558 results about "Data entity" patented technology

The invention relates to the technical field of intelligent prevention and control of typical coal mine disasters, and discloses a coal mine fire and gas disaster multi-modal knowledge base construction and optimization method, which comprises the following steps: constructing a unified ontology structure based on typical coal mine fire and gas disasters; based on the unified ontology structure, extracting entities, attributes and relationships among the entities from the multi-modal data source; integrating the extracted entities, attributes and relationships by using a graph neural network model to form a structured knowledge graph; based on the structured knowledge graph, identifying and linking the multi-modal data entities, and establishing a multi-modal knowledge base; historical data and real-time monitoring data of a to-be-assessed area are acquired, and dynamic risk assessment is performed by using the constructed risk assessment hybrid model based on entities and relationships in the multi-modal knowledge base; wherein the risk assessment hybrid model fuses GNN, DBN and LSTM; and according to the real-time monitoring data and the risk assessment result, updating the attribute values and relationships of the entities in the multi-modal knowledge base.

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 provides a time-space dynamic graph construction method and system for an engineering construction project, and relates to the field of railway tunnel engineering management.The method comprises the steps that construction data entities are extracted, and an entity set is generated; taking entities in the entity set as nodes to be added into the graph, and configuring attributes for each node; adding a relation edge for each node according to a logic structure between the construction data; adding a time label and a space label to each node so as to form a data graph; associating each entity in the data atlas with a time chain and a space chain to form a data atlas attached to the time-space chain; combining a node merging strategy and a graph neural network to optimize a graph structure; and dynamically modeling dimensional changes of entities and relationships in time by means of a time sequence diagram neural network. According to the method, the dynamic change graph of the relationship between the construction data entities is constructed, so that the spatial-temporal information and the entity relationship of each process are accurately expressed and efficiently associated, and the retrieval efficiency and the management level are favorably improved.

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).

The present disclosure relates to a method of operating a neural network with clipped input data. The method includes defining lower and upper threshold values for integer numbers in data entities of input data for at least one neural network layer. If a value of an integer number in a data entity of the input data is smaller than the defined lower threshold value, the method includes clipping the value of the integer number comprised in the data entity of the input data to the defined lower threshold value. If a value of an integer number in a data entity of the input data is larger than the defined upper threshold value, the method includes clipping the value of the integer number comprised in the data entity of the input data to the defined upper threshold value. Integer overflow of an accumulator register is thereby avoided.

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.

The invention discloses a coal enterprise knowledge management system construction system and method in the technical field of computer information. The method comprises the following steps: collecting coal enterprise knowledge data; preprocessing the coal enterprise knowledge data to obtain knowledge graph data and large model module data; after performing data entity identification on the knowledge graph data by using an entity identification technology, creating a knowledge graph module; after large model module data are classified and labeled, a large model module is created; and creating a coal enterprise knowledge management system based on the knowledge graph module and the large model module. Through a mode of fusing the knowledge graph and the large model, the knowledge base answering field is enriched, the knowledge base question answering mode is optimized, the large model is enhanced by using the professional knowledge graph knowledge, the professional question answering result is generated, the question answering ability of the knowledge base in the professional field is improved, and contribution is made to digital, intelligent and green development of the coal industry.

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.

The invention relates to a big language model data analysis method and device, computer equipment, a computer readable storage medium and a computer program product, and is applied to an enterprise resource planning system (ERP) and the like. The method comprises the following steps: acquiring to-be-analyzed question number information, and matching a target data index in a preset data index set according to the to-be-analyzed question number information; matching a corresponding target data entity according to the to-be-analyzed question number information under the condition that the matching of the target data index fails; under the condition that the target data entity is successfully matched, acquiring a data index template, and determining a target data index according to the target data entity and the data index template; and obtaining target data according to the target data index, and obtaining a data analysis result according to the target data. By adopting the method, the intelligent number asking accuracy of the complex form can be improved.

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.

A method of operating a neural network based on conditioned weights includes defining integer lower and upper threshold values for values of integer numbers comprised in data entities of input data for the neural network layer. If a value of an integer numbers comprised in a data entity of the input data is smaller than the lower threshold value, the value of the integer number comprised in the data entity of the input data is clipped to the lower threshold value, or if a value of an integer number comprised in a data entity of the input data is larger than the upper threshold value, the value of the integer number comprised in the data entity of the input data is clipped to the upper threshold value. Integer valued weights are determined based on the lower threshold value, the upper threshold value, and a pre-defined accumulator register size, such that integer overflow of the accumulator register can be avoided.

The invention provides a big data intelligent decision analysis method and system based on machine learning. The method comprises the following steps: receiving real-time data streams of a plurality of sources, the real-time data streams comprising structured data and unstructured data; analyzing the unstructured data by applying semantic understanding and context sensing technologies so as to extract themes and emotional tendencies in the unstructured data; the emphasis of data analysis is dynamically adjusted, and an association network between data is constructed by using a graph technology; evaluating mutual influence among different data entities, and predicting future state change to obtain a prediction result; based on the mode, the trend and the prediction result, generating a strategy suggestion oriented to a specific application scene; and the strategy suggestions are converted into executable service rules to support intelligent decision making. According to the technical scheme provided by the invention, different types of data can be comprehensively analyzed to identify key trends, understand emotions of consumers and predict market trends, so that the data analysis efficiency is effectively improved.

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.

A technology for the adaptive compression of time series of complex monitoring data records is disclosed. Variants of complex monitoring data may describe multiple observations of the same type and from the same source from different timestamps, or they may describe multiple, related observations from the same source from the same timestamp. Meta-data for individual complex monitoring data records is analyzed to identify portions of the monitoring data record that can be omitted without loss of data to improve the compression rate. Different base compression methods may be applied for individual entities of monitoring data, depending on the data type used to represent the monitoring data entity, like XOR compression for floating point types. Compressed data is stored to a storage stream, where meta-data that is required to determine the omitted portions of complex data records is stored before corresponding observation payload data.

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.