6666 results about "Data access" patented technology

The present application relates to the technical field of power digital twin, and discloses a digital twin system for a power grid. The system comprises: a data access module, which accesses multi-source heterogeneous data of a power grid; a modeling module, which is used for receiving the data output by the data access module and establishing digital twin models in multiple service scenes; a fusion module, which is used for performing association and fusion of the digital twin models in the multiple service scenes; a computing module, which is used for carrying out multi-scene collaborative simulation and derivation by using the digital twin models; and an optimization module, which is used for optimizing the digital twin models, wherein the multi-source heterogeneous data comprises real-time monitoring data, historical operation data, meteorological environment data, device model data, and geographic information data. The problem in the prior art of low fusion efficiency of multi-source heterogeneous data of a power grid is solved. A standardized data access interface is constructed, and association analysis, knowledge graph technology, etc. are applied, so that seamless integration of the multi-source heterogeneous data of the power grid is achieved, thereby improving the data fusion efficiency.

The invention discloses a computer equipment fault monitoring system and method based on artificial intelligence, and relates to the technical field of computer equipment fault monitoring. The system comprises a data access module, a semantic analysis module, a knowledge graph construction module, a dynamic semantic association module, a data fusion processing module, a decision output module and an adaptive optimization module. The data access module collects and standardizes hardware, software and network data; the semantic analysis module extracts and enhances semantic tags; the knowledge graph construction module forms a data semantic relation network; the dynamic semantic association module screens potential semantic relationships; the data fusion processing module generates a multi-dimensional feature vector; the decision output module triggers fault early warning; and constructing a feedback knowledge graph of the self-adaptive optimization module. According to the method, through event-driven interpolation, dynamic weight fusion, closed-loop feedback optimization and the like, the problems of multi-source data alignment, semantic fusion and dynamic adaptation are solved, the fault monitoring accuracy and the system adaptability are improved, and the method is suitable for fault monitoring and early warning of computer equipment.

The invention discloses an optical fiber data storage management system and method based on big data, and relates to the technical field of storage management. According to the optical fiber data storage management system and method based on big data, the spatial-temporal characteristics of the historical access records are extracted through the LSTM network, the data heat value is calculated in combination with the time decay function, and the thermal distribution diagram of the data blocks is generated. And based on the thermal distribution map, constructing a storage resource allocation model by using a deep reinforcement learning algorithm, and dynamically adjusting the optical path priority and copy distribution. A data prefetching strategy is optimized through the spatio-temporal joint index and the vector similarity index, and a high-frequency data cache index distribution diagram is generated. And comprehensively considering the data access delay, the index hit rate and the copy migration frequency, obtaining an optical fiber storage efficiency index, and adjusting a storage strategy according to the index. According to the method, the dynamic scheduling and utilization efficiency of data storage resources is effectively improved, the data access delay is reduced, the copy management is optimized, and the overall performance of a storage system is remarkably improved.

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention relates to the technical field of data processing, in particular to a storage and calculation integrated parallel processing system and method. The method comprises the following steps of obtaining original stored data and performing topological skeleton projection, generating a projection skeleton structure to realize characteristic hierarchical integration, determining a weight relation matrix based on multi-scale space fusion data, realizing matching of a memory and a calculation unit through gradient optimization adjustment, and obtaining a multi-scale space fusion matrix. And the mining storage and calculation unit activates the chain type interaction relationship of the mapping data and performs recursive self-calibration, generates self-calibration update data, predicts a heterogeneous access channel and reconstructs a data access link, and finally performs task recombination and pipeline scheduling and eliminates parallel conflicts, thereby obtaining an efficient parallel processing result. According to the method, high efficiency and intelligentization of storage and calculation are realized, and reliable support is provided for execution of complex calculation tasks.

The invention provides a multi-source heterogeneous data integration and governance-oriented data weaving method, which comprises the following steps of: performing data acquisition from an accessed multi-source heterogeneous data source to generate an original multi-source heterogeneous data stream; performing standardization processing on the original multi-source heterogeneous data stream to generate a standardized multi-source heterogeneous data set; performing active content scanning processing on the standardized multi-source heterogeneous data set to determine business metadata, and performing consanguinity tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to carry out standardized constraint on the enhanced service metadata to obtain standardized service metadata without cross-data source semantic ambiguity, and carrying out implicit association mining processing on the standardized service metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logic abstraction processing on the distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

The invention relates to the technical field of data processing, and discloses a multilevel cloud storage cluster construction and data storage method. The method comprises the steps that S1, multiple levels of storage layers are constructed and comprise a cache layer, a performance layer and a capacity layer, and the storage layers are provided with different storage media which are used for storing hot data, warm data and cold data respectively; s2, determining data popularity by monitoring data access frequency, access time interval and data importance; and S3, dynamically adjusting the storage hierarchy of the data according to the data popularity, and triggering hierarchical migration of the data. By constructing a multi-level storage layer and adopting a hierarchical architecture that the cache layer stores high-frequency data, the performance layer stores intermediate-frequency data and the capacity layer stores low-frequency data, high-heat data is preferentially stored in a high-performance medium, access delay is reduced, and data reading and writing efficiency is improved. Meanwhile, in combination with a dynamic data migration strategy, the storage position can be adjusted in real time according to the data access popularity, and the overall performance of the system is further optimized.

The invention discloses a distributed multi-tenant data security isolation system and method, and relates to the technical field of data isolation, and the system comprises a data isolation module which is responsible for distributing independent container instances and hardware resources for each tenant, and adjusting the data access authority according to an access control strategy; the dynamic access control module is responsible for generating an access control strategy according to the user identity, the authority, the tenant strategy and the access behavior; the distributed data management module is responsible for storing data fragments on different storage nodes; the resource scheduling and optimizing module is responsible for collecting resource demand data of each tenant, calling an intelligent scheduling algorithm, performing dynamic prediction and analysis on resource demands, and then automatically adjusting a resource allocation strategy according to a prediction result; and the security auditing and monitoring module is responsible for collecting operation logs and event records of each module and carrying out comprehensive analysis. The invention aims to solve the problems of data security and resource management in a multi-tenant environment.

The invention relates to a data access method and system of an intelligent Internet of Things gateway. The method comprises the following steps: performing feature matching and protocol identification on a plurality of sensors and terminal equipment connected to the intelligent Internet of Things gateway by using an equipment feature library to obtain an equipment protocol type; analyzing the original data to obtain standardized data; performing encryption processing to obtain an encrypted security data packet and an encrypted session key; performing multi-factor transmission channel selection to obtain an optimal transmission channel combination; and based on the optimal transmission channel combination, sending the encrypted security data packet to the Internet of Things platform, verifying the received data confirmation information based on a consensus algorithm, and outputting a data access state report through a data visual interface and an AI voice assistant. The processing strategy can be dynamically adjusted according to the equipment characteristics, the data characteristics and the network environment, and the flexibility and adaptability of the system are improved.

The invention relates to the technical field of railway data security, and provides a railway data asset security monitoring and risk early warning method and system, and the method comprises the steps: collecting multi-dimensional feature data of railway data assets, including equipment operation state data, network communication data, data access logs and environment perception data; performing heterogeneous data fusion preprocessing on the multi-dimensional feature data to generate a standardized feature vector; calculating security evaluation indexes of the standardized feature vector based on a dynamic weight distribution algorithm, wherein the security evaluation indexes comprise an integrity evaluation value, an availability evaluation value and a confidentiality evaluation value; generating a multi-level risk early warning signal according to the deviation degree of the safety evaluation index and the dynamic threshold value; and triggering dynamic control of a data asset protection strategy based on the risk early warning signal, wherein the dynamic control comprises access permission adjustment, data encryption strength grading and flow limitation. According to the invention, the safety and reliability of railway data assets can be improved, and data leakage and system fault risks are reduced.

The invention provides a data security analysis method and an intelligent calculation data security workstation, and the method comprises the steps that the intelligent calculation data security workstation obtains data of different data sources, and the data comprises a user operation log and a data access and transmission log; classifying and grading the data based on a preset rule base and a machine learning model, and marking sensitive data; analyzing a user behavior mode based on the user operation log, and identifying an abnormal behavior; and based on the data access and transmission log, evaluating a data leakage risk, and generating a risk report.

The invention provides a cross-domain data integration and fusion method based on a large model, a terminal and a storage medium, and the method comprises the steps: carrying out the format conversion and cleaning of cross-domain original data through a unified data access adaptation layer, and outputting a standardized data set; carrying out semantic understanding integration by utilizing a large model, analyzing field names, generating field semantic tags in combination with a business term knowledge base, judging business relevance by calculating similarity, and generating an inter-domain field mapping relationship; and performing technical standard conversion and data fusion processing based on the mapping relationship, and performing intelligent quality treatment by adopting a mode of combining a quality treatment standard library and a large model. According to the method, a large model is introduced to assist data understanding and treatment, so that the technical problems of insufficient semantic understanding, non-uniform standards and the like in existing cross-domain data integration are effectively solved, and the accuracy and automation level of data integration are remarkably improved.

The invention discloses a big data distributed storage and parallel processing collaboration method based on cloud computing. The method comprises the following steps: sensing data stream characteristics in real time through a self-adaptive dynamic partitioning engine, dynamically adjusting a partitioning strategy and generating a metadata label; constructing a node selection model through a comprehensive evaluation algorithm, selecting storage nodes to form an optimal storage cluster, and dynamically adjusting a resource matching weight coefficient based on a load state through a load optimization module; decomposing a data processing task into parallel subtask units, and constructing a dual-objective optimization model; triggering a dynamic rebalance mechanism through a distributed monitoring agent in combination with a hierarchical early warning strategy; and constructing a multi-level cache system to optimize a data access path, outputting a final result, pushing the final result to the user terminal, and updating the knowledge base. Through collaborative optimization of dynamic partitioning, multi-dimensional resource scheduling, elastic scaling and intelligent caching technologies, the resource utilization rate, the load balancing capacity and the stability in a high-concurrency scene of the system are remarkably improved.

Methods, systems, and computer storage media for providing security posture management using an artificial intelligence security engine in a security management system. Security posture management supports security management of a computing environment based on contextual information associated with artificial-intelligence-supported applications. The security management system provides an artificial intelligence security graph associated with the artificial-intelligence-supported applications. The artificial intelligence engine uses the artificial intelligence security graph to correlate artificial intelligence attack monitoring data with operational data of the artificial-intelligence-supported applications. In operation, artificial intelligence attack monitoring data is accessed. An artificial intelligence security graph associated with a plurality of artificial-intelligence-supported applications is accessed. Based on the artificial intelligence attack monitoring data and the artificial intelligence security graph, operational data of an artificial-intelligence-supported application is accessed. The artificial intelligence attack monitoring data and the operational data are analyzed to identify an artificial intelligence security alert. The artificial intelligence security alert is communicated.

The invention relates to the technical field of financial data security, and discloses an intelligent ERP financial system data security management and authentication method. The method comprises the following steps: acquiring an original transaction data stream in an ERP system, extracting key financial fields, and dividing the key financial fields into a sensitive data set and a common data set according to a preset rule; a dynamic encryption strategy framework is constructed based on sensitive data set attributes, the framework comprises multiple levels of encryption strength parameters, and the corresponding encryption strength can be automatically matched according to the authentication level of an access request. And monitoring a system data access behavior in real time, collecting feature data, inputting the feature data into the anomaly detection model, and triggering access blocking when an output anomaly access probability exceeds a threshold value. And generating a periodic integrity verification instruction according to the sensitive data updating frequency, performing integrity verification by using a hash chain technology, recording a result and marking a tampering risk level. And based on the association relationship between the tampering risk level and the abnormal access probability, generating an updated security policy and synchronizing the updated security policy to each data access node.

The invention relates to the technical field of computer storage, in particular to a multi-level cache management method, system and equipment for solid-state storage equipment and a medium. The method comprises the following steps that multiple layers of caches are configured in the solid-state storage device and comprise a high-speed cache layer, a medium-speed cache layer and a low-speed cache layer, and each layer of cache has different access speeds and capacities and is used for storing data blocks with different priorities and access frequencies; dynamically scheduling the data blocks to a proper cache layer through a cache allocation algorithm according to the real-time access frequency, size, life cycle and importance of the data blocks; establishing a comprehensive data block priority evaluation model, and calculating a priority score of each data block; according to the multi-level cache management method, by configuring the multi-layer cache, the priority evaluation model and the multi-path scheduling mechanism, dynamic scheduling and load balancing of the data blocks among different cache layers are achieved, and the overall efficiency of data access is improved.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

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 provides a multi-private-domain visitor portrait sharing and privacy protection routing method based on federal learning, and relates to the technical field of data sharing and privacy protection, and the method comprises the steps: calculating the feature sensitivity and distributing a differential privacy budget, and adding mixed noise to the original data of a visitor portrait for privacy protection, according to the method, feature vectors are encrypted by adopting fully homomorphic encryption based on ideal lattices, threshold password segmentation is performed by utilizing a Viterbi algorithm, sub-key components are distributed to private domain environments, a hybrid network structure is constructed to extract features, federal optimization is performed based on data quality and historical contribution degree, and a feature extraction network is updated. The method comprises the following steps: carrying out feature dimension reduction by using a dual auto-encoder, extracting key features and generating user feature fingerprints, carrying out user grouping by calculating the similarity of the feature fingerprints, and realizing safe sharing of user similarity information and visitor portrait original data in combination with multi-party safe calculation and a hierarchical data access strategy.

The invention belongs to the technical field of computer data protection, and particularly relates to a computer data hierarchical protection method and a dynamic protection system. According to the method, the to-be-protected data is graded and scored, dynamic adjustment of the data protection grade is realized in combination with the dynamic risk of the computer network environment, and more detailed data grading is performed in combination with the semantic risk value by identifying sensitive information in the to-be-protected data, so that the pertinence and effectiveness of data protection are ensured, and the data protection efficiency is improved. Meanwhile, multiple risk parameters of the computer network environment are comprehensively considered, the security state of the network environment is reflected in real time through calculation of the dynamic risk score value, the data protection grade is intelligently adjusted according to the grading score and the dynamic risk score value, and dynamic optimization of the data protection strategy is achieved; and through fusion of historical behavior records and current session contexts of operators, fine management of access permissions is realized, and the security of data access is further enhanced.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention relates to the technical field of electric power government affair management, and provides a cross-department government affair big data business co-processing method based on a computing power platform and data fusion, comprising the following steps: S1, deploying a heterogeneous computing power cluster, and establishing a computing power resource dynamic scheduling data processing platform comprising a CPU, a GPU, an FPGA and an edge computing node; s2, acquiring multi-dimensional data of power grid equipment data, power grid load data, industry power consumption data and government policy data; and S3, designing a cross-department data access system based on the zero-trust architecture, and developing a multi-dimensional model of a power grid risk prediction model, an industry energy consumption analysis model and a carbon emission accounting model in power management. The space-time joint probability prediction and conflict priority ranking algorithm is applied to power-government affair emergency disposal, intellectualization and precision of cross-department business collaboration are achieved, power faults with serious consequences are rapidly handled according to the priority level, and the harmfulness and unpredictability of the power faults can be reduced.

The invention relates to the technical field of data storage and processing, in particular to an RAID card static cache management method and device based on data popularity, and the method comprises the steps that the physical position of a data block needing to be accessed in an SSD array is acquired according to a read-write request; historical data access information of the RAID card is collected, a historical data set is generated, and a neural network model is trained by using the historical data set to obtain a cache management model; predicting and outputting data blocks which are possibly accessed in the future and the access probability of the data blocks through the cache management model; the RAID controller dynamically adjusts a cache strategy in combination with an LRU strategy according to a prediction result output by the cache management model and a read-write request of a file system, and optimizes a storage position and an updating mechanism of a data block in a cache space; and when the hit rate or the data access efficiency does not reach the preset value, adjusting the parameters of the cache strategy and updating the cache strategy. According to the method, cache resources can be more effectively distributed, the cache hit rate is improved, the cache replacement overhead is reduced, and therefore the performance of a storage system is optimized.

A text-to-structured query language conversion method based on a large language model comprises the steps that database outline information is cut, and tables and columns needed by query of a non-target structured query language SQL are eliminated through correlation scoring and Steiner tree problem solving; screening context samples, predicting a similarity score from a text to a structured query language Text-to-SQL by using a cross encoder language model, and selecting a sample most related to a query problem; and injecting the cut database table and column information and the screened context sample into a cue word template, and generating an SQL query statement by using a large language model. The method can be used for optimizing the automatic conversion process from the natural language to the SQL and improving the conversion accuracy, and is used for providing a quick and convenient database retrieval mechanism for a non-SQL user and providing applications such as a data access mode in a wider form for mechanisms such as big language model retrieval enhancement generation RAG and the like.

The invention relates to the technical field of data desensitization, in particular to an electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence, which comprises a data classification module, an access evaluation module, a dynamic desensitization module and a security audit module. According to the method, the data of the e-commerce platform is intelligently classified and evaluated by adopting an artificial intelligence technology, dynamic desensitization and finer access control of the data are realized, various data access requirements can be dynamically met by evaluating the sensitivity level of the file and the security level of the access request, data processing is performed according to requirements, and the data processing efficiency is improved. The security and privacy of user data are effectively protected, through automation of security audit, the recognition efficiency of abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, through real-time analysis and response, the risk of data leakage is reduced, and the practical value of data and the fluency of service operation are kept.

The invention provides an erasure code compatible read-write method and system based on a bidirectional data access agency, and relates to the technical field of distributed storage, and the method comprises the steps: employing multi-agent reinforcement learning to construct a heterogeneous resource portrait and optimize task allocation, combining a space-time graph convolutional network and a federated learning framework to construct a data format conversion model, and constructing an erasure code compatible read-write model; data features are analyzed through an encoder based on an attention mechanism, a fragmentation strategy is generated, self-adaptive encoding is achieved through a capsule neural network, data integrity verification and repairing are conducted through a double-layer space attention network and a graph attention network, the data access efficiency of a heterogeneous storage system is improved, data format compatibility is enhanced, and the data access efficiency is improved. And the data integrity is ensured.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on a context agreement, comprising: receiving a business demand event, extracting a demand text, and disassembling the demand text to generate a plurality of subtasks; obtaining a data source context according to the sub-tasks and task subject information, generating a corresponding data extraction path and an analysis model strategy, connecting a target data source through an access strategy, collecting original data, preprocessing the original data, and dividing the original data into training data and verification data; and training the model based on the analysis model strategy and generating a reasoning result, and completing verification output through a result verification model. According to the method, task disassembly, data access and model generation processes are driven through a model context protocol, and a knowledge graph reasoning and verification mechanism is combined, so that a closed-loop automatic analysis path is formed, and the processing efficiency and intelligent adaptation capability of cross-domain data tasks are effectively improved.

Systems and methods include discovering and classifying any of data discovered by inline cloud inspection, data stored across one or more cloud services, and data stored across one or more endpoints; continuously monitoring access to and usage of classified data, wherein the monitoring is performed in real-time and includes analyzing data access patterns, user behaviors, and application interactions; evaluating a security posture of the classified data by identifying misconfigurations, compliance violations, excessive permissions, and vulnerabilities; and enforcing one or more security policies based on the evaluated security posture.

The invention discloses a data access permission control method and system based on a block chain, and relates to the technical field of block chains, the system is composed of a plurality of function modules, and the system comprises a hierarchical permission module which adopts a role hierarchical strategy and defines the access granularity of a role through an intelligent contract; the dynamic permission adjustment module is used for dynamically adjusting the permission range by analyzing user behaviors in real time through machine learning according to the principle of following the minimum permission in the permission distribution process; attribute encryption and zero-knowledge proof are introduced, an access key is dynamically generated according to user attributes, and sensitive information is hidden in the verification process; the multi-chain collaboration module is used for coding an intelligent contract through an authority rule, automatically executing access request verification, storing encrypted data through a private chain, managing an index and an access strategy through an alliance chain, isolating a storage domain from an access domain, and carrying out process traceability on a data request through a cross-chain verification mechanism; and the key management module is used for generating a true random number private key by adopting a hardware security unit.