83 results about "Metadata description" patented technology

Automatically investigating security incidents and generating security incident reports using a Large Language Model (LLM). A computerized system receives an incoming Security Alert Message pertaining to a possible security-related incident. The system automatically feeds into the LLM at least: the content of the Security Alert Message; the metadata of the Security Alert Message; context information describing a security domain; and organization context information pertaining to users and machines of that organization. The system automatically prompts the LLM to automatically investigate the Security Alert Message and to automatically generate a detailed Incident Report pertaining to the Security Alert Message.

The invention discloses a fusion method for automatic coprocessing of multi-source heterogeneous data, which comprises the following steps of: analyzing the format and semanteme of the multi-source heterogeneous data to obtain metadata features of each data source, the metadata features comprising data types, coding modes and semantic tags, and generating a standardized metadata description set; according to the metadata description set, a pre-established protocol template library is adopted, an applicable template is matched, an initial unified exchange protocol is generated, and the initial unified exchange protocol comprises a format conversion rule and a semantic mapping relation; and if the format conversion rule of the initial unified exchange protocol cannot adapt to the metadata features of the newly added data source, dynamically updating the protocol template through a machine learning algorithm to obtain an exchange protocol adaptive to the new data source.

The invention discloses a Direct3D memory model compatible method based on adaptive placeholder resources, which comprises the following steps: establishing resource metadata, accessing a scene library, checking an instruction template and double sandboxes when a DXVK is started, describing related resources by the DXVK through the metadata after a D3D application is started, completing resource mapping and metadata dynamic updating, and allocating the resources to the corresponding sandboxes; the DXVK compiles an application shader code, identifies a resource access instruction, matches an access scene and a check template by combining a pipeline stage and binding slot query metadata, instantiates the check instruction and adds the check instruction to the front of a target instruction; according to the unbound resources, adaptive generation of corresponding occupied resources is carried out, the authority and the state are configured, the unbound resources are bound to a Vulkan descriptor set to replace VKNULLHANDLE, and adaptation of the shader interface and the pipeline state parameters is completed to create PSO (Particle Swarm Optimization); and intercepting an application rendering instruction analysis parameter to construct a command buffer area, submitting the command buffer area to a Vulkan queue to trigger a GPU (Graphic Processing Unit) to execute rendering, and finally completing complete conversion and adaptation from the D3D rendering logic to the Vulkan.

The invention provides a collaboration method and system for enhancing interoperability of AI agents. The collaboration method comprises the steps that S1, the agents are described through structured metadata; s2, decomposing the target task; s3, extracting capability types required by all the sub-tasks according to all the sub-tasks, broadcasting the capability types required by all the sub-tasks, and screening to obtain intelligent agents meeting the capability types required by the sub-tasks; s4, comprehensively scoring the multiple agents corresponding to each sub-task obtained through screening, and screening the agent with the highest score as an executor of the current sub-task; s5, screening a minimum candidate agent subset according to all agents corresponding to all screened subtasks, so that a union set of capabilities of the minimum candidate agent subset can completely cover the total demand of the target task; and S6, based on the current minimum candidate agent subset, executing and finishing the target task according to the dependency relationship among all the sub-tasks.

The invention provides a monitoring data linkage method between code-free applications, automatic identification and mapping of data structures between the applications are realized by constructing a metadata description model and a field similarity function, and data change detection is performed by adopting an improved locality sensitive hash algorithm. Through a distributed transaction control mechanism, data consistency in cross-application data linkage is ensured, and a multi-stage bloom filter and a distributed transaction feature evaluation model are designed. Furthermore, data synchronization is realized by utilizing a synchronous vector field, and data synchronization batch processing and concurrency adjustment are optimized in combination with a Gaussian divergence theorem. Besides, the invention provides abnormal recovery path optimization based on the minimum action quantity, the reliability of data linkage is ensured, and a dynamic data security protection mechanism is constructed through a wave function, a diffusion equation and nonlinear mapping.

Data structures and applications are defined with an application development environment which employs the use of a natural language artificial intelligence interface and recorded metadata descriptions, which are converted at runtime to generate underlying software code (e.g., C#, SQL, JavaScript, etc.) to implement the application. The data structures include application data elements described in a data serialization format. Each application data element includes fields that each represent an individual value. Each field is defined with a label, a data type, and attributes. The data structures also describe relationships or links between application data elements, as well as data-driven or time-based rules that drive further data manipulation and interactions within and external to the programming development environment through a variety of interfaces (e.g., HTTP, SMTP, SMS).

Knowledge-based audio-text modeling via automatic multimodal graph construction is performed. An audio dataset is received, the audio dataset including clips of audio data, wherein each of the clips of the audio data is paired with corresponding metadata descriptive of the audio contents of the respective clip of the audio data. Graph nodes of interest are identified from a sematic network, the graph nodes being descriptive of semantics of the knowledge domain of the contents of the audio dataset. A large language model (LLM) is utilized for categorizing the metadata into the graph nodes and for inferring supplemental data for the graph nodes for which there is no metadata, producing an extracted knowledge graph. The extracted knowledge graph is validated utilizing the LLM to perform relation verification of edges between the graph nodes of the extracted knowledge graph, thereby mitigating hallucination effects in the categorizing and inferring of the supplemental data.

The invention provides a method and a device for generating a customized report of an SAAS (Software as a Service) system based on dynamic template configuration. The method comprises the following steps: acquiring a customized visual report template for realizing business data decoupling; editing the report template based on a preset editor, and generating a template configuration file; analyzing a structured marker in the template configuration file to generate a metadata description file; querying a business database through a unified data interface according to a data requirement defined by the metadata description file, and obtaining and assembling customized business data corresponding to the tenant ID; and at least based on the customized business data, controlling a rendering process through a dynamic position calculation algorithm, and dynamically filling the data to a corresponding position of the report template to generate a customized report. According to the method, the problems of code redundancy and difficulty in maintenance of the SAAS multi-tenant report are solved, zero code configuration is realized, and report generation efficiency and tenant personalized adaptation capability are improved.

The invention discloses a self-defined component configuration method and system based on a low-code platform, and belongs to the technical field of low-code development, and the method comprises the following steps: constructing a reusable dynamic structure unit library by deconstructing a component function into an atomized dynamic structure unit and defining metadata and context contracts of the dynamic structure unit; component functions are disassembled into template fragments, logic controllers and metadata descriptors in an atomized mode, standardized metadata and context contracts are defined, a reusable dynamic structure unit library with high cohesion and low coupling is constructed, label retrieval according to function categories is supported, a version control mechanism guarantees historical version backtracking and automatic upgrading, and the reliability of the system is improved. Resource packaging simplifies an integration process for a standardized module, so that the repeated development cost is remarkably reduced, and an efficient and extensible unit basis is provided for dynamic configuration.

The invention discloses a data processing method and system based on AIGC content, and the method comprises the steps: obtaining the content generated through AIGC, and distributing a unique identifier for the content; the content is subjected to metadata description, metadata of the content is generated, and the metadata comprises content types, generator IDs, generation time, content abstracts and content quality indexes; carrying out fragmentation processing on the content, and encrypting each piece of processed fragmented content to obtain each encrypted content block; and for each encrypted content block, forming an encrypted storage block by the encrypted content block, the unique identifier and the metadata, storing the encrypted storage block in a block chain, and dynamically adjusting the storage position of the encrypted storage block. By utilizing the embodiment of the invention, the efficiency and security of content management can be improved by means of unique identifier distribution, metadata description, encrypted storage, dynamic block chain management and the like of the AIGC generation content.

The invention discloses a multi-modal data quality evaluation method based on deep learning. The method comprises the steps of obtaining first modal record data and second modal record data which are marked to be aligned in a database; respectively carrying out semantic feature extraction on the first modal record data and the second modal record data to obtain a text structured data semantic coding feature tensor and a metadata description semantic coding feature vector; performing cross-modal joint coding on the text structured data semantic coding feature tensor and the metadata description semantic coding feature vector to obtain a structured-metadata cross-modal quality evaluation joint coding feature tensor; generating reconstruction record data based on the structured-metadata cross-modal quality evaluation joint coding feature tensor, and calculating offset features of the reconstruction record data; performing quality evaluation on the offset features through a first deep learning model; according to the method, through semantic coding feature extraction, fine-grained hash cross-modal coding and bidirectional reconstruction offset evaluation, the accuracy of data quality evaluation is improved.

The invention provides a data acquisition and integration method, system and device and a medium, and belongs to the technical field of information. The method comprises the following steps: packaging multi-source heterogeneous data into an original data packet in a standard format through a unified packaging protocol; cleaning, converting and standardizing the original data packet by using a rule engine to generate a standardized data object; based on the metadata description information and the field mapping relation, intelligent fusion of the multi-source data is achieved, and a fusion data record with complete business semantics is generated; a full-link quality monitoring system is established, a quality report is generated through multi-dimensional quality index evaluation, and an alarm is triggered; displaying the operation state through a visual interface and receiving configuration parameters; and realizing dynamic scheduling and resource optimization of the distributed tasks based on the task dependency graph. According to the invention, standardization, automation and intellectualization of the whole data acquisition and integration process are realized, and the data processing efficiency and the quality reliability are effectively improved.

The invention discloses a generation type intelligent large screen construction method and system based on natural language questions and answers, and belongs to the technical field of computer software, and the method comprises the steps: data preprocessing and knowledge base construction: constructing an interface metadata description system based on a data interface, generating a high-dimensional vector through large-scale pre-training, and constructing a knowledge base; dense distributed representation of text semantics is realized; performing question segmentation and extraction: performing automatic segmentation on an intelligent large screen generation question proposed by a user by adopting a generative pre-training model, and matching an interface corresponding to an optimal interface description based on a model reasoning capability; index data acquisition: performing iterative processing by adopting a polling mode, and acquiring index data according to the interface metadata information; and large screen code generation and analysis to realize code generation of an intelligent large screen. According to the method, the problems that traditional large screen development needs to be participated by professional technicians and the response period is long are solved, automatic analysis of multi-source heterogeneous data and visual code generation are achieved, and finally an intelligent large screen application capable of being directly deployed is generated.

A method, apparatus, non-transitory computer readable medium, and system for data processing include obtaining a query relating to a document and identifying metadata for the document based on the query, where the metadata describes a structure including a plurality of portions of the document. Some embodiments including generating, using a machine learning model, a retrieval command based on the query and the metadata, selectively retrieving at least one of the plurality of portions of the document based on the retrieval command, and generating, using the machine learning model, a response to the query based on the at least one of the plurality of portions of the document.

Systems and methods for authoring workflows for processing data from a large-scale dataset include defining a metadata schema for the large-scale dataset, and receiving user input defining a workflow as a plurality of operations to be performed on the data. Each of the operations includes input metadata formatted according to the metadata schema. The input metadata describes input data to be processed by the operation and identifying a location for the input data in the data storage system, programmed instructions for performing an atomic operation on the input data to generate output data; and output metadata formatted according to the metadata schema. The output metadata describes the output data and identifying a location for the output data in the data storage system.

A natural language query is received from an operator of a telecommunications network. A metadata request is computed from the natural language query. The metadata request is sent to a repository of metadata, the metadata describing telemetry data of the telecommunications network, the telemetry data stored in a relational database. Metadata is received from the metadata repository in response to the metadata request. Using the received metadata and a language model a relational database query is computed and the relational database is queried. A response is received from the relational database, triggering an action.

The embodiment of the invention relates to the technical field of information, in particular to an intelligent data query method and system based on big data. The method comprises the following steps: reading multi-mode data, and establishing a multi-mode data row according to a source and acquisition time; generating a metadata description, a content abstract, a preset scene label set and a preset scene effect description for each multi-mode data line according to the multi-mode file description and the multi-mode file metadata; generating guidance data for each of the multi-mode data lines; storing the guide data in a memory space which is opened up in advance; receiving a query demand expressed in a natural language, and generating a tag set matched with the query demand by using a pre-accessed large language model; querying matched guide data according to the tag set to obtain matched multimode data; and generating a query result report according to the metadata description, the content abstract and the preset application scene effect description of the matched multi-mode data line.

The invention provides a clinical test data table collaborative auditing method and system based on file state driving, and relates to the technical field of clinical test data processing.The method comprises the steps that firstly, an initial file set of clinical test data table files is obtained, and the initial file set comprises a to-be-audited table file unit and a metadata description information set; analyzing the data unit structure to generate a table data unit topological graph; generating an auditing operation event in response to the auditing operation; inputting the auditing operation event into a constraint propagation analysis model to determine an affected associated data unit node set; and finally, generating a file state driving instruction based on the associated data unit node set, and triggering the cooperative auditing terminal to auditing the associated data field. According to the invention, data can be accurately positioned, the structure can be visually presented, the influence range can be rapidly determined, automatic coordination auditing is realized, and the clinical test data auditing efficiency and accuracy are improved.

This invention discloses a method for constructing logical associations and generating privacy-compliant code for heterogeneous databases in intelligent programming assistance. It introduces a custom metadata description file independent of the database's physical schema, defining cross-domain logical association attributes and data security attributes, including the target database identifier. The description file is parsed, comparing the associated target with the database identifier of the current context, and automatically selecting either an intra-database join strategy or an application-layer aggregation strategy based on the comparison result. An enhanced context hint containing architectural constraints and security rules is constructed, and the target code is generated by inputting a large language model. De-identification processing logic is automatically embedded based on the data security attributes. This invention effectively solves the problems of AI programming context breakage, cross-database association code generation errors, and data privacy compliance risks caused by missing physical foreign keys in microservice architectures, achieving automatic code generation and source-level data privacy protection for complex cross-service business logic.

The application provides a double-knowledge base-based MCP tool calling system, comprising: a user request module; a tool description knowledge base module, used for storing metadata description information of all registered MCP tools; a tool calling sequence template knowledge base module, used for storing tool calling sequence templates, the tool calling sequence templates containing function descriptions, user question examples, tool calling processing logic and answer examples; a dynamic retrieval and matching module, used for simultaneously retrieving in the tool description knowledge base module and the tool calling sequence template knowledge base module according to a natural language request proposed by a user; a prompt word dynamic construction module; a tool service module; and an intelligent decision and answer generation module. The application effectively solves the token consumption bottleneck problem in MCP tool calling through the double-knowledge base architecture and the dynamic retrieval mechanism.

The application discloses a kind of based on deep learning multi-modal data quality evaluation method, comprising obtaining database in first modality record data and second modality record data labeled as alignment;First modality record data and second modality record data are respectively carried out semantic feature extraction to obtain text structured data semantic encoding feature tensor and metadata description semantic encoding feature vector;Text structured data semantic encoding feature tensor and metadata description semantic encoding feature vector cross-modal joint coding to obtain structured-metadata cross-modal quality evaluation joint coding feature tensor;Based on structured-metadata cross-modal quality evaluation joint coding feature tensor, reconstruct record data is generated, and the offset feature of reconstruct record data is calculated;Offset feature is evaluated by quality by first deep learning model;The application improves the accuracy of data quality evaluation by semantic encoding feature extraction, fine-grained hash cross-modal coding and bidirectional reconstruction offset evaluation.

This application discloses a metadata management method, apparatus, device, and storage medium based on lineage analysis, belonging to the field of artificial intelligence technology. This application obtains the metadata description corresponding to the data to be processed, performs lineage analysis based on the metadata description to obtain the lineage information of the data to be processed, divides the data according to the lineage information to obtain a lineage dataset, inputs the lineage dataset into a pre-trained data classification model to obtain data classification results, wherein the data classification results record the mapping relationship between each data to be processed, constructs a data map of the data to be processed based on the mapping relationship and the metadata description, and manages the data to be processed based on the data map. Furthermore, this application also relates to blockchain technology, and the data to be processed can be stored in the blockchain. This application combines the lineage information and metadata description of the data to classify and manage the data to be processed, improving the reliability of data management and thus enhancing the value of data utilization.

A network intrusion system for a protected network includes a ruleset module configured to receive metadata for rules. The metadata describes, for each of the rules, a set of associated network vulnerabilities. The ruleset module is configured to access vulnerability information describing a set of cumulative vulnerabilities that each is present in at least one network device within the protected network. The network intrusion system includes a rule management module configured to, for each rule of the plurality of rules: identify the set of associated network vulnerabilities described by the metadata for the rule, determine whether there is a match between any of the set of associated network vulnerabilities and the set of cumulative vulnerabilities, and, in response to determining that there is no match, transmit a first command signal to a network security module. The first command signal instructs the network security module to disable the rule.

A storage system includes a storage device including a nonvolatile memory device and a host device including a file system to generate and update a file system metadata to manage data stored in the nonvolatile memory device. The file system of the host device determines a relocation of a plurality of data blocks and transfers, to the storage device, a synchronous move request including a move data descriptor indicating information on a storage location of the plurality of data blocks and a metadata descriptor indicating information on a change in the file system metadata according to the relocation. The storage device performs a data move operation to change the storage location of the plurality of data blocks based on the move data descriptor, and a metadata update operation to write a new file system metadata according to the relocation in the nonvolatile memory device based on the metadata descriptor.

Automatically investigating security incidents and generating security incident reports using a Large Language Model (LLM). A computerized system receives an incoming Security Alert Message pertaining to a possible security-related incident. The system automatically feeds into the LLM at least: the content of the Security Alert Message; the metadata of the Security Alert Message; context information describing a security domain; and organization context information pertaining to users and machines of that organization. The system automatically prompts the LLM to automatically investigate the Security Alert Message and to automatically generate a detailed Incident Report pertaining to the Security Alert Message.

The application discloses a kind of seismic remote sensing data management method and system, it is related to earth science information technology field, including, access multi-source seismic remote sensing data, define the standardized metadata description format to each type of multi-source seismic remote sensing data, store multi-source seismic remote sensing data and corresponding standardized metadata description into data resource library;Real-time monitoring of earthquake event information, when target earthquake event is captured, the epicenter of target earthquake event is generated as dynamic space-time range, according to dynamic space-time range, aggregate multi-source seismic remote sensing data from data resource library, and construct dynamic data cube for target earthquake event;Based on dynamic data cube, generate data product through data processing workflow, record the complete processing process log of data product.The application forms integrated management scheme from data intelligent aggregation, product quality controllable to problem accurate traceability and closed-loop correction.

A method is disclosed herein. The method includes receiving (200) a three-dimensional medical image (122) and presenting (202) a cross-sectional view (126) of the three-dimensional medical image on a user interface (124). The user interface includes a split input tool (128); the method further includes repeatedly: receiving (204) split edit data (132) from the split input tool, where the split input tool includes tool configuration parameters (130) configured to control how the split input tool generates the split edit data; collecting (206) tool use metadata (134), the tool use metadata description inputting the split edit data using the split input tool; modifying (208) the tool configuration parameters using the tool usage metadata; and construct (210) a segmentation (136) of the three-dimensional medical image using the segmentation editing data.

The invention discloses a relational data importing method and device suitable for a retrieval enhancement generation system, equipment and a medium, and relates to the technical field of data processing and artificial intelligence crossing. The method comprises the following steps: analyzing metadata in a relational database to obtain table structure information, field attribute information and inter-table association relationship information; constructing a knowledge set for describing data semantics, wherein the knowledge set comprises metadata description knowledge generated on the basis of table structure information and field attribute information, association rule knowledge generated on the basis of association relationship information between tables, and instance semantization knowledge generated on the basis of field attribute information and business data content; and converting the knowledge set into an unstructured knowledge unit which can be retrieved by the RAG system, and updating the unstructured knowledge unit based on an incremental synchronization mechanism. By means of the technical scheme, the problems that the adaptability of relational data and an RAG system is poor, and knowledge extraction is insufficient can be solved.

This document discloses a medical system (100, 300, 400). Execution of machine-executable instructions (120) causes a computing system (110) to repeatedly perform the following operations: receive (204) segmentation editing data (130) from a segmentation entry tool (128); collect (206) location-specific segmentation tool usage metadata (134) describing the use of the segmentation entry tool to enter segmentation editing data at the editing location; determine (208) an anatomical location by using the editing location; retrieve (210) expected segmentation tool usage metadata from a segmentation tool usage database (122) by using the anatomical location; determine (212) the presence of anomalous segmentation editing data (806) by comparing the location-specific segmentation tool usage metadata with the expected segmentation tool usage metadata; provide (214) signals (142, 404, 808) if anomalous segmentation editing data is determined to exist; and construct (216) a segmentation result of a medical image (144) by using the segmentation editing data.

The application discloses a component self-defining configuration method and system based on a low-code platform, belongs to the technical field of low-code development, and comprises the following steps: a reusable dynamic structure unit library is constructed by decomposing component functions into atomized dynamic structure units and defining metadata and context contracts of the dynamic structure units, component functions are atomized and decomposed into template segments, logic controllers and metadata descriptors, standardized metadata and context contracts are defined, a reusable dynamic structure unit library with high cohesion and low coupling is constructed, and the reusable dynamic structure unit library supports function category tagging retrieval, a version control mechanism guarantees historical version backtracking and automatic upgrading, resources are packaged into standardized modules to simplify integration processes, and the application significantly reduces the cost of repeated development, thereby providing an efficient and extensible unit basis for dynamic configuration.