280 results about "Data entity" patented technology

Various embodiments of the present disclosure provide machine learning architectures and data processing techniques for improving computer-based text comprehension. The techniques may include identifying a plurality of data entity tokens from a target section of a multi-section natural language document and generating, using an embedding layer of a semantic chunking model, a text span embedding for a text span of the target section. The techniques may include leveraging the semantic chunking model to generate an attended span representation for the text span based on the text span embedding and the plurality of data entity tokens. The techniques may include identifying an entity topic that corresponds to the text span based on the attended span representation and, responsive to an identification of the entity topic, generating a subgraph data object for a knowledge graph using the text span.

Disclosed in the embodiments of the present disclosure are a knowledge graph construction method and apparatus, a storage medium and a terminal. The method comprises: generating at least one user entity on the basis of user data in a target scenario, and generating at least one data entity on the basis of unstructured data in the target scenario; for the data entities of each type, separately extracting a data association relationship for every two data entities of the type; and on the basis of a corresponding relationship between each user entity and each data entity and the data association relationships between the data entities, constructing a knowledge graph corresponding to the target scenario. The present application generates the user entity and the data entities of the unstructured data on the basis of original data in scenarios; and by means of analysis of the unstructured data, the present application extracts the association relationship between any two entities among data entities of each type, and uses same as edges for connecting entity nodes, thus effectively integrating data of various types in the scenarios, and constructing knowledge graphs with richer and more comprehensive knowledge in the target scenarios.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.

The invention discloses an AI-based digital project performance evaluation data processing method and system, and relates to the technical field of artificial intelligence and digital project management, and the method comprises a multi-source data collection module which is used for obtaining data; the entity alignment module is used for establishing an association relationship among cross-system data entities; the dynamic index generation module is used for dynamically adjusting the evaluation index weight by utilizing a reinforcement learning framework; the efficiency prediction module is used for modeling a task dependency relationship according to the time sequence diagram convolutional network and outputting a delay risk probability; and the visual interface is used for displaying the data. According to the method, cross-system entity alignment, dynamic index weight adjustment, task delay risk prediction and root cause analysis are realized through AI technologies such as BERT semantic matching and a graph neural network, visual data display and interaction functions are provided by virtue of a visual interface, the problems of data dispersion, index static state, risk lag and the like in traditional project evaluation are solved, and the project evaluation efficiency is improved. And the decision support capability of project management is improved.

The invention provides an automatic data management method and system based on a large model, and the method comprises the steps: obtaining a native data set corresponding to a to-be-managed data field, carrying out the cross-domain feature association mining of the native data set based on a pre-trained large model, generating a semantic feature map of data entities and an implicit association link between the data entities, and carrying out the data management through the implicit association link. Calling a pre-trained large model, performing quality dimension evaluation based on the semantic characteristic spectrum and the implicit association link, outputting a quality defect vector of a data entity, performing source tracing analysis based on the quality defect vector, and determining a data quality defect type existing in a to-be-treated data field and a cross-entity propagation path of the data quality defect; a strategy evolution module in the pre-training large model is driven to generate a self-adaptive governance strategy containing defect positioning anchor points, and the self-adaptive governance strategy is deployed to a data governance execution environment to complete automatic data governance, so that the governance strategy can better adapt to different data quality scenes; and the pertinence and effectiveness of data management operation are improved.

The invention relates to the field of heterogeneous data storage, in particular to a heterogeneous data storage method and system for a civil aviation data platform, and the method comprises the steps: collecting metadata of each subsystem in the heterogeneous data storage system in real time, inputting the metadata into a pre-constructed metadata unified model, and carrying out standardization processing to generate a global metadata view; establishing a data traceability tracking mechanism according to the global metadata view, and mapping dispersed metadata items and data entity operation records according to the data traceability tracking mechanism to generate a traceable metadata change track; and a metadata quality label is embedded in the metadata unified model, a metadata knowledge graph is constructed according to the metadata change track and the metadata quality label, and a resource allocation strategy of the heterogeneous data storage system is dynamically optimized according to an analysis result of the metadata knowledge graph. The problem of low storage management efficiency caused by difficulty in integrating and tracing metadata in a heterogeneous data storage system in civil aviation data is solved.

The invention relates to a multi-level data collection method, system and device and a storage medium. The method comprises the steps that an engineering project cost file is received; performing structured analysis on the construction cost file of the engineering project, and extracting an original data field; performing term mapping processing on the original data field to generate a standardized data entity; and according to a preset project cost index system, carrying out hierarchical collection on the standardized data entities to generate a structured knowledge base, and storing the project cost file and the corresponding structured knowledge base into a data collection case library. The method has the effect of improving the data collection efficiency and accuracy of the engineering project cost file.

The invention provides a water conservancy data informatization management system and method. The method comprises the following steps: constructing an object association map of a water conservancy data entity object and business logic based on basic object data, mapping relation data and business logic data in water conservancy data; performing cryptographic hash operation on nodes and connecting lines in the object association map to obtain a data consanguinity fingerprint with uniqueness and tamper-proof characteristics, and storing the data consanguinity fingerprint and an authority strategy matrix for accessing the water conservancy data into an intelligent contract to obtain an authority control contract of the water conservancy data; capturing a data access request of an access user, and determining a data access permission of the access session according to the permission control contract and the data access request; and performing business scene-oriented controlled visual sharing on the water conservancy data extracted from the object association map based on the data access permission. By adopting the scheme of the invention, a water conservancy data security sharing system considering data dynamic adaptation, multi-attribute permission constraint and full-link credible tracing can be constructed.

The invention provides a knowledge graph-driven medical data agent collaborative governance method, system and device and a medium, and belongs to the technical field of medical data governance, and the method comprises the following steps: constructing a knowledge graph containing a quintuple, and defining mapping relationships among medical data entities, attributes and standards; extracting multi-modal medical data knowledge elements through a structured / unstructured engine, and converting the multi-modal medical data knowledge elements into a quintuple for storage; executing table / field / value domain mapping based on the knowledge graph to generate a data mapping result; binding the quality control rules and the medical specifications to form a quality control quintuple; arranging the task into a directed acyclic execution sequence through an agent group cooperation task; and dynamically updating the knowledge graph and performing version management. Medical data full-link governance, knowledge graph unification heterogeneous data semantics, intelligent agent cooperation automatic processing complex tasks, update guarantee knowledge timeliness and quality control rule binding guarantee data quality are achieved, and medical data governance efficiency and accuracy are improved.

The invention relates to a data circulation traceability method of a data security event and a data entity mode, and aims to solve the technical problems that the similar technology in the existing digital power grid lacks the technical combination of block chains, Internet of Things and meta universe and the big data and AI artificial intelligence technology. The method is characterized in that a unified operation interface of the method is provided with a digital twinborn body generated and constructed by a meta-universe technology as a data flow traceability model, edge intelligence of a digital power grid is accessed, when data in the edge intelligence of the digital power grid is abnormally tampered, the CNN discovers data abnormity, the intruded edge intelligence is accurately positioned, and the data flow traceability of the digital power grid is realized. The range of the abnormal behavior is predicted; meanwhile, pressure testing and virtual deduction are immediately carried out in a digital twinborn body formed by the meta universe technology, a possible data security event of system crash or data leakage is predicted, an accurate disposal scheme and risk level division are given in advance, and low-level risks are autonomously handled.

The invention provides a test data diversification generation method and system based on a data factory, and the method comprises the steps: carrying out the multi-dimensional semantic deconstruction of a basic test data set stored in the data factory, and obtaining a basic concept component set through the recognition of a semantic association relationship and a concept hierarchical structure between data entities; constructing a dynamic semantic association network on the basis, and generating an association weight matrix by calculating semantic association strength among different concept components so as to recombine the basic concept component set and generate a structured semantic template set; the method comprises the following steps: acquiring a target test data format, inputting the target test data format into a pre-trained feature evolution generation network, optimizing a feature evolution direction, obtaining an evolved feature set, performing data form conversion and relevance verification, converting the evolved feature set into a target test data format, and generating a target test data set meeting diversified test scene requirements after verification. According to the invention, the requirements of diversified banking business test scenes on high-quality test data can be better met.

The embodiment of the invention provides an enterprise information architecture (4A)-based AI Agent planning enhancement system, a control device and equipment, and the system comprises a Prompt construction module which is used for analyzing an unstructured demand to obtain a structured demand, and cooperating with other modules to generate a standardized Prompt comprising a business process, a data entity and a tool strategy; the knowledge matching module is used for matching related knowledge based on a knowledge graph employment number fusion knowledge base and sorting the related knowledge into a knowledge list containing business processes, practical experience, a data list and a tool list; and the context reasoning module is used for generating a reasoning result based on the historical data and the knowledge list and feeding back the reasoning result to the Prompt construction module so as to assist in generating the normalized Prompt. According to the system, the integration capability of the AI Agent on business logic and data resources in the task planning process is remarkably improved, so that the AI Agent can more accurately understand enterprise-level business scenes, and standardized and intelligent planning support is provided for practical application of the AI technology in enterprise digital transformation.

The invention relates to the technical field of purchase supervision, in particular to a purchase supervision active early warning method based on an artificial intelligence algorithm. Comprising the steps of building a multi-source purchase data acquisition and preprocessing module, building an implicit association risk mining model based on an improved graph neural network, building a dynamic risk trend prediction model based on a time sequence attention long-term and short-term memory network, and building a multi-dimensional risk fusion and early warning decision module. According to the procurement supervision active early warning method based on the artificial intelligence algorithm, a procurement subject-business link-data entity association graph is constructed by improving a graph neural network, and an attention mechanism and a time attenuation factor are combined, so that a cross-subject and cross-link implicit association risk can be accurately captured, a risk identification range is expanded from a single dimension to an association dimension, and the procurement supervision accuracy is improved. Through actual scene verification, the hidden risk identification coverage rate is improved, the industrial pain point that hidden illegal behaviors are difficult to find is successfully solved, and source risks in the purchasing process are effectively prevented.

The invention discloses an oil and gas pipeline fault prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining and preprocessing pipeline associated data corresponding to an oil and gas pipeline based on sensing equipment, and obtaining pipeline target data; defining an entity type and a relationship type, and constructing a dynamic knowledge graph based on the pipeline target data, the entity type and the relationship type; when the leakage point is detected, determining a target sub-graph corresponding to the leakage point based on the dynamic knowledge graph, generating a migration sequence of the target sub-graph, and converting the migration sequence into a vector to be matched; and matching the to-be-matched vector with a sub-graph in a historical graph library to obtain a similar sub-graph, and determining a target fault probability of the oil and gas pipeline based on the similar sub-graph. The problem that an existing method is difficult to meet the requirements for dynamism, accuracy and the like of corrosion prediction in a complex pipeline system is solved, a dynamic knowledge graph is constructed as a core drive, Internet of Things perception, multi-modal data analysis and artificial intelligence are fused, and real-time prediction of the pipeline state is achieved.

The invention relates to the technical field of logistics information management, and particularly discloses a corrugated board logistics information tracking method and system based on RFID, logistics state data of corrugated boards are obtained through RFID and preprocessed, a knowledge graph including entities such as corrugated cartons, storage nodes and transportation tools and association relationships is constructed, the nodes are converted into low-dimensional vectors, and the low-dimensional vectors are stored in a database; key factors and causal effect values which cause anomalies in entity association are mined by utilizing a causal forest algorithm, an abnormal propagation path is reasoned through a graph neural network, a root cause report is generated, targeted decisions such as sorting adjustment, equipment maintenance and path re-planning are generated accordingly, and a graph and a model are updated in real time to adapt to dynamic changes. According to the method, multi-dimensional data, entity relation modeling, causal effect analysis, propagation path reasoning and dynamic optimization design are integrated, the problems of unclear explanation of abnormal root causes and lack of pertinence in decision making in traditional corrugated board logistics tracking are effectively solved, and the accuracy of logistics information tracking and decision making effectiveness are improved.

The invention provides a data security protection system based on a reinforcement learning large model, and relates to the technical field of data processing, comprising the following steps: associating a historical attack chain feature library to extract corresponding attack behavior sequence features; fusing the knowledge-based data entity, the real-time environment baseline offset and the historical attack chain features to construct a comprehensive security state representation body; generating a protection strategy instruction set from the safety state representation body through a pre-training reinforcement learning large model; deploying a protection strategy instruction set in the zero-trust architecture, and collecting strategy effective time delay, a security entropy change gradient and a defense efficiency index to generate a feedback tensor; and constructing a reward function based on the feedback tensor, and updating strategy network parameters of the reinforcement learning model by adopting a near-end strategy optimization algorithm to form a closed loop. According to the invention, the intelligence, dynamic nature and adaptability of data security protection are improved.

The invention provides a data processing method and system based on a business intelligence system. The method comprises the following steps: receiving request data, and standardizing the request data to obtain standardized data; analyzing the standardized data based on a pre-constructed dynamic semantic map to obtain an analysis result; the dynamic semantic graph is a directed attribute graph constructed based on business terms, data entities and business concepts; performing logic compiling on the analysis result to generate an executable operation chain; the executable operation chain comprises sequenced atomic operation steps, and the atomic operation steps comprise data processing logic and metadata binding information; and running the executable operation chain to obtain an analysis result corresponding to the request data. The method comprises the following steps: analyzing request data based on a dynamic semantic map; and the analysis result is logically compiled, an executable operation chain is generated, and the data processing process is decomposed into sequenced atomic operation steps, so that the data processing flow becomes clear, and the accuracy and efficiency of request data processing are improved.

The invention relates to the field of task automatic decision making, in particular to a task disassembly analysis decision making system and method based on a knowledge graph, and aims to solve the problems that task decision making depends on a structured database, so that decision making lacks deep association, and task target disassembly is easily influenced by personal experience. A data set establishment module specially processes unstructured data, actively extracts data entities, records association relationships, finally generates a dynamic graph, splits a task target into a plurality of sub-task chains, automatically generates a plurality of execution schemes to form an alternative scheme pool, and finally generates an alternative scheme pool through real-time environment data and resource constraint data. It is ensured that evaluation is carried out under the dynamic condition close to reality, the feasibility and accuracy of decision making are improved, deduction is carried out through the graph neural network, chain reactions and potential conflicts possibly occurring in the scheme execution process can be simulated, quantitative risk coefficients are output, risk evaluation is made to be quantitative from qualitative, and the risk evaluation efficiency is improved. And finally, an optimal execution scheme is obtained.

The invention relates to the technical field of data analysis, and discloses a low-code data analysis platform and method based on multi-source data real-time access, and the platform comprises a data access module, a modeling module, a rendering engine and an early warning module. The method corresponds to the platform. According to the method and the device, the problems of real-time integration and standardization of the multi-source heterogeneous data are solved by obtaining the standardized data entity, and a high-quality data basis is provided for subsequent analysis; a dynamically constructed knowledge graph and a graph embedding technology are deeply fused into a data analysis process, so that semantic association among data entities is intelligently understood, an optimized query statement is automatically generated, intelligent recommendation and rationality verification are provided for establishing a data association relationship for a user, and the technical threshold of data modeling is reduced; a second-level response closed loop from data access to visual presentation is formed based on intelligent chart recommendation of data features and early warning rules and instant calculation and triggering in a real-time data stream.

The invention relates to a power operation data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring power data of each data source in a power system; analyzing data attributes of the power data, and converting the data attributes into a predetermined format to obtain target data; based on the target data and the corresponding data attributes, establishing a knowledge graph; wherein the knowledge graph comprises nodes and association relationships between the nodes; when it is monitored that the electric power data of any data source has a change condition, updating the knowledge graph based on the change condition; and screening out key nodes from the knowledge graph, and carrying out real-time monitoring on the key nodes according to an anomaly detection algorithm to obtain an anomaly detection result. Therefore, the knowledge graph is established based on the target data and the data attributes, the dispersed data sources and data entities in the power system are presented in a node form, and the association between the data sources and the data entities is reflected by using the association relationship, so that workers can intuitively understand the internal relationship among the elements of the power system.

This invention provides a method and system for predicting diabetes by integrating knowledge graphs and deep learning. The method constructs an entity vector library of physical examination data, and obtains vector representations of patient physical examination data based on this library, resulting in physical examination data vectors. It then constructs a diabetes domain knowledge graph; based on this knowledge graph, it extracts entities and relationships from patient symptom descriptions and represents them as vectors, obtaining symptom description vectors, and thus a matrix representing the patient's condition; finally, it integrates a self-attention mechanism and a convolutional neural network to construct a classification prediction model, which yields the prediction results. Compared with existing methods, this invention, by simultaneously considering patient physical examination data and symptom descriptions and based on a dual-driven approach of knowledge and data, achieves higher accuracy and interpretability.

The invention discloses a data blood relationship management analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining original data, carrying out the cleaning, conversion and standardization processing of the original data, and constructing a blood relationship model; storing the blood relationship model based on a JanusGraph graph database, and performing blood relationship analysis through a Gremlin query statement to obtain a blood relationship analysis result; when the change of the data entity is detected, the incremental update of the affected blood relationship is automatically triggered, so that the update delay of the blood relationship information can be effectively compressed from the traditional hour level to the minute level, and the technical bottlenecks of blood relationship information islands, low analysis efficiency and insufficient large-scale data expansibility are solved; the timeliness and the accuracy of data change influence evaluation and the data governance decision support capability are remarkably improved, and the speed and the efficiency of data blood relationship management analysis are improved.

The invention relates to the technical field of electric power storage, and particularly discloses an electric power storage resource global collaborative management method and system, and the method comprises the steps: constructing an electric power professional warehouse data graph with an Internet of Things physical ID as a core node, the Internet of Things physical ID being a unique physical identifier of an electric power storage resource; associating material data of a safety supervision warehouse, a regulation and control warehouse and a logistics warehouse, and integrating key data entities of a safety tool verification system, a scheduling equipment management system and a work order management system; a unified data format and a data mapping rule are defined, and standardized interaction of cross-system heterogeneous data is achieved. According to the method, standardized real-time synchronization of cross-system data is realized by constructing a power professional warehouse data graph and relying on an internet of things entity ID active synchronization engine; by using a multi-dimensional rule engine, based on a professional warehouse type-material attribute-business scene three-dimensional rule base, accurate management and control are provided for warehouse-in, warehouse-in, warehouse-out and exception handling of materials, and the asset utilization efficiency is effectively improved.

The invention relates to the technical field of deduction engines, in particular to a distributed joint combat deduction engine architecture based on multi-agent reinforcement learning, which comprises a data processing layer capable of acquiring target area environment data, entity historical state data and trust data, target area environment data and entity historical state data are processed through a distributed computing framework, and original trust vectors are generated through trust data; the simulation engine layer can perform environment modeling and entity modeling through an entity modeling engine and an environment simulation engine; carrying out credibility dynamic modeling based on the historical behaviors of the intelligent agent; the joint command layer can coordinate and dispatch a target environment entity and environment resources through a coordination engine according to a preset command scheme, and performs cross-domain trust conduction; and the analysis and evaluation layer can deduce and restore in real time, predict the situation of the target area and evaluate the robustness of the command scheme.

The invention provides a multi-source fusion heterogeneous data extraction method and system and a storage medium, and the method comprises the steps: constructing a dynamic hypergraph neural network and a dynamic hypergraph structure, carrying out the feature aggregation, and obtaining a space-time enhancement feature representation; performing causal reasoning and meta-learning optimization to generate robust feature representation; and by executing data reconstruction and fusion, outputting a heterogeneous data extraction result after multi-source fusion. According to the method, a dynamic hypergraph neural network, a sliding window and an attention mechanism are combined, data entity spatio-temporal evolution is modeled in real time, and the problem of correlation lag of a traditional static hypergraph is solved; then, a causal element learning confrontation module integrates causal reasoning, element learning and confrontation training to improve model robustness; finally, data reconstruction, meta-learning and adversarial loss are subjected to weighted fusion through a joint optimization strategy, the feature expression ability is improved, and the generalization performance of the model in a small sample scene is ensured.

The invention provides a distributed multi-cloud-node remote sensing data synchronous transmission method, system and device, and the method comprises the steps: receiving a data synchronization request of a user, the data synchronization request taking a data set defined by business logic as a synchronization unit, and the logic of the data set being defined by at least one piece of business dimension information; in response to the data synchronization request, extracting all data entities corresponding to the data set logic from the distributed data source according to the service dimension information; decomposing a synchronization task for all the data entities into sub-tasks corresponding to different data types; and on the basis of the transmission strategy, after to-be-transmitted data in the subtasks is preprocessed, transmission is performed through the dynamically selected network link, so that the destination cloud node executes post-processing operation corresponding to preprocessing on the received data, and a data set is restored.

The invention provides a digital main line processing method based on an industrial data atlas, a medium and a system, and belongs to the technical field of the industrial data atlas. Unified access of cross-system data is realized by constructing a multi-source data acquisition adapter, cleaning and standardization processing are performed on heterogeneous data, an integrated data set is constructed, and the data collection efficiency is improved. Data entities are abstracted as nodes, cross-system association relationships are abstracted as edges to construct an industrial data graph, a graph index optimization strategy and a hierarchical storage architecture are adopted to improve cross-system query performance, a graph adaptive evolution model is utilized to realize dynamic adjustment of a graph structure, and a cross-system deep association analysis function is realized through a graph traversal algorithm. And an access control and data security mechanism is established to complete system protection, so that the technical problem that the industrial data processing system is difficult to realize cross-system deep correlation analysis is solved.

The invention provides a banking business data change influence analysis method and related equipment. The method comprises the following steps: acquiring a business data blood relationship map constructed for a banking business system; each node in the business data blood relationship map corresponds to each bank data entity, and each edge in the business data blood relationship map corresponds to a data flow relationship among the bank data entities; based on the change type, determining a target sub-graph corresponding to the change type from preset sub-graphs; taking the change node as a starting point, and screening out candidate nodes matched with the root node attribute of the target sub-graph from the business data blood relationship graph; based on the target sub-graph and each candidate node, performing sub-graph matching in the business data blood relationship graph to obtain a matching result isomorphic with the target sub-graph; based on a preset influence scoring rule, scoring each matching path in the matching result; and on the basis of each matching path and the score corresponding to the matching path, a change influence analysis result is generated, so that the change influence analysis efficiency and accuracy are improved.

The invention relates to the technical field of real estate data management, and particularly discloses a real estate data exchange system based on ownership survey data, which comprises the following steps of: establishing an association relationship among a data entity, a processing interface and an operation main body by constructing an ownership data blood relationship network; receiving a data exchange request and extracting context information; extracting a multi-hop association sub-graph from the blood relationship network based on the context information to form a dynamic risk association view; by analyzing a view topological structure, evaluating path sensitivity and connectivity, and quantitatively calculating a single risk value representing a data association leakage risk; and finally, according to a matching result of the risk value and the threshold interval, selecting and executing a corresponding data exchange response action from the security policy library. According to the method, the problem of permission leakage caused by legal operation sequence combination in the prior art is solved, the conversion from static permission control to dynamic risk perception is realized, and the security of real estate data exchange is improved.