444 results about "Data entity" patented technology

The invention discloses an Internet of Things data integration method and system based on dynamic semantic atlas and edge collaboration, and the method comprises the steps: carrying out the equipment access and protocol conversion, capturing the original data of equipment through an edge node, analyzing the average communication interval of 100ms + / -20ms through an LSTM model, generating an equipment fingerprint hash value, analyzing a message structure through a TinyBERT model, and extracting a key field, a temperature value and a timestamp. The method comprises the following steps: establishing a dynamic semantic graph, converting the dynamic semantic graph into a standard JSON format, uploading the standard JSON format to a cloud end through a service grid, constructing a dynamic semantic graph, collecting multi-source metadata by the cloud end, generating an entity relationship by utilizing a Transform model, dynamically optimizing a mapping rule based on reinforcement learning, automatically associating a Pressures field to a database column PSI, and carrying out collaborative optimization by an edge cloud; according to the method, the knowledge graph and the Transform model are combined, the data entity relation is updated in real time, the semantic drift problem of a traditional static graph is solved, the cross-domain data association accuracy is improved by 35%, the unique device fingerprint is generated through the message frequency and load distribution, and dynamic tracking of the mobile device is supported.

The invention provides a knowledge graph construction and intelligent question and answer method and device based on deep learning. The method includes; obtaining target text data; information extraction is conducted on the target text data, entity relation data is generated, and the entity relation data comprises key data, entity data and relation data used for indicating the relation between entities; according to the entity relationship data, importing the target text data into a graph database to form a knowledge graph; obtaining an input question statement; the question sentence classification model is used for classifying the question sentences, question sentence features in the question sentences are determined, and the question sentence features comprise question sentence types and question sentence entity data; and retrieving text data matched with the question features in the knowledge graph as answer data of the question statements. According to the method, the public text information retrieval efficiency is improved, and the accuracy of intelligent question answering is improved.

Systems, computer program products, and methods are described herein for building material data management. With reference to an example method, the method receives a first dataset formed of first data entries associated with a building material data source and generates a building material data entity based on the building material data source. The method includes outputting a representation of the building material data entity. The first dataset may be formed of data records (e.g., construction documents, user inputs, sensor data, or machines data), and the representation may be a visual representation may include notifications, visual presentations of the building material entity, etc.

The invention provides a data consanguinity full-link traceability method and system based on multi-source heterogeneous metadata and a pre-training large model. The method comprises the following steps: S1, collecting metadata and preprocessing the data; s2, identifying the data processed in the S1 by using a data consanguinity detection model, and outputting a consanguinity probability matrix between data entities; s3, filtering the blood relationship probability matrix based on a dynamic threshold value to generate a blood relationship map; and S4, rendering the blood relationship graph obtained in the S3 to visualize the data blood relationship. By means of metadata multi-dimensional feature extraction and end-to-end training of a deep learning large model, the problems that a traditional technology is rigid in rule, low in calculation efficiency, insufficient in semantic understanding and the like are solved, and high-precision and self-adaptive data blood relationship modeling is achieved.

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.

Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated source code review using sentiment analysis with magnitude of entities. Known compliant and non-compliant source code may be used to generate dictionaries for evaluating lines of code using AI and ML techniques, such as by clustering data entities (lines of software code) and performing sentiment analysis on the data entities (lines of software code) which accounts for a magnitude of the data entities in the software code. The dictionaries enable automated review and correction of non-compliant code, such as vulnerable or insecure code, during the coding process. For example, sentiment analysis may be performed using the dictionaries on in-development code to determine a polarity and magnitude score for each line of code. The scores for each line can be compared to one or more conditions to determine a remediation action for individual lines of code.

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.

The invention relates to an anti-cross-labeling intelligent atlas identification method and device, electronic equipment and a storage medium. The anti-cross-bidding intelligent atlas identification method comprises the following steps: collecting related data in a bidding and tendering process; the data is cleaned, integrated and formatted, so that subsequent graph construction and analysis are facilitated; a bidding data atlas is constructed based on the collected data, nodes represent data entities including bidders and bidding files, and sides represent the relationships including bidding relationships, association relationships and bidding file repetition similarity degrees among the entities; extracting features related to the trademark behavior from the atlas; training the extracted features by using a machine learning algorithm, and constructing a string mark behavior recognition model; evaluating the trained model, and performing parameter adjustment and optimization according to an evaluation result; and the trained model is used to carry out automatic detection of the string marking behavior. According to the anti-cross-labeling intelligent atlas identification method, cross-labeling behaviors can be automatically, efficiently and accurately identified.

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 discloses an infrared and visible light image data association method based on multi-view clustering, graph construction is completed by aiming at obtained data of different data sources, nodes represent data entities, and edges represent similarities or relationships among the entities. By means of a graph convolution network, input multi-view data features and a structure graph are mapped step by step through a series of nonlinear transformation, and features in a feature graph are extracted. After feature representation of data is independently learned from each view angle, the features are fused to obtain global feature representation, and meanwhile, features of similar samples in other different view angles are integrated to learn a common space. In addition, the multi-view clustering is guided and compared by using the pseudo labels, so that the multi-view data is effectively processed, and the accuracy and efficiency of association are improved.

The invention relates to a metadata management system and method based on a universal metadata model of a data lake, and belongs to the technical field of metadata models, and the system comprises a metadata uptake module which uptakes metadata in the process that multi-source heterogeneous data enters the lake; the modeling module is used for modeling the acquired metadata through a metadata model and supporting dynamic expansion; the metadatabase is used for receiving and storing the ingested metadata; the metadata management module is used for managing metadata and data consanguinity of the multi-source heterogeneous data in the data lake and carrying out visual display in a graph form; wherein modeling is carried out on the acquired metadata through the metadata model, and dynamic expansion is supported; comprising the steps that metadata is modeled in a graph form, nodes are used for representing data entities and metadata entities, and a data structure is self-defined and comprises structured data, semi-structured data and unstructured data; and the incidence relation between the nodes is represented by edges. The method supports multi-source heterogeneous data cases in a data lake.

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.

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 updates a database, which comprises a current list of false data entities, with the one or more differences.

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.

Revising data management indices by defining a plurality of data entity buckets, assigning bucket hash values to each data record according to the plurality of data entity buckets, generating a record index including data record bucket membership data, determining a combined record score for each data record according to the bucket membership data, revising data record relationship indices of a data management system according to the combined record score of a data record, and responding to a user query according to the record index including the revised record relationships.

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.