1406 results about "Data resources" patented technology

The invention relates to a dynamic data pipeline construction method based on artificial intelligence and multi-modal data processing, and belongs to the technical field of data fusion treatment and artificial intelligence. According to the method, a multi-modal deep learning model is adopted, semantic analysis is performed on business documents, user requirements and function specification instructions, and key indexes, data requirements and logic constraints are extracted; constructing a multi-modal knowledge graph, performing unified representation and feature extraction on structured and unstructured data resources, and performing embedded learning by using a graph neural network; based on a deep semantic matching model and a hybrid recommendation system, realizing accurate matching of business requirements and data resources; modeling generation of a data processing flow into a Markov decision process by adopting a reinforcement learning strategy, and automatically generating an optimized data processing workflow; the adaptability of the model to new business requirements is improved by utilizing a meta-learning algorithm; and dynamic scheduling and execution of tasks are realized through a workflow engine. According to the invention, the data processing efficiency can be improved.

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 belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention belongs to the technical field of data storage, and discloses a multi-source marine environment data storage method and system based on HDF5. According to the method, a hierarchical storage framework is constructed, a domain-type-source three-level directory architecture is constructed, a space-time dimension dynamic extension mechanism is executed, and space-time composite coding is performed; designing an HDF5 nested packet storage architecture, wherein grid topological structure definition, dynamic resolution self-adaptive storage, multi-source data processing and metadata management are carried out; constructing distributed data association and indexes, including data standardization preprocessing and cross-source data association; the storage performance is optimized and expanded, wherein density sensitive blocking and layered compression are carried out. According to the method, redundant storage of a high-latitude region is greatly reduced, and dynamic adaptation of a local encryption grid is supported. According to the invention, open circulation and collaborative utilization of interdisciplinary data resources are facilitated.

The invention relates to a hydropower station safety comprehensive management system based on a digital twinborn scene, which designs a hydropower station data resource coding system, establishes a hydropower engineering digital twinborn base, integrates all related geospatial data, basic data, monitoring data and business management data of a hydropower station, and constructs the hydropower station digital twinborn scene. Predicting displacement and deformation conditions of a building structure of the hydropower station and a change trend of important facility and equipment state indexes of the hydropower station, and predicting and pre-judging safety risks therein; the reservoir flow of the hydropower station, the upstream and downstream river section water level, the flow change and the power generation output and the power generation capacity of the hydropower station are forecasted or predicted, corresponding plans are provided for flood early warning or meteorological disaster early warning or water level over-limit early warning, and key links of the plans are simulated in combination with a simulation engine; and performing simulation calculation on different power generation and reservoir scheduling strategies to obtain an optimal scheduling scheme. According to the invention, the intelligent management of hydropower station engineering safety, scheduling safety and power generation safety is realized.

This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.

The invention relates to the technical field of access control, in particular to an access control strategy generation method and system based on a large language model.Automatic cue words are generated through cue word engineering, enhanced data are generated for an example document according to the automatic cue words, and the example document is a text marked with access control elements; learning an access control element reasoning process in the example document and the enhanced data by using a plurality of large language models, and generating a corresponding access control strategy thinking chain, so as to obtain an access control strategy set of a target document in the attribute mining and strategy generation task based on the thinking chain; and based on a multi-model voting mode, obtaining a final access control attribute and strategy for the access control strategy to be optimized in the access control strategy set according to an optimization result. According to the method, the access control strategy with relatively high quality can be generated, and the method has a relatively good application prospect in the aspects of dynamic and mass data resource access authorization and management under novel open environments such as big data and the Internet of Things.

A system and method for dynamically visualize and modify user authority and security resource edge based on access behaviors in a cloud computing environment. The method provides a new access control perspective to ensure the credibility of database object usage patterns, build clearer and explicit security data consumption mode for data usage requests, and guide tenants to have a more comprehensive understanding and concise use. Processing components and method are configured to gather data and analytics including collecting data resource and database object usage tagging both resources and users with security level by machine learning and building an access model for each role. The method uses graph modeling with created, trained access patterns for users protected resources and produces in-memory implicit sub-groups for users with similar access requirements and limits their resource accesses according to their history behaviors. Further provided are outlier activities prediction and in-real time access authorization modification.

The invention discloses a self-supervised learning and multi-modal biomedical signal processing method, system and terminal, and belongs to the technical field of biomedical engineering and information, and the method comprises the steps: employing an unsupervised learning auto-encoder to extract the feature representation of multi-modal data; and taking the feature representation of the source domain data as input, taking the feature representation of the target domain data as output, and training the large language model based on the Transform architecture based on maximum likelihood estimation. According to the method, relevance and common features among multi-modal data are learned through an auto-encoder, and feature representation extracted by the auto-encoder is applied to pre-training of a large language model, so that the large language model learns more abstract and advanced feature representation, and the processing capacity and generalization performance of the multi-modal data are improved; meanwhile, due to the fact that an unsupervised learning method is adopted, a large amount of unmarked data can be used for pre-training the large language model, and therefore data resources can be better utilized.

A data management system may include data connectors configured to establish connections with access control systems and receive heterogeneous metadata related to the data access history. The system may include a transformer that stores graph objects in memory. The graph objects may include application account nodes representing application accounts associated with the domain and resource nodes representing data resources associated with the domain. The system may further store event objects. The system may render for display a data access graph that illustrates a data permission traversal path from a source node to a particular data resource. The data permission traversal path may include a graphical representation representing the particular application account having permission to access the particular data resource. At least a portion of the graphical representation may be rendered according to the data access activity level of the particular application account accessing the particular data resource.

The invention provides a resource scheduling method and system for a converged network, an intelligent terminal and a medium, and particularly relates to the technical field of network communication, and the scheme comprises the steps: carrying out the priority division and sorting of target data collected in real time according to a preset priority evaluation strategy through employing a 5G network, and obtaining the target data with the marked priority; performing time synchronization processing on all the target data marked with the priorities by using the TSN equipment to obtain the target data after time synchronization; and according to the priority and the current network state, carrying out dynamic resource allocation on the target data after time synchronization, obtaining a resource allocation result and executing a resource scheduling operation. According to the scheme, priority scheduling and a time synchronization strategy are combined based on the 5G-TSN fusion network, priority processing and accurate time synchronization of key data are ensured, the reliability and effectiveness of data transmission can be improved, bandwidth allocation is dynamically adjusted according to the data priority, and the accuracy of data resource scheduling and the data transmission efficiency can be improved.

The invention discloses an AI-based meter reading data management and resource scheduling optimization method and system, and relates to the technical field of intelligent management of meter reading data. The method comprises the steps that meter reading data of a meter is obtained, and the meter is provided with a corresponding two-dimensional code metal identification piece; meter reading data are managed according to geographic areas and user types, a multi-level data feature system is formed, and the user types comprise resident types and industrial types; carrying out trend modeling on the multi-level data characteristic system by adopting an AI model so as to determine an energy consumption trend in a future period; dynamically adjusting a meter reading task according to an energy consumption trend and a network load condition so as to dynamically schedule human resources, data resources, network resources and hardware resources; wherein the power grid topology position code is used for binding an association relationship between the meter reading task and the power grid physical conduction path. According to the invention, full-link optimization from meter reading data acquisition to resource scheduling is realized.

The invention relates to the technical field of fault diagnosis, and particularly discloses a bearing fault diagnosis method based on adaptive feature migration fusion. According to the method, an adaptive feature migration fusion algorithm is utilized, and multi-mode signals of acceleration, load, temperature and rotating speed are comprehensively processed, so that accurate fault diagnosis of the embedded intelligent bearing under a complex working condition is realized. Aiming at the problems that a traditional diagnosis method is difficult in signal feature extraction and large in data distribution difference under different working conditions, the invention provides a self-adaptive feature migration fusion algorithm, and the robustness and expression ability of fault features in different working conditions and data sets are improved by constructing a migration learning model and combining a feature selection and fusion mechanism. Compared with the dependence of a traditional deep learning algorithm on large-scale labeled samples, the method can efficiently utilize a small amount of label data, the requirements for hardware computing power and data resources are remarkably reduced, and an efficient and flexible embedded bearing fault diagnosis method is provided for online fault diagnosis of an industrial site.

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 embodiment of the invention provides a data access method and device, equipment and a readable storage medium. The method comprises the following steps: in response to a demand for processing a data resource generated in a target application, sending a data access authorization request for the data resource to a plurality of clients of the target application, the plurality of clients being associated with the data resource. And respectively receiving authorization information for the data access authorization request from at least one client in the plurality of clients. And obtaining at least one access credential corresponding to the at least one client based on the authorization information. And accessing target data associated with the at least one client in the data resource by using the at least one access credential to process the target data. Through the process, the data security is enhanced, and unauthorized data resource access and leakage risks are avoided, so that the data security is improved.

The invention relates to the technical field of data generation of sparse scenes, and discloses a data set generation method and system of a sparse scene, and the method comprises the steps: inputting an image which is actually collected by a target sparse scene into a target detection model, and obtaining a detection frame of a target foreground and a background; using a preset segmentation large model to take the detection frames of the target foreground and the background as cue words to perform model reasoning, respectively obtaining masks of the target foreground and the background, and obtaining a target foreground image and background area coordinate information based on the masks of the target foreground and the background; and integrating the target foreground image and the background area coordinate information by using an adaptive image fusion technology to generate a new target sparse scene image, and adding the newly generated target sparse scene image into the training data set of the target detection model. According to the method, only a small number of target objects and background images are needed, a large number of new high-quality images can be generated, the problem of scarcity of scene data is solved, and rich data resources are provided for subsequent model training.

The invention discloses a Frechet distance-based abnormal inflow traceability method and system for a sewage treatment plant, and the method comprises the steps: carrying out the unit division of the inflow range of the sewage treatment plant through combining land parcel information, pipe network information, convergence conditions, monitoring stations and the like, and analyzing the correlation between total phosphorus and conductivity; arranging a pipe network water quality monitoring micro station, and carding a factory-network-land parcel-source pointer relation library; a sewage treatment plant big data warehouse is constructed, related data are cleaned and treated, and the data are accessed to a pollutant traceability visualization platform; a sewage plant water pollution traceability model is constructed, a water inlet total phosphorus standard exceeding event is selected to carry out traceability research, the traceability model is embedded into a visual platform, and sewage plant total phosphorus concentration standard exceeding traceability visualization is achieved. Compared with the abnormal inflow tracing method of the sewage treatment plant, the large data resource utilization rate can be greatly improved while a large amount of manpower and material resources are saved, and a basis is provided for locking secretly-discharged enterprises, predicting pollution diffusion and environment emergency.

Systems and methods for risk assessment of user accesses to data resources are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include: a processor; and a memory coupled to the processor, where the memory includes program instructions store thereon that, upon execution by the processor, cause the IHS to: obtain a plurality of resource risk weights of a respective plurality of resources, and a plurality of access permissions of a user for the respective plurality of resources; and generate based, at least in part, on the plurality of resource risk weights and the plurality of access permissions of the user, a risk score for the user that represents a level of security impact of the user on the plurality of resources.

The invention provides an agricultural problem prediction and decision support system based on a large model, and the system comprises a data collection and preprocessing module which is used for collecting data resources of a plurality of channels, and carrying out the data cleaning, data conversion and data storage through a distributed calculation framework; the intelligent agent large model module comprises a large model for agricultural problem prediction, and the large model is trained by adopting historical data; the policy response module is used for extracting information in the policy file, converting the information into structured data and providing a calling interface; and the decision support module is used for generating specific farming activity suggestions based on the structured data in the policy response module and the prediction result of the agent large model module. The system provided by the invention can dynamically provide suggestions in response to farming activities according to crop growth conditions.

The invention discloses an enterprise cloud asset global data integration management method based on a data center station, and aims to solve the problems of data source dispersion, complex management, data islanding and the like, and the method comprises the following steps: automatically identifying and classifying various data sources of an enterprise through an evolutionary algorithm, and generating a dynamic data source directory; based on the directory, a self-adaptive data access strategy is generated, unified access of structured, semi-structured and unstructured data and data synchronization of a cross-cloud platform are realized, a unified data model is constructed in a data platform, and unified mapping of data of different sources is realized through a semantic mapping technology. In the data transmission process, the optimal path set is adopted to ensure high efficiency and stability of data transmission. According to the method, the received data is dynamically treated through a self-adaptive strategy, a cross-cloud data virtualization layer is constructed based on the treated data, transparent management of data resources is realized, and data analysis is performed by adopting real-time processing and a differential privacy enhancement algorithm.

The invention relates to the technical field of data resource management, in particular to an online evaluation method for data resource value, which comprises the following steps of: constructing a multi-dimensional evaluation index set, and acquiring metadata and market dynamic information of to-be-evaluated data in real time through a distributed interface; carrying out weight distribution on the index set based on a dynamic weighting algorithm, generating a weight coefficient set and constructing a value calculation model; performing standardized preprocessing on the collected original data, inputting the data into the model, and calculating an initial estimated value; and dynamically correcting the initial value estimation value based on the timeliness attenuation factor and the data scarcity factor, and generating a final value estimation value and an estimation report. Through multi-dimensional index construction, a dynamic weighting algorithm, standardized preprocessing and two-factor correction, automatic evaluation of the data value is realized, the problems of single dimension, static weight and neglect of timeliness scarcity of a traditional method are solved, the real-time performance and scientificity are improved, and a quantitative decision basis is provided for data capitalization.

The invention discloses a data governance method and device based on an MCP protocol and a medium, and the method comprises the steps: obtaining a language instruction of a user through a data center regulation and control MCP client, determining a data governance demand according to the language instruction, and determining an obtaining request according to the data governance demand; and sending the acquisition request to the MCP server to acquire the data resource through the MCP server, and performing data processing according to the data resource. The data governance process is started through the natural language instruction, user operation is facilitated, and the use threshold is lowered. And the data governance demand and the acquisition request are accurately determined through the analysis instruction, so that the governance pertinence is improved. And resources are obtained and processed by means of the MCP server, so that efficient data management is realized. According to the process, user requirements and data acquisition and processing links are integrated, so that data management is more convenient, accurate and efficient.

A system establishes connections with a data resource system and connections with a security monitoring system. The system receives metadata related to data access history of the data resources from the data resource system and risk related signals associated with data access activities from the security monitoring system. The system generates an access graph comprising graph objects that are connected by access paths signaling access levels of the data resources controlled by the data resource system. The system aggregates the metadata from the data resource system, the risk related signals from the security monitoring system, and data associated with the access graph to generate normalized risk signals and identifies a cybersecurity risk-related instance associated with an access paths in the access graph. The system generates an alert which allows a user to adjust access privilege of a data resource associated with the cybersecurity risk-related instance.

The invention relates to the technical field of intelligent customer acquisition, in particular to a cross-border supply chain financial service AI intelligent customer acquisition system based on big data analysis. Functional components such as a big data access engine, an artificial intelligence analysis model, a customer portrait modeling engine, a preference setting interface, a public opinion and risk identification module, an intelligent screening engine and a docking output module are integrated; multi-source data such as customs data, trade data, enterprise industry and commerce data, judicial public data, international manufacture and cutting lists and internet public information from all places around the world are widely fused. The system performs modeling calculation on industry categories, regional distribution, export history, transaction records, public opinion trends and compliance risks of import and export enterprises, establishes customizable customer optimization rules, accurately outputs a target customer list which can be covered by credit insurance and financing service in combination with a machine learning algorithm, and attaches contact information. Various import and export related data resources can be accessed, automatic screening and output of credit insurance and account receivable financing adaptive exporters are realized through preference setting and AI analysis, the customer obtaining efficiency is remarkably improved, the marketing and risk control cost is reduced, and the economic benefit is improved. The problems that traditional cross-border supply chain financial service customer obtaining depends on manual recommendation, offline visit and a static customer library, the efficiency is low, and small and medium-sized export enterprises are difficult to cover are solved.

The invention discloses an intelligent intervention management recommendation method and device based on a health portrait, and belongs to the technical field of health management. And an intelligent and scientific health management auxiliary tool is provided for primary doctors, so that the problem of insufficient primary medical resources is solved. The health state of an individual is dynamically reflected by using a health portrait, personalized recommendation is generated in combination with an algorithm, and a one-step intervention scheme is avoided. By continuously tracking and evaluating the effectiveness of intervention measures, the recommendation strategy is dynamically adjusted, and the health management efficiency is remarkably improved. And health data acquisition from multiple platforms is supported, heterogeneous data resources are fully utilized, and the health condition of the user is comprehensively described.

The invention provides an urban operation index monitoring method based on multi-source heterogeneity, and the method comprises the steps: determining an urban operation monitoring target, determining a field needing to be monitored, carrying out the design of a target monitoring index, analyzing a data source needed by an attention index, converging urban operation multi-source data, and carrying out data analysis and standardization processing. The method comprises the following steps: setting a data sample, establishing an analysis model, carrying out model training based on historical data, and constructing an urban operation index monitoring model with adaptive capacity through rule setting and model parameter adjustment and optimization. According to the method, data resources in the fields of industrial economy, urban governance, green ecology, happiness and livability and the like produced in the urban operation process are fused, and data decision support is provided for the urban operation comprehensive situation and sensing of urban operation risks and development trends through data aggregation, data mapping, data governance, model analysis and other operations.

The invention discloses a multi-dimensional evaluation method for credential environment migration, and particularly relates to the field of evaluation. According to the method, an evaluation index system covering infrastructure, an application system, data resources and security compliance is constructed, and comprehensive scanning is realized through instruction set detection, binary analysis and equal security compliance inspection; dynamically calculating the weight by adopting an AHP-entropy weight hybrid model, and triggering weight rebalance based on the volatility; through combination of static scanning and a dynamic probe, the compatibility, performance and safety differences are quantitatively analyzed; and finally, generating a visual risk assessment report, integrating data of each stage to calculate a compatibility score, a performance loss rate and a security level, providing instruction level difference comparison, a performance flame graph and a vulnerability attack path interaction view, and outputting a migration suggestion comprising a priority reconstruction list, an NEON instruction optimization scheme and a staged reinforcement plan.

Transaction platform systems and methods include those in which various systems interact with each other to conduct transactions in various ways. A method for configuring and launching a marketplace includes: identifying, by a processing system having one or more processors, opportunities to facilitate the configuration of a new marketplace; receiving marketplace opportunity data, where the marketplace opportunity data includes information related to a set of one or more types of assets; determining configuration parameters to be implemented in the new marketplace; determining the feasibility of implementing the configuration parameters in the new marketplace; determining data resources to support the new marketplace; determining an architecture for the new marketplace; determining a configuration of data resources in a data model for the marketplace; configuring marketplace objects; connecting selected data resources to populate the marketplace objects; and launching the new marketplace.

The invention belongs to a data generation method, provides a multi-modal medical data generation method and a related device, and aims to solve the technical problems of multi-modal medical data resource limitation, modal missing and data scarcity of an existing medical data generation method. Obtaining a fine-tuning medical large model; obtaining a text instruction and a multi-modal data stream associated with the task, and obtaining a task type, an existing modal list, a target modal list and a task instruction after analysis of the text instruction according to the text instruction and the multi-modal data stream; according to the existing modalities in the existing modality list and the task instruction, obtaining a comprehensive shared feature representation; encoding and decoding the comprehensive shared feature representation to obtain a converted feature instruction, and generating a modal mark for a target modal in the target modal list; and updating a generation state through dynamic estimation based on the converted feature instruction.

The invention relates to the technical field of data circulation, in particular to a method and a system for realizing data standard management in the field of data circulation, which are used for managing common data standards, providing term standard basic information management and providing code standard content structured management. The method and the system for realizing data standard management in the data circulation field provided by the invention have the beneficial effects that efficient circulation of data resources is promoted, an access party is promoted to supply data according to a data standard, and all parties are ensured to have consistent understanding on data; the core data quality is improved, the platform can perform quality verification on the core data resources according to the data standard, and the core data resources can be circulated after passing the verification; semantic-level data circulation service is achieved, ontology models are standardized in a unified mode, universal-level data semantic association data models are standardized in a unified mode through platform organization, and data resource semantic interconnection and intelligent search service are gradually promoted.