539 results about "Data governance" patented technology

The invention discloses a hydraulic engineering construction operation parameter optimization and data management method and system. The method comprises the steps of 1, multi-source data access and vectorization; step 2, time sequence feature extraction and multi-modal fusion; step 3, identifying interpretable influence factors; 4, performing multi-task dynamic anomaly prediction; 5, risk weighted fusion and uncertainty quantification are carried out; 6, carrying out digital twinborn simulation verification; step 7, governance strategy optimization and security auditing; and step 8, feedback acquisition and model adaptive updating. The method has the advantages that multi-modal unified representation is constructed by fusing structured data such as water level and flow with unstructured information such as meteorological images and operation and maintenance logs, time sequence feature extraction is carried out in combination with the space-time diagram neural network, the dynamic sensing and prediction capability of the complex hydrological process is remarkably improved, and the method is suitable for being popularized and applied. According to the invention, the conversion from static threshold alarm to multi-task and prospective abnormal early warning is realized, and the adaptability of the system to multiple scenes is enhanced.

The invention provides a data weaving platform-oriented self-service data preparation method, which comprises the following steps of: accessing original data and associated structured metadata of a target data source into a data weaving platform to generate standardized accessed original data and associated structured metadata; based on a semantic model of the data weaving platform, performing classified semantic annotation on the associated structured metadata, and positioning a transfer node of the associated structured metadata to generate a data blood relationship record; performing bidirectional quality verification on the data consanguinity record according to semantics and path continuity, and matching with a federated governance rule of a data weaving platform to generate a metadata governance list; and generating a unified metadata directory based on the metadata governance list, so that a user of the data knitting platform can obtain key segments containing complete data consanguinity records. According to the method, the problem of insufficient accuracy and efficiency in self-service data preparation can be effectively relieved, and the efficient preparation requirement of a data weaving platform for multi-source data is met.

The invention relates to the technical field of new energy power data governance, in particular to a multi-source fusion cleaning and closed-loop quality control method and device for wind power data and a medium, and the method comprises the steps: generating an initial data set through collecting SCADA time sequence data and at least one type of external meteorological data of a wind turbine generator; based on the initial data set, executing physical consistency check, grouping adaptive statistical detection, unsupervised machine learning detection and time sequence residual analysis in parallel, and generating four independent exception judgment vectors; inputting the four abnormal judgment vectors into a preset fusion decision model, and outputting a comprehensive abnormal label; obtaining a repaired data set according to the comprehensive abnormal label; and calculating a multi-dimensional quality score representing data quality based on the repaired data set, and triggering a quality control closed-loop operation according to the multi-dimensional quality score and a comparison test result. Through the arrangement, multi-dimensional fusion detection and self-adaptive intelligent repair can be realized, and integrated cleaning of quality quantitative evaluation and closed-loop verification can be realized.

The invention provides an intelligent agent-based automatic verification and rule matching system for insurance-waiting evaluation reports. The intelligent agent-based automatic verification and rule matching system comprises a report acquisition module which pulls to-be-verified and historical insurance-waiting reports; the auditing processing module calls an attention Bi-LSTM model which is subjected to reinforcement learning fine adjustment and is fused with knowledge graph embedding, and scores the report sentence by sentence; the rule management module is used for storing and analyzing an equal-guarantee 2.0 structured machine rule and carrying out secondary accurate matching on low-confidence sentences; the knowledge graph module is used for maintaining a multi-dimensional association graph of a standard family, a control measure and an evaluation item and providing graph reasoning; the agent decision-making module generates actions of passing, returning, manual rechecking or automatic correction according to the auditing state; the feedback learning module collects artificial recheck differences, an incremental training model, a strategy network and atlas vectors; and the storage module stores reports, models, rules, maps and logs in a multi-modal manner through a relation library, a map library and a vector library, and implements data governance and access control. According to the invention, the audit efficiency, accuracy and standard consistency of the evaluation report can be improved.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

The invention provides an intelligent solitary policy processing method based on a neural network and machine learning, and relates to the field of data processing. According to the method, strategy byte codes and execution contexts are generated by extracting business rules, supervision calibers and field dependency relationships of the single insurance policy, discovery, repair and arbitration of field abnormity are achieved in combination with a dynamic data governance model, an account-falling confirmation receipt is output through a voting game and a compensation transaction, and a replayable trace and a rare mode index are generated. And a high-confidence label and an error correction prior increment are obtained in combination with triggering verification, and finally, full-link evidence is solidified through continuous distillation, chain type evidence storage and an auditable snapshot library, so that a traceable record of the single insurance policy is formed. By implementing the technical scheme provided by the invention, full-link data processing can be conveniently carried out on the single insurance policy, so that the processing efficiency is effectively improved.

The invention discloses a multi-source heterogeneous data integration analysis treatment method, system and device and a medium, and the method comprises the steps: obtaining multi-source heterogeneous original data, carrying out the semantic mapping through a power grid business ontology library, and carrying out the coordinate dynamic normalization through a self-adaptive conversion matrix, and obtaining standardized spatial data; carrying out spatial query, dynamic buffer area calculation and protection area grading fusion through a spatial superposition optimization algorithm, and carrying out topological relation identification and regularization processing on the power line and an external linear ground feature to obtain a spatial conflict analysis result; through a three-level conflict resolution mechanism, recognition and cooperative resolution operation of geometry, attribute and temporal conflicts are carried out, and a resolved compliance space data set is obtained; and performing governance flow driving and rule adaptation through a dynamic business rule engine to obtain a spatial data governance report. According to the method, efficient integration and intelligent treatment of spatial data in the power transmission line project are realized, the data processing timeliness is remarkably improved, and spatial conflicts are eliminated.

The invention provides a knowledge graph-driven medical data agent collaborative governance method, system and device and a medium, and belongs to the technical field of medical data governance, and the method comprises the following steps: constructing a knowledge graph containing a quintuple, and defining mapping relationships among medical data entities, attributes and standards; extracting multi-modal medical data knowledge elements through a structured / unstructured engine, and converting the multi-modal medical data knowledge elements into a quintuple for storage; executing table / field / value domain mapping based on the knowledge graph to generate a data mapping result; binding the quality control rules and the medical specifications to form a quality control quintuple; arranging the task into a directed acyclic execution sequence through an agent group cooperation task; and dynamically updating the knowledge graph and performing version management. Medical data full-link governance, knowledge graph unification heterogeneous data semantics, intelligent agent cooperation automatic processing complex tasks, update guarantee knowledge timeliness and quality control rule binding guarantee data quality are achieved, and medical data governance efficiency and accuracy are improved.

The invention relates to the technical field of data processing, and discloses a full-life-cycle-oriented engineering technology data governance method and a full-life-cycle-oriented engineering technology data governance system. The method comprises the steps that project object attributes are divided into an anchor point attribute set and an extended attribute set, a locking mark is added to the anchor point attribute set, the extended attribute set is layered according to stages to obtain a data object meta-model, a dependency relationship mapping table is established according to the data object meta-model to achieve automatic verification when the attributes are changed, and the attribute change is completed. And establishing a spatial topological graph and a process graph to diagnose inconsistent defects, establishing a weighted graph to quantitatively analyze change influences, evaluating data quality according to scene types, and intelligently complementing missing attributes. According to the method, the integrity and consistency of cross-stage transmission of engineering data, the real-time performance and accuracy of multi-professional data collaboration, the depth and automation degree of data quality diagnosis, the scientificity and comprehensiveness of change impact analysis and the flexibility and efficiency of data utilization are improved.

The invention discloses a data governance method, electronic equipment and a computer readable storage medium, and the method comprises the steps: responding to a received table data governance request, inputting the table data governance request into a large language model for information extraction processing, and obtaining data source information of to-be-governed table data; determining to-be-governed table data based on the data source information; and performing standardization processing on the to-be-governed table data to obtain target table data. Therefore, efficient and accurate data governance is realized by combining the natural language processing capability of the large language model and the high accuracy of the non-large language model.

The invention discloses a semantic matching-based field name standard abbreviation mapping method and system, and particularly relates to the technical field of data governance, a source field set to be subjected to abbreviation and a standard field set in a standard field library are acquired, field names and field descriptions are normalized, and field text representation is generated; using the pre-training language model to generate a semantic vector and recalling a candidate mapping pair set; calculating character string similarity, synonym expansion matching score, data type consistency score and sample value pattern consistency score for the candidate mapping pairs; generating a neighbor confusion risk index based on semantic neighbor density, entity category distribution deviation, synonym expansion and context anti-verification, performing risk gating correction, and judging a score in combination with a difficult case; and applying table-level consistency constraints in the same service table or entity domain to carry out joint consistency solution to obtain a table-level optimal mapping scheme, generating confirmation confidence, and outputting an automatic confirmation or manual recheck mapping result.

The invention discloses a unified management system and method based on multi-source heterogeneous industrial data, and relates to the technical field of industrial data management, and the method comprises the steps: S1, collecting furnace condition monitoring data in real time, and carrying out the preprocessing of the collected data; s2, constructing a semantic classification data set, establishing a working condition label, periodically evaluating the disturbance level of the hearth temperature, and performing disturbance attribute labeling on the semantic classification data set; s3, screening out a temperature distortion candidate period, quantifying the distortion risk of the hearth temperature, and performing differential repair on the temperature sequence; and S4, evaluating the risk intensity of the multi-source furnace condition features in the fusion stage, screening out an abnormal period, and executing field consistency check and priority coverage correction. The problems that temperature reading abnormity cannot be repaired in time and multi-source data fusion is unreliable due to the fact that an existing industrial data management technology cannot identify temperature distortion and cannot conduct consistency verification on multi-source furnace condition data are solved.

The invention relates to the technical field of data security, and discloses a dynamic hierarchical compliance data governance method, system and device and a storage medium, and the method comprises the steps: accessing multi-modal data; the method comprises the following steps: analyzing an input law and regulation text based on a large language model, converting into a structured data governance rule, and storing in a rule base; semantic understanding and correlation analysis are carried out on the multi-modal data, and sensitive level labels are dynamically distributed; in response to a received data access request, analyzing context information and user attributes, dynamically selecting and executing a target desensitization strategy, and generating desensitization data; performing real-time scanning on the multi-modal data based on the multi-modal large model, and identifying and marking sensitive information in the multi-modal data; the sensitive information is fed back and used for dynamically updating the sensitive level label, and the data governance rule and the desensitization strategy are dynamically adjusted according to the feedback information. According to the method, high-standard requirements on agility, accuracy and comprehensiveness of data governance in a complex dynamic environment can be met.

The invention relates to the technical field of data compliance. According to the AI-driven data compliance rule intelligent method, device and equipment and the medium, the method comprises the steps that natural language input of a user is obtained, and user demand description is obtained; performing multi-dimensional semantic analysis on the user demand description through a pre-trained large language model, and generating a structured semantic framework comprising an intention vector, an entity relation graph and a constraint condition set; performing compliance correction on the structured semantic framework to generate an optimized semantic framework; performing logic conflict detection and performance simulation test on the domain-specific language rule to generate an executable compliance rule; and deploying the executable compliance rule to the target data governance platform so as to achieve the technical effects of reducing the learning threshold and the manual error rate generated by the domain specific language rule, improving the semantic adaptation capability in a complex business scene and optimizing the operation stability of the rule on the data governance platform.

The invention relates to the technical field of data management, in particular to a data weaving semantic integration method based on a semantic network and a knowledge graph, which comprises the following steps: acquiring multi-source heterogeneous data and preprocessing to obtain standardized data, the multi-source heterogeneous data comprises structured data, semi-structured data and unstructured data from different business scenes; performing rule injection on the standardized data to generate semantic web ontology data; extracting entities and attribute values thereof in the standardized data to generate structured knowledge graph data of the instance layer; and establishing a semantic association mapping network of the semantic network ontology data and the structured knowledge graph data, and generating a semantic integration result of data weaving. According to the method, the basic differences of the multi-source data in the aspects of formats, codes and the like are eliminated, a unified semantic standard is provided, and conflicts caused by non-unified semantics are reduced.

The invention particularly relates to a multi-modal enhancement and graph retrieval system based on structure language two-stage alignment. The multi-modal enhancement and graph retrieval system based on structure language two-stage alignment comprises a multi-source data access module, a data governance and standardization processing module, a data training module, a graph knowledge vector library and a retrieval and generation engine. According to the multi-modal enhancement and graph retrieval system based on structure language two-stage alignment, through a two-stage alignment mechanism, the deep structure cognition and reasoning ability of a large language model is improved, and the question and answer accuracy and decision intelligence in the fields of communication operation and maintenance, urban governance and the like are remarkably improved.

The application discloses a data management platform for smart park construction and belongs to the technical field of smart park management, and specifically comprises the following steps: collecting resource demand data and resource supply data of each enterprise in the park, performing demand feature extraction operation on the collected resource demand data to generate a structured demand description, performing supply feature extraction operation on the resource supply data to generate a structured supply description, calculating the matching degree between demand and supply based on the structured demand description and the structured supply description and generating an initial matching scheme, performing constraint condition checking operation on the initial matching scheme according to the park operation strategy to identify matching pairs that violate the constraint conditions, performing scheme adjustment operation on the matching pairs that violate the constraint conditions to generate an optimized matching scheme, and finally scheduling the park resources to complete resource data sharing between enterprises; and the application realizes intelligent management and efficient sharing of resource data in the park, and improves the overall resource utilization efficiency of the park.

The invention relates to the technical field of power-carbon collaboration, and discloses a block chain-based power-carbon collaboration data management and intelligent optimization method, system, device and medium, and the method comprises the steps: collecting power-carbon multi-source data in real time, and carrying out the data preprocessing; establishing a correlation model between electricity and carbon data so as to quantify a correlation relationship between unit power generation and carbon emission, and obtaining carbon emission reduction data; a double-objective optimization function is adopted to construct an electricity-carbon collaborative decision-making large model, and an optimal unit output scheme is output through deep learning; and generating a block chain hash identifier for each green electricity transaction, performing contract automatic verification, and if a performance risk is monitored, triggering early warning and sending an optimization suggestion. According to the invention, by constructing a technical system based on power-carbon collaborative data management and intelligent optimization, accurate conduction of carbon emission technical constraints, safety interaction of cross-system data, credible tracing of environmental rights and interests and dynamic prevention and control of risks are realized, and fusion and efficient collaborative operation of a power system and carbon emission management are promoted.

The invention discloses a data governance strategy dynamic execution system based on active metadata and AI recommendation, and relates to the technical field of data governance, and the system comprises an active metadata sensing layer which is used for actively monitoring and capturing metadata real-time changes of various data sources, and reporting the metadata real-time changes in a standardized data format; the intelligent analysis decision-making layer is used for receiving the standardized metadata, taking a preset knowledge base as a basic support, performing metadata association analysis by relying on an AI recommendation module, generating a governance strategy and converting the governance strategy into an executable instruction; the strategy dynamic execution layer is used for receiving an executable instruction, performing task splitting, priority ranking and dependency management through a workflow scheduler, and calling a corresponding executor to automatically complete execution of a governance strategy; and the unified management and control portal layer is used for providing a visual interface for an administrator so as to carry out treatment strategy approval, global monitoring, blood relationship analysis, audit query and manual intervention. According to the invention, intelligent generation and automatic execution of the data governance strategy can be realized.

Examples relate to an adaptive AI coworker system for enhancing organizational operations. The system generates personalized AI coworkers based on role requirements, employing adaptive learning to understand unique organizational practices. It utilizes multi-agent coordination for complex task execution, automatically generating, prioritizing, and allocating tasks based on organizational context. The system integrates data from various sources, implementing data governance measures. Customized large language model instances are created for specific roles and organizations, incorporating organization-specific data and operational practices. The system provides explainable AI features for operational decision-making, ensuring transparency in critical tasks. Security measures and access controls are implemented to maintain data integrity and compliance with relevant regulations and standards.

The invention relates to the technical field of industrial data management. The data management method, system and device based on the industrial digital base and the medium are provided, the method comprises the steps that block chain evidence storage is conducted on version hash, micro-differential packet hash and an obtained operator certificate, an on-chain evidence storage block is generated, distributed storage processing is conducted on a micro-differential packet, and an off-chain differential packet storage entity is obtained; when it is detected that the data is abnormal, abnormal version positioning is carried out based on the record-level blood relationship chain, and an abnormal version sequence is determined; on the basis of a predefined inverse operation mapping relation, performing reverse playback on a micro-differential packet corresponding to the abnormal version sequence to realize historical data state recovery; and carrying out signature verification on the incremental service pack, carrying out dynamic loading processing after the verification is passed, updating a preset blood relationship extraction rule and a preset difference algorithm, and generating a hot update strategy, so as to achieve the technical effects of improving the data blood relationship tracing precision, reducing the storage redundancy overhead and enhancing the credibility of an evolution process.

The invention provides an intelligent data management method and system, and relates to the technical field of data processing. Standardized packaging and automatic execution of the data governance instruction are achieved, and the problem that a traditional storage medium cannot dynamically adapt to the multi-source data processing requirement is solved. Through the decoupling design of the pre-compilation instruction and the runtime environment, the same storage medium can adapt to data management nodes of different hardware architectures, and the deployment complexity during system upgrading is effectively reduced. Furthermore, based on an instruction execution mechanism of a streaming computing engine, the real-time performance of data quality evaluation and early warning triggering is ensured, and decision lag caused by batch processing delay is avoided. Through the on-demand loading characteristic of the modular instruction set, the function expansion requirement of a specific business scene is supported while the core governance function is ensured.

The invention discloses a data asset management system and method, and relates to the field of data management, and the system comprises a data collection layer which is used for collecting multi-source heterogeneous data; the data integration layer is used for integrating the multi-source heterogeneous data and constructing a data warehouse; the data storage layer is used for carrying out structured storage on the data in the data warehouse; the data governance layer is used for performing standardization processing and quality management on the data stored in the data storage layer to obtain target data; and the data application layer is used for performing dynamic tracking inventory and classification level-to-level management on the data assets based on the target data. Through cooperative work of five layers of architectures, full-process closed-loop management of the multi-source heterogeneous data from collection to application is realized, and the problems of difficulty in integration of the multi-source data, low management efficiency, uncontrollable quality and the like are effectively solved by the overall scheme.

Access control may involve receiving a request from a computing device of a user for access to data available through a computer system, where at least some of the data is stored locally in the computer system. Access control may further involve identifying one or more tags associated with the data, each tag including a metadata label characterizing the data. One or more data governance policies can be determined as being applicable to the request based on the identified tags and further based on one or more attributes of the request. The one or more data governance policies can be applied to derive filtered data for output to the user's computing device in response to the request. In some implementations, the computer system includes a cloud-based datastore and is configured to automatically assign or recommend tags for incoming data from remote computer systems.

A system and method are disclosed for policy-constrained symbolic rendering of artificial intelligence outputs using deterministic evidence structures and authenticated display controls. Governed records derived from device, clinical, operational, environmental, or multi modal data are normalized, lineage-tracked, and assembled into deterministic evidence packs whose ranking, augmentation, pruning, and ordering are reproducible and traceable. Governed prompts constructed from these evidence packs are provided to internal or external artificial intelligence models under controlled, deterministic invocation parameters. Candidate model outputs—textual or multimodal—are symbolically interpreted, evaluated by a multi-policy engine, and constrained into compliant renderable forms. A cryptographic render-token architecture binds each constrained output to its governing evidence, policy state, model and tokenizer identity, viewport or session binding, environmental conditions, temporal validity, and decision lineage. A display gate renders only upon successful token validation and revocation checks. Correction-propagation mechanisms revoke affected tokens, perform deterministic replay, and issue superseding views for reliable, auditable use in safety-critical environments.

The invention discloses an aeronautical component heterogeneous data semantic mapping and logic conversion method and system, and relates to the technical field of aeronautical data processing.Multi-granularity candidates are generated by constructing unified intermediate representation and fusing accurate matching of a standard mapping table and fuzzy matching of an aeronautical field fine-tuning semantic embedding model; in combination with multi-dimensional features such as mapping confidence, semantic similarity and structural consistency, a combined decision is made through a fusion sorting model and a rule engine, and three types of results are output; unit standardization and dimension consistency verification is executed on the high-confidence result loading parameter logic relation graph, and incremental learning and versioning self-evolution of a semantic model, a mapping table and a PLG rule base are achieved based on manual feedback; and finally generating a standardized component parameter record and a complete auditing evidence chain. According to the method, high-reliability, traceable and self-evolution semantic alignment and logic verification of the aviation heterogeneous data are realized, and the data governance efficiency and the engineering credibility are remarkably improved.

The invention provides an operator-level data consanguinity automatic generation method based on a large model, and relates to the field of electric digital data processing, and the method comprises the steps of S1, heterogeneous script preprocessing and semantic normalization, S2, symbol enhanced LLM consanguinity reasoning, S3, causal logic verification and consanguinity correction, S4, dynamic adaptation, and S5, consanguinity intelligent analysis and visualization. The method is suitable for multi-source, multi-format and multi-language data processing scenes, the automation degree, the refinement capability and the intelligent level of data blood relationship construction can be improved, and more accurate and efficient technical support is provided for data governance, data security, system migration / reconstruction, data quality management and control, cost optimization and the like.

The invention belongs to the field of artificial intelligence, particularly relates to an integrated artificial intelligence analysis and modeling platform supporting large-scale data processing, and aims to solve the problems of low resource scheduling efficiency, model training reasoning splitting, data algorithm coupling, limited cross-modal modeling and the like under high-dimensional heterogeneous mass data. The platform comprises a distributed data access layer, a unified metadata governance engine, a heterogeneous computing resource scheduling center, a model life cycle management module, a multi-modal fusion modeling framework and a dynamic service orchestration interface, and realizes automatic data access governance, dynamic resource scheduling, model full-cycle management and control and cross-modal intelligent fusion. And the resource utilization rate, the modeling accuracy and the service deployment efficiency are remarkably improved.

The invention provides an enterprise data management system and method based on artificial intelligence. The system comprises a data acquisition module which acquires enterprise energy consumption data and equipment operation parameters in a cross-level manner through an interface; the data preprocessing module is used for performing exception repair on the energy consumption data by adopting an exception detection model, and performing deletion repair on equipment operation parameters by combining a knowledge graph and a filtering algorithm so as to generate an effective data set with a space-time label; the knowledge graph generation module constructs an ontology model containing equipment entities, energy media, metering points and process links on the basis of the effective data set, and mines an association relationship between equipment states and energy consumption data through a graph neural network to generate an energy association knowledge graph; and the governance strategy generation module takes energy consumption optimization and data integrity as targets, generates governance rules in combination with the carbon emission data, and performs root cause analysis based on the energy association knowledge graph, so as to generate an enterprise data governance strategy and govern enterprise energy consumption data and equipment operation parameters according to the enterprise data governance strategy.

The invention relates to a metadata-driven data consanguinity analysis and influence evaluation method, system and device and a medium. The method comprises the following steps: performing parameter analysis on an original query language script, and constructing a parameter function model in combination with a parameter dimension table; performing parameter replacement on an original query language script according to the model, and analyzing the replaced script to obtain a table-level blood relationship, a field-level blood relationship and the like; constructing a record-level query template based on the field-level blood relationship and the conversion logic, and constructing a dynamic blood relationship map; and executing a record level query template, carrying out record node mounting on the atlas, obtaining and responding to a data change event, carrying out hierarchical influence propagation calculation, obtaining and responding to a data exception event, carrying out root cause node positioning based on the mounted dynamic blood relationship atlas, and generating a repair suggestion. According to the method, through dynamic parameter analysis and record-level blood relationship analysis, the accuracy and automation level of data blood relationship analysis are improved, and the efficiency of data governance is improved.