10 results about "Common Data Format" patented technology

Described are examples for creating a graph model for data obtained from multiple data sources and stored in a common data format. A selection of columns of data stored in the common data format that are to be associated with one another in the graph model can be obtained. Based on the selection, a graph model can be created including metadata that defines a list of vertices corresponding to at least a portion of the columns and a list of edges between two or more of the vertices identifying a relationship between corresponding columns. A graph query or graph algorithm can be performed over the graph model of the data as stored in the common data format without requiring copying of the data to a separate graph database.

Methods and systems are described for assessing a computer network using a graph model. In some instances, the methods comprise: receiving data from at least one data stream of a plurality of data streams, wherein the plurality of data streams comprise computer network data provided by one or more data brokers, and wherein the data received from different data streams of the plurality comprise different data formats; converting the data received from the at least one data stream to a common data format comprising a node or an edge; updating a graph model comprising a plurality of nodes and edges stored within a graph database according to the node or edge of the converted data; and providing a user of the computer network with a visualization of a status of the computer network.

The application discloses a kind of novel photovoltaic device transient dynamics calculation design system and method, system includes: user parameter input module is used to receive and manage the input parameter required by simulation;Device structure construction module is used to construct the layered structure model of target device based on input parameter, and define the space interval, physical property and interface boundary condition of each layer;Physical model selection and coupling module is used to construct corresponding coupled partial differential equation set according to the device type of target device;Space grid division module is used to construct the space discrete grid of layered structure model, and the calculation precision of interface and thin layer area is enhanced using non-uniform grid division mode;Core numerical solution module is used to solve coupled partial differential equation set, and obtain simulation result;Data export module is used to output simulation result as general data format.

By converting various types of data into data in a more easily usable format, failure diagnosis can be performed more efficiently using the data.A failure diagnosis system includes: an edge system which is an electronic system; a failure diagnosis device configured to diagnose a failure of the edge system; and a computer which is an external device. The failure diagnosis device distinguishes data acquired from the edge system and the computer according to a type of the data, extracts a data element included in the data based on predetermined interpretation processing for the data, converts the data element into a predetermined data code corresponding to an item of a common data format regardless of the type of the data, and generates diagnosis intermediate data in which the data code is assigned to the corresponding item, and performs diagnosis analysis of a failure in the edge system using the diagnosis intermediate data.

The invention provides a student wrong question automatic acquisition method based on an intelligent correction system. The method comprises the following steps: S1, acquiring a homework set of any student and inputting the homework set into the intelligent correction system to obtain a correction result; s2, judging whether the correction result meets a triggering condition or not: if so, turning to S3; if not, returning to the step S1; s3, extracting multi-modal wrong question contents including question contents, student answering contents and correction results from the homework set; s4, based on the knowledge graph and the error classification system, performing structured information labeling on the multi-modal error content to obtain an error mastering result; step S5, obtaining a redo correction result of the student for the wrong questions and updating a mastering result of the wrong questions; and S6, storing the updated error question mastering result, the updated multi-modal error question content and the updated structured information according to a general data format. The method has the beneficial effects that full-process automation can be realized, multi-modal information is covered, and high-quality structured error data is generated.

A system is provided.SOLUTION: A system comprising: means for acquiring sales data of real stores; means for acquiring online sales performance data; means for converting the sales data of the real stores and the online sales performance data into a common data format; means for detecting and correcting duplicate data and missing data; means for storing the converted data in a database; means for acquiring price information of the real stores and the online stores from the database; and means for displaying the acquired price information on a user terminal.SELECTED DRAWING: Figure 1

This invention belongs to the field of large language model technology, specifically a method for constructing large language models based on DSL, SYSML, and UML constraints. The method includes: constructing a five-layer unified capability stack, embedding constraint concepts into the large language model at the architectural level; defining a unified intermediate representation (UIR) as a universal data format; injecting constraint concepts into model weights at the parameter level through a four-stage unified adjustment process of corpus immersion, graph semantic learning, inference pattern learning, and capability alignment; constructing a unified inference pattern library covering requirements analysis, architecture design, behavioral modeling, constraint verification, code generation, and document generation; and using the UIR and the inference pattern library to guide inference, achieving synchronous generation and constraint verification of multiple output types. This invention enables the large language model to form structured constraints internally, resulting in outputs that naturally meet engineering requirements, and solves the problems of easy failure of external constraints and poor cross-domain adaptability.

A media content management server is configured to receive, from a first remote server, first metadata associated with a first set of content items, the first metadata representing a first parameter for each of the first set of content items in a first data format, and further to receive, from a second remote server, second metadata associated with a second set of content items, the second metadata representing a second parameter for each of the second set of content items in a second data format different than the first data format. Reformatted metadata is generated by reformatting the first parameter and the second parameter to a common data format. Rules engine circuitry is configured to generate a data transfer package by comparing the reformatted first and second parameters to content access rule data to determine subsets of content items for inclusion in the data transfer package.

Store a subscription dataset for each tenant of a multi-tenant system, each of the subscription datasets having a common data format native to the system, the subscription datasets including billing data and not behavioral data. Determine primary features from a particular subscription dataset. Derive secondary features from the primary features. Generate a churn prediction model based on the primary features and the secondary features. Obtain a second subscription dataset, the second subscription dataset comprising billing data that is more recent than the particular subscription dataset. Identify, using the particular churn prediction model and the second subscription dataset, one or more subscribers as a churn-risk. Report the one or more subscribers identified as a churn-risk.

Provided herein are methods and materials using multi-omics assay to detect DNA, RNA, and / or polypeptides. In some cases, the methods and materials provided herein can be used for simultaneous detection of one or more target analytes (e.g., a target gene of interest) in a sample. In some cases, the outputs generated are standardized into a common data format, thereby allowing integrated processing, interpretation, and comparison across multiple different types of analytes (e.g., biomolecular classes). For example, the method includes simultaneous detection of DNA, RNA, and / or polypeptides in a single sample.