1263 results about "Data governance" patented technology

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention provides a multi-source heterogeneous corpus fusion method and system based on government affair service data, and the method comprises the steps: obtaining an original corpus set of a plurality of data sources in government affair service, carrying out the cross-modal semantic alignment processing of each corpus unit in the original corpus set, generating a normalized data block corresponding to each corpus unit, and carrying out the fusion of the data blocks; carrying out multi-modal semantic coding on the standardized data blocks to obtain semantic feature vectors of all corpus units, carrying out topological structure coding on association attribute sets among the standardized data blocks to generate a global structure relation graph, and carrying out dynamic weight distribution on the semantic feature vectors based on node connection weights in the global structure relation graph to obtain semantic feature vectors of all corpus units; and generating a fusion weight matrix, performing cross-modal feature fusion on the semantic feature vector to obtain a target semantic embedding representation, and generating a standardized corpus associated with the government affair service. According to the method, the semantic aggregation problem of the non-uniformly distributed corpus units is solved, and the government affair data governance efficiency and the cross-department cooperation capability are greatly improved.

The invention relates to a multi-data standard definition conflict resolution method based on a large model and a knowledge graph, and belongs to the field of public data governance and data standardization. The method comprises the following steps: extracting and preprocessing a multi-data standard definition; defining semantic representation and large model analysis: performing semantic analysis on the definition of each data item by using a large model, and generating a semantic embedding vector and a key feature; knowledge graph construction and entity alignment: constructing a knowledge graph containing all data items, and connecting data item nodes with similar semantics in different standards by adopting an entity alignment algorithm to form a candidate alignment relationship; conflict detection and type identification: aiming at the aligned data item definition, comparing attributes and values, identifying definition conflict points, and classifying and marking conflict types; and carrying out conflict resolution and unified definition generation by using a globally optimized conflict resolution algorithm. According to the method, the problems of data islands and semantic conflicts caused by inconsistent data standard definitions in the prior art are solved.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention relates to the technical field of data processing, in particular to a data management method for automatically detecting data abnormal values based on deep learning, which comprises the following steps of: based on an input data set, performing synchronization and missing value filling on a plurality of data sources, and performing standardization and normalization processing on data to obtain a preprocessed data set; according to the method, the data preprocessing is executed, the consistency and quality of data input are optimized, feature extraction and model layer-by-layer training are utilized, the recognition capability of the model for abnormal data is enhanced, the precision and efficiency of data anomaly detection are improved, calculation of residual errors and reconstruction errors is combined, the accuracy of abnormal data scoring is improved, and the accuracy of data anomaly detection is improved. The detection threshold value is dynamically adjusted by utilizing data characteristics, the false alarm rate is reduced, and the reliability and the stability of the enhanced data processing flow are ensured by combining time sequence analysis, evaluating the abnormal development trend, performing comparative analysis on abnormal data and normal data modes and performing data correction.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention provides a domain data management method and system based on an artificial intelligence platform, and the method comprises the steps: obtaining financial transaction data through a data warehouse, carrying out the preprocessing, and extracting transaction metadata and semantic association thereof through a natural language technology; and classifying the metadata by adopting a hierarchical clustering algorithm, and constructing a transaction data consanguinity map. And extracting time sequence and spatial features from the atlas, calculating a node interaction index by using a pre-configured data interaction prediction model, and marking a high-heat node. And extracting high-dimensional transaction association features based on the graph neural network model, and identifying abnormal transaction data. And marking an abnormal node in the blood relationship map, and tracking and positioning an origin node of abnormal data. According to the method, multiple advanced technologies are comprehensively utilized, and rapid and accurate risk identification can be realized in the face of massive financial transaction data.

The invention relates to a digital twin workshop management and control method and system based on data multilayer fusion, and belongs to the field of service-oriented industrial system data science, and the method comprises the steps: obtaining the global multi-source heterogeneous data of a digital twin workshop, and carrying out the data classification, data processing and data storage of the global multi-source heterogeneous data, carrying out data-level fusion to obtain digital twin workshop standard data; extracting standard data of the digital twin workshop, performing semantic association, feature extraction and feature analysis on the standard data of the digital twin workshop, and performing feature level fusion to obtain key state features of the digital twin workshop; the method comprises the following steps: constructing a decision-making model oriented to a digital twin workshop, inputting key operation features of the digital twin workshop, performing decision-making level fusion based on the decision-making model, decision-making optimization and decision-making method solution, and outputting a workshop production management and control decision. The method can provide support for digital twin workshop operation data governance analysis and production management and control accurate decision.

The invention provides a data governance knowledge graph construction method based on metadata, and the method comprises the steps: defining a meta-model structure of each class of metadata and a relationship between meta-models, summarizing and abstracting all meta-model structures, and forming a unified meta-model structure after constructing a corresponding relationship; dynamically adjusting the uniform meta-model structure; constructing a dynamic ontology model, mapping the unified meta-model structure to the dynamic ontology model, and adjusting the dynamic ontology model according to the dynamic change of the unified meta-model structure; performing data processing on the metadata in the metadata lake; according to a corresponding mapping relation between the unified meta-model structure and a dynamic ontology model, enabling the structured data after data processing to correspond to the dynamic ontology model, and completing the construction of a primary knowledge graph; and detecting and preprocessing the real-time data stream to form updated content of the knowledge graph. The method aims at achieving effective fusion and dynamic updating of multi-source heterogeneous metadata knowledge in the data governance process.

The invention relates to the technical field of campus management, and particularly discloses a smart campus management system based on big data, an event-driven data management architecture is used for dynamically collecting, integrating and associating multi-source heterogeneous data in a campus, and generating a standardized event stream; the dynamic resource scheduling engine is in communication connection with the event-driven data governance architecture, generates a resource allocation instruction based on event types and priorities in event streams, and dynamically deploys campus resources; the closed-loop evaluation optimization module receives a resource scheduling result of the dynamic resource scheduling engine and generates a multi-dimensional evaluation index, and the multi-dimensional evaluation index is fed back to the data governance architecture through root cause analysis so as to optimize a subsequent decision; the privacy enhancement processing unit integrates a federated learning framework and a differential privacy algorithm, performs collaborative analysis on cross-system data and ensures the anonymity of individual data; through three core technologies of dynamic data management, intelligent resource scheduling and closed-loop evaluation optimization, intelligent upgrading of the whole campus management process is realized.

The invention discloses a marketing data link abnormity diagnosis method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of multi-source data format difference, lack of real-time anomaly detection mechanism and low data management efficiency exist in anomaly diagnosis. The method comprises the steps that multi-source heterogeneous data of a marketing data link is collected in real time, and data cleaning, missing value filling and abnormal value processing are completed; extracting statistical characteristics of the link data, performing clustering analysis on the data, and generating a self-supervision label; constructing a support vector machine classification model, and detecting normal and abnormal data in the link; optimizing the anomaly detection model through a deep neural network; and realizing interpretability analysis and decision support of anomaly diagnosis by using a multi-classification result generated by the LSTM model and a self-supervised learning strategy. According to the invention, data link abnormity diagnosis is efficiently and intelligently realized, and data quality management, abnormity detection precision and service decision support capability are improved.

The invention relates to the related technical field of data governance analysis, in particular to an intelligent data governance system based on behavior path analysis, and the system comprises a path generation module which generates a data flow path through a graph neural network according to a data behavior log, and draws up a behavior path graph; the first risk propagation path set determination module and the second risk propagation path set determination module respectively use graph diffusion and characteristic parameter marks to determine risk paths so as to operate a blocking mechanism to carry out data access management. The technical problems that data access depends on single rule matching and single-point anomaly detection, global correlation analysis on data operation behaviors is lacked, and complex attacks cannot be effectively detected are solved, the behavior path map is generated through the graph neural network, the influence range of abnormal operation is quantified by using the graph diffusion model, and the data access efficiency is improved. The technical effects of predicting the potential path of sensitive data leakage in advance, dynamically adjusting the confidence, improving the risk node identification accuracy, carrying out real-time blocking treatment and guaranteeing the data access security are achieved.

The invention provides a commodity data management method based on a block chain, and the method comprises the steps: obtaining commodity attribute data, carrying out the standardization processing of the commodity attribute data according to a pre-established commodity attribute standardization mapping table, and obtaining the standardized commodity attribute data; writing the standardized commodity attribute data into a target data fragment of a distributed account book storage architecture according to a preset data routing rule, and indexing by adopting a Merkle tree data structure; legality and integrity verification is carried out on the data writing operation through an intelligent contract automation mechanism, and data index information is generated; and according to the commodity attribute and the transaction identification code, determining the data integrity and non-tampering through the block hash value, obtaining the traceability rule in the smart contract, judging the data security, and obtaining an automatic traceability process.

The invention relates to the technical field of natural language processing, and discloses a power generation industry data intelligent treatment method, device and equipment based on a large model, and the method comprises the steps: constructing a multi-source heterogeneous task data set covering structured and unstructured information, completing the fine tuning training of a plurality of industry sub-fields based on a language model, forming a large language model set with specific scene adaptability; constructing a multi-view semantic representation structure for actual input data, integrating modeling task intention, application scene and model adaptability, predicting an optimal target model and a Top-K candidate model, and generating a unified semantic embedding vector; and realizing accurate matching of the structured knowledge fragments through the graph neural network. The problems that an existing model is insufficient in semantic understanding, inflexible in model selection and inaccurate in knowledge calling in the data management process are solved, the requirements of diversified tasks for accuracy and specialty are met, and then management of data assets is facilitated.

The invention discloses a power marketing knowledge graph construction method, system and device and a storage medium, and relates to the technical field of power system data management. At present, integration and application of multi-source heterogeneous data are difficult, data anomaly detection and treatment efficiency is low, and data circulation is unstable. The method comprises the following steps: collecting data of a plurality of sources, and carrying out data cleaning and standardization processing; classifying the data according to structuralization, semi-structuralization and non-structuralization, and forming feature vectors through a feature extraction method; extracting knowledge units from the feature vectors by using natural language processing and machine learning technologies, fusing knowledge from different sources through rules or models, and constructing a unified knowledge base; generating a knowledge graph in a graph database by using a knowledge base, and constructing a graph structure of nodes and edges; and carrying out real-time monitoring on data flow in the knowledge graph, and analyzing path efficiency. According to the technical scheme, the data management efficiency is improved, data circulation is monitored in real time, and the business process is optimized.

The invention provides a multi-source data management method based on machine learning and a related device, and the method comprises the steps: obtaining a multi-source data set in an emergency command rescue scene, carrying out the data preprocessing of the multi-source data set, obtaining a standardized data set with a unified data form, extracting a data element information set from the standardized data set, and carrying out the processing of the data element information set. And calling a pre-trained blood relationship modeling model to perform association analysis processing on the data element information set, generating a blood relationship description set among the data entries in the standardized data set, and generating a data blood relationship tracking result based on the blood relationship description set. The data consanguinity tracking result comprises a historical record of the full life cycle of the data and cross-data entry dependency graph information. According to the invention, the method can provide a governance support with a data source capable of being checked, a processing process capable of being traced and a clear dependency relationship for emergency command rescue, enables a rescue decision to be developed based on reliable data consanguinity information, and effectively improves the support capability of data governance for the rescue decision.

The invention relates to the technical field of CIM (common information model) platforms, in particular to a multi-element and multi-scale data management method and system based on a CIM basic platform, and relates to data acquisition, access and classification in a service interface, incremental acquisition and offline copying mode. Data needs to be translated to conform to spatial, elevation and temporal references of a CIM platform, unify geocoding and spatial expression, and semantic-based place name address association. And the BIM and the CIM are fused through a knowledge graph. And performing three-dimensional data grading processing, and constructing CIM1 to CIM5 grade data. Government affair data are subjected to graph falling through a unified address library. Overall data governance covers data aggregation, classified governance, two-dimensional and three-dimensional fusion, BIM and CIM fusion technology and government affair data association, classification and level-to-level management of data is realized, different data combination application is supported, and CIM business application and government affair process auxiliary approval and decision making are assisted.

The invention relates to the technical field of coal mine informatization and data management, in particular to a data governance method and system for a coal mine and a storage medium, and provides a layered architecture from edge node preprocessing to central platform deep fusion in order to solve the problem of coal mine multi-source heterogeneous data governance. The edge nodes realize real-time anomaly judgment and output clustering data through an auto-encoder, a generative adversarial network and deep reinforcement learning; the central platform generates global collaborative model parameters and logic semantic data by using federated learning and a self-attention mechanism, then constructs a coal mine knowledge graph through unified semantic mapping and a graph convolutional network, and synchronously updates the digital twinborn platform with real-time verification data to generate real-time digital twinborn data. And finally, outputting an early warning decision under the action of a long short-term memory network and a self-attention mechanism, and performing traceability management in combination with a block chain technology, thereby realizing efficient and safe coal mine data management.

The invention provides a data privacy protection method for a data governance system, and belongs to the technical field of data governance, and the method comprises the steps: carrying out the cross verification of multi-source feature data and the unique identification information of an object collected on site, generating an original data set, carrying out the sensitive information recognition and grading, and generating the preprocessing data with a sensitive grade label; core identification information in the preprocessed data is disassembled to generate standardized desensitized data conforming to privacy protection, a bidirectional encryption mapping relation is established between object unique identification information collected on site and the standardized desensitized data, an encryption index is formed, the encryption index and preset multi-dimensional compliance data are fused, and a data fusion result is obtained; generating standardized fusion data; and based on the access token, generating a differential authorization data set divided according to permission granularity, performing privacy disclosure risk assessment, generating a risk level, performing privacy processing on the risk level, and outputting the risk level to a risk control system. And the data management efficiency is improved.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

The invention discloses a multi-level governance method for real-time data of new energy station power generation equipment, and particularly relates to the field of power data governance, and the method comprises the steps: cooperatively generating an edge governance rule and a central processing logic according to a global data governance strategy, and transmitting the edge rule to an edge node; and meanwhile, the data quality is monitored in real time, and a result is fed back to dynamically adjust a global data governance strategy, so that closed-loop optimization of data governance is realized. According to the multi-level treatment method for the real-time data of the new energy station power generation equipment, an edge treatment rule set and a central processing logic set are generated through driving of a global data treatment strategy, and the strategy is dynamically adjusted based on third treatment data; the problem that the edge node processing rule and the central data processing logic lack collaboration and dynamic adaptation is effectively solved; by implementing cross-level timestamp synchronous calibration, secondary alignment processing of the center node on the multi-source heterogeneous data is avoided, and the equipment state evaluation precision is effectively improved.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

Various embodiments relate generally to data science and data analysis, computer software and systems, based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to generate configurable automation templates and automation executable programs that automate workflows based on process models to effectuate automated data governance of datasets, including graph-based data stored in one or more graphs, such as a knowledge graph, a data catalog, etc., whereby in at least one example, automated workflows can provide automatic enrichment of datasets, such as multi-layered knowledge graph data that may include data catalog data. In some examples, a method may include receiving data representing activation of an automated workflow template configured to perform a process workflow, receiving configuration data, linking an automated workflow template to executable instructions, and retrieving data representing a type of process flow, which may data including enriched graph data to modify graph data.

The invention particularly relates to a data governance method and system based on a large model. According to the data governance method based on the large model, data are automatically collected, and the data are preliminarily cleaned and preprocessed; carrying out deep analysis, mining and prediction on the data by utilizing the large model, and carrying out data conversion and formatting according to business requirements; a data quality standard, a data security policy and a data management process are formulated and executed according to service requirements, so that the accuracy, integrity and security of data are ensured; the data quality and the data flow are monitored in real time, and if a data quality problem is found, the data quality problem is fed back to the large model so that the large model can solve the data quality problem in time. According to the data governance method and system based on the large model, intelligentization and automation of data governance are achieved, the burden of manual operation is relieved, the data governance efficiency is improved, the data security and compliance are optimized, intelligent decision support is achieved, and the data value mining capacity is improved.

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 relates to the technical field of data governance, and discloses a data governance method based on an industrial big data platform, which comprises the following steps: processing an organizational structure document and a business process definition through a text semantic analysis technology, and generating a responsibility allocation matrix; analyzing the historical data access behavior of the user, and depicting the data use characteristics of the user; according to the user model and the responsibility matrix, in combination with data sensitivity scoring, scene feature extraction and multi-dimensional risk calculation, dynamic permission decision is realized; for the decision-making mechanism, verifying the security influence of the authorization strategy through a simulation test; performing adaptive adjustment on model parameters according to a test result, and dynamically updating the initial responsibility matrix to form closed-loop management; through organizational structure intelligent analysis, user behavior modeling, risk awareness permission decision, digital twin verification and adaptive optimization, accurate division and dynamic adjustment of data governance responsibilities are realized, and the intelligence and dynamic adaptability of data permission management are improved.

The invention relates to the technical field of data management and information, and discloses a data asset identification system and method based on multi-dimensional rule engine blood relationship analysis, and the method comprises the steps: obtaining static code information and dynamic operation tracking information of to-be-analyzed data; constructing a theoretical data blood relationship map based on the static code information; constructing a real-time data blood relationship map based on the dynamic operation tracking information; generating a fusion data consanguinity map by adopting a time window alignment strategy; constructing a five-dimensional data asset identification rule model; starting a blood relationship driven multi-dimensional rule engine; executing the multi-dimensional rule engine, and evaluating and determining the nature of the to-be-analyzed data; in the evaluation process, a conflict resolution mechanism is started. According to the method, the multi-dimensional rule engine is driven through dynamic and static combined blood relationship analysis, and conflict judgment is performed by using blood relationship information.

The invention discloses a data quality governance method, and relates to the technical field of privacy calculation and data governance. According to the method, various kinds of original data are obtained, one kind of original data comes from a data source, and the data source comprises a hospital, a company and a medicine production enterprise; normalizing the original data to obtain standardized data; inputting the standardized data into a preset federal data quality evaluation model for data quality evaluation to obtain a quality evaluation result corresponding to each kind of standardized data, the quality evaluation results including an integrity evaluation result, a consistency evaluation result and a timeliness evaluation result; aggregating the quality evaluation results to obtain a global quality evaluation report; and determining a corresponding quality optimization strategy according to the global quality evaluation report, and treating data according to the quality optimization strategy. And the data quality treatment effect is improved.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

The invention discloses a data governance agent system and an implementation method, and relates to the field of data processing, and the system comprises a data analysis module, a data storage module, a user response module, a quality monitoring and feedback module and a field large language model module. The data analysis module performs unified analysis modeling on structured and unstructured data; the data storage module adopts a graph database and a vector database to store knowledge data; the user response module supports natural language interaction and intelligent question and answer; the quality monitoring and feedback module optimizes data quality through closed-loop feedback; and the field large language model module constructs an optimization model adaptive to the vertical field. Through multi-modal analysis, knowledge graph construction, double-library collaborative storage and model dynamic optimization, a data barrier is broken through, manual dependence is reduced, the data treatment efficiency, quality and intelligent level are improved, and the method is suitable for specialized data treatment requirements of industries such as manufacturing and finance.