313results about "Semi-structured data mapping/conversion" patented technology

The invention relates to an enterprise-level simulation knowledge graph construction method based on multi-modal data integration, and belongs to the technical field of knowledge graphs. The method comprises the following steps: integrating structured data, semi-structured data and unstructured data through a multi-modal data warehouse; performing knowledge extraction on the semi-structured data and the non-structured data to obtain entities and relationships, and performing knowledge fusion; storing the fused entities and relationships by using a graph database, and constructing a simulation knowledge graph; a vector database is embedded in combination with a simulation knowledge graph, semantic extension search is realized through multi-modal joint search, similar cases are searched through a simulation result graph, and a simulation scheme comparison matrix is automatically generated. The multi-modal data is effectively integrated, the comprehensiveness and accuracy of knowledge graph construction are improved, more powerful, efficient and intelligent support is provided for simulation analysis of enterprises, and the enterprises can be assisted in rapidly making scientific decisions in complex and changeable business scenes.

Systems and methods for Artificial Intelligence (AI)-driven computer system utilities for generating dynamic User-Interface (UI) elements and loading parsed semi-structured data into a structured data schema, which may then be readily queried utilizing known end-user tools.

In an example embodiment, a solution for matching entities in a query table with one or more entities in a target table, in the presence of a constraint on the value sum of the matching targets (hereinafter called the “value constraint”), using machine learning techniques, is provided. Specifically, a non-linear objective function is converted to a linear objective function and a machine learning model is trained using the linear objective function, allowing for the use of solver functions from libraries in order to speed matching over existing methods.

Data associated with one or more data sources is transformed into a format associated with a common ontology using one or more transformers. One or more machine learning models are generated based at least in part on the transformed data. The one or more machine learning models and the one or more transformers are provided to a remote device.

Computer implemented systems and methods are disclosed for importing data from electronic data files. In accordance with some embodiments, a file format is assigned to a source electronic data files by a data importation system. The data importation system may further identify a file type identifier associated with the source electronic data file and map the source electronic data file to a transformation template. The data importation system may further store the file format, file type identifier, and an indication of the transformation template as a file type profile associated with the source electronic data file in a database.

The invention provides an authentication document form generation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining an authentication document; converting the authentication document into an XML (Extensible Markup Language) file so as to map authentication document data to an XML tree node of the XML file; standardizing the XML file through the global unique identifier and the tag system to obtain a standardized XML file; in combination with the standardized XML file, template candidate items conforming to different manageable data templates in the authentication document are extracted respectively; in combination with the template candidate item, mapping the authentication document to a target form; and establishing a bidirectional mapping relationship between the target form and the authentication document to realize bidirectional conversion between the target form and the authentication document and complete the form processing of the authentication document. Through establishment of the bidirectional mapping relation, synchronous updating of the form and the document content can be achieved, errors caused by manual operation are avoided, and automation, accuracy and practicability of document conversion are remarkably improved.

Disclosed are various embodiments for leveraging large language models (LLMs) and retrieval-augmented generation (RAG) for data standardization of clinical AI data. A query comprising a target dataset that needs to be standardized and a dataset dictionary can be obtained. Augmented data can be extracted from an external source. A prompt including the query and the augmented data can be generated and applied to a large language model configured to output a response to the query. The response can include the target dataset standardized according to a standard format.

The invention discloses a complex system comprehensive multi-view consistency detection method based on a large model, and relates to the technical field of information system architecture design. The method comprises the steps that N view models to be detected are determined, and a rule base is constructed and formed; forming a triple set; performing rule retrieval on the triple set to obtain a corresponding rule subset; and constructing a complete prompt statement according to the prompt template and the rule subset, and reasoning the prompt statement by the large language model to obtain a consistency detection result. Based on the MBSE method, the ability of understanding, reasoning and applying knowledge in the field of system architecture and system engineering is remarkably improved, the automation degree, accuracy and robustness of multi-view consistency detection are improved, and the correctness, completeness and consistency of complex information system architecture design are guaranteed.

A system for a visual design system (VDS) includes storing at least one layout and an associated layout signature where the associated layout signature represents a hierarchical composition of the semantic types of the components; a unit to analyze components of an existing layout provided by a user of the VDS, to determine a component set signature, to compare it with at least one stored associated layout signature and to find a set of candidate layouts which are visually diverse and semantically similar to the existing layout; where the unit presents the set of candidate layouts to the user, updates the existing layout according to a user selected layout. It also includes an experiment system to create, run and analyze the results of at least one experiment using at least one of: A / B and multivariate testing on user selected layouts to provide information on user preferred layouts for the VDS.

The invention discloses a geological data description data structured analysis method based on a multi-modal large model. The method comprises the following steps: acquiring multi-modal elements including texts, tables, curves and sheet symbols; dynamically generating a limit mapping matrix of a Sheaf neural network according to the geological description ontology and the multi-modal elements; splicing and consistency evolution from a local cross section to a global cross section are executed, and description field candidates are obtained; performing consistency verification on the description field candidates, and identifying fields with contradictions; calculating a corresponding minimum evidence cut set to form minimum conflict interpretation; inputting the minimum conflict interpretation as an additional loss function into a training process, and updating parameters; and outputting the optimized geological data description field result. According to the method, the consistency, interpretability and traceability of geological data multi-modal description analysis are improved through the dynamic constraint field and the minimum conflict interpretation mechanism.

A power grid fault coping strategy generation method based on a large model driven knowledge graph is characterized by comprising the following steps: extracting an entity triple from an existing relational database, converting the entity triple into a graph database, and constructing an equipment entity graph by using the graph database; extracting knowledge elements from semi-structured and non-structured data related to fault handling through a knowledge extraction technology; and eliminating ambiguity between reference items and fact objects in knowledge elements by using a knowledge fusion technology, forming a power grid fault knowledge graph, and constructing a thinking graph according to a reasoning process. According to the method, the power grid information which is most matched with the power grid fault event disposal is retrieved and recommended, and the fault disposal scheme which meets the requirements is quickly and accurately generated.

The invention belongs to the technical field of computer big data, and discloses a relation graph construction and layout method, device and system based on spectral clustering and a storage medium, a clustering center is initialized through a genetic algorithm, the clustering center serves as genetic information and is coded into a character string, the operation time can be shortened, and the classification precision can be improved; furthermore, a weighted Euclidean distance is constructed as a distance function of a K-means algorithm, mutual relation weighting between the features can be reflected, features of different weights are counted into the distance, the classification precision can be effectively improved, the loss is reduced, and the classification efficiency is improved. According to the method, an initial similarity matrix, obtained through a traditional similarity calculation method, between XML documents is corrected through an affinity propagation algorithm, the similarity between the hidden similar XML documents can be reflected, on the basis, the correct clustering number and the correct clustering result are obtained by applying a multi-path spectral clustering method NJW, the method is irrelevant to the sequence of the XML documents, and the method has the advantages of being high in practicability and easy to popularize. The method is suitable for clustering the retrieval results of the XML documents arranged in any sequence.

The invention discloses a thermal power plant intelligent question and answer method based on multi-source corpus fusion, and aims to solve the problems that knowledge construction depends on manual annotation, the corpus format is single, the context understanding ability is insufficient and the credibility of a generated result cannot be guaranteed in an existing question and answer method. The method comprises the following steps: introducing a vectorization processing mechanism and a semantic block model generation module to perform unified embedding and compression expression on various types of texts such as thermal power plant operation regulations, equipment manual, operation records and the like, so as to realize accurate retrieval and structured answering based on semantic correlation; and an answer judgment mechanism is introduced to reduce the risk that a language model generates an illusion phenomenon, so that the accuracy and interpretability of question and answer output are improved, and the intelligent question and answer requirements of operators in the aspects of equipment diagnosis, regulation query, accident handling and the like are met.

The present invention discloses a parameter control method under the QT framework, belonging to the technical field of information transmission and processing. The present invention includes a series of processing procedures such as defining the types and interfaces of function controls based on QT, implementing and forming a function control library, selecting function controls to form a local XML file, dynamically creating function controls to implement interface layout, performing message passing when the attributes of function controls change, and triggering the business logic of function controls. By using the signal and slot communication mechanism in QT, the present invention realizes the business requirements of controlling device parameters by applying various controls in different usage scenarios, and has good adaptability and scalability. The present invention has the advantages of cross-platform, easy expansion, strong reusability, etc., can significantly improve the efficiency of business design and development, and makes the man-machine interface for parameter control friendly, convenient, flexible and consistent.

Disclosed is an apparatus for optimizing a query of a graph database, the apparatus comprising: an interface for converting a graph query into a relational logic plan; and a relational query optimizer for optimizing the converted relational logic plan, wherein the interface converts an optimized relational physical plan generated by the relational query optimizer into a physical plan executable in a graph database, and provides the converted physical plan to a graph query processor.

A structured document storage format is provided in which tokens used for encoding and decoding are contained within the encoded document in an inline dictionary. Each document is encoded independently, and there is no central dependency or any dependency on other documents during DML and query execution. Each encoded document has all information with it on disk so that the document can be independently shared, decoded, or distributed. A mechanism is provided for quickly determining a mapping between tags and tokens without having to scan the entire inline token dictionary for each document. This allows the database system to process multiple documents, each having its own inline token dictionary, without having to fully scan the inline dictionary of every document for each tag that referenced in an operation.

The invention discloses a low-code configurable system integration implementation method based on XML (Extensible Markup Language). The algorithm relates to the technical field of application software development and low code. According to the method, through cooperative work of a task scheduling layer, an XML model layer, a connection establishment layer, a data exchange layer, a data conversion layer and a business logic layer, integrated adaptation of multiple modes of SOAP and RESTFUL is achieved. The functions of data pushing, data pulling, process triggering, data mapping and a data analysis module between systems are highly abstracted into code models irrelevant to services, so that integration implementation personnel do not need to write codes, and the functions of data integration and application integration can be operated only by defining the service models on XML (Extensible Markup Language) files. The business model comprises configuration of a data source address, an operation token, data mapping and flow triggering. According to the method, through code packaging, an implementation engineer does not need to consider implementation details and more focuses on business logic implementation. The development efficiency of system integration can be greatly improved, and the integration online debugging time is shortened.

The invention discloses an Internet webpage content feature extraction method based on artificial intelligence, which comprises the following steps: S1, acquiring a webpage HTML source file and a rendering image, and preprocessing the webpage HTML source file and the rendering image; s2, performing semantic classification on DOM nodes, encoding the DOM nodes into three types of identifiers, and constructing a node label sequence; s3, performing time sequence synchronization on the node attribute vector, the node tag sequence and the visual area set, and performing block-level slicing; s4, inputting the block-level slices into a gated recursive attention network, extracting multi-modal joint representation, and executing attention aggregation; s5, performing structure alignment on the feature fusion sequence, calculating a cross-node consistency distance, and screening a target slice set with high structure cohesion; and S6, mapping the target slice set to a content feature space, and generating a content feature tag set. According to the method, the structural accuracy, the semantic integrity and the multi-modal fusion precision of webpage content extraction are improved.

A computing system includes: server; client; broker-dealer database(s) storing personally identifiable information for accounts; and distributed ledger. Server receives request to obtain the personally identifiable information (PII) regarding public information for first account from first user of client. Server determines that a database of the database(s) includes the PII for the first account. Server determines whether the first user has permission to obtain the PII for the first account from the database that includes the PII for the first account. Server receives public information for the first account from distributed ledger when the first user has permission to obtain the PII for the first account from the database that includes the PII for the first account. Server provides the PII regarding the public information to the first user of the client.

A semantic model shall be completed or corrected in a comfortable way. Therefore, there is provided a method of generating a semantic model of a technical process or technical unit including the step of preparing (S1) a data model structure of the technical process or technical unit. An artificial intelligence unit determines (S2) a data element, whereby the data model structure is input into the artificial intelligence unit. The semantic model is generated by incorporating the data element into the data model structure.

A system, computer program product, and method are presented for leveraging structured data and unstructured data, and, more specifically, to ranking documentation from unstructured data sources through leveraging insights provided by the structured data to facilitate associated business risk inquiries. The method includes identifying, by researching subject business entities, one or more structured data sources that include relevant structured data directed to the subject business entities. The method also include extracting the relevant structured data directed toward the subject business entities and leveraging the relevant structured data to identify unstructured data sources. The method further includes identifying documents from the unstructured data sources that have relevant information, thereby identifying relevant unstructured data, and leveraging the relevant structured data to determine relationships with the relevant unstructured data. The method also includes scoring each relationship and ranking each document from the unstructured data sources as a function of the scoring.

Disclosed are systems and methods for XBRL tag outlier detection. In some embodiments, the method includes the steps of: receiving a first set of XBRL data records; generating a second set of XBRL data records based upon a subset of the first set of XBRL data records; training a machine learning model using the first set of XBRL data records and the second set of XBRL data records; receiving an XBRL document associated with one or more assigned XBRL tags; and analyzing the XBRL document using the trained machine learning model to identify a set of outlier XBRL tags in the one or more assigned XBRL tags.

The invention discloses a hospital examination and inspection result sharing method based on big data, and relates to the technical field of hospital examination and inspection result sharing. Examination data and inspection data are respectively stored in independent databases, and the two types of data are intelligently linked by using a unique identity label of a patient and a time stamp; the format, the time sequence and the semantic information are accurately matched to form a continuous and visual comprehensive disease course record; introducing a deep learning model, carrying out natural language processing on an unstructured text in the inspection data, and automatically converting an image description and a diagnosis report into structured data and a predefined semantic tag; according to the method, the workload of manual structuring is reduced, distributed storage, dynamic updating and multi-dimensional index query modules are constructed based on a big data analysis platform, real-time retrieval and joint query are conducted on comprehensive disease course data records, meanwhile, an early warning report is generated through an anomaly detection algorithm, and a basis is provided for clinical decision making.

Described herein are systems and methods for extracting and combining XML files of an XFA document. The systems include processors and memory for efficient processing and data storage. The systems can identify XFA documents and generate XML files by parsing the XFA documents. The systems can identify XML nodes within XML files, with each node corresponding to a particular node type, and generate web forms including web nodes, with each web node mapped to a corresponding XML node. The systems can receive input corresponding to the respective node type and store an association between the input received for the web node and the node type and an identifier of an XML node to which the web node is mapped. The systems can update the XML files using the association and generate the populated XFA document by combining the updated XML files according to the schema of the XFA document.

Disclosed are a data processing and storage method and apparatus based on a graph database and a vector database. On the basis of a graph database and a vector database in combination with a LayoutLMv3 model, a Transformer model, and OCR technology, the invention aims to efficiently parse, store, and retrieve unstructured documents. In the present invention, first, a document is converted into an image, and a layout analysis model, namely the LayoutLMv3 model, is used to identify several types of regions in the image, such as text, images, and tables. Then, three types of parsers are used to analyze the regions containing data. In particular, due to the complexity of table data structures, a table analysis model is used to convert tables into textual representations. Finally, all obtained data is structurally partitioned and respectively stored in a graph database and a vector database to achieve high accuracy and high efficiency in data retrieval, thereby providing strong support for big data analysis and large language model applications.

Migrating computing systems to zero trust compliant architectures using templates that are constructed from multimodal unstructured sources. The unstructured input data is converted to structured data using a trained machine learning model. The structured data output by the model may be reviewed. If errors are found, a revision script can be revised or refined and the unstructured input data may be re-ingested. When the structured data is suitable, the structured data is parsed and stored as templates in a template database. A database of zero trust approved hardware and / or software may be generated. Migration operations may be performed using the templates in the template database and / or the information in the hardware and software database.

The application discloses an object processing method and device, electronic equipment and storage medium, and relates to the technical field of network. An object sharing request of an object sharing initiator is received, the object sharing request carries identification information of an object, content information of the object is obtained based on the identification information, the content information of the object is processed by format conversion by using an object processing system, resource address generation and storage processing are performed, activity information is returned to an object sharing acquirer in response to an activity information acquisition request sent by the object sharing acquirer, and a resource address of the object is returned to the object sharing acquirer in response to an object acquisition request sent by the object sharing acquirer. The above method can split the activity information and the content information of the object after format conversion into two data packets to be delivered, reduce the size of a single data packet, and reduce the possibility of being discarded by a long link system.

A method (300) of storing semi-structured data (12U) includes receiving user data (12) comprising semi-structured user data from a user (10) of a query system (150). The method includes receiving an indication (14) that the semi-structured user data fails to include a fixed schema. In response, the method further includes parsing the semi-structured user data into a plurality of data paths (210) and extracting a data type (220) associated with each respective data path of the plurality of data paths. The method additionally includes storing the semi-structured user data as a row entry in a table (204) of a database in communication with the query system, wherein each column value associated with the row entry corresponds to a respective one of the plurality of data paths and the data type associated with the respective data path.

The application relates to a knowledge-driven multi-agent cooperative reactor scheme demonstration system and a design method, medium and equipment thereof, the system comprising: a knowledge internalization unit for converting unstructured and semi-structured documents into a dynamic knowledge source which can be queried and utilized by a model in real time; a knowledge structuring unit for extracting core entities, relationships and attributes from the knowledge source and constructing a professional knowledge graph of a reactor design field; and a knowledge application unit for constructing intelligent agents and a collaborative working mechanism of multi-professional intelligent agents based on the knowledge source and the knowledge graph, and constructing an intelligent question and answer interface. Through automatic knowledge management and intelligent agent collaborative work, the application significantly reduces the time and effort of manual intervention and improves the efficiency of reactor scheme demonstration.

The present invention discloses a method for intelligently generating an ontology, and its steps include: 1) converting the elements for describing entities in the XSD document to be processed into class nodes; converting the elements for describing entity attributes in the XSD document to be processed into data attribute nodes; 2) determining the edges between the nodes corresponding to each element according to the nested hierarchical relationship between the elements in the XSD document to be processed, and generating a directed graph corresponding to the XSD document to be processed; 3) generating semantic embedding vectors for each node in the directed graph, calculating the semantic similarity between nodes according to the semantic embedding vectors of the nodes; merging the nodes with semantic similarity greater than a set threshold into cluster nodes; 4) obtaining an ontology of resource knowledge content described in OWL language according to the directed graph processed in step 3). The present invention can reveal more knowledge content in the original XML resources and improve the description and revelation ability of the ontology for the original knowledge content.