1724 results about "Data objects" patented technology

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention relates to a distributed data security storage and encryption system based on an AI technology. The system comprises a data acquisition and preprocessing layer used for acquiring original data and preprocessing the original data to obtain an identifiable data object; the AI identification and strategy decision layer is used for identifying data features of identifiable data objects based on an interpretable AI model to obtain an encrypted instruction packet; the encryption execution and distributed storage layer is used for performing encryption storage processing on the original data content according to an encryption instruction packet to obtain data access interface information; and the dynamic access control layer is used for obtaining user information in response to a data access request instruction of a user, recombining and decrypting the data by using the data distribution mapping index based on the access control strategy table, and returning a plaintext data fragment. By adopting the system, a high-strength, differentiated and multi-level security mechanism can be realized through the AI, intelligent encryption strategy decision is carried out, and the security requirements of multiple scenes and multiple types of data are met.

The invention provides a digital factory flexible production scheduling system based on MES, and relates to the technical field of digital management, and the method comprises the steps: capturing an order change instruction in real time through an integrated interface of the MES and ERP, and converting the order change instruction into a standard chemical order data stream; based on the structured data object, fusing the equipment real-time sensing data with a pre-stored process constraint matrix and dynamic material topological data, and generating a three-layer real-time resource model containing an equipment state vector, the process constraint matrix and the material topological data; layered optimization is executed based on a three-layer real-time resource model, in the first layer of optimization, an equipment state vector and a process constraint matrix are used as hard constraints, and an initial feasible solution set is generated; and the second-layer optimization executes multi-target evaluation on the initial feasible solution set to obtain a non-dominated solution set as a production scheduling plan. According to the invention, order-resource-execution full-process intelligent collaboration is realized.

The invention discloses an intelligent data storage method based on a cloud platform, relates to the technical field of data storage management, and is used for solving the problem that cold and hot data identification is lagged. According to the method, the access behavior graph is constructed, the concurrent access coupling weight is calculated, the data object with high access frequency and deep path hierarchy is identified, the structure adjustment instruction is dynamically generated, the access link complexity and index hierarchy redundancy are reduced, the response delay and the popularity prediction error before and after the structure adjustment are combined, and the access efficiency is improved. According to the method, the scheduling efficiency of the hotspot data is improved, the adaptability to the change of a storage access mode is enhanced, the dynamic adjustment capability and the structure response performance are good, and the method can be used for realizing the closed-loop linkage of the access behavior perception, the structure reconstruction and the model optimization. Therefore, the method is more suitable for a large-scale data intelligent management scene under a cloud platform.

The invention provides a data security method and system fusing an MIN and a unified AI theory, and the method comprises the steps: S1, completing the multi-dimensional identification registration and synchronization of a data space in a multi-identification network MIN based on a data space identity identification DSID and an ontology semantic mapping frame; s2, acquiring a data acquisition record from a multi-identifier network MIN, and constructing an access control atlas in combination with a semantic vector of a data subject identifier DSID-Item; s3, analyzing the access request and verifying the authority, identifying the sensitivity level of the access request, and triggering a privacy enhancement mechanism when the sensitivity level is marked as medium-high-level privacy data; and S4, judging whether the data object reaches a value end point in the life cycle of the data object or not by taking a data space identity (DSID) registered in the multi-identifier network (MIN) as a retrieval anchor point, and if so, executing a verifiable multi-stage destruction operation. According to the invention, a full-life-cycle trusted data security technical scheme can be realized, and intelligent, explainable and efficient security management of data streams in a network space is realized.

The present disclosure relates to techniques for automatically generating new data objects from user input. The system receives user input comprising a plurality of words and executes a first query on a vector store to identify schema elements similar to keywords in the user input. The vector store provides a response with similarity scores for identified elements. A second query is executed on a knowledge graph to identify association paths between data objects that include the identified elements. The knowledge graph response includes association information linking source and target data objects through selected elements. Full association paths are constructed from this information, and a command is generated to instantiate a new data object with elements corresponding to the user input. This approach leverages the strengths of large language models, vector stores, and knowledge graphs to efficiently and accurately create new data objects, ensuring data integrity and relevance.

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 discloses an intelligent medical record quality control method based on a rule and large language model hybrid engine, and relates to the technical field of natural languages, and the method comprises the steps: obtaining original electronic medical record data from a medical information system; performing post-structured processing on the original electronic medical record data, and converting an unstructured or semi-structured text in the original electronic medical record data into a unified structured data object readable by a machine; traversing a plurality of quality control points in a preset quality control rule base, and for each quality control point, adaptively selecting and executing a corresponding quality control path based on the converted structured data object; and based on the quality control results of all the quality control points, performing score calculation and generating a quality control report. According to the method, the automatic and deep quality control of the full-dose medical record can be realized, form inspection is covered, connotation logic analysis can be executed, the system is endowed with excellent flexibility and regional expansion capability, and reliable technical support is provided for improving the overall level of medical quality management.

The invention relates to the technical field of data storage, in particular to a mass data multilayer hierarchical storage method fusing access frequency and life cycle weight, which comprises the following steps: calculating the value life cycle weight of a data object and the physical life cycle health degree of a target storage medium in parallel, and outputting a composite weight factor through a coupling function; calculating a net benefit score value based on the composite weight factor and the data real-time access frequency in combination with a migration I / O loss estimated value of cross-layer migration; when the net benefit score value breaks through a preset migration threshold value, data migration is executed, and physical storage positions are dynamically distributed on the target storage layer according to the physical life cycle health degree. According to the method, the migration threshold is dynamically adjusted through the business key factors, the core data migration responsiveness is guaranteed, and meanwhile, high-cost migration behaviors are prevented from being triggered by temporary or edge data by mistake.

Various embodiments of the present disclosure provide a feature engineering framework for generating task-specific contextual embeddings to improve machine learning model performance, while reducing memory requirements for the performance of a predictive task. The techniques may include identifying a plurality of entity predictive identifiers from historical data objects for an entity. The entity predictive identifiers are used with a task-specific data store to generate a textual entity document for the entity by concatenating a set of contextual text descriptions from the task-specific data store that correspond to a set of task relevant identifiers from the plurality of entity predictive identifiers. The techniques include generating, using a machine learning encoder model, a contextual entity embedding for the entity based on the textual entity document and then training, using the contextual entity embedding, a machine learning classification model for the performance of the predictive task.

Apparatuses, systems, and techniques to generate one or more data items related to an object from a set of multiple data items related to the same object using a generative adversarial network. In at least one embodiment, data objects in a set are disentangled into common and unique components and encoded such that they may be used to train one or more neural networks to generate missing data from the data objects in the set, such as generating missing medical images from a set of related medical images.

The invention provides a real-time data acquisition buffer throughput management method and device and electronic equipment, and relates to the field of data processing. Receiving a data object containing a sampling value, a timestamp, a measuring point number and a state mark through an input memory queue, and constructing a logic mapping identifier by a mapping agent unit to generate a logic write-in state; the logic write-in state is stored in a delay sensing buffer area, and a transaction confirmation delay sequence returned by a target database is monitored in combination with a distributed delay monitoring unit; establishing a time regression model based on the delay sequence, and generating a prediction projection window of transaction confirmation time; when the window meets a confidence threshold condition, releasing the logic write-in state to an output memory queue; and finally, writing into a database through an output thread, generating a completion mark table, and feeding back for delay buffer dynamic management. By implementing the technical scheme provided by the invention, the buffer data release rhythm and the database processing capability are conveniently coordinated, so that the overall throughput efficiency is improved.

Methods, systems, and apparatus, including computer-readable media, for enhancing artificial intelligence chatbots with search functionality. In some implementations, a system stores a search index for data sets, where the search index describes data objects of the data sets and values for the data objects in the data sets. The system receives a user prompt to a chatbot and searches for data objects and values that are relevant to the user prompt, including using the search index to search for data objects and values of one or more data sets that the chatbot is configured to access. The system uses one or more results obtained using the search index to generate a chatbot response to the user prompt, including providing the one or more results to an artificial intelligence and / or machine learning (AI / ML) model. The system provides the chatbot response as a response to the user prompt.

Automated system for predicting software application framework deployment changes for incident mitigation is provided. An incident indication for an incident may be received. One or more incident features associated with the incident may be extracted. One or more data objects associated with the incident may be identified based on the one or more incident features. Candidate incident mitigation dataset comprising a plurality of incident mitigating predictions may be generated based on relation data and a topology graph structure associated with the one or more data objects. A ranked candidate incident mitigation dataset may be generated using one or more ranking models. One or more incident mitigating predictions from the ranked candidate incident mitigation dataset may be selected based on a rank value associated with each of the one or more incident mitigating predictions.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

The invention discloses an automatic auditing method and system based on ship-shore model matching fusion, and belongs to the technical field of data processing.The method comprises the steps that ship-shore matching related documents of a user are interacted, and the ship-shore matching related documents support multiple formats; activating a multi-model fusion processing module to perform preprocessing and multi-modal analysis on the ship-shore matching related document, extracting a text, a visual element and structured data, and generating a structured data object; a preset rule engine and the large language model are called to process the structured data object in parallel, the rule engine executes deterministic rule verification, and the large language model processes uncertainty semantic recognition determination; and fusing output results of the rule engine and the large language model to generate an audit result and a risk prompt. The problems that in the prior art, ship-shore matching auditing depends on labor, the efficiency is low, errors are large, multi-format documents are difficult to process through a single technology, rule and semantic coverage is incomplete, information fragmentation is caused, and auditing accuracy and efficiency are difficult to consider at the same time are solved.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for retrieving a subgraph that is used to generate one or more answer outputs responsive to an input query by: (i) generating one or more context embeddings that are associated with an input query, (ii) identifying one or more candidate node paths and one or more node relations based on a knowledge graph, (iii) identifying, using a predictive machine learning model, one or more context-relationship rankings based on the one or more candidate node paths, the one or more node relations, and the one or more context embeddings, and (iv) generating one or more subgraph data objects based on the one or more context-relationship rankings.

Embodiments provide for dynamic generation and updating of quote data objects using machine learning. An example apparatus is configured to receive a quote request and parse the quote request to extract one or more quote parameters. The example apparatus is further configured to determine, based at least in part the one or more quote parameters and by querying a plurality of repositories that are each updated periodically or in real-time, available inventory associated with the product identifier, and to determine a set of alternative quote values for the product identifier. The example apparatus is further configured to generate, using a first trained machine learning model and based at least in part on the available inventory and the set of alternative quote values, a first quote feature for the quote request and a confidence score, and to cause transmission of a quote data object to a user device.

A system including a media data optimization engine (MDOE) and a method for automatically creating structured data objects for media content rendered in one or more languages in an electronic document of a business entity are provided. The MDOE identifies non-textual objects including media content rendered in one or more languages in the electronic document and generates textual objects in the corresponding language(s) therefrom. The MDOE transforms the textual objects into structured data objects based on configurable criteria and generates a dynamic index-oriented object for the structured data objects specific to the business entity. The MDOE connects the structured data objects to the dynamic index-oriented object by creating linked data nodes therefrom with the dynamic index-oriented object as a core. The MDOE connects the dynamic index-oriented object with the linked data nodes to the electronic document, thereby facilitating dynamic changes to the electronic document and dynamically optimizing the electronic document.

The invention relates to the technical field of distributed architectures, and discloses a multi-screen cooperative control method based on an OpenHarmony distributed architecture and an all-in-one machine system.The method comprises the steps that a broadcast frame found by equipment is sent through an OpenHarmony distributed soft bus, security authentication is conducted on a plurality of display terminals responding to the broadcast frame, a virtual bus channel is established, and the virtual bus channel is sent to the display terminals; obtaining a virtual device group; creating a distributed industrial data object on the main control equipment of the virtual equipment group and synchronizing the distributed industrial data object to each slave display terminal to obtain a distributed data model; mapping the anonymous shared memory to an annular buffer area; and writing real-time data acquired by the industrial field equipment into an annular buffer area, and transmitting the real-time data to each slave display terminal of the virtual equipment group, in the method, each display terminal accesses an agent instance of an industrial data object through a distributed data management framework; and the requirements of high reliability, low delay and high consistency on multi-screen cooperative control in an industrial field are met.

The invention discloses an electric power multi-protocol data conversion method and system based on object model dynamic mapping, and the method comprises the steps: receiving an original protocol message, recognizing a protocol type, extracting a logic name as a unique identifier of equipment, extracting an object identifier as a measurement point identifier, and extracting an electric energy array as a measurement point value; according to the protocol type and the equipment unique identifier, matching a corresponding object model template from a unified object model library; mapping the equipment unique identifier, the measuring point identifier, the measuring point numerical value and the acquisition timestamp obtained by analysis to corresponding fields in a template; and generating an object model data object conforming to a standard format, packaging the object into a message, executing an encryption authentication mechanism of a network layer and a transmission layer through the power Internet of Things security access gateway, and sending the message to an Internet of Things management platform. According to the invention, standardized, automatic and secure access of different protocol terminals is realized through protocol analysis and the unified object model library.

The invention belongs to the technical field of data statistics, and particularly discloses a Python-based dynamic SQL (Structured Query Language) generation and data statistics method and system, a terminal and a medium. The method comprises the following steps: acquiring statistical demand configuration in an Excel file; analyzing the Excel file to generate a structured data object, and carrying out semantic analysis and metadata consistency verification on the natural language description; constructing an SQL abstract syntax tree through a Python analysis module, and executing syntax mapping and function replacement according to a database adaptation rule; connecting a target database through a Python database driver and executing an SQL query statement; and after a query result is obtained, outputting an Excel file, a visual chart or interface data. According to the method, automatic analysis, SQL dynamic generation, safe execution and intelligent result output of data statistics requirements are realized.

Various embodiments of the present disclosure provide prompt engineering and iterative, feedback-based generative techniques that improve traditional LLM technology, including extractive LLM techniques. The techniques may include generating, using a machine learning classification model, a resolution capability classification for an input data object that comprises an input question and an input document; generating using a large language model (LLM), an initial predictive output for the input data object based on an initial generative model prompt for the input data object; identifying a classification model output divergence based on a comparison between the resolution capability classification and the initial predictive output; and in response to the classification model output divergence: generating an augmented generative model prompt by modifying the initial generative model prompt with a representation of the resolution capability classification, and generating, using the LLM, an updated predictive output based on the augmented generative model prompt.

One or more data objects from at least one of HR data system may each contain data fields storing HR information. A graphical user interface (GUI) may present primitive coding blocks representing standard graphic programming functions and custom business blocks representing high-level operations customly directed to the one or more data objects. A visual workflow may be constructed using at least one block selected, by a user of the GUI, wherein the at least one block represents operations linking the data fields of the one or more data objects to corresponding input fields of the at least one block. The at least one block of the visual workflow may be translated into a custom data connector comprising executable instructions for obtaining values for the corresponding input fields from the linked data fields and generating, based on the values, at least one corresponding data object storing the HR information.

The invention provides an intelligent hierarchical storage method based on cold and hot data, and the method comprises the steps: collecting the access features of a plurality of dimensions of data objects in real time, and calculating the dynamic comprehensive popularity value of each data object through a pre-trained machine learning model; and carrying out automatic migration and storage optimization on the data object between the high-speed storage layer and the low-speed cheap storage layer. According to the method, the machine learning model is adopted to comprehensively analyze the multi-dimensional access features, so that the problems of insufficient judgment precision and poor adaptability caused by dependence on a single-dimensional strategy in a traditional hierarchical storage method are solved; accurate prediction of the cold and hot states of the data and dynamic self-adaptive adjustment of the layering strategy are achieved, the utilization efficiency of storage resources is effectively improved, and the overall storage cost is reduced.

A set of locations in a data object to be annotated is identified as corresponding to metadata of the data object. A natural language text query is generated using the metadata of a data object. A set of scores is generated for the set of locations using a generative neural network, and the set of scores indicate whether individual candidate locations satisfy the natural language text query. Based on the set of scores, a location in the data object is annotated to generate an annotated location as corresponding to the metadata.

Techniques for automated query response determination using a hybrid AI are disclosed herein. An example computer-implemented method includes receiving a data file and a request including at least one query associated with the data file and applying a hybrid model to the data file. Applying the hybrid model includes segmenting the data file into one or more portions, embedding the one or more portions into a vector space, extracting, from the data file, data associated with one or more classifications, storing (i) the embedded portions in a first database and (ii) the extracted data in a second database, and determining a response to request queries based on the embedded portions and the extracted data, wherein the hybrid model constrains each response based on a parametric input prompt associated with the extracted data. The example computer-implemented method further includes storing one or more data objects indicating each response.

The invention relates to the technical field of data life cycle management, and discloses a dynamic self-adaptive data life cycle management system and method.The dynamic self-adaptive data life cycle management method comprises the steps that a data object and a business lineage log thereof are logically bound, and a lineage entropy value is calculated in a non-semantic counting mode based on the log; according to the method, a data management path which does not depend on an external complex rule engine to carry out event analysis is established, so that a management strategy can be generated by business process records of data in an endogenous and deterministic manner; and meanwhile, for the data which is subjected to high-level control due to accidental high-risk events, after the risk of the data is actually dissipated, automatic falling of the control level is realized, and the coupling degree and the maintenance complexity of the whole management system are reduced.

The present invention provides a data network, a data ingestion system and a method of data ingestion in the data network for a supply chain management enterprise application. The data network includes one or more data objects of different data types received from different data sources structured on multiple distinct architecture, connected to each other for executing multiple functions in the enterprise application.