Metadata statistical analysis method and system, electronic equipment and storage medium

Abstract

The invention provides a metadata statistical analysis method and system based on a data tag, electronic equipment and a storage medium. The method comprises the following steps: configuring a metadata tag rule and storing the metadata tag rule in a rule configuration database; registering metadata according to the design scheme of the metadata meta-model, and collecting the metadata of the current system through a metadata collection system; comparing the metadata with a configured label rule, and generating a registration metadata label and an acquisition metadata label; a comparison function of the metadata management system is utilized to finely compare the two types of labels according to a metadata table and a field so as to judge whether the two types of labels are synchronous or not, and whether the use of the metadata in the current system meets a design standard or not is evaluated accordingly; and based on the comparison information, data sharing and statistical analysis functions of the metadata are realized. The real-time and efficient data analysis requirement can be met, and the problem that data sharing and interoperability between different systems are poor is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of metadata management, and in particular relates to a metadata statistical analysis method, system, electronic device and storage medium based on data tags. Background Art

[0002] In today's era of rapid information development, data has become an important resource for promoting social progress and corporate development. Especially in the field of big data analysis, data collection, organization, analysis and application have become an indispensable part of all walks of life. However, in the statistical analysis of metadata (data that describes data), existing technologies still face many challenges.

[0003] From a general perspective, most of the existing metadata statistical analysis methods are based on traditional data warehouse and database technologies, which are incapable of processing massive, multi-dimensional, and dynamic data. At the same time, the existing technologies also have significant deficiencies in the generation and analysis of metadata tags, which are mainly reflected in the following problems:

[0004] 1. The data label generation method is backward. Currently, most systems still use manual methods to generate data labels. This method is not only inefficient but also prone to errors. It cannot meet the needs of quickly and accurately generating data labels in a big data environment.

[0005] 2. Data analysis methods are limited: Existing systems often use fixed data analysis methods, which makes it difficult to adapt to the data analysis needs of different fields and industries. In addition, these methods often have poor results when processing complex and dynamic data, and it is difficult to reveal the deep laws and connections behind the data.

[0006] 3. Limited scope of data application: Due to the limitations of existing systems in data label generation and data analysis methods, the scope of data analysis results is limited. Many valuable data analysis results cannot be fully utilized, resulting in a waste of data resources.

[0007] In response to the above problems, domestic and foreign peers have conducted a lot of research and practice. Some advanced technical solutions, such as automatic label generation algorithms based on machine learning and data association analysis methods based on graph databases, etc. However, these technical solutions still have some problems in practical applications: machine learning algorithms have high requirements for data quality and annotation quality, and it is difficult to adapt to the diversity and complexity of data in practical applications; graph databases have limited performance when processing massive data, and it is difficult to meet the needs of real-time and efficient data analysis; existing technical solutions often lack unified standards and specifications, resulting in poor data sharing and interoperability between different systems. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention proposes a metadata statistical analysis method, system, electronic device and storage medium based on data tags. The method advances the implementation of data tags to the metadata registration and collection stage; at the same time, it provides a comparison between metadata registration and actual application, realizes the monitoring and analysis of the entire metadata life cycle, and realizes data sharing. The specific technical solutions of the present invention are as follows:

[0009] In one aspect of an embodiment of the present invention, a metadata statistical analysis method based on data tags is provided to meet the needs of real-time and efficient data analysis, realize monitoring and analysis of the entire metadata life cycle, and solve the problem of poor data sharing and interoperability between different systems; the method specifically comprises the following steps:

[0010] Step 1: Configure metadata tag rules and store them in the rule configuration database;

[0011] Furthermore, the configuration of metadata tag rules also includes the following steps:

[0012] Step 101: Understand the purpose and objectives of metadata tags and understand the functions, attributes, and syntax of each tag.

[0013] Step 102: Based on the determined metadata tag usage and objectives, metadata tag rules, types, and attributes are defined;

[0014] Step 103: Create a metadata tag template according to the defined metadata tag rules, types and attributes;

[0015] Step 104: Integrate the created metadata label template into the metadata management system, and configure data binding in the metadata management system, and finally obtain the configured metadata label rule;

[0016] Step 105: Test and verify the configured metadata tag rules. After the test and verification pass, deploy and put them online, that is, store them in the rule configuration database.

[0017] Step 2: Register metadata based on the design scheme in the metadata metamodel, collect metadata of the current system based on the metadata collection system, compare the registered metadata and the collected metadata with the configured metadata label rules respectively, and generate corresponding registered metadata labels and collected metadata labels;

[0018] Furthermore, in step 2, the following steps are specifically included:

[0019] Step 201: Fill in the metadata according to the attribute content set in the metadata metamodel, complete the metadata registration, and store the corresponding registration information;

[0020] Step 202: The metadata collection system collects metadata of the current system according to the link information configured in the current system, and stores the collected metadata;

[0021] Step 203: Combine the registered metadata and the collected metadata, and use the metadata tag rules in the rule configuration database to perform comparisons respectively, generate registered metadata data tags and collected metadata tags, and store corresponding tags.

[0022] Step 3: Use the metadata comparison function in the metadata management system to compare the registered metadata tags with the collected metadata tags according to the same metadata tables and fields, and determine whether the registered metadata tags are synchronized with the collected metadata tags, and then determine whether the use of metadata in the current system meets the design standards;

[0023] Step 4: Based on the comparison information between the collected metadata tags and the registered metadata tags, metadata data sharing and statistical analysis are realized.

[0024] Furthermore, the metadata data sharing includes: providing a visual data analysis panel based on metadata tag information to help users understand data usage, sharing status and access frequency; ensuring that data can be accurately classified and effectively shared by tagging metadata; and dynamically managing metadata based on sensitivity and access frequency;

[0025] Furthermore, the statistical analysis of metadata includes: statistical analysis of metadata creation time, statistical analysis of metadata update time, statistical analysis of metadata access time, statistical analysis of metadata effective time range, statistical analysis of metadata time granularity, metadata time trend analysis, metadata time synchronization analysis, statistical analysis of metadata total amount, number of metadata attributes, metadata size (storage space), metadata growth rate, metadata redundancy, metadata coverage rate and metadata utilization rate.

[0026] Another aspect of the present invention provides a metadata statistical analysis system based on data tags, the system comprising:

[0027] A rule configuration module is used to configure metadata tag rules and store the configured metadata tag rules in a rule configuration database;

[0028] The metadata registration and collection module is used to register metadata based on the design scheme in the metadata metamodel and collect metadata of the current system using the metadata collection system;

[0029] The tag generation module is used to compare the registered metadata with the configured metadata tag rules to generate the corresponding registered metadata tags, and compare the collected metadata with the configured metadata tag rules to generate the corresponding collected metadata tags;

[0030] The label comparison and synchronization judgment module is used to compare the registered metadata labels with the collected metadata labels by using the existing metadata comparison function in the metadata management system. According to the same metadata tables and fields, the comparison is performed to determine whether the two are synchronized. Based on the comparison results, it is determined whether the use of metadata in the current system meets the design standards.

[0031] The data sharing and statistical analysis module is used to realize the data sharing and statistical analysis of metadata based on the comparison information between the collected metadata tags and the registered metadata tags.

[0032] According to another aspect of an embodiment of the present invention, based on the same inventive concept as the above-disclosed content, there is provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes all steps of a metadata statistical analysis method based on data tags as described above.

[0033] According to another aspect of the embodiments of the present invention, a computer storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it is used to implement all the steps of the metadata statistical analysis method based on data tags as described above.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] The present invention uses data tags as a bridge to put metadata management and application analysis in front; by comparing the registered metadata in the design stage and the collected metadata in the application stage, it can more intuitively understand the current status of metadata and the implementation of relevant designs and standards, and realize real-time, efficient and comprehensive data analysis, sharing and statistical analysis according to the comparison situation; at the same time, it provides a comprehensive indicator analysis application based on the metadata time dimension and data volume dimension, provides more references for the development trend of metadata and the design of new metadata in the future, and also provides real-time data support for the management of existing metadata.

[0036] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a flowchart of a metadata statistical analysis method.

[0039] Figure 2 The diagram is a flowchart of a metadata tag rule configuration method.

[0040] Figure 3 The invention is a flowchart of automatic label generation of a metadata label rule configuration method.

[0041] Figure 4 The present invention is a tag synchronization flowchart of a metadata tag rule configuration method.

[0042] Figure 5 It is a metadata statistical analysis flow chart of a metadata label rule configuration method. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In one embodiment, a metadata statistical analysis method is provided. Figure 1 , Figure 1 The present invention is a flowchart of a metadata statistical analysis method, wherein the method comprises the following steps:

[0045] Step S1: configure metadata tag rules and store them in a rule configuration database;

[0046] In this embodiment, a metadata tag rule configuration method is provided. Figure 2 , Figure 2 The present invention is a flowchart of a method for configuring metadata tag rules, the method comprising:

[0047] Step S101: clarify the purpose and purpose of metadata tags, and understand the function, attributes and syntax of each tag;

[0048] Step S102: Based on the determined metadata tag usage and objectives, metadata tag rules, types and attributes are defined;

[0049] Furthermore, the metadata tag rules are defined, including the usage principles, naming rules and format requirements of tags; the metadata tag types and attributes are defined, including title, description, keywords, author and date.

[0050] Step S103: creating a metadata tag template according to the defined metadata tag rules, types and attributes;

[0051] Furthermore, the metadata tag template is created as follows:

[0052] First, according to the defined metadata tag rules, the basic structure of the metadata tag template is defined;

[0053] Furthermore, the basic structure of the metadata tag template includes page layout, area division, and content to be filled in each area.

[0054] Then, choose a suitable development template engine to write the template code;

[0055] Furthermore, the development template engine is thymeleaf.

[0056] Finally, according to the basic structure of the defined metadata tag template, the template code written by the developed template engine is combined with it to generate the final HTML page, namely the metadata tag template.

[0057] Step S104: Integrate the created metadata label template into the metadata management system, and configure data binding in the metadata management system, and finally obtain the configured metadata label rules;

[0058] Furthermore, the configuration data binding is to establish a valid relationship between the configuration corresponding tags and metadata, so as to ensure that the tags can be correctly applied to the corresponding metadata.

[0059] Step S105: Test and verify the configured metadata tag rules. After the test and verification pass, deploy and put them online, that is, store them in the rule configuration database.

[0060] Furthermore, the test verifies the configured metadata tag rules, mainly through two methods: rule consistency check verification and tag function verification, where:

[0061] The rule consistency check and verification is to verify the label naming specifications and classification specifications; to check whether there are spaces, special characters, etc. in the naming by writing scripts to ensure that the naming uses a unified format; to manually check whether the status label contains only appropriate values ​​such as "pending review" and "approved";

[0062] The tag function verification is to manually add tags to different data items to ensure that the tag functions normally.

[0063] Step S2: registering metadata based on the design solution in the metadata metamodel, collecting metadata of the current system based on the metadata collection system, comparing the registered metadata and the collected metadata with the configured metadata label rules respectively, and generating corresponding registered metadata labels and collected metadata labels;

[0064] Furthermore, the metadata metamodel is a model about metadata, an abstract description of metadata, used to define the organization and structure of metadata, and to facilitate understanding of how to design, modify and operate metadata. The metadata metamodel consists of elements such as classes, attributes, relationships, constraints, inheritance and polymorphism; the attributes include tables, columns, primary keys, foreign keys, indexes, creation dates and modification dates, etc.

[0065] Furthermore, the design scheme in the metadata metamodel refers to the scheme information such as the specific format, definition and constraint of data carriers such as database tables, message information and files involved in the process of designing and developing the metadata metamodel;

[0066] Furthermore, before collecting metadata of the current system, the metadata collection system needs to configure the collection data according to the current system to improve collection efficiency; the configuration of the collection data includes configuring the system information, database information and database link information of the current system.

[0067] In this example, see Figure 3 , Figure 3 The present invention is a flowchart of automatic tag generation of a metadata tag rule configuration method, wherein the automatic tag generation specifically includes:

[0068] Step S201: Fill in the metadata according to the attribute content set in the metadata metamodel, complete the metadata registration, and store the corresponding registration information;

[0069] Step S202: the metadata collection system configures link information according to the current system, collects metadata of the current system according to the link information, and stores the collected metadata;

[0070] Step S203: combining the registered metadata and the collected metadata, using the metadata tag rules in the rule configuration database to perform comparisons respectively, generating registered metadata data tags and collected metadata tags, and storing corresponding tags.

[0071] Step S3: using the existing metadata comparison function in the metadata management system, the registered metadata tags and the collected metadata tags are compared according to the same metadata tables and fields, to determine whether the registered metadata tags are synchronized with the collected metadata tags, and further determine whether the use of metadata in the current system meets the design standards;

[0072] In this example, see Figure 4 , Figure 4 The present invention is a tag synchronization flow chart of a metadata tag rule configuration method, wherein the tag synchronization is based on tag comparison between registered metadata and collected metadata; specifically:

[0073] Compare the collected metadata tags and registered metadata tags according to the same metadata table and fields. If the comparison results match, it means that the comparison is successful and the use of metadata in the current system meets the design standards.

[0074] If the comparison results do not match, it means that the use of metadata in the current system does not meet the design standards. At this time, the data tags of the registered metadata are synchronized with the data tags of the collected metadata, ultimately ensuring the unity of metadata design and implementation.

[0075] Step S4: Based on the comparison information between the collected metadata tags and the registered metadata tags, data sharing and statistical analysis of the metadata are realized.

[0076] Furthermore, the metadata data sharing includes: providing a visual data analysis panel based on metadata tag information to help users understand data usage, sharing status and access frequency; ensuring that data can be accurately classified and effectively shared by tagging metadata; and dynamically managing metadata according to its sensitivity and access frequency.

[0077] In this example, see Figure 5 , Figure 5The invention discloses a metadata statistical analysis flow chart of a metadata tag rule configuration method; the metadata statistical analysis is a statistical analysis based on the metadata time dimension, specifically including: metadata creation time statistical analysis, metadata update time statistical analysis, metadata access time statistical analysis, metadata effective time range statistical analysis, metadata time granularity statistical analysis, metadata time trend analysis, metadata time synchronization analysis, metadata total statistical analysis, number of metadata attributes, metadata size (storage space), metadata growth rate, metadata redundancy, metadata coverage rate and metadata usage rate, wherein:

[0078] The metadata creation time statistical analysis refers to the time point when the metadata entry is first created in the current system; when the metadata is created, the system automatically records the current timestamp as the creation time, obtains the metadata attribute in the collected metadata, and counts the dimensions of table, schema, data source, and current system respectively, outputs the name of each dimension, the creation time of each metadata, and arranges them in descending order according to the creation time;

[0079] The metadata update time statistical analysis refers to the time point when the metadata entry was last modified or updated; when the metadata is modified or updated, the system automatically updates the timestamp attribute of the metadata, obtains the attribute in the collected metadata, and counts the dimensions of table, schema, data source, and current system respectively, outputs the name of each dimension, the update time of each metadata, and arranges them in descending order according to the update time;

[0080] The metadata access time statistical analysis refers to the time point when the metadata entry is queried or accessed. Each time the metadata is accessed, the current system records the current timestamp as the access time. The timestamp is recorded in the metadata access record attribute. The statistics are respectively counted with table, schema, data source, and current system as dimensions. The names of each dimension and the access time of each metadata are output and arranged in descending order according to the access time. The access frequency and mode of the metadata can be understood.

[0081] The metadata effective time range statistical analysis refers to the start and end time of the metadata entry that is considered effective in the current system. When creating metadata, the start and end time of the effective time range are set according to business needs; these time values ​​are stored in the attributes of the collected metadata. When it is necessary to determine whether the metadata is valid, the current time can be compared with the effective time range;

[0082] The metadata time granularity statistical analysis refers to the accuracy or detail of the metadata time-related information recorded or processed by the current system. Determine the time granularity requirements (for example, seconds, minutes, hours, days, etc.) according to business needs; when designing and implementing the system, ensure that the storage and processing of timestamps meet the required time granularity; by querying and analyzing the timestamp attributes of the collected metadata, you can understand the time granularity settings of the current system;

[0083] The metadata time trend analysis refers to identifying and understanding the patterns or trends of metadata changes over time by analyzing the time-related attributes of metadata (such as creation time, update time, access time, etc.); taking a single metadata as the basic unit, the creation time, update time, and access time of the metadata are counted respectively, and the time trend of the metadata is obtained, such as growth, decrease, periodic change, etc.;

[0084] The metadata time synchronization analysis refers to the accuracy and consistency of metadata time-related information between different metadata entries in the current system or between different systems. Regularly check whether the time-related attributes of metadata are accurate and consistent; if the time information is inconsistent or wrong, correct and synchronize it. In a distributed system, use a time synchronization protocol (such as NTP) to ensure time consistency between different nodes. Ensure the accuracy of metadata time synchronization through regular data quality checks and verifications;

[0085] The metadata total amount statistical analysis refers to traversing all metadata warehouses or databases, counting the metadata items in each warehouse or database; adding up all the counting results to obtain the metadata total amount;

[0086] The number of metadata attributes refers to the sum of the attributes in a single metadata record or the average of the attributes in metadata records of a specific type. The metadata records in the metadata repository are traversed, and the attributes in each record are counted. If necessary, the average number of attributes in metadata records of a specific type can be calculated;

[0087] The metadata size (storage space) refers to the total storage space required to store metadata; all files or records in the metadata warehouse are traversed, the size of each file or record is obtained (usually in bytes), and the sizes of all files or records are added together to obtain the total storage space;

[0088] The metadata growth rate refers to the speed at which metadata grows over time. The total amount of metadata at the initial time point is recorded. After a period of time (such as monthly or quarterly), the total amount of metadata is recorded again to calculate the metadata growth between the two time points; the growth rate is calculated based on the time interval (such as daily or monthly growth rate);

[0089] The metadata redundancy refers to the degree of repeated or redundant data in the metadata warehouse. Define the standard of redundant metadata (such as identical records, records with extremely high similarity, etc.); use algorithms or tools to search for redundant data in the metadata warehouse, count the redundant data found, and calculate the proportion of redundant data to the total metadata;

[0090] The metadata coverage rate refers to the proportion of data assets or business areas covered in the metadata warehouse. Define the scope of data assets or business areas that need to be covered by metadata, traverse all metadata records in the metadata warehouse, check whether each record belongs to the defined coverage scope, and calculate the ratio of covered data assets or business areas to the total coverage scope;

[0091] The metadata usage rate refers to the frequency or proportion of metadata being actually used (such as being queried, referenced, analyzed, etc.); monitor the access logs or usage data of the metadata warehouse, count the number of metadata records accessed or used within a period of time, and calculate the proportion of used metadata records to the total amount of metadata; if possible, you can also analyze the usage of different metadata types.

[0092] In one embodiment, a metadata statistical analysis system based on data tags is provided, the system comprising a rule configuration module, a metadata registration and collection module, a tag generation module, a tag comparison and synchronization judgment module, and a data sharing and statistical analysis module; wherein:

[0093] The rule configuration module is used to configure metadata tag rules and store the configured metadata tag rules in a rule configuration database;

[0094] The metadata registration and collection module is used to register metadata based on the design scheme in the metadata metamodel, and collect metadata of the current system using the metadata collection system;

[0095] The tag generation module is used to compare the registered metadata with the configured metadata tag rules to generate corresponding registered metadata tags, and compare the collected metadata with the configured metadata tag rules to generate corresponding collected metadata tags;

[0096] The tag comparison and synchronization judgment module is used to compare the registered metadata tags with the collected metadata tags by using the existing metadata comparison function in the metadata management system, and to compare them respectively according to the same metadata tables and fields to determine whether the two are synchronized, and to determine whether the use of metadata in the current system meets the design standards based on the comparison results;

[0097] The data sharing and statistical analysis module is used to realize data sharing and statistical analysis of metadata based on the comparison information between the collected metadata tags and the registered metadata tags.

[0098] In one embodiment, based on the same inventive concept as the above disclosed content, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes all steps of a metadata statistical analysis method based on data tags as described above.

[0099] In one embodiment, a computer storage medium is further provided, on which a computer program is stored. When the computer program is executed by the processor, it is used to implement all steps of the metadata statistical analysis method based on data tags as described above.

[0100] It should be noted that the "current system" mentioned in the present invention refers to the system whose metadata is collected.

[0101] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A metadata statistical analysis method based on data tags, characterized by: Configure metadata tag rules and store them in the rule configuration database; Register metadata based on the design scheme in the metadata metamodel, collect metadata of the current system based on the metadata collection system, compare the registered metadata and the collected metadata with the configured metadata label rules respectively, and generate corresponding registered metadata labels and collected metadata labels; The registered metadata tags and the collected metadata tags are compared using the existing metadata comparison function in the metadata management system according to the same metadata tables and fields to determine whether the registered metadata tags and the collected metadata tags are synchronized, and then determine whether the use of metadata in the current system meets the design standards; Based on the comparison information between the collected metadata tags and the registered metadata tags, metadata data sharing and statistical analysis are achieved.

2. The metadata statistical analysis method based on data tags according to claim 1, characterized in that: The configuration metadata tag rules are stored in the rule configuration database, and further include: Clarify the purpose and objectives of metadata tags, and understand the functions, attributes, and syntax of each tag; Define metadata tagging rules, types, and attributes based on the identified metadata tagging purposes and goals; Create metadata tag templates based on defined metadata tag rules, types, and attributes; Integrate the created metadata label template into the metadata management system, and configure data binding in the metadata management system to finally obtain the configured metadata label rules; Test and verify the configured metadata tag rules. After the test and verification pass, deploy them online and store them in the rule configuration database.

3. The metadata statistical analysis method based on data tags according to claim 2, characterized in that: The metadata tag rules described in the definition include the usage principles, naming rules and format requirements of tags; the metadata tag types and attributes described in the definition include title, description, keywords, author and date.

4. The metadata statistical analysis method based on data tags according to claim 2, characterized in that: The step of creating a metadata tag template further includes: Define the basic structure of the metadata label template according to the defined metadata label rules; Choose a suitable development template engine to write template code; According to the basic structure of the defined metadata tag template, the template code written by the developed template engine is combined with it to generate the metadata tag template.

5. The metadata statistical analysis method based on data tags according to claim 4, characterized in that: The basic structure of the metadata tag template includes page layout, area division and content to be filled in each area; The development template engine is thymeleaf.

6. The metadata statistical analysis method based on data tags according to claim 2, characterized in that: The test verifies the configured metadata tag rules, mainly through rule consistency verification and tag function verification, where: The rule consistency check and verification is to verify the tag naming specifications and classification specifications, and to check whether there are spaces and special characters in the naming by writing scripts to ensure that the naming uses a unified format; The tag function verification is to manually add tags to different data items to ensure that the tag functions normally.

7. The metadata statistical analysis method based on data tags according to claim 1, characterized in that: The generating of corresponding registration metadata tags and collection metadata tags also includes: Fill in the attributes set in the metadata metamodel, complete the metadata registration, and store the corresponding registration information; The metadata collection system configures link information according to the current system, collects metadata of the current system according to the link information, and stores the collected metadata; The registered metadata and the collected metadata are combined and compared respectively using the metadata label rules in the rule configuration database to generate registered metadata data labels and collected metadata labels, and the corresponding labels are stored.

8. The metadata statistical analysis method based on data tags according to claim 1, characterized in that: The determining whether the registered metadata tag is synchronized with the collected metadata tag, and then determining whether the use of metadata in the current system complies with the design standard, further includes: Compare the collected metadata tags and registered metadata tags according to the same metadata table and fields. If the comparison results match, it means that the comparison is successful and the use of metadata in the current system meets the design standards. If the comparison results do not match, it means that the use of metadata in the current system does not meet the design standards. At this time, the data tags of the registered metadata are synchronized with the data tags of the collected metadata, ultimately ensuring the unity of metadata design and implementation.

9. The metadata statistical analysis method based on data tags according to claim 1, characterized in that: The data sharing of metadata is realized, including: providing a visual data analysis panel based on metadata tag information to help users understand data usage, sharing status and access frequency; ensuring that data can be accurately classified and effectively shared by tagging metadata; and dynamically managing metadata according to its sensitivity and access frequency; The implementation of metadata statistical analysis is a statistical analysis based on the metadata time dimension, specifically including: metadata creation time statistical analysis, metadata update time statistical analysis, metadata access time statistical analysis, metadata effective time range statistical analysis, metadata time granularity statistical analysis, metadata time trend analysis, metadata time synchronization analysis, metadata total amount statistical analysis, number of metadata attributes, metadata size, metadata growth rate, metadata redundancy, metadata coverage rate and metadata utilization rate.

10. A metadata statistical analysis system based on data tags, characterized by: The system includes a rule configuration module, a metadata registration and collection module, a label generation module, a label comparison and synchronization judgment module, and a data sharing and statistical analysis module; wherein: The rule configuration module is used to configure metadata tag rules and store the configured metadata tag rules in a rule configuration database; The metadata registration and collection module is used to register metadata based on the design scheme in the metadata metamodel, and collect metadata of the current system using the metadata collection system; The tag generation module is used to compare the registered metadata with the configured metadata tag rules to generate corresponding registered metadata tags, and compare the collected metadata with the configured metadata tag rules to generate corresponding collected metadata tags; The tag comparison and synchronization judgment module is used to compare the registered metadata tags with the collected metadata tags by using the existing metadata comparison function in the metadata management system, and to compare them respectively according to the same metadata tables and fields to determine whether the two are synchronized, and to determine whether the use of metadata in the current system meets the design standards based on the comparison results; The data sharing and statistical analysis module is used to realize data sharing and statistical analysis of metadata based on comparison information between collected metadata tags and registered metadata tags.

11. An electronic device, comprising a memory and a processor, characterized in that: A computer program is stored in the memory. When the computer program is executed by the processor, the processor executes a metadata statistical analysis method based on data tags as described in any one of claims 1 to 9.

12. A computer storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, is used to implement a metadata statistical analysis method based on data tags as described in any one of claims 1 to 9.

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