884 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 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.

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.

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 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 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 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 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.

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 discloses a multi-source data resource key information mining method and system, and the method comprises the steps: obtaining multi-source data of various forms and types, and carrying out the preprocessing; performing division according to data types; word frequency statistics and word correlation analysis are carried out on the character data, and numerical value meanings of the digital data are extracted through context semantic correlation; constructing a cross-modal key information data set based on the key information data set of each data type; designing a multi-branch long-short-term memory neural network model, inputting a cross-modal key information data set into the model for training, and generating key information mining models for different fields by dynamically adjusting network parameters; and selecting to carry out key information real-time extraction and updating on the optimal model, generating a knowledge graph, and carrying out visual operation. The method has the advantages that through multi-source data fusion and processing, the long-short term memory neural network model is combined, the knowledge graph is generated, and domain-specific key information mining and visualization operation are efficiently carried out.

The invention relates to an enterprise multi-dimensional business index analysis data resource optimization method, a medium and equipment, and the method comprises the steps: collecting a multi-dimensional business index analysis request of an enterprise business system, carrying out the mode recognition and feature extraction of the multi-dimensional business index analysis request, and generating business entity features; and inputting the business entity features into the resource allocation model, outputting a priority score and a resource demand prediction result, calculating an optimal resource allocation scheme through an optimization decision model based on the priority score and the resource demand prediction result, and performing automatic resource scheduling according to the optimal resource allocation scheme to generate a business decision instruction. And finally outputting a business decision instruction and a data resource management report. The resource reuse potential among different requests is automatically identified and quantified by adopting a set similarity algorithm, so that the enterprise data resource utilization efficiency is remarkably improved, the redundancy calculation consumption is reduced, and the resource allocation is more reasonable and efficient; manual intervention is reduced, and enterprise data resource management efficiency and response speed are improved.

An autonomous risk investigation system and methods are provided that are configured to automate investigation tasks during a plurality of investigation stages using an intelligent decision automation framework. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform investigation recommendation operations which include receiving a task for a risk investigation of a fraud alert for an entity, determining a prioritization of the fraud alert, allocating the task for the fraud alert to an investigator, determining investigation data for the task, collecting the investigation data from at least one data resource, orchestrating the investigation data with at least one additional task, and outputting the investigation data for the risk investigation in compliance with data security requirements and the required legal standards and regulations.

The invention discloses a data resource management system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. According to the method, multi-source data is collected, and data cleaning and preprocessing are carried out; selecting meteorological indexes and soil indexes, and calculating an influence factor of each index in the meteorological indexes and the soil indexes; determining the weight of each influence factor, and calculating to obtain an agricultural product external environment influence factor; historical data are collected, meteorological data and soil data are matched with agricultural product planting areas and planting periods, and a multi-dimensional data set is constructed; performing feature engineering on the multi-dimensional data set to obtain a feature set; training a long short-term memory network model, and performing model evaluation and optimization; after training is completed, meteorological data and soil data are input into the model, and agricultural product yield prediction data are output; and performing data alignment, comparing the predicted agricultural product yield value with the average value of the market demand, and analyzing a confidence interval to obtain a decision suggestion.

The invention discloses a data resource scheduling management system and method based on a directory chain, and relates to the technical field of data resource management and scheduling, and the system comprises directory chain management, authority management, approval and data sharing scheduling modules; the directory chain management module unifies catalog resources and builds and updates a directory chain; the permission management module monitors permission conflicts in real time, judges the priority according to a multi-dimensional rule and adjusts conflicting permissions; the examination and approval module refers to permission, historical records and resource availability examination and approval application; the data sharing and scheduling module searches data according to a directory chain and judges and schedules the data in combination with permission; according to the method, the problems of dispersed data resource management, authority conflict, low data sharing scheduling efficiency and the like are effectively solved, the resource management efficiency is improved, the authority management security is enhanced, and the data sharing scheduling process is optimized.

The invention discloses a user authority control system for a power system. The user authority control system comprises a multi-level organization and user management module, a data resource management module, a dynamic authority control module and a monitoring and auditing module, the multi-level organization and user management module is used for realizing organization maintenance, post management and employee management; the data resource management module realizes controlled resource management and business data management; the dynamic authority management and control module realizes authority distribution, role life cycle management, authority inheritance management, organization and authority transfer management and data authority management; and the monitoring and auditing module is used for realizing permission use recording, auditing log recording, abnormal behavior monitoring, data classification management, function permission management, dynamic permission control and logic extension. According to the invention, through organization and user management, data resource management, dynamic authority management and monitoring auditing, the control of the user authority of the power system is realized, the reliability is higher, the compatibility is better, and the expandability is better.

In a cross-security-region resource access method in a cloud computing system, a virtual block device, which is simulated by a virtio protocol-based BLK front-end driver in the cloud computing system and taken as an RPC calling client, receives an IO access request for data resources maintained by a resource service program triggered by a business application installed on each virtual node through a simulated RPC channel on the virtual block device; in response to the IO access request, encapsulates the IO access request into an RPC request for performing an RPC calling for target data resources, sends the RPC request to a RPC calling server program simulated by a virtio protocol-based BLK service through the RPC channel, so that the RPC calling server program submits the RPC request to a resource access agent program, which submits the RPC request to the resource service program to complete cross-security region resource access.

The invention discloses a data transmission method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining a to-be-sent data volume quota application; sending the to-be-sent data volume quota application to a receiving end, so that the receiving end determines a data volume quota and issues the data volume quota based on application information carried by the to-be-sent data volume quota application; distributing the data volume quota issued by the receiving end to a data transmission queue corresponding to the to-be-sent data volume quota application, so as to update the data volume quota balance of the data transmission queue corresponding to the to-be-sent data volume quota application, and obtaining the updated data volume quota balance; and through a data transmission queue corresponding to a to-be-sent data volume quota application, transmitting a data stream to a receiving end based on the updated data volume quota balance. According to the invention, the receiving end can dynamically allocate a proper amount of data volume quota according to actual needs, and the data resource waste of the receiving end in the transmission process is reduced.

The invention discloses an early warning method, system and terminal based on a fault instability evaluation model, and the method comprises the steps: obtaining crack data collected by a plurality of sensors, carrying out the crack propagation analysis of a rock according to the crack data, and obtaining the comprehensive crack multi-modal data; inputting the fracture multi-modal data into a fault instability evaluation model, and coupling the fracture multi-modal data by the fault instability evaluation model to generate predicted fracture parameters; and when the predicted crack parameter reaches a target early warning threshold, matching a corresponding early warning level according to the predicted crack parameter to obtain a target early warning level, and taking a corresponding response measure according to the target early warning level. According to sensor data, cracks are analyzed, multi-modal data are generated, the instability risk is evaluated, the early warning level is matched, corresponding response measures are taken, fracturing related parameters are rapidly adjusted, valuable data resources are provided, and potential risks such as resource consumption or geological disaster aggravation caused by excessive fracturing are avoided.