geographic information data management method and system based on cloud computing, and relates to the field of geographic information, and the method comprises the steps: constructing a semantic segmentation model based on a convolutional neural network Deep Lab, and carrying out the feature extraction and semantic annotation of a surveying and mapping geographic data set through the semantic segmentation model, and obtaining a geographic element set; performing spatial division and indexing on the geographic element set by adopting an R tree spatial indexing method to obtain a hierarchically organized geographic element tree; constructing a graph neural network model based on a graph convolutional network GCN, and performing topological relation modeling on the geographic element tree by using the graph neural network model to obtain a time-space attribute graph containing geographic semantics and topological relations; and constructing a space-time prediction sequence model based on the space-time diagram neural network STGCN, and performing evolution trend prediction on the space-time attribute diagram by adopting the space-time prediction sequence model. Aiming at the problem that dynamic prediction of geographic elements is difficult to realize in the prior art, the spatial-temporal evolution trend of the geographic elements is predicted, and dynamic prediction is realized.

The invention discloses a safe and rapid storage method and system for image data of an imaging department, and relates to the technical field of medical image data processing. The method comprises the following steps: 1, designing a distributed storage system, and selecting and configuring storage nodes; 2, selecting and updating an encryption algorithm, and implementing file-level and block-level encryption; 3, realizing accurate data deduplication by utilizing a machine learning technology, and selecting a proper compression algorithm; 4, constructing an index system, and establishing a data warehouse and a data lake by using big data analysis and machine learning technologies; 5, establishing an image data backup and recovery mechanism; and 6, constructing a data integrity checking and monitoring system. According to the invention, by adopting the technologies of distributed storage, encryption, data deduplication, compression, big data analysis, machine learning, backup and recovery, data integrity verification, monitoring and early warning and the like, efficient storage and safety protection of image data are realized.

The invention, which relates to the technical field of intelligent logistics management, discloses an AI-based intelligent logistics management system comprising a warehouse management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehouse management unit is used for managing warehouse-in and warehouse-out information and cargo information of a warehouse, reminding inventory risks, and carrying out demand prediction and inventory optimization; the data fusion acquisition unit is used for acquiring personnel, goods, equipment and environment information of a warehouse; the digital twin supervision unit comprises an intelligent AI recognition unit and a data analysis processing unit, the intelligent AI recognition unit is used for carrying out recognition analysis and risk reminding on personnel, goods, equipment and environment of a warehouse through an AI intelligent algorithm, and the data analysis processing unit is used for mapping data of an entity warehouse to a digital twin warehouse. And visual interaction management and intelligent scheduling of personnel, goods and equipment are carried out on the digital twin warehouse, so that the warehouse can be comprehensively monitored, and the goods storage and management efficiency is improved.

The invention is suitable for the technical field of anomaly detection, and particularly relates to a cloud edge multi-mode abnormal event detection system based on a dynamic decision-making mechanism, and the system comprises a data collection module which is used for collecting videos and audios, and obtaining multi-mode data; the data preprocessing module is used for preprocessing to obtain preprocessed data; the key feature extraction module is used for carrying out key frame feature extraction and key frequency band feature extraction; the multi-mode anomaly detection module is used for carrying out anomaly judgment; and the cloud large-model intelligent processing module is used for analyzing the detected abnormal fragments and outputting a generative text result. According to the cloud edge multi-mode abnormal event detection system based on the dynamic decision-making mechanism provided by the invention, more comprehensive event understanding and anomaly detection are provided through combination of video and audio data. And deep features are extracted at the edge end by using the convolutional neural network, so that the detection accuracy is improved, and the false alarm rate is effectively reduced.

The invention relates to the technical field of data distributed storage, and discloses a distributed storage method and system for clinical test data, and the method comprises the steps: carrying out the ring signature encryption processing of the patient test data of a plurality of data centers, and obtaining a patient anonymization identifier and standardized clinical data; performing two-stage random storage node configuration to obtain an optimal storage node configuration scheme; constructing an LPPMM-DA data aggregation model based on the optimal storage node configuration scheme, and performing fragmentation processing on the patient test data to obtain a plurality of target data fragments; performing dynamic operation envelope analysis and alliance graph game calculation on the multiple target data fragments to obtain a storage resource scheduling scheme; according to the storage resource scheduling scheme and the anonymization identifier, distributed data block storage and access authentication are executed on the multiple target data fragments, data block storage distribution information is generated, a complete data access control system is constructed, and safe and reliable storage of clinical test data is ensured.

The invention relates to the technical field of data storage management, in particular to a data storage method based on a large model. The method comprises the following steps: performing input class cluster fragmentation optimization on a target pre-training large model to obtain storage modulus fragmentation data; constructing an intelligent fragmentation storage strategy by using the storage modulus fragmentation data; obtaining target model training data; performing intelligent fragmentation storage on the target model training data by using an intelligent fragmentation storage strategy, and performing redundancy compression optimization to obtain dynamic redundancy storage index data; and training data storage space optimization is carried out according to the dynamic redundancy storage index data, and an optimized data storage strategy is obtained. According to the method, intelligent fragmentation storage and refined data redundancy compression are performed on the data, so that efficient and stable model training data access is realized, the problem of low data storage and access efficiency in a large model training process is solved, and the model training efficiency and reasoning real-time performance are remarkably improved.

The invention discloses a negative carbon coplanar filling system arrangement and mining method, which comprises the following steps of: stoping by adopting a rear gangue high-porosity filling coal mining technology, retaining a stoping roadway on one side of a filling section by utilizing a gob-side entry retaining technology, and reinforcing by adopting a roadway-side support body; the mining roadway serves as a CO2 filling roadway of the working face and a mining roadway on one side of an adjacent filling working face; when the working face advances by 300-500 m, the working face is separated from a rear goaf through a sealing wall, and a sealed storage space is formed; then, drill holes are formed in the goaf through the CO2 injection and filling roadway, and CO2 injection and filling pipelines are arranged in the mining roadway and the drill holes; and CO2 is injected into the goaf by the CO2 injection pump through the CO2 injection pipeline, and after injection is completed, CO2 injection in the next closed space is started until injection of the working face is completed. The system is simple in arrangement, CO2 filling and coal mining filling do not interfere with each other, operation is flexible, the sealing performance of the sealing space is good, the dual purposes of solid waste disposal and CO2 sealing are achieved, and green mining of coal resources is achieved.

The invention discloses an intelligent file cabinet management method based on the RFID technology, and relates to the technical field of intelligent file management. According to the method, intelligent retrieval is adopted, file positioning is fast and accurate through double indexing of content attributes and RFID tags, and a user only needs to input conditions such as keywords, themes or dates, so that the file can be rapidly and accurately positioned; according to the method, relevant archives can be screened out through similarity matching, the actual storage positions of the archives can be positioned, the retrieval efficiency is remarkably improved, meanwhile, a virtual digital library is established to conduct digital mapping on the archives, and dependence on actual storage space is reduced; under the condition of dense storage, the position information of the archives is recorded through the RFID tags, so that the archives can be flexibly put on a shelf or readjusted in position without influencing the management and positioning of the archives, meanwhile, the archives are digitized when being put in storage, and all content attributes, classification tags and position information are synchronously stored in a virtual digital library; even if the paper archives are actually damaged or lost, the digital information of the paper archives is still completely stored in the digital library.

The invention discloses a personalized digital human image creation and generation system based on deep learning, which relates to the technical field of digital human generation and comprises a multivariate data acquisition module, an image creation module, a model storage module, a matching module and a rendering interaction module. The multivariate data acquisition module collects facial images and whole body photos through a camera, and records voice samples through a microphone. According to the invention, by constructing the multivariate data acquisition module, the image creation module, the model storage module, the matching module and the rendering interaction module, full-process automatic processing from data acquisition to digital human image generation is realized; the multivariate data acquisition module can comprehensively capture the appearance and voice features of a user, and the image creation module extracts feature vectors by using a deep learning technology and generates a personalized digital human image by using a generative adversarial network technology.

The invention relates to the technical field of data management, in particular to a data asset sorting method, which comprises the following steps: preliminarily classifying all data assets in an enterprise, performing quality evaluation on the preliminarily classified data assets, generating a quality evaluation report, and performing data redundancy detection and optimization based on the quality evaluation result. Cleaning redundant data by adopting a deduplication algorithm and an optimization algorithm, carrying out data correlation analysis, generating a data correlation graph through a graph model and a statistical model, and determining the relationship between data sets; and finally, according to a correlation analysis result, integrating the data sets with high correlation into a unified data set by adopting a data fusion algorithm, and reducing a storage space through a data compression technology. According to the method, the accuracy of data classification and management can be improved, the data redundancy is reduced, the data storage and access efficiency is optimized, and the data security and privacy protection are enhanced through security evaluation and protection measures.

The invention discloses an operation and maintenance platform data acquisition system based on big data, and relates to the technical field of big data, and the system comprises the steps: capturing a data packet from a network flow of an operation and maintenance platform, and storing the data packet in a buffer area; performing preliminary screening on the data packets in the buffer area, and allocating priorities for the data packets based on a dynamic weight scheduling mechanism to complete preliminary preprocessing of the data packets; based on a circular sorting and block optimization technology, distributing the plurality of decomposed analysis tasks to a plurality of threads for parallel processing; formatting the analysis result, and performing batch parallel processing according to the data scale of the analysis result; formatting the analyzed data, writing the formatted data into a time sequence database according to a time sequence, and generating an index based on the tag field; through a dynamic cache management mechanism, the cache size is dynamically adjusted according to the throughput and the maximum delay of the network flow, and the problems of data loss and processing delay are avoided.

The invention relates to the technical field of hierarchical storage, in particular to a photo-electromagnetic storage method and system based on hierarchical management, and the method comprises the following steps: collecting read-write speed, error rate, durability and cost data of optical storage and electromagnetic storage media, carrying out the sorting and grading processing of the data according to the read-write speed and the error rate, and carrying out the sorting and grading processing of the data; and performing score calculation on each storage medium to obtain a medium performance score. According to the method and the device, the data is effectively distributed to the most suitable storage medium by accurately scoring the performance of the storage medium and combining specific requirements of the to-be-stored data, so that the data access speed and efficiency are improved, the total storage cost is reduced, and particularly, when a large amount of data is processed, the data compression level and the encoding format are dynamically adjusted, and the data compression efficiency is improved. The storage efficiency of the data is further optimized, data redundancy is reduced, the overall performance of data storage and access is remarkably improved through optimization measures, and meanwhile long-term safety and integrity of the data are ensured.

The invention discloses an energy storage cabinet temperature control method based on a cloud platform and a temperature control energy storage cabinet, and relates to the technical field of electric energy storage, the energy storage cabinet comprises a cabinet body, an energy storage module, a data acquisition module, a communication module and a temperature control module, and the temperature control method comprises the following steps: S1, acquiring environmental parameters and battery state data; s2, determining a temperature adjustment factor of the energy storage cabinet through the battery state data; s3, generating a composite temperature index based on the environmental parameters and the temperature adjustment factor; s4, performing region division on the energy storage cabinet, determining temperature requirements of different regions according to the composite temperature index, and formulating an energy storage cabinet temperature control strategy based on the temperature requirements in combination with the environment prediction temperature; s5, the cloud platform generates a control instruction based on the temperature control strategy and issues the control instruction to the energy storage cabinet to execute the control instruction; accurate control over the temperature in the energy storage cabinet can be achieved, it is ensured that energy storage equipment operates within the optimal temperature range, the energy storage efficiency is improved, and the service life of the equipment is prolonged.

The invention discloses a customs intelligent specimen cabinet monitoring system, relates to the technical field of specimen cabinet monitoring, and aims to solve the problems that the monitoring strength of an existing specimen cabinet is reduced, and abnormality checking cannot be carried out in time. According to the invention, the most suitable transmission channel is flexibly selected through different transmission requirements and environmental conditions, the adaptability and flexibility of the system are enhanced, the feature extraction of the environmental monitoring data covers the monitoring parameters of each sensor, and the comprehensive monitoring of environmental factors is ensured. The monitoring of real-time video data provides intuitive visual information, so that workers can intuitively know the real-time condition in the specimen cabinet, manual intervention is reduced through automatic detection, recognition and alarming, the automation and intelligence level of the monitoring system is improved, and the monitoring efficiency is improved through automatic data classification, visual display and anomaly analysis. And the intelligent level of the customs intelligent specimen cabinet monitoring system is improved.

The invention discloses a method for generating a three-dimensional building GLTF file based on index compression. The method comprises the following steps: S1, collecting three-dimensional coordinate data of a building; s2, grouping and classifying the data according to floors to form a classification index structure based on a hash table; s3, selecting a reference model and recording Mesh data and vertex coordinates of the reference model; s4, calculating a translation matrix T of the polyhedral structure, and performing coordinate transformation; s5, generating Mesh data of other polyhedrons based on index compression, and reducing redundant surface data by adopting a topology preserving surface optimization algorithm; s6, independently processing the geometric data of each floor, and generating corresponding Mesh data and Node nodes; and S7, combining all floor data to generate a complete GLTF file, and performing compression by using a dynamic Huffman coding algorithm. Through the improved index compression and dynamic Huffman coding technology, the file size is remarkably reduced, the transmission efficiency is improved, and the geometric and topological integrity of the model is ensured.

The invention relates to the technical field of database establishment, in particular to an establishment method and system based on an aircraft aerodynamic model database. The method comprises the following steps: obtaining an aircraft pneumatic data storage demand and an aircraft pneumatic data query demand, and carrying out multi-level storage architecture design to obtain a basic data layer, a processing data layer and a high-level model data layer; the method comprises the following steps: collecting aircraft original aerodynamic data sets of wind tunnel experiments, flight tests and CFD numerical simulation, and transmitting and storing the aircraft original aerodynamic data sets to a basic data layer; carrying out aerodynamic load, flow field parameter and aerodynamic pressure distribution analysis on the original aerodynamic data set of the aircraft in the basic data layer, and transmitting and storing the aerodynamic load, flow field parameter and aerodynamic pressure distribution to the processing data layer; and carrying out aircraft aerodynamic model construction on the data in the processing data layer, transmitting and storing the data to a high-level model data layer, and carrying out R-tree multi-dimensional index connection to obtain an aircraft aerodynamic model multi-level storage database. According to the invention, an efficient and intelligent aerodynamic model database can be established.

The invention provides a social network-based block chain storage method and system, and the method comprises the steps: constructing a network transaction graph according to node transaction data, and mining a social relation between nodes from the graph, which represents the probability of possible transaction in the future; according to the social relation, the nodes are divided into different communities, the nodes store related transactions of the nodes of the same community, and the burden of storing the whole block chain account book is avoided; according to the storage and query cost of a single node, modeling the total storage and query cost in the whole network into a multi-objective optimization model, and minimizing the storage cost and the query cost in the network; a multi-population ant colony algorithm is adopted for solving, and the storage position of the transaction is allocated by adjusting the storage saving rate and the query cost tolerance rate, so that the balance of the storage cost and the query cost in the network is achieved. The technical problems that the node storage burden is large, the storage and query efficiency is low, and the storage cost balance degree is not high are solved.

The invention relates to the technical field of bioinformatics, in particular to a gene pool data retrieval method and system and a storage medium. The method comprises the following steps: acquiring multi-source genome data, performing quality control on the multi-source genome data based on an FAIR principle, and performing efficient storage and indexing by utilizing a distributed storage system so as to obtain a distributed genome database; performing cross-species homology analysis on the distributed genome database, and constructing a multi-level evolutionary tree model, so as to obtain evolutionary relationship map data; mapping gene structures and functions among different species according to the evolutionary relationship map data so as to obtain cross-species gene mapping data; receiving retrieval query data input by a user; and performing query type identification on the retrieval query data to obtain optimized query data. Through the multilevel evolutionary tree model and cross-species gene mapping, gene function comparison and prediction among different species are realized.

The invention provides a digital twin city construction and operation optimization method based on cloud computing and a big data technology. The method comprises the following steps: optimizing layout of various sensor devices through a particle swarm optimization algorithm and an adaptive sampling algorithm, and adjusting acquisition frequency; establishing a distributed database, and realizing uniform distribution and efficient access of data in the distributed database through a load balancing algorithm; mining a time rule of urban operation by using a time sequence model, and discovering association between different data; a city model is constructed by using a city three-dimensional live-action fusion modeling method which takes unmanned aerial vehicle oblique photography as a main part and takes ground laser radar as an auxiliary part, different areas in a city are identified by applying a machine learning method, and a refined model of each field of the city is constructed; real-time simulation of a city operation state is realized through virtual reality and augmented reality technologies; the system is continuously optimized by using an online learning algorithm, so that the system continuously learns and adapts to a new data mode in the operation process, and the construction and operation optimization capability of the system is improved.

The invention relates to the technical field of material storage management, in particular to a BIM-based construction site material monitoring management method and system. The method comprises the following steps: obtaining a construction project drawing; performing drawing information extraction on the construction project drawing to obtain construction project material information data and construction project structure data; performing construction project three-dimensional modeling on the construction project material information data and the construction project structure data to generate construction project three-dimensional modeling data; obtaining total construction material data; and carrying out material information modeling on the total construction material data by using a BIM technology to generate total construction material modeling data. According to the invention, accurate three-dimensional modeling, intelligent marking, regionalized storage and real-time allocation of the construction site materials are realized through the BIM technology, and the accuracy of construction site material monitoring management and the emergency response real-time performance are improved.

According to the big data analysis method based on adaptive data management and privacy protection, an efficient, safe, visual and automatic data analysis solution can be provided, and the requirements of the big data era for data processing and analysis are met. The core innovation point of the invention lies in a self-adaptive data storage architecture and a dynamic data compression and decompression mechanism. The adaptive data storage architecture intelligently selects an optimal storage format and an optimal index strategy by analyzing data characteristics and an access mode in real time, so that the data storage efficiency and the query performance are remarkably improved. Meanwhile, a dynamic data compression and decompression mechanism can automatically adjust a compression algorithm and parameters according to a real-time access mode and content characteristics of the data, so that optimization of a data storage space and balance of an access speed are realized. The two innovations not only solve the efficiency problem in big data storage and access, but also provide a solid foundation for efficient processing and analysis of data.