1050 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 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 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 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 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 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 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.

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.

The invention provides an automatic data management method and system based on a multi-modal large model, and the method comprises the steps: collecting multi-source heterogeneous industrial data, and carrying out the standardization processing, and forming standardized multivariable time series data; constructing a process knowledge base, and performing semantic embedding coding on a process knowledge text and storing the process knowledge text; constructing and finely tuning a KTSF multi-modal large model, and fusing process knowledge semantics and multivariable time sequence data through a cross-modal attention mechanism to generate joint semantic representation; based on prediction of a KTSF multi-mode large model, outputting a residual error with actual data, and dynamically identifying abnormal data; performing attribution analysis; based on an attribution result, calling a KTSF multi-mode large model to generate a repair value, and performing intelligent correction on the abnormal data; the design quality evaluation and feedback learning module is used for calculating a data quality score and driving incremental updating of the model; and the design rule self-learning module is used for automatically extracting the governance rule through clustering analysis and updating the knowledge base.

The invention discloses an auditing and supervising system and method for real-time traceability of multi-source data, and relates to the technical field of data governance, and the method comprises the steps: when data is generated, automatically collecting original feature information of the data, writing the original feature information into a block chain, generating a unique data consanguinity identifier, and building a data consanguinity index table; during data circulation, recording node identifiers, timestamps and operation types in real time, acquiring attenuation values from the attenuation coefficient comparison table, calculating current credibility coefficients, and chaining the current credibility coefficients as new nodes to form chained association; the data integrity is periodically verified, when it is detected that the data quality is abnormal, a complete blood relationship chain corresponding to the abnormal data is called, and the attenuation value and the offset cumulant of each link are associated; and screening links of which the decreasing amplitude of the credibility is greater than a threshold value, calling an uplink record of a maximum attenuation point in combination with a feature offset jump point, and positioning a specific operation behavior. According to the invention, the maximum attenuation point and the specific operation behavior of the abnormal data are accurately positioned, and the auditing efficiency is improved.

The invention discloses a medical data management method and system based on multi-modal fusion and privacy protection, and the method comprises the steps: receiving multi-modal medical data, and executing cross-modal embedding learning to generate a joint embedding vector; constructing a patient multi-modal graph based on the joint embedded vector, and detecting abnormal inconsistency between the cross-modal data by using a graph attention network to generate an abnormal detection result; a privacy protection strategy is dynamically adjusted according to the anomaly detection result and the access context information, and desensitization processing is conducted on the medical data; and constructing a global model through security aggregation based on the desensitized medical data to generate an optimal governance action and apply the optimal governance action to a data management process. Through organic cooperation of multi-modal fusion, anomaly detection, adaptive privacy protection, security aggregation and dynamic strategy optimization, a closed-loop medical data governance platform is successfully constructed, and the problems of multi-modal data fragmentation, privacy compliance conflict and dynamic governance deficiency are effectively solved.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention relates to the technical field of intelligent agriculture, in particular to a multi-source heterogeneous big data governance method and device.The multi-source heterogeneous big data governance method comprises the following steps that S1, multi-source heterogeneous data collection and semantic alignment are conducted, hidden semantic conflicts are eliminated; s2, performing dynamic data source adaptation and quality monitoring; s3, performing cross-domain permission penetration control; s4, building an industrial model driven by federal learning; and S5, double-chain block chain evidence storage and traceability are carried out. The method is used for disambiguating dialect term conflict, federated learning cross-domain privacy modeling and double-chain block chain evidence storage traceability through a dynamic semantic alignment neural network, and systematically solves the problems of dialect semantic segmentation, data islands and sensitive information leakage caused by small scattered distribution of peasant households in the field of traditional Chinese medicinal material planting. And a trust closed loop of government-enterprise-peasant household three-party cooperative treatment is constructed.

The invention discloses a data governance method and device based on an MCP protocol and a medium, and the method comprises the steps: obtaining a language instruction of a user through a data center regulation and control MCP client, determining a data governance demand according to the language instruction, and determining an obtaining request according to the data governance demand; and sending the acquisition request to the MCP server to acquire the data resource through the MCP server, and performing data processing according to the data resource. The data governance process is started through the natural language instruction, user operation is facilitated, and the use threshold is lowered. And the data governance demand and the acquisition request are accurately determined through the analysis instruction, so that the governance pertinence is improved. And resources are obtained and processed by means of the MCP server, so that efficient data management is realized. According to the process, user requirements and data acquisition and processing links are integrated, so that data management is more convenient, accurate and efficient.

The invention provides a data classification and grading method and device based on an artificial intelligence large model technology, and relates to the field of data security. The method comprises the following steps: constructing a data classification and grading catalog according to a preset classification rule, and storing each row of the classification and grading catalog as a document record in a target vector database; respectively carrying out first retrieval operation, second retrieval operation and third retrieval operation on the data classification and grading catalog by taking the business system name, the data table name and the field name as first query conditions so as to obtain a first matching document, a second matching document and a third matching document in the target vector database; sorting the first matching document, the second matching document and the third matching document according to a preset sorting mode, and obtaining a sorting result; and inputting a sorting result into the large language model, and performing data security early warning through the large language model. According to the method and the device, the security problems that corresponding security measures cannot be taken, sensitive data is leaked and the like when a data governance engineer identifies the metadata according to the data classification and classification catalog and judges the classification and security level of the metadata are solved.

The invention relates to a medical data dynamic management method based on multi-modal data fusion and deep reasoning, which comprises the following steps: establishing a three-layer dynamic management architecture, collecting various medical data of a patient in real time through a bottom layer, performing primary processing on the various medical data of the patient, receiving the primarily processed medical data through a middle management layer, and performing dynamic management on the medical data through the middle management layer. The double-flow Transform network architecture performs medical event prediction, the event-driven architecture triggers a data processing flow, medical data after primary processing is subjected to dynamic extraction, data conversion, data loading and multi-modal data fusion processing, a data processing result is transmitted to the application layer, the application layer performs dynamic field generation, and the event-driven architecture performs multi-modal data fusion processing. And data automatic dynamic management and real-time feedback calibration are carried out based on a data fusion analysis result. The limitation of a static field structure existing in an existing medical data management SEER database is solved, the data quality is improved, automatic management and real-time feedback calibration of follow-up visit data are achieved, and separation of a follow-up visit system and a data management platform is avoided.

The invention discloses a data component encapsulation construction method based on a standardized description model, and provides a new data encapsulation normal form of data components. According to the method, a standardized encapsulation model and a protocol system are constructed around a data component, and the semantics, the structure and the operation of heterogeneous multi-source data are subjected to standardized encapsulation by adopting a unified identifier, metadata, data routing and interface structure; lightweight packaging is carried out based on a JSON format, and a unified access interface is provided through RESTful API, DOIP and other protocols; a Handle system is introduced to realize globally unique data component identification, and dynamic addressing and analysis access of the data component identification are supported; a data component registration publishing mechanism is constructed, metadata registration, index management and identification mapping of the data component are supported, and efficient discovery, acquisition, sharing and integrated utilization of the data component among multiple systems and multiple subjects are ensured. Meanwhile, a component interoperation and scheduling mechanism oriented to a cross-domain circulation scene is provided, and data management is promoted to be transformed into a loose-coupling, autonomous and orchestrable new normal form.