3476 results about "Data access" patented technology

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 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 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 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 discloses a protocol self-adaption method and device for household photovoltaic data access, and the method comprises the steps: transmitting a predefined multi-protocol detection instruction set to a newly accessed household photovoltaic terminal when the access of the household photovoltaic terminal is detected for the first time, and the predefined multi-protocol detection instruction set comprises standardized instructions of at least two heterogeneous communication protocols; monitoring response data of the household photovoltaic terminal, and identifying a communication protocol type supported by the household photovoltaic terminal based on a feature matching relationship of the response data; dynamically generating a corresponding data analysis template based on the identified communication protocol type, and establishing a protocol adaptation channel; and data acquisition and analysis are carried out on the household photovoltaic terminal through the protocol adaptive channel, so that protocol adaptive access is realized. Parallel detection of heterogeneous protocols is realized by pre-defining a multi-protocol detection instruction set, non-preset library identification of protocol types is completed in combination with response feature matching, and edge-side adaptive access is achieved by using a dynamically generated analysis template and a protocol adaptive channel.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The invention belongs to the technical field of computers, and particularly relates to an information integration management method and system supporting real-time updating, and the method comprises the steps: accessing a heterogeneous data source through a distributed gateway, carrying out the adaptive analysis of a protocol, and standardizing a structure; adding a millisecond timestamp and a unique identifier, and generating a semantic data cluster through streaming engine sliding window association analysis; after multi-dimensional consistency verification is executed, writing and storing are carried out by adopting a double-buffering and snapshot isolation mechanism; and dynamically assembling the customized view according to the user role and the context and safely returning the customized view. The system correspondingly comprises a protocol analysis module, a time sequence marking module, a streaming association module, a semantic verification module, a double-buffer control module, an index updating module, a view assembling module and a secure transmission module. According to the invention, millisecond-level real-time convergence, conflict-free fusion and on-demand view output are realized, and the data access flexibility, the quality guarantee capability and the service adaptation flexibility are comprehensively improved.

The invention discloses a data storage control method and device, a storage medium and electronic equipment, and relates to the technical field of computers, and the data storage control method comprises the steps that the access frequency of multiple pieces of data accessed in a future time period is predicted according to historical access characteristics of the multiple pieces of data in a storage system; detecting whether the data access types of the data are changed or not according to the frequency range in which the access frequency falls; under the condition that it is detected that the data access type of the target data is changed, a storage strategy of the target data is adjusted according to the target data access type obtained after the target data is changed, and a target storage strategy is obtained; and executing the target change operation on the target data, the target change operation being used for changing the storage strategy of the target data to the target storage strategy, thereby solving the technical problem that it is difficult for a storage system to consider both storage overhead and storage performance during data storage. The technical effect that the storage cost and the storage performance are both considered when the storage system stores the data is achieved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a data hierarchical storage method, device, equipment and medium based on access characterization. Writing the data into a matched first target storage device according to a preset device performance corresponding relation; after the data are stored, continuously monitoring subsequent access characteristics of the data, and dynamically judging whether the heat state is changed or not; and if the popularity state is changed and a specific migration condition is met, redistributing the second target storage device and completing data migration operation so as to realize automatic hierarchical storage management of cold and hot data. By acquiring the data access representation and dynamically identifying the popularity state, hierarchical migration of data with different popularity among storage devices with different performances is realized, the resource use efficiency and access performance of a storage system are improved, and the storage cost is reduced.

The invention discloses a Direct3D memory model compatible method based on adaptive placeholder resources, which comprises the following steps: establishing resource metadata, accessing a scene library, checking an instruction template and double sandboxes when a DXVK is started, describing related resources by the DXVK through the metadata after a D3D application is started, completing resource mapping and metadata dynamic updating, and allocating the resources to the corresponding sandboxes; the DXVK compiles an application shader code, identifies a resource access instruction, matches an access scene and a check template by combining a pipeline stage and binding slot query metadata, instantiates the check instruction and adds the check instruction to the front of a target instruction; according to the unbound resources, adaptive generation of corresponding occupied resources is carried out, the authority and the state are configured, the unbound resources are bound to a Vulkan descriptor set to replace VKNULLHANDLE, and adaptation of the shader interface and the pipeline state parameters is completed to create PSO (Particle Swarm Optimization); and intercepting an application rendering instruction analysis parameter to construct a command buffer area, submitting the command buffer area to a Vulkan queue to trigger a GPU (Graphic Processing Unit) to execute rendering, and finally completing complete conversion and adaptation from the D3D rendering logic to the Vulkan.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

The present invention relates to the technical field of network security protection. Disclosed are a security protection method and apparatus for a customer service management system. The method comprises: acquiring an employee data set, and performing permission assignment, so as to generate an employee operation permission set; performing identity verification on an access user, and determining a data management operation permission range of the access user; acquiring a sensitive data set for encryption processing, and storing same in a customer service management system; accessing data of the customer service management system, implementing an access control policy, and performing risk assessment, so as to generate a system risk level; extracting detected anomalous data, synchronizing same to a security protection network, and outputting a protection control policy; and executing the protection control policy for security assessment, regularly updating the protection control policy, and performing intelligent security protection on the customer service management system. The present invention solves the technical problem in the prior art that customer service management systems have low security protection capacity, which leads to difficulty in preventing network attacks, thus achieving the technical effect of improving the security protection capacity of customer service management systems.

The invention provides an edge fusion diagnosis system and method for multi-source heterogeneous power transmission monitoring data, and belongs to the technical field of power transmission line monitoring. The multi-source heterogeneous power transmission monitoring data edge fusion diagnosis system comprises a multi-source heterogeneous data access module, a data preprocessing and standardization module, an edge computing node, an edge node collaborative diagnosis module, a data compression and feature extraction module and a cloud fusion analysis platform. The method comprises the following steps: accessing multi-source heterogeneous data, preprocessing and standardizing, locally diagnosing by an edge computing node, triggering multi-node collaborative diagnosis when the credibility is insufficient, and uploading key feature data to a cloud for global analysis and model optimization. According to the method, comprehensive integration and efficient processing of multi-source data are realized, the real-time performance, accuracy and comprehensiveness of power transmission monitoring diagnosis are improved, and the efficient monitoring requirement of a complex power transmission network can be met.

The invention discloses a big data risk control decision support system based on multi-modal data, and the system comprises a data access module which is used for accessing and analyzing multi-source entity information, building a grouping calibration rule base, and generating a compliance multi-modal data set; the preprocessing and characterization module is used for preprocessing the data, generating basic characterization of each modal and establishing a micro verifier library; the causal hypergraph module is used for constructing a causal hypergraph and outputting a stable causal structure and a gating mask; the fusion and anti-fact module is used for executing cross-modal fusion to generate a risk representation, generating and verifying a minimum change anti-fact sample, and outputting a reachable identifier and an adjustment representation; the evidence chain module is used for calculating a shortest evidence overpass, generating a structured evidence chain and outputting an evidence index; and the strategy module is used for generating and correcting scores, triggering strategy actions and recording execution information. Through multi-modal data fusion and causal reasoning, high-precision, low-delay and interpretable big data risk control decision support is realized.

The invention discloses a self-adaptive data knitting performance optimization method based on artificial intelligence. The method comprises the steps of obtaining multi-source heterogeneous data based on a data source list and analyzing the multi-source heterogeneous data to generate a metadata set at least comprising technical metadata, business metadata, operation metadata and management metadata; based on the virtual table interface, performing virtual table abstraction and packaging processing on the metadata set to generate a logic data carrier; determining an operational attribute of the metadata set to generate a data knitting path of the logical data carrier based on the operational attribute; monitoring the data access task queue and the access index feedback in real time, extracting resource load association features from the data access task queue and the access index feedback based on the constructed resource scheduling model, and generating a resource scheduling strategy according to the resource load association features; and optimizing the data knitting path of the logic data carrier based on a resource scheduling strategy to obtain an optimized data knitting path so as to perform data knitting on the multi-source heterogeneous data.

Systems, apparatus, and methods of managing access to private data via tokens are disclosed. The disclosed techniques provide for constructing digital tokens that represent a right-to-access private data where the tokens can be managed as individual objects. Further, such digital access tokens (DATs) can be recorded on a notarized ledger, a blockchain for example. Additionally, the rights-to-access can be embodied as a non-fungible token (NFT), which could further include support for establishing homomorphic encryption workspaces in which private data can be accessed or manipulated without exposing the private data.

A data management system establishes connections with access control systems which are delegated by a domain to control data access and maintain data access history associated with the domain. The system receives a group access permission of a set of data resources to a group of named entities and heterogeneous sets of metadata related to the data access history and generates graph objects. The graph objects include named entity nodes and resource nodes. The named entity node represents a named entity associated with an organization, and the resource node represents a data resource. The system traverses access paths that connect the named entity node and the resource node determines a utilization level of a set of access paths. Based on the utilization level, the system revokes the set of access paths, thereby revoking an access permission of the set of named entity in the group to the specific data resource.

The invention provides a real estate full life cycle intelligent management method and system based on Internet of Things perception, and the method comprises the steps: distributing a unique hardware identifier of equipment, combining a lightweight digital certificate with a pre-shared key to achieve two-factor authentication, and storing a root certificate in a cloud end; an access authentication process is optimized, an identity label and a certificate signature request need to be submitted when a node accesses for the first time, a temporary session key is generated after the gateway verifies, and abnormal access is immediately isolated and reported; data integrity verification is implemented, and after a sensor collects data, an abstract is generated and data encryption transmission is carried out; after decryption, the receiving end recalculates the abstract for comparison, and if not, the abstract is discarded and nodes are marked to be abnormal; a data anomaly detection model is constructed, energy consumption fluctuation is determined based on historical data, a frequency baseline is collected, data reasonability is monitored in real time, secondary authentication is triggered when the data is abnormal, and node data access is suspended if the data fails; a hierarchical key system is established, hardware is solidified by a root key, session keys are alternated for 24 hours, and data encryption keys are dynamically derived and distributed through encryption channels.

The invention provides an automatic visual screen configuration method, and belongs to the technical field of data processing, and the method comprises the steps: obtaining initial data from a plurality of data sources, carrying out the analysis processing of the types of the data sources through a dynamic protocol adaptation layer, and carrying out the data cleaning and standardization of the analyzed data, and obtaining a standard data set; extracting feature information based on the standard data set, and matching the feature information through a pre-configured rule base to obtain a visual display component; and obtaining a target screen physical parameter, determining the priority of the visual display component, and obtaining a component layout coordinate scheme through an adaptive layout algorithm, the target screen physical parameter and the priority. A visual component template is automatically matched by analyzing a data source type, and an optimal layout scheme is generated by utilizing an algorithm, so that full-process automation from data access to visual display is realized, and the problems of low efficiency and high cost of traditional manual configuration are avoided.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention relates to a data access prediction scheduling method and system based on a dynamic threshold value, and belongs to the technical field of data processing. The method comprises the following steps of: dynamically adjusting an abnormal detection threshold in an access behavior by utilizing reinforcement learning and an error feedback mechanism, realizing preposition identification of potential high-frequency access data by combining abnormal point classification processing and access trend prediction, and generating a scheduling request to drive cache preheating or interface priority scheduling of the data. According to the method, access behavior modeling, anomaly recognition, trend prediction and strategy feedback mechanisms are combined, interface access optimization requirements in a mass data storage system are met, and the key problems that access behaviors are unpredictable, scheduling response lags and the like are solved.

Systems and methods disclosed herein automatically authorize, audit, and manage usage of protected digital content via agentic artificial intelligence (AI) models. A data access / usage request is received (e.g., from a graphical user interface) that is associated with digital assets licensed from third parties. The system uses a first AI agent set to identify the digital content and retrieve corresponding access policies from a distributed database. The system uses a second AI agent set (same as or different from the first AI agent set) to evaluate the request against the retrieved policy to generate a permission set and / or settlement instructions. The system uses a third AI agent set (same as or different from the first and / or second AI agent sets) to embed digital watermarks and / or cryptographic signatures into the accessed content, and to record an audit trail of access, authorization, and / or settlement events in a distributed ledger or database.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention discloses a hospital Internet of Things equipment intelligent monitoring and fault early warning system based on edge computing, relates to the technical field of equipment monitoring and early warning, and aims to solve the technical problem that fault discovery lags in a high-real-time scene of an existing intelligent monitoring and fault early warning system. The system is used for collecting various data of hospital Internet of Things equipment and sending the data to a preset storage position, and comprises a local database, a data processing module and an abnormal data judgment unit; the Internet of Things base station is used for being connected with Internet of Things equipment, collecting equipment data and achieving intelligent monitoring and fault early warning through an algorithm model, and the algorithm model is constructed based on a core algorithm and rules; and the hospital Internet of Things edge computing platform is used for carrying out edge computing management on the data sent by the Internet of Things base station, feeding back an analysis result and storing the data. The method has the advantage of improving the fault discovery speed of the Internet of Things equipment.

The invention relates to a distributed data security storage and encryption system based on an AI technology. The system comprises a data acquisition and preprocessing layer used for acquiring original data and preprocessing the original data to obtain an identifiable data object; the AI identification and strategy decision layer is used for identifying data features of identifiable data objects based on an interpretable AI model to obtain an encrypted instruction packet; the encryption execution and distributed storage layer is used for performing encryption storage processing on the original data content according to an encryption instruction packet to obtain data access interface information; and the dynamic access control layer is used for obtaining user information in response to a data access request instruction of a user, recombining and decrypting the data by using the data distribution mapping index based on the access control strategy table, and returning a plaintext data fragment. By adopting the system, a high-strength, differentiated and multi-level security mechanism can be realized through the AI, intelligent encryption strategy decision is carried out, and the security requirements of multiple scenes and multiple types of data are met.

The invention relates to the technical field of storage control, in particular to a memory control method, which comprises the following steps: acquiring a target logic address in response to a reading request from a host system; determining a data access mode associated with the target logic address in real time based on the historical access information, wherein the data access mode at least comprises a continuous mode, a random mode and a hotspot mode; according to the determined data access mode, triggering a data prefetching strategy and a flash translation layer query strategy matched with the access mode; obtaining target data from a memory module based on a data prefetching strategy and a flash translation layer query strategy; and scheduling the cache resources and the input / output bandwidth according to a predetermined resource management strategy, and returning the target data to the host system. The prefetching accuracy and the cache utilization rate are improved.

The invention discloses a data access method based on an industrial internet and an industrial internet integrated system, and the method comprises the steps: collecting real-time operation parameters, packaging the parameters into an information packet, recognizing nodes by an edge gateway, dynamically discovering a model by an edge node, evaluating the nodes to generate availability scores, verifying the consistency of data, and recombining the data to form a complete unit. And distributing to a corresponding processing queue to analyze and extract features. According to the system, through an edge node dynamic discovery model and a data consistency verification algorithm, in combination with industrial internet parameters, collaborative optimization of node dynamic discovery and data verification is achieved, all units are connected and interacted through an industrial Ethernet bus, data access reliability and real-time performance are improved, and the system is suitable for data processing requirements in industrial internet complex scenes.

The invention provides a priority resetting method of a cache line and an artificial intelligence chip, and relates to the technical field of artificial intelligence chips, the method comprises the following steps: detecting that a cache group contains a cache line of a first priority state (namely, the priority is greater than a preset level), and in addition, detecting that the cache group contains the cache line of the first priority state (namely, the priority is greater than the preset level); detecting a request state between the processing core and the cache when the target duration (namely the interval duration from the previous access of the data request carrying the priority prompt to the cache group) is greater than the preset duration; when the request state is that the data request sent by the processing core is not detected, spontaneously initiating a priority resetting process, and adjusting the priority of the cache line in the first priority state in the cache group to a preset level; in this way, hardware can automatically reset the priority of the cache line, the situation that the cache line with the high priority occupies the cache space for a long time is avoided, the cache space reserved for non-important data access is improved, the overall performance of the cache is improved, and meanwhile processing of a data request (normal access) of a processing core is not affected.

The invention relates to the technical field of ecological environment monitoring and geographic information, in particular to a coastal wetland soil sample point quality evaluation method and system based on multi-source environmental data. According to the method, remote sensing, climate, terrain, ocean and historical land coverage data are fused, and indexes are extracted and standardized; an environment similarity score (weighted Markov / neighbor weighted Euclidean), a historical consistency score (multi-temporal coverage + transfer penalty) and a source reliability score (metadata integrity + anomaly detection + NDVI-SOC residual) are constructed respectively; comprehensive scores are obtained through self-adaptive weight fusion, A / B / C grades are divided, and a report and a high-quality sample point set are output. The system comprises a data access module, a scoring engine module and a visualization module, and the precision and robustness of soil mapping and carbon storage evaluation can be remarkably improved.

The invention discloses a dynamic strategy data access method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a dynamic strategy parameter corresponding to a data access object according to a data access request under the condition of obtaining the data access request corresponding to the data access object; determining a dynamic data access strategy corresponding to the data access object according to the dynamic strategy parameter; and determining target access data corresponding to the data access request based on the dynamic data access strategy, and sending the target access data to the data access object. Based on the technical scheme, the dynamic strategy corresponding to the data access request is determined, and then the target access data is dynamically determined according to the dynamic strategy, so that the data access strategy can be dynamically changed, the strategy flexibility problem in a specific scene of urban and rural fusion is solved, and the data credibility is improved.