Dynamic data asset operation optimization method for multi-source metadata intelligent access checking

Abstract

The invention belongs to the technical field of data asset operation optimization methods, and particularly relates to a dynamic data asset operation optimization method for multi-source metadata intelligent access inventory, which comprises the following steps: deploying a multi-source data probe, and aiming at different types of data sources such as a relational database, a distributed file storage, a streaming data platform and an API (Application Program Interface) according to a preset capture rule and mode; the blood relationship, authority configuration and quality characteristics of metadata are collected, and collected original information is temporarily stored in a middle buffer area after being subjected to duplicate removal; according to industry specifications and business requirements, a standardized metadata model containing four core dimensions of asset basic information, blood relationship, permission configuration and quality characteristics is constructed, and after original information of a middle buffer area is subjected to field mapping, conversion and association verification and data conflict processing according to the model, structured metadata is stored in a metadata warehouse.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data asset operation optimization method, and particularly relates to a dynamic data asset operation optimization method for multi-source metadata intelligent access and inventory. BACKGROUND

[0002] With the deepening of digital transformation, the scale of enterprise data assets continues to expand, and the types of data sources are increasingly diversified, covering relational databases, distributed file storage, streaming data platforms, API interfaces and other forms. The blood relationship, permission configuration and quality characteristics of data assets present complex and heterogeneous characteristics. The traditional data access mode relies on manual configuration of adaptation rules, and the adaptation flexibility for new data sources is insufficient. Moreover, the quality checking rules are single and fixed, making it difficult to effectively filter abnormal data, resulting in low data access efficiency and low standardization. A large amount of raw data cannot be quickly converted into usable structured metadata. At the same time, the existing metadata management lacks a unified standardized model, and the metadata information of different data sources is scattered and disordered. The blood relationship is not complete, and the permission and quality characteristics are not closely related, making it difficult to form a comprehensive asset view.

[0003] In the process of data asset operation, the label system in the traditional mode is static and fixed, and cannot be automatically updated with metadata changes and business demand iterations, resulting in insufficient asset classification and grading refinement. Metadata retrieval relies on precise field matching, lacks semantic understanding ability, and cannot meet the diversified needs of users such as fuzzy query and natural language query, resulting in poor discoverability of data assets. In addition, the operation monitoring lacks full-link real-time sensing capability, the abnormal early warning is lagging and the root cause positioning is difficult, the asset value assessment dimension is single, and it is difficult to accurately identify high-value assets and low-value redundant assets. At the same time, the fine-grained access control and compliance control mechanism is not perfect, further restricting the efficient use and safe operation of data assets. SUMMARY

[0004] The purpose of the present application is to provide a dynamic data asset operation optimization method for multi-source metadata intelligent access and inventory in view of the above-mentioned technical problems.

[0005] Therefore, the present application provides a dynamic data asset operation optimization method for multi-source metadata intelligent access and inventory, which comprises the following steps:

[0006] Step 1: Deploy multi-source data probes. For different types of data sources such as relational databases, distributed file storage, streaming data platforms and API interfaces, collect the blood relationship, permission configuration and quality characteristics of metadata according to the preset capture rules and modes. The collected raw information is temporarily stored in the intermediate buffer area after deduplication.

[0007] Step two: According to industry standards and business needs, a standardized metadata model is constructed, which includes four core dimensions of asset basic information, blood relationship, permission configuration, and quality characteristics. The original information in the intermediate buffer zone is field-mapped, converted and associated with the model. After handling data conflicts, the structured metadata is stored in the metadata warehouse.

[0008] Step three: Define business scenarios, data types, sensitive level label core dimensions and standardized labels, establish label hierarchy relationship, realize automatic generation and association mapping of labels based on structured metadata, support manual definition of labels, set label iteration update trigger conditions and record update logs.

[0009] Step four: Build a search engine that supports structured data and semantic search, synchronize metadata warehouse information and build multi-dimensional indexes, integrate semantic models to generate semantic vectors, optimize query parsing, matching and sorting modules, support multiple types of queries, adjust sorting weights combined with user behavior feedback, and provide search result preview function.

[0010] Step five: Build a multi-source data adaptation template library, embed an AI-driven adaptive template generation module, establish a multi-dimensional quality verification rule set, embed a rule engine into the standardized access link, configure differential verification mode, build a rule self-learning module to optimize verification rules, and use plug-in design and multi-thread asynchronous processing mechanism to improve adaptability and concurrency capability.

[0011] Step six: Build a full-link real-time monitoring module, build monitoring dashboards around core operation indicators, train anomaly early warning models based on historical data and implement hierarchical early warning and root cause analysis; build a label-driven asset intelligent operation engine to realize asset personalized recommendation and multi-dimensional value evaluation, and dynamically optimize asset inventory; decouple core functions using microservice architecture, integrate AI algorithm modules to build access strategy self-optimization closed loop, and strengthen data security protection.

[0012] Preferably, the blood relationship in step one includes data generation source, upstream and downstream dependence, and field-level mapping relationship; the permission configuration covers data owner, access role, read-write permission level and authorization validity period; the quality characteristics include data integrity, field non-empty rate, format compliance, data timeliness, and duplication rate indicators; the capture mode includes real-time capture for high-frequency update data sources and timed offline capture for low-frequency update data sources, and the probe has a built-in log recording module.

[0013] Preferably, the asset basic information dimension in step two includes asset unique ID, name, data source type, and storage location; the data conflict handling adopts the method of "latest captured data priority + manual review", and the missing dependence relationship is supplemented during integration, and the quality characteristic raw statistical data is converted into standardized indicators.

[0014] Preferably, the tag automatic generation rule in step three is based on a structured metadata preset, and the tag association mapping is realized through a semantic matching algorithm; the tag iteration update trigger condition includes a metadata change trigger, a business requirement change trigger, and a periodic check trigger, and supports user manual triggering of tag iteration.

[0015] Preferably, the core function module of the search engine in step four includes a query analysis module, a matching module, and a sorting module; the query analysis module improves accuracy through keyword segmentation, synonym replacement, and ambiguity elimination techniques, and supports fuzzy query, multi-condition combination query, and natural language query; the matching module combines structured field matching scores and semantic similarity scores; the sorting module introduces a user behavior feedback mechanism; the search recall rate and precision rate are verified through test data sets, and continuous iteration optimization is carried out based on search logs after going online.

[0016] Preferably, the adaptive template library in step five divides sub-template sets according to data source types, each template contains fixed configuration items and extensible configuration items, the fixed configuration items cover data source type identification and default transmission protocol basic information, and the extensible configuration items support custom field mapping; the adaptive template generation module includes a feature collection unit, a feature matching model, a template generation engine, and a template verification unit, and the effectiveness of the template is verified through simulation access testing; the quality check rule set covers integrity, consistency, accuracy, timeliness, and format compliance, and the rules are defined in a standardized manner and support version management.

[0017] Preferably, the core operation indicators in step six include asset access success rate, data usage rate, and quality compliance rate; the asset value evaluation dimensions include data quality, reuse rate, business value, and storage cost, low-value assets can be automatically identified and the elimination process is triggered; the data security guarantee function includes fine-grained access control based on metadata permission information, data desensitization, and operation audit.

[0018] The data asset operation optimization system based on metadata intelligent access and inventory includes a multi-source metadata capture module: deploy multi-source data probes to collect the blood relationship, permission configuration, and quality characteristics of metadata for different types of data sources, and perform deduplication processing on the original information and temporarily store it in an intermediate buffer area;

[0019] The metadata standardization integration module: built-in standardized metadata model for field mapping, conversion, association verification, and conflict processing of raw information, output structured metadata and store in the metadata warehouse;

[0020] The dynamic label management module: used to define standardized labels and hierarchical relationships, realize automatic generation, association mapping, and iteration update of labels, and support manual definition of labels;

[0021] Metadata retrieval engine module: used for building multi-dimensional index and semantic vector, providing multi-type query function, improving retrieval efficiency and accuracy through optimizing query analysis, matching and sorting logic;

[0022] Smart access engine module: containing multi-source data adaptation template library, adaptive template generation module, quality checking rule set and rule self-learning module, used for realizing template-based and automated access and quality control of multi-type data sources;

[0023] Monitoring and operation optimization module: including full-link real-time monitoring unit, abnormal early warning unit, asset intelligent operation unit, used for operation index monitoring, hierarchical early warning, asset personalized recommendation and value evaluation, adopting micro-service architecture and integrating AI self-optimization and data security guarantee function.

[0024] Preferably, the probes of the multi-source metadata capture module include database probes, file probes and stream data probes, the database probes acquire relevant information by reading system tables, SQL execution logs and ETL script analysis, the file probes analyze file metadata and transmission logs, and the stream data probes listen to data production and consumption links.

[0025] Preferably, the smart access engine module adopts plug-in design and supports dynamic update of template library and rule library; the metadata retrieval engine module contains a retrieval result preview unit, and the abnormal early warning unit of the monitoring and operation optimization module has a bottleneck root cause analysis function.

[0026] The beneficial effects of the present application are:

[0027] By constructing a multi-source data smart access engine and a standardized metadata model, template-based and automated access of heterogeneous data sources is realized, an AI-driven adaptive template generation module greatly reduces the adaptation cost of newly added data sources, a multi-dimensional quality checking rule set and a self-learning optimization mechanism effectively filter abnormal data, and data access efficiency and standardization degree are significantly improved. At the same time, the unified metadata model integrates asset basic information, blood relationship, permission configuration and quality characteristics, completes the missing dependent relationship, solves the problems of scattered and disordered metadata and loose correlation, forms a comprehensive and structured asset panoramic view, and lays a solid foundation for subsequent operation management.

[0028] The automatic iteration and correlation mapping capability of the dynamic label system realizes the refinement and dynamic adaptation of asset classification and grading. The semantic retrieval engine integrates multi-dimensional indexing and natural language understanding technology, greatly improving the discoverability and search accuracy of data assets, and meeting the diversified retrieval needs. The full-link real-time monitoring and intelligent early warning mechanism realizes the rapid perception and root cause positioning of abnormalities. The multi-dimensional asset value evaluation system accurately identifies high-value assets and redundant assets, promoting the dynamic optimization of asset inventory. In addition, the flexible expansion capability of micro-service architecture and the fine-grained security control mechanism not only guarantee the compliance and safe operation of data assets, but also significantly improve the asset reuse rate and operation efficiency, fully releasing the value of data assets. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The road map of the dynamic data asset operation optimization method for multi-source metadata intelligent access inventory of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0031] It should be noted that all directional and positional terms used in the present application, such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "inner", "outer", "top", "low", "transverse", "longitudinal", "center", etc., are used only to explain the relative position relationship, connection condition, etc. between components in a certain state (as shown in the drawings), and are only for the convenience of describing the present application, and are not required to construct and operate the present application in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the description of "first", "second", etc. in the present application is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implying the number of the indicated technical features.

[0032] In the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection; it can be directly connected, or indirectly connected through intermediate medium; it can be the communication inside two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0033] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the exemplary description of the above terms does not necessarily mean the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0034] A dynamic metadata panoramic management system is constructed, the blood relationship, permission configuration and quality characteristics of the data asset are automatically captured by a multi-source data probe, a standardized metadata model is established to structure and integrate the captured information, a dynamic label system is designed based on business scenarios, data types, sensitivity levels and other dimensions, automatic iterative updating and associated mapping of the label are supported, fine classification and grading of the asset are realized, a metadata retrieval engine is built, semantic retrieval technology is combined to improve the efficiency and accuracy of asset search, and the problem of insufficient discoverability of data assets is solved.

[0035] The types of data sources covered by the target data asset are combed, including relational databases, distributed file storage, streaming data platforms, API interfaces, etc., and special data probes are designed for each type of data source; the capture range and rules are specified for each probe, and the capture content of the blood relationship, permission configuration and quality characteristics is defined, wherein the blood relationship includes data generation source, upstream and downstream dependence and field-level mapping relationship, the permission configuration covers data owner, access role, read-write permission level and authorization validity period, and the quality characteristics include data integrity, field non-empty rate, format compliance, data timeliness, and duplication rate; at the same time, the capture mode is configured to support real-time capture for high-frequency update data sources and offline capture for low-frequency update data sources, and a log recording module is built into the probe to record the capture process information for problem tracing.

[0036] Start multi-dimensional metadata capture and original information collection by multi-source data probe: database probe captures blood relationship and permission configuration by reading system table, SQL execution log and ETL script parsing, and captures quality characteristics by pre-defined quality verification script; file probe parses file metadata, data Schema and file transmission log to obtain relevant information; streaming data probe listens to data production and consumption links to capture data stream upstream and downstream node relationship and transmission delay characteristics; all the original information collected by the probes is temporarily stored in the intermediate buffer in a unified format, and is de-duplicated based on asset unique identifier and capture timestamp to avoid waste of storage resources.

[0037] According to the industry metadata specification and business requirements, a standardized metadata model is defined, which includes asset basic information, blood relationship, permission configuration, and quality characteristics. The field definition, data type, format standard, and value range of each dimension are clearly defined to ensure the structure and consistency of the metadata. The asset basic information dimension covers key information such as asset unique ID, name, data source type, and storage location.

[0038] The original capture information of the intermediate buffer is mapped and converted according to the standardized metadata model. The upstream and downstream relationships of the blood relationship are verified. The missing dependency relationship is completed through the asset unique identifier. The quality characteristic raw statistical data is converted into standardized indicators. If data conflicts occur during integration, conflict alerts are triggered, and the "latest capture data priority + manual review" method is used for processing. Finally, the integrated structured metadata is stored in the metadata warehouse.

[0039] Determine the core dimensions of business scenarios, data types, sensitivity levels, data quality levels, and asset values. Standardize the labels under each dimension, define the label name, unique code, description, and association rules, and establish the label hierarchy to provide a foundation for label association mapping.

[0040] Implement label automatic generation and association mapping mechanism: design label automatic generation rules based on structured metadata, automatically label corresponding labels when metadata meets preset conditions; build label association mapping engine, realize automatic association between labels through semantic matching algorithm; support manual addition of custom labels, and custom labels can be linked through association rules and automatically generated labels.

[0041] Set the trigger conditions for label iteration update, including metadata change trigger, business requirement change trigger, and periodic check trigger; establish label update log to record label addition, modification, deletion operation and trigger reason, and support manual trigger of label iteration to meet special business scenario requirements.

[0042] Select an engine framework that supports structured data and semantic retrieval, synchronize structured metadata and label information in the metadata warehouse to the retrieval engine, and build multi-dimensional indexes such as asset name, label combination, and metadata field; integrate semantic models to perform semantic coding on metadata text information and labels, generate semantic vectors and store them; design core function modules including query analysis module, matching module, and sorting module to realize query analysis, matching calculation, and result sorting respectively.

[0043] Optimize semantic search function and improve search experience: Optimize query analysis module, support fuzzy query, multi-condition combination query and natural language query, improve analysis accuracy through keyword segmentation, synonym replacement and ambiguity elimination technology; In the matching module, the structured field matching score and the semantic similarity score are combined to get the comprehensive matching score; The sorting module introduces user behavior feedback mechanism to dynamically adjust the sorting weight; Build a search result preview module to support users to quickly view asset core information and improve search efficiency.

[0044] Build a test data set containing different types, tags and business scenarios, design multiple test cases to verify the recall rate and precision rate of the search engine; Collect problems during testing, optimize index structure, semantic encoding model and matching algorithm; Monitor search logs after going online, analyze user search behavior, continuously iterate tag system, search rules and semantic model, continuously improve data asset search efficiency and accuracy, and solve the problem of insufficient discoverability of data assets.

[0045] Optimize the architecture of intelligent access engine, design a multi-source data adaptation template library, support template configuration of multiple data sources such as relational databases, file storage, and streaming data, and embed an AI-driven adaptive template generation module that can automatically generate adaptation templates based on the characteristics of new data sources, reducing the cost of manual configuration; Integrate multi-dimensional quality verification rules in the access process, covering data integrity, consistency, and accuracy verification, automatically filter abnormal data through the rule engine, and support self-learning optimization of verification rules to realize automation and intelligence in the access process, greatly improving data access efficiency.

[0046] Comprehensively inventory the types of data sources involved in the target data assets, including relational databases, file storage, streaming data platforms, API interfaces, and distributed storage, and clearly define the specific forms corresponding to each type of data source (such as MySQL, Oracle for relational databases, and CSV, Parquet for file storage); For each data source, sort out its core access requirements, covering data transmission protocols, authentication methods, data reading modes, Schema parsing rules, and data conversion requirements, to provide clear basis for subsequent adaptation template library design.

[0047] An adaptive template library is built using a modular architecture, sub-template sets are divided according to data source types, and each template includes fixed configuration items and extensible configuration items; the fixed configuration items cover data source type identification, default transmission protocol, basic authentication field and data reading basic rules, and the extensible configuration items support user-defined field mapping, data filtering conditions and conversion functions; basic templates are developed for various data sources, the database template has built-in JDBC connection parameters, table structure automatic parsing rules and incremental synchronization strategies, the file template has built-in parsing engine selection, field separator identification and table header matching rules, the stream template has built-in topic subscription configuration, serialization / deserialization rules and consumer group settings, and all templates are stored in JSON format for easy engine parsing and modification.

[0048] An adaptive template generation module is built, including a data source feature acquisition unit, a feature matching model, a template generation engine and a template verification unit; the feature acquisition unit automatically obtains the key features of the new data source through the detection script, including data source type identification, transmission protocol, authentication method, Schema structure, data volume and update frequency; based on historical template data and corresponding data source features, a data source type identification model and an adaptive rule generation model are trained; when a new data source is connected, the feature acquisition unit obtains its features, the model first identifies the data source type, then matches the most similar basic template in the template library, adjusts the configuration items combined with the unique features of the new data source, supplements the adaptive rules, and generates a dedicated template; the template verification unit checks the validity of the template through simulated access testing, and automatically stores it in the template library after verification.

[0049] Around the quality requirements of data access, a multi-dimensional verification rule set covering integrity, consistency, accuracy, timeliness and format compliance is built; the integrity rules include field non-empty, record number matching and other checks, the consistency rules include field format, data logic and other checks, the accuracy rules include data format, content accuracy and other checks, and the timeliness rules include transmission delay, update frequency and other checks; each rule is standardized, with clear rule name, unique code, verification logic, applicable data source type, threshold parameter and exception level, forming a structured rule library, supporting rule addition, deletion, modification and query, and version management.

[0050] The data access engine is restructured into a standardized link of "data source connection → data reading → quality verification → data conversion → storage", and the rule engine is embedded in the quality verification link; the rule engine uses an extensible architecture and works with the template library to automatically load corresponding verification rule sets according to data source types and template configurations; verification modes are configured for different access scenarios, real-time per-item verification for stream data, batch verification for batch file data, and single request verification for API interface data; rules are executed according to exception level priority during verification, serious error data is automatically filtered, warning exception data is marked, and exception logs are recorded to support exception tracing.

[0051] A rule self-learning module is built, which includes a data collection unit, a model training unit, and a rule iteration unit. The data collection unit continuously collects verification data, misjudgment / omission judgment feedback, and business demand change records in the historical access process. The model training unit uses a supervised learning algorithm to analyze rule effectiveness indicators, including hit rate, accuracy, misjudgment rate, and omission rate. For low-accuracy rules, the threshold is adjusted or the logic is optimized. For high-omission-rate scenarios, abnormal patterns are mined and new rule recommendations are generated. The rule iteration unit pushes the optimized rules and newly recommended rules to the rule library, which takes effect after manual confirmation. At the same time, iteration logs are recorded, version rollback is supported, and rule updates can be automatically triggered based on business demand change signals.

[0052] The plug-in design is used to extend the engine adaptation capability. When a new data source type is added, the corresponding template and rule plug-ins can be quickly developed. The template library and rule library support version management and dynamic updating, allowing users to customize rules through a visual interface. The engine integrates a general data conversion component that automatically performs format standardization, encoding conversion, and other operations in conjunction with template configuration. A multi-thread asynchronous processing mechanism is used to optimize concurrent processing capability, supporting batch and stream data source parallel access.

[0053] A test data set covering multiple types of data sources and multiple scenarios is constructed, and test cases are designed to verify template adaptation accuracy, AI template generation effectiveness, verification rule hit rate, and access process stability. Through stress testing, high-concurrency scenarios are simulated to optimize engine processing performance. After going online, a monitoring module is deployed to track key indicators such as access success rate and rule verification accuracy in real time. User feedback is collected, and the template library, AI adaptation model, and verification rule set are continuously iterated and optimized to improve the engine's automation, intelligence level, and access efficiency.

[0054] A full-link real-time monitoring module is built, focusing on key operational indicators such as asset access success rate, data usage rate, and quality compliance rate. Flow computing technology is used to realize real-time collection and statistical analysis of indicators. Based on historical data, an abnormal early warning model is trained, and the early warning threshold is dynamically adjusted. For access failures, quality abnormalities, and low usage rates, hierarchical early warning is triggered. At the same time, a bottleneck root cause analysis function is integrated, which automatically locates the problem source by associating metadata information and monitoring data, providing accurate decision support for operational optimization.

[0055] The metadata tag-driven asset intelligent operation engine is constructed, a personalized recommendation engine is built based on a semantic matching algorithm of asset tags and business demand tags, data assets conforming to business scenarios are accurately pushed to promote asset reuse, a multi-dimensional asset evaluation system is established to quantify asset value from dimensions of data quality, reuse rate, business value, storage cost, etc., an asset value score and a life cycle evaluation report are generated, low-value assets are automatically identified and a retirement process is triggered to realize dynamic optimization of data asset inventory.

[0056] The system is decoupled and designed by using a micro-service architecture, core functions such as metadata management, intelligent access, monitoring and early warning, recommendation and evaluation are split into independent micro-services to support on-demand expansion and flexible deployment, an AI algorithm module is integrated to build an access strategy self-optimization closed loop, historical access data and operation indicators are analyzed to automatically adjust access priority, template configuration and quality verification rules, a data security module is strengthened, fine-grained access control is realized based on metadata permission information, and data desensitization, operation audit and other functions are combined to ensure compliant operation and safe use of data assets.

[0057] The embodiments of the application are described above in combination with the drawings, the embodiments in the application and the features in the embodiments can be combined with each other without conflict, the application is not limited to the above specific embodiments, the above specific embodiments are only illustrative and not restrictive, and those skilled in the art can make many forms under the inspiration of the application without departing from the scope of the application and the protection scope of the claims, all of which belong to the protection of the application.

Claims

1. A dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata, characterized by: Includes the following steps: Step 1: Deploy multi-source data probes to collect metadata lineage, permission configuration, and quality characteristics for different types of data sources, such as relational databases, distributed file storage, streaming data platforms, and API interfaces, according to preset capture rules and patterns. The collected raw information is temporarily stored in an intermediate buffer after deduplication. Step 2: Based on industry standards and business needs, construct a standardized metadata model that includes four core dimensions: basic asset information, lineage, permission configuration, and quality characteristics. Map, transform, and verify the fields of the raw information in the intermediate buffer according to the model. After handling data conflicts, store the structured metadata in the metadata repository. Step 3: Define core dimensions and standardized tags for business scenarios, data types, and sensitivity levels; establish tag hierarchy relationships; automatically generate and map tags based on structured metadata; support manually customized tags; set tag iteration update trigger conditions and record update logs. Step 4: Build a search engine that supports structured data and semantic retrieval, synchronize metadata repository information and build multi-dimensional indexes, integrate semantic models to generate semantic vectors, optimize query parsing, matching and sorting modules, support multiple types of queries, adjust sorting weights based on user behavior feedback, and provide a search result preview function; Step 5: Build a multi-source data adaptation template library, embed an AI-driven adaptive template generation module, establish a multi-dimensional quality verification rule set, embed the rule engine into the standardized access link, configure differentiated verification modes, build a rule self-learning module to optimize verification rules, and adopt a plug-in design and multi-threaded asynchronous processing mechanism to improve adaptability and concurrency. Step Six: Build a real-time monitoring module across the entire chain, construct a monitoring dashboard around core operational indicators, train anomaly warning models based on historical data and implement tiered warnings and root cause analysis; build a tag-driven intelligent asset operation engine to achieve personalized asset recommendations and multi-dimensional value evaluation, and dynamically optimize asset inventory. The core functions are decoupled by adopting a microservice architecture, and an AI algorithm module is integrated to build a self-optimizing closed loop for access strategies, thereby strengthening data security.

2. The dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata as described in claim 1, characterized in that: The lineage relationship mentioned in step one includes the data generation source, upstream and downstream dependencies, and field-level mapping relationships; the permission configuration covers the data owner, access role, read and write permission level, and authorization validity period; The quality characteristics include data integrity, field non-empty rate, format compliance, data timeliness, and duplication rate; the capture modes include real-time capture for high-frequency updated data sources and timed offline capture for low-frequency updated data sources, and the probe has a built-in log recording module.

3. The dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata as described in claim 1, characterized in that: The asset basic information dimensions mentioned in step two cover the asset's unique ID, name, data source type, and storage location; data conflict handling adopts the method of "newest captured data first + manual review", and missing dependencies are filled in during the integration process and the original statistical data of quality characteristics are converted into standardized indicators.

4. The dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata as described in claim 1, characterized in that: The automatic tag generation rules in step three are based on pre-defined structured metadata, and the tag association mapping is achieved through a semantic matching algorithm. The tag iteration update triggering conditions include metadata change triggering, business requirement change triggering, and periodic verification triggering, and users can manually trigger tag iteration.

5. The dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata as described in claim 1, characterized in that: The core functional modules of the retrieval engine described in step four include a query parsing module, a matching module, and a sorting module. The query parsing module improves accuracy through keyword segmentation, synonym replacement, and ambiguity elimination techniques, and supports fuzzy queries, multi-condition combination queries, and natural language queries. The matching module integrates structured field matching scores and semantic similarity scores. The ranking module incorporates a user behavior feedback mechanism; the retrieval recall and precision are verified using a test dataset, and continuous iteration and optimization are performed based on retrieval logs after deployment.

6. The dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata as described in claim 1, characterized in that: In step five, the adaptation template library is divided into sub-template sets according to the data source type. Each template contains fixed configuration items and extensible configuration items. The fixed configuration items cover the data source type identifier and basic information of the default transmission protocol, while the extensible configuration items support custom field mapping. The adaptive template generation module includes a feature acquisition unit, a feature matching model, a template generation engine, and a template verification unit. The validity of the template is verified through simulated access testing. The quality verification rule set covers completeness, consistency, accuracy, timeliness, and format compliance. The rules adopt standardized definitions and support version management.

7. The dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata as described in claim 1, characterized in that: The core operational metrics mentioned in step six include asset access success rate, data utilization rate, and quality compliance rate; the asset value evaluation dimensions include data quality, reuse rate, business value, and storage cost, which can automatically identify low-value assets and trigger the elimination process. The data security protection functions include fine-grained access control based on metadata permission information, data anonymization, and operation auditing.

8. A data asset operation optimization system for intelligent access and inventory of metadata, based on the dynamic data asset operation optimization method for intelligent access and inventory of multi-source metadata as described in claims 1-6, characterized in that: Multi-source metadata capture module: Deploys multi-source data probes to collect metadata lineage, permission configuration and quality characteristics for different types of data sources, deduplicates the original information and temporarily stores it in an intermediate buffer; Metadata standardization and integration module: It has a built-in standardized metadata model, which is used to map, transform, verify and handle conflicts of raw information, output structured metadata and store it in the metadata repository; Dynamic tag management module: used to define standardized tags and hierarchical relationships, realize automatic tag generation, association mapping and iterative updates, and support manual customization of tags; Metadata retrieval engine module: used to build multi-dimensional indexes and semantic vectors, providing multi-type query functions, and improving retrieval efficiency and accuracy by optimizing query parsing, matching and sorting logic; Intelligent access engine module: includes a multi-source data adaptation template library, an adaptive template generation module, a quality verification rule set and a rule self-learning module, used to realize the templated and automated access and quality control of multiple types of data sources; Monitoring and Operation Optimization Module: Includes a full-link real-time monitoring unit, an anomaly warning unit, and an intelligent asset operation unit, used for monitoring operational indicators, tiered warnings, personalized asset recommendations, and value evaluation. It adopts a microservice architecture and integrates AI self-optimization and data security protection functions.

9. The data asset operation optimization system for intelligent metadata access and inventory as described in claim 8, characterized in that: The probes of the multi-source metadata capture module include database probes, file probes, and streaming data probes. The database probes obtain relevant information by reading system tables, SQL execution logs, and parsing ETL scripts. The file probes parse file metadata and transmission logs. The streaming data probes monitor the data production and consumption chain.

10. The data asset operation optimization system for intelligent metadata access and inventory as described in claim 8, characterized in that: The intelligent access engine module adopts a plug-in design and supports dynamic updates of the template library and rule library; the metadata retrieval engine module includes a retrieval result preview unit, and the anomaly warning unit of the monitoring and operation optimization module has a bottleneck root cause analysis function.

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