371 results about "Data entity" patented technology

The invention provides an enhanced generation method based on question matching retrieval, and belongs to the field of matching generation, and the method comprises the following steps: S1, a semantic feature coding stage: carrying out real-time feature extraction and vector space mapping on a natural language query input by a user by adopting a deep neural network model, generating high-dimensional distributed representation with semantic representation capability; s2, a knowledge base intelligent retrieval stage: executing multi-dimensional semantic matching in the vectorized knowledge base based on an approximate nearest neighbor search algorithm, and screening out a candidate knowledge set highly related to query semantics through a similarity measurement function; s3, retrieval matching results are automatically associated to the structured knowledge base through the established semantic-knowledge mapping relation, the preprocessed standardized response content is directly obtained, and the response content adopts a multi-modal data organization form and comprises a structured data entity and retains a rich text expression form.

The invention discloses an e-commerce personalized recommendation method based on cross-domain collaborative filtering, and relates to the technical field of e-commerce, and the method comprises the steps: collecting and preprocessing data, including user behavior data and commodity attribute data, of different fields of an e-commerce platform; materializing the collected data in different fields, and integrating the data into a structured e-commerce knowledge graph; and constructing a user-based cross-domain collaborative filtering model based on the behavior data of the user in the multiple fields, and predicting commodities in which the user is interested. By integrating data in different fields and constructing user portraits and commodity portraits, the problem of inaccurate recommendation caused by data heterogeneity in a traditional recommendation system is effectively solved, interests and preferences of users in different fields can be more accurately captured through cross-domain data association analysis, and the recommendation efficiency is improved. Therefore, personalized recommendation better meeting the requirements of the user is provided for the user, the recommendation accuracy and satisfaction degree are remarkably improved, and the user stickiness and the platform competitiveness are enhanced.

Predicting the salience of one or more data entities to a particular (target) data entity from among a plurality of data entities may comprise generating a graph of the plurality of data entities and a machine-learned model architecture that predicts the salience of the one or more data entities output by the machine-learned model architecture using the graph. For example, the machine-learned model architecture may comprise a first machine-learned model for generating an embedding using the content of the target data entity, a second machine-learned model for generating a vector using the data type indicated by the target data entity, and a third machine-learned model (e.g., a graph neural network or other feed-forward neural network) for generating a contextual representation of the target data entity to which other contextual representations associated with the plurality of data entities may be compared (e.g., using Euclidean distance, cosine similarity, dot product).

Methods and corresponding systems and apparatuses for configuring user access to data stored in and / or accessed through an external computer system are described. Access permissions can be configured through defining a permission set relative to a proxy entity and assigning the permission set to one or more users. A proxy entity is a local representation of an external data entity. A proxy entity can be a virtual entity in that the proxy entity does not contain underlying data. A proxy entity can, however, include metadata describing its corresponding external data entity. A computer system maintaining a proxy entity can store mapping information linking the proxy entity to an external data entity. The mapping information and the permission set can be used to determine an access permission relative to the external data entity and to communicate this access permission to the external computer system so that access can be provided accordingly.

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.

False data entities attempt to evade getting caught by changing their information repeatedly over time. Systems and methods are provided to detect false data entities. A computing system ingests a plurality of data files respectively from a plurality of external data sources using the network interface within a current specified time period. It consolidates a plurality of data entries of false data entities from the plurality of data files into a consolidated list for the current specified time period. It compares the consolidated list for the current specified time period with a previous consolidated list corresponding to a previous specified time period to identify one or more differences. It then displays, using a graphical user interface (GUI), the one or more differences in association with a given false entity.

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 the field of user interface design, in particular to a table generation and optimization method and device based on demand analysis, and the method comprises the steps: receiving demand data inputted by a user, and recognizing the attributes of a table, data entities and the relation between the data entities according to the demand data; generating a table structure according to the data entity and the attribute; according to the data entities and the relation between the data entities, target demand parameters are matched, and the target demand parameters comprise data fields and constraint conditions; based on a matching result, filling a table structure with the target demand parameter to obtain an initial table; obtaining an interaction behavior of the user on the initial table in real time, and analyzing demand change data of the user according to the interaction behavior; and updating the initial table based on the demand change data. By analyzing the interaction behavior and demand change of the user, the system can continuously optimize and adjust the table in the use process, so that the table can be automatically adjusted along with the demand change.

The invention provides a multi-source heterogeneous data entity alignment method and device and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: merging two knowledge maps according to a predicate alignment algorithm to obtain a merged knowledge map; learning semantic abstract embedding and relation structure embedding of each entity in the combined knowledge graph; according to the dependency relationship of words in the attribute text of each entity, performing part-of-speech learning on attribute semantic embedding of each entity; determining a combined embedding vector of each entity according to semantic abstract embedding, relation structure embedding and attribute semantic embedding of each entity; and aligning the entities in the combined knowledge graph according to the combined embedding vectors of the entities. According to the method, the accuracy of knowledge graph entity alignment in the scene of insufficient seed quantity and sparse attribute is effectively improved.

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 discloses a label-based digital equipment model dynamic creation method and system, and relates to the field of complex data integration and equipment management technology application in the power industry, and the method comprises the following steps: predefining a general data label model, dynamically configuring a query model, and establishing a database; and constructing a digital equipment data entity and designing a technical process of parallel computing and asynchronous processing. Data standardization is realized, and the query efficiency is improved; through a dynamic configuration mechanism, the system can automatically generate the query model, and the flexibility is improved; through the data entity construction method, different business data can be associated through a standardized model, and the data consistency is improved; according to the method, parallel computing and cache optimization technologies are utilized, and data query and processing are accelerated; according to the method, an incremental updating mechanism is adopted, so that the real-time performance and consistency of data are ensured; according to the method, the creation efficiency, query performance and system flexibility of the equipment model can be remarkably improved, and better technical support is provided for digital transformation of the power industry.

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 relates to the technical field of government affair shared data encryption, and discloses a self-adaptive government affair shared data conformal encryption method and system, and the method comprises the steps: recognizing a data entity containing potential sensitive content in original government affair data after structural analysis through employing an improved entity recognition model, and generating a sensitive tag; carrying out sensitivity evaluation on the data entities, and carrying out sensitive grading processing on the data entities by utilizing an improved clustering algorithm; and carrying out encryption processing and data replacement on the data entity after sensitive grading by adopting a self-adaptive encryption algorithm to obtain government affair shared data. The invention provides a government affair data protection method fusing entity identification, sensitivity evaluation and an adaptive encryption strategy, and the method achieves the conformal encryption protection of a high-sensitivity entity and the desensitization replacement of a low-sensitivity entity through the construction of a multi-dimensional sensitivity matrix, the fine grading of potential sensitive fields, and the matching of an adaptive encryption mode. And the privacy security and the structure availability of government affair data sharing are improved.

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 discloses a multi-source heterogeneous big data aggregation analysis method based on master data modeling. The method comprises the following steps: modeling main data, defining a relation mode of a business entity and a data entity, and outputting a graph structure storage mode; classifying the service entities, and defining different data entity fusion algorithms for different data entity types by taking the main attributes of the service entities as the identification basis of the data entities; accessing data, and cleaning the accessed data; service entities and data entities are analyzed from the cleaned data based on a data entity fusion algorithm, and a relation theme library is constructed according to the incidence relation between the service entities and the data entities; storing the cleaned data and a relation theme library thereof; and carrying out modeling analysis on the cleaned data by combining the characteristics of the business entity and the relation theme library. According to the method, unified management of multi-source heterogeneous mass data is carried out from a global perspective, the blood relationship of the data is clear, and the authority is controllable.

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.

Example implementations relate to automatically identifying matching data entities. A determination request is received and for each of a first data entity and a second data entity, a sequence of demarcated attributes is generated. A similarity score is generated, using a trained classification model, between the first data entity and the second data entity that accounts for feature interactions between the first data entity and the second data entity. The trained classification model receives the demarcated attributes for each of the first data entity and the second data entity as inputs, and is trained using a dataset of annotated pairs of data entities. Weights in the trained classification model are determined based on a customizable loss function. In accordance with a determination that the similarity score is greater than a predetermined threshold, an indication that the first data entity matches the second data entity is generated.

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 belongs to the crossing field of computer technology, geographic information science and environmental science, and discloses a hierarchical knowledge graph-based wetland semantic virtual geographic environment construction method, which comprises the following steps of: constructing a data graph, and constructing a wetland monitoring entity, the multi-source heterogeneous wetland monitoring data set is converted into a knowledge graph with entities as nodes and semantic relations as edges; constructing a calculation graph, integrating a wetland executable calculation entity into the knowledge graph, and enabling the calculation entity to execute calculation and conversion operation on entities in the data graph based on an algorithm rule; and a scene graph is constructed to represent a knowledge graph formed by different wetland application scene entities, and the application scene entities are associated with data entities from the data graph and calculation entities from the calculation graph. According to the method, the problems that in the existing wetland virtual geographical environment, the wetland monitoring data is too much and too little due to lack of semantic information and the calculation entity is too strong and too weak due to insufficient intelligence can be solved.

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.