Government metadata management method and system, electronic device, and readable storage medium

Abstract

The present disclosure provides a government metadata management method and system, electronic device, and readable storage medium, which belongs to the field of data management technology. The method includes: generating government metadata based on government data, determining the target data category of government data based on the government metadata; the target data category includes internal data and external data; determining the access rights of government data based on the target data category; the access rights include full access, partial access, and prohibited access; performing management operations on government data based on the access rights and government metadata; the management operations include data collection and data query. The government metadata management method and system, electronic device, and readable storage medium provided by the present disclosure can improve the management efficiency of government data.

Description

Technical Field

[0001] The present disclosure belongs to the field of data management technology, and more specifically, to a government metadata management method and system, an electronic device, and a readable storage medium. Background Art

[0002] With the widespread application of information technology in the field of government affairs, the amount of government data has exploded. As a key means to effectively manage and utilize government data, government metadata management is becoming increasingly important. In modern government management, government departments need to deal with a large amount of complex and diverse government data, including policy documents, statistical data, business process data, geographic information data, etc. These data come from different departments, systems and channels, and have different formats, quality and security requirements.

[0003] However, existing metadata management systems mainly focus on the Hadoop ecosystem and lack support for structured data storage and processing technologies. When the data structure changes, the change results cannot be automatically captured, resulting in inaccurate metadata information. Without an effective metadata management method, it will be difficult to achieve efficient and accurate data management, which will affect the scientific nature of government decision-making, the quality of public services, and the effectiveness of social governance. Summary of the invention

[0004] The purpose of the present disclosure is to provide a government metadata management method and system, an electronic device, and a readable storage medium to improve the management efficiency of government data.

[0005] A first aspect of the embodiments of the present disclosure provides a method for managing government metadata, including:

[0006] The government metadata is generated based on the government data, and the target data category of the government data is determined based on the government metadata. The target data category includes internal data and external data.

[0007] Determine the access rights to government data based on the target data category. Access rights include full access, partial access, and prohibited access.

[0008] Manage government data based on access rights and government metadata. Management operations include data collection and data query.

[0009] A second aspect of the embodiments of the present disclosure provides a government metadata management system, including:

[0010] The data classification module is used to generate government metadata based on government data and determine the target data category of the government data based on the government metadata. The target data category includes internal data and external data.

[0011] The access module is used to determine the access rights of government data based on the target data category. The access rights include full access, partial access and prohibited access.

[0012] The data management module is used to manage government data based on access rights and government metadata. Management operations include data collection and data query.

[0013] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned government metadata management method when executing the computer program.

[0014] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned government metadata management method are implemented.

[0015] The beneficial effects of the government metadata management method and system, electronic device, and readable storage medium provided by the embodiments of the present disclosure are: by generating government metadata and determining the target data category, internal data and external data can be clearly distinguished. This helps to improve the pertinence and accuracy of data management, and enables managers to better understand the source and nature of data.

[0016] By setting access rights, on the one hand, we can ensure that sensitive internal data is properly protected, prevent unauthorized access and leakage, and ensure the security of government information. On the other hand, by setting access rights reasonably according to the characteristics of different data, we can improve the availability and sharing of data while ensuring security. Some access rights can meet specific demand scenarios, while prohibiting access can eliminate unnecessary risks.

[0017] In terms of management operations, data collection and query based on access rights and government metadata can improve the efficiency and standardization of data management, ensure that data acquisition and use comply with regulations, and provide reliable data support for government decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, 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 only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flowchart of a government metadata management method provided by an embodiment of the present disclosure;

[0020] Figure 2 A structural block diagram of a government metadata management system provided by an embodiment of the present disclosure;

[0021] Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0023] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0024] Please refer to Figure 1 , Figure 1 A flowchart of a government metadata management method provided in one embodiment of the present disclosure, the method may include S101 to S103.

[0025] S101: Generate government metadata based on government data, and determine target data categories of the government data based on the government metadata. The target data categories include internal data and external data.

[0026] In this embodiment, government data includes various types of data generated, collected, stored and processed by government departments in the process of performing their functions, such as population census data, enterprise registration information, fiscal revenue and expenditure data, land resource utilization data, etc. Government metadata refers to the attribute information describing government data, including the source, format, creation time, update frequency, data owner, security level, etc. of the data. For example, for a population census data table, its government metadata may include data source as the National Bureau of Statistics, creation time as a specific year, update frequency as once every ten years, data format as a spreadsheet, security level as internal use, etc. Internal data refers to data generated, used and managed within government departments. For example, work flow records within government departments, internal meeting minutes, internal personnel information, etc. External data refers to data from outside government departments, such as data provided by enterprises, social organizations, the public, etc. For example, economic data reported by enterprises, survey data from social organizations, opinions and suggestions from the public, etc.

[0027] In this embodiment, government data is divided into internal data and external data because they differ in source, nature, and management requirements. Internal data is usually closely related to the daily work of government departments and requires strict security control and internal management. External data comes from a wide range of sources and needs to be screened, verified, and integrated to ensure its reliability and availability. Government metadata provides an important basis for data management. Metadata can be used to quickly determine the category, source, and security level of data, so as to reasonably allocate access rights. In the process of data collection and query, metadata can help users accurately find the required data and improve management efficiency.

[0028] For example, metadata of government documents may include: Take a document on urban construction planning issued by the government as an example: File name: "XX City 2024-2026 Urban Construction Master Plan". Document number: XX Zhengfa

[2024] No. XX. Issuing unit: XX Department of XX City. Issuing date: May 10, 2024. Keywords: urban construction, planning, development. File type: policy document. Confidentiality level: public. Number of pages: 50 pages.

[0029] Exemplarily, the metadata of the government database table class may include: Assume that there is a "resident basic information table" in a government population information database: Table name: resident basic information table. Table description: Stores the basic identity information, contact information, etc. of the city's residents. Field name and description: ID number: resident's unique identity identifier, string type. Name: resident's name, string type. Age: resident's age, integer type. Contact number: resident's contact number, string type. Data source: public security household registration system, community registration, etc. Update frequency: updated (collected) once a month. Data volume: 1 million records.

[0030] For example, in the field of tax management, government metadata can be generated based on various government data in the tax system. For example, for data such as corporate tax return records, metadata information is generated including data source as corporate declaration system, data format as a specific table, and update cycle as monthly. These metadata are used to determine that they are internal data because they are data generated and managed in the business system of the tax department. In terms of urban planning, the data on citizens' opinions on urban construction obtained from social survey institutions are determined as external data based on their metadata so that they can be managed separately from internal planning scheme data.

[0031] S102: Determine the access rights to the government data based on the target data category. The access rights include full access, partial access, and prohibited access.

[0032] In this embodiment, full access means having unlimited rights to view, use, modify and delete government data. For example, senior government leaders can have full access to key government data involving major decisions in order to fully understand the situation and make accurate decisions. Partial access means that only specific parts of government data can be viewed or limited operations can be performed. For example, staff of a certain department have only partial access to data of other departments. For example, when civil affairs department staff are handling social assistance work, they may only be able to view the result information of whether the enterprise has paid taxes normally for the tax data of the tax department, but cannot see detailed data such as the specific tax amount. Prohibited access means being explicitly restricted from accessing specific government data. For example, government data involving state secrets, such as military deployment, national security strategy and other information, are prohibited from access by ordinary government staff and the public.

[0033] In this embodiment, different access rights settings can ensure the security and confidentiality of government data. Full access rights are limited to a few key personnel to prevent data from being abused or leaked. Partial access rights can meet the business needs of most staff members while limiting their access to sensitive data. No access protects important information involving state secrets and personal privacy.

[0034] For example, in the public security household registration management system, internal staff, such as those responsible for household registration information entry and daily management, can be granted partial access rights. They can view and modify household registration information within a specific range, but cannot operate on some data involving high security levels. Senior managers may have full access rights and can conduct comprehensive inquiries and management adjustments on all types of data in the system. For external personnel, such as ordinary people, access to specific household registration details is generally prohibited, and they can only query some processed public information, such as the location of the household registration, through specific channels.

[0035] S103: Perform management operations on government data based on access rights and government metadata. Management operations include data collection and data query.

[0036] For example, in the field of medical insurance, government data can include insured person information, medical insurance reimbursement records, etc. For the staff of the medical insurance management department, different access rights can be granted according to their job responsibilities and security levels. For example, the staff responsible for data entry and daily maintenance can perform data collection operations. They can add new insured person information and reimbursement records, but they may only have partial access rights to some sensitive information such as the complete content of personal ID numbers to protect personal privacy. The personnel responsible for data analysis and decision-making can perform data query operations, and quickly find the required medical insurance data through the data description and classification information in the government metadata, and conduct statistical analysis and policy formulation. At the same time, for external institutions such as scientific research units, after strict approval, they can obtain access rights to some medical insurance data for medical research, but are prohibited from accessing sensitive information involving personal privacy.

[0037] For example, in the field of tax management, the internal data of the tax department includes detailed tax records of taxpayers, tax audit reports, etc. These data can be set with different access rights according to staff in different positions. For example, tax auditors may have partial access rights and can view the tax records and related financial data of specific taxpayers for the purpose of auditing. Ordinary office staff may only have the authority to query statistical data and cannot view the information of specific taxpayers. For external data, such as financial statements submitted by enterprises, they can be used as reference data after review, but the access rights are strictly restricted. At the same time, these data are managed through government metadata, and information such as the source, format and update time of the data is recorded to facilitate staff to query and analyze.

[0038] From the above, it can be concluded that government data can be better understood and managed based on government metadata. Metadata can provide a detailed description of the data, including the source, format, content and other information of the data, thereby providing a basis for subsequent data classification and authority determination. The division of government data into internal data and external data takes into account the different sources of data, and their security, sensitivity and scope of use should also be different. Determining different access rights can ensure the security and reasonable use of government data. The settings of full access, partial access and prohibited access can strictly control the degree of user access to data according to the importance and sensitivity of the data. Management operations based on access rights and government metadata can ensure the standardization and precision of data management and improve the efficiency and quality of data management.

[0039] The disclosed embodiments improve the security of government data. By clarifying data categories and access rights, it is possible to effectively prevent unauthorized access and data leakage, and protect state secrets, personal privacy, and sensitive information. For external data, reasonable permission control can also be performed according to its risk level to reduce security risks.

[0040] The disclosed embodiments improve the efficiency of government data management. With clear metadata and access rights, the required data can be quickly located during data collection and query, reducing unnecessary search time and waste of resources. At the same time, it is also convenient for data managers to uniformly manage and monitor data to ensure the accuracy and completeness of the data. In addition, the disclosed embodiments also help promote the reasonable sharing and use of government data. For internal data, data sharing between departments can be achieved within the appropriate scope of authority to improve the efficiency of collaborative work. For external data, it can be conditionally opened and shared according to actual needs to provide better services for the public and enterprises.

[0041] In one embodiment of the present disclosure, determining access rights to government data based on target data categories includes:

[0042] In response to the target data category being internal data, access rights to the government data are determined based on the user role.

[0043] In response to the target data category of the government data being external data, the access rights of the government data are determined based on the access type of the government data, which includes public data, restricted data, and non-public data.

[0044] In this embodiment, in the government affairs system, the responsibilities and scope of work undertaken by different staff members determine their different needs and operating permissions for government affairs data. Different staff members can be assigned different user roles. Public data includes government affairs data that can be disclosed to the public, and usually does not involve sensitive information such as state secrets, commercial secrets, and personal privacy. Restricted data includes data with certain restrictions on access, which requires specific identity authentication, authorization, or access in specific scenarios, or can only access part of the data. Non-public data includes strictly confidential data, which is prohibited from being disclosed to the public and unauthorized persons.

[0045] For example, in the environmental protection department, internal data such as environmental monitoring data. Environmental monitors can collect and partially query monitoring data to understand environmental conditions in a timely manner; department leaders can comprehensively view and analyze data and formulate environmental protection policies. For external data, air quality index and other data are public data, and the public can query and understand environmental quality at any time; the pollution emission data of enterprises can be used as restricted data for scientific research institutions to study environmental protection measures after the consent of the enterprise and the approval of the environmental protection department; and confidential data involving major national environmental protection projects are non-public data.

[0046] This embodiment effectively protects sensitive data, reduces the risk of data leakage, and improves data security through fine division of permissions. Reasonable disclosure of some data meets the public's demand for government information, improves the government's credibility, and enhances government transparency. On the premise of ensuring security, data can better support decision-making, research, and public services.

[0047] In one embodiment of the present disclosure, management operations are performed on government data based on access rights and government metadata, including:

[0048] Determine the data lineage relationship between government data.

[0049] Manage government data based on access rights, data lineage and government metadata.

[0050] In this embodiment, the data lineage relationship represents the generation and dependency relationship between government data. It can describe how a piece of data is derived from other data and which data depends on the data. This relationship can help understand the source, evolution process and impact range of the data.

[0051] For example, in government statistics, the comprehensive economic development index data of a region is calculated from multiple sub-indicator data, such as regional GDP, fiscal revenue, fixed asset investment, etc. These sub-indicator data are the upstream data of the comprehensive economic development index data, and there is a data lineage relationship between them and the comprehensive index data. The comprehensive economic development index data may be used to formulate policies, evaluate the level of regional development, etc. These decision-making and evaluation result data are the downstream data of the comprehensive index data, and there is also a data lineage relationship.

[0052] Exemplarily, in the specific implementation process, two modules, data discovery module and metadata management module, can be set. Among them, the data discovery module can include the creation of a metadata collector, and the metadata collector information includes database type (relational database, nosql database, etc.), driver information, service address, service port, user name, password, driver package upload, script, collection frequency, etc., and different information items are displayed according to different database types. If the database type is selected as "other", it is necessary to upload the driver package and edit the script online. The module obtains the table information of the database regularly according to the collection frequency, such as table name, field name, field type, field length, whether the field is required, field remarks, etc. If the database type is "other", the data discovery module will automatically call the thermal analysis engine and compile the execution script into an executable file. Then, according to the preset data collection frequency, the table information of the database is obtained regularly. The data discovery module converts the collected database table information into basic metadata information and stores it in the corresponding database.

[0053] The metadata collector can dynamically adjust the data collection frequency when it is running. After the most recent collection task is completed, the metadata collector can adjust the execution time of the next collection task based on the latest collection frequency.

[0054] The metadata management module can be divided into two sub-modules: metadata basic information management module and metadata lineage information management module. The metadata basic information management module can provide metadata basic information maintenance functions, for example, metadata basic information can include subject (database table field information), document, tag, field, note and other information.

[0055] The metadata lineage information management module can be used to enter and import metadata lineage information, as well as to view metadata lineage relationship maps. Users can download metadata lineage information import templates, which can include upstream metadata information identifiers and downstream metadata information identifiers.

[0056] Import the edited metadata lineage information import template into the database to provide metadata lineage map query function, and obtain all upstream metadata information of the metadata in a data set recursive manner through the metadata unique identifier. In the same recursive manner, obtain all downstream metadata information of the metadata and form a metadata information tree structure object. The front-end client can parse the tree structure object through the js script and display the tree structure on the front-end page.

[0057] Among them, the upstream metadata information identifier includes a mark used to identify and point to metadata that affects or serves as the source of specific metadata. For example, it can indicate which metadata is the "predecessor" or "upstream factor" of the metadata currently concerned. For example, there is a database table about citizens' social security payment records. Its metadata includes basic information such as "social security payment record subject", "document description is social security payment record storage table", "label is social security data", "field is social security", and "remarks are updated monthly". For this metadata, the upstream metadata information identifier can include "citizen identity information metadata identifier", because the citizen's identity information is the basis of the social security payment record, and without identity information, the object of social security payment cannot be determined; there can also be a "social security policy and regulations metadata identifier", because policies and regulations determine the rules and standards for social security payments, thereby affecting the content and format of social security payment records.

[0058] The downstream metadata information identifier includes an identifier for identifying and pointing to metadata that is affected by or depends on specific metadata. That is, it can indicate which metadata is the "derivative result" or "downstream product" of the metadata currently concerned. Exemplarily, continuing to take the metadata of the above-mentioned social security payment records as an example, the downstream metadata information identifier may include a "social security benefit accounting metadata identifier", because the accounting of social security benefits needs to be based on social security payment records, and the data of social security payment records directly affects the results of benefit accounting; there may also be a "social security data analysis report metadata identifier", because the data analysis report is generated based on data such as social security payment records, so the metadata of the report depends on the metadata of the social security payment records.

[0059] This embodiment allows users to clearly understand the ins and outs of government data by determining data lineage relationships, which helps to fully grasp the process of data generation, circulation and use, so as to better manage data and make decisions. Through data lineage relationships, the source of the data can also be traced. When data quality problems are found, the problem can be quickly located and appropriate measures can be taken to correct it. Combined with access rights and government metadata, relevant data can be uniformly managed according to data lineage relationships. For important upstream data, security protection and quality monitoring can be strengthened to ensure its accuracy and reliability, thereby ensuring the quality of downstream data.

[0060] In one embodiment of the present disclosure, generating government metadata based on government data includes:

[0061] Determine the type of government data based on the characteristic information of the government data.

[0062] Generate government metadata based on government data types.

[0063] In this embodiment, the government data types include basic information, business management, and decision support.

[0064] Generate government metadata based on government data types, including:

[0065] In response to the government affairs data type being a basic information type, first government affairs metadata is generated based on characteristic information of the government affairs data and a first metadata template.

[0066] In response to the government data type being a business management type, second government metadata is generated based on characteristic information of the government data and a second metadata template.

[0067] In response to the government affairs data type being a decision support type, third government affairs metadata is generated based on characteristic information of the government affairs data and a third metadata template.

[0068] In this embodiment, the characteristic information represents the key attributes or features that distinguish different types of government data. The characteristic information may include information on the content characteristics, source characteristics, and usage characteristics of government data. Basic information government data may include population information, geographic information, and economic information. Business management government data may include administrative approval, law enforcement supervision, and public services. Decision support government data may include statistical analysis reports, policy evaluation data, and public opinion monitoring data.

[0069] The first metadata template represents the metadata template corresponding to basic information government data, the second metadata template represents the metadata template corresponding to business management government data, and the third metadata template represents the metadata template corresponding to decision support government data.

[0070] For example, the business management metadata template for administrative approval government data may include: Data name: Administrative approval data. Data description: Information about the process, conditions, results, etc. of various approval items. Approval item name: [Specific approval item]. Approval process: [Detailed process step description]. Acceptance conditions: [List various acceptance requirements]. Processing time limit: [Specified number of working days]. List of required materials: [Specify required materials]. Approval result status: approved / disapproved / pending review, etc. Responsible department: [Specific name of approval department]. Data update mechanism: Real-time update as business is processed. Data storage format: Electronic documents, database records, etc.

[0071] This embodiment generates metadata through customized government data types, making government metadata more in line with actual needs and improving the management efficiency and utilization value of government data. This embodiment also provides multiple types of government metadata templates to facilitate users to quickly generate corresponding metadata according to different government data types, improve work efficiency and accuracy, and provide a more comprehensive, accurate and efficient solution for the management and utilization of government data.

[0072] In this embodiment, determining the type of government data based on the characteristic information of government data includes:

[0073] Determine a feature vector based on feature information of government affairs data;

[0074] Calculate multiple similarities between the feature vector and multiple standard feature vectors; the multiple standard feature vectors are feature vectors corresponding to multiple government data types.

[0075] Determine the government data type based on multiple similarities.

[0076] In this embodiment, the government data type is determined based on multiple similarities, including:

[0077] The government data type is determined to be the government data type corresponding to the standard feature vector with the highest similarity.

[0078] In this embodiment, a set of standard feature vectors can be determined for each type of government data (basic information, business management, and decision support). For example, the basic information class can include features such as data stability greater than a stability threshold (i.e., high data stability), data update frequency lower than an update frequency threshold (i.e., low update frequency), and a relatively fixed data source; the business management class can include features such as being closely related to a specific business process, data update frequency higher than an update frequency threshold (i.e., high update frequency), and involving collaboration among multiple departments; the decision support class can include features such as being based on large amounts of data analysis, the results being used to guide policy formulation, and having high requirements for data accuracy.

[0079] For each type of government data to be determined, the corresponding feature information is extracted according to its first feature information, and a feature vector is constructed. For example, if the first feature information of a government data indicates that its data source is a statistical department, the content is relatively stable, and the update cycle is relatively long, then a feature vector can be constructed.

[0080] Appropriate similarity calculation methods, such as cosine similarity, Euclidean distance, etc., can be used to calculate the similarity between the feature vector of the government data to be determined and the standard feature vector corresponding to each government data type. For example, when using cosine similarity calculation, the two feature vectors are regarded as vectors in space, and the similarity is determined by calculating the cosine value of the angle between them. The closer the cosine value is to 1, the more similar the two vectors are.

[0081] When the similarity between the government data to be determined and a certain type is the highest and exceeds a certain similarity threshold, the government data is determined to be of this type. For example, if the similarity between the government data to be determined and the basic information type is 0.8, the similarity between the government data to be determined and the business management type is 0.6, and the similarity between the government data to be determined and the decision support type is 0.5, and the threshold is set to 0.7, then the government data can be determined to be the basic information type.

[0082] This embodiment uses a method for generating government metadata based on government data, which significantly improves the efficiency of data management and utilization. This embodiment achieves accurate identification of data by automatically classifying government data types, providing strong support for subsequent data processing. This embodiment uses customized metadata templates for different types of data to ensure the integrity and accuracy of metadata, providing a high-quality data foundation for subsequent data analysis and mining. This embodiment is also flexible and scalable, and can be adjusted as the type and characteristics of government data change to meet the needs of different scenarios. In summary, this embodiment has significant beneficial effects in improving the efficiency of government data management, promoting data resource sharing, and promoting intelligent government decision-making.

[0083] In one embodiment of the present disclosure, a management operation is performed on government data based on government metadata, including:

[0084] The target data level of government data is determined based on government metadata. The target data level includes primary data and secondary data. The priority of primary data is higher than that of secondary data.

[0085] Determine the frequency of data collection based on the target data level.

[0086] The government affairs data is collected based on the data collection frequency to obtain the government affairs collection data.

[0087] The government affairs database is updated based on the government affairs collection data. The government affairs database stores multiple government affairs data tables, and one government affairs data table stores one type of government affairs data.

[0088] In this embodiment, determining the data collection frequency based on the target data level includes:

[0089] Determine the frequency of data collection for government data based on government metadata.

[0090] In response to the target data level of the government data being primary data, the data collection frequency of the secondary data is calculated based on the data collection frequency of the primary data.

[0091] In this embodiment, there is a correlation between the secondary data and the primary data. When the primary data is updated, the secondary data associated with it is also updated. The primary data may include government data such as policies and regulations, business processes, departmental functions, and technical environment, and the secondary data may include basic information government data. Among them, the technical environment may include the architecture of metadata management technology, database storage format, etc.

[0092] Exemplarily, an inheritance mapping table between primary data and secondary data may be established. A table may be established for each data affected / associated with primary data. When primary data is updated or the collection frequency of primary data changes, the secondary data in the inheritance mapping table corresponding to the primary data may be updated or the collection frequency may be changed.

[0093] For example, for primary data, due to its importance and high priority, a higher data collection frequency will be determined separately according to its characteristics to ensure the timeliness and accuracy of the data and meet the needs of government decision-making. For secondary data, its collection frequency is based on the collection frequency of primary data, and is calculated and updated on the basis of the initial collection frequency. Because secondary data is correlated with primary data, when primary data is updated, the related secondary data often also needs to be updated to maintain data consistency and integrity. When primary data is updated or the collection frequency changes, by querying the inheritance mapping table, the affected secondary data can be quickly determined, and corresponding update operations can be performed or the collection frequency can be adjusted to ensure that the data in the government database is always kept up to date, providing reliable data support for government management and decision-making.

[0094] Exemplarily, the data level, collection frequency, and table name or table ID of the inheritance mapping table are set in the government metadata corresponding to the government data. When the primary data is updated, the corresponding government metadata is automatically queried to obtain the inheritance mapping table information, and then the affected secondary data is found, and the secondary data is synchronously updated through data push, triggering update tasks, etc. When the collection frequency of the primary data changes, the inheritance mapping table is also queried to adjust the collection frequency of the corresponding secondary data. A scheduled task can be set to regularly check the changes in the primary data and process the secondary data accordingly. A monitoring mechanism can be established to monitor the update status of government data and the execution of the collection frequency in real time to ensure the effectiveness of data management.

[0095] In this embodiment, the data collection frequency of the secondary data is calculated based on the data collection frequency of the primary data, including:

[0096] The data collection frequency of the secondary data is calculated based on the data collection frequency and frequency update formula of the primary data.

[0097] The frequency update formula is:

[0098]

[0099] in, Indicates the collection frequency after the secondary data is updated. It indicates the initial collection frequency of secondary data, that is, the original collection frequency of secondary data when the impact of primary data changes is not considered. It can be preliminarily determined based on factors such as the type and importance of secondary data.

[0100] Indicates the collection frequency after the primary data is updated. For example, when the primary data changes due to factors such as its importance and timeliness, or is adjusted according to business needs, the newly determined collection frequency.

[0101] It indicates the initial collection frequency of primary data, that is, the regular collection frequency initially set for primary data, reflecting the frequency of collecting primary data when no update or adjustment is performed.

[0102] Represents the weight of the correlation coefficient of data sources, represents the weight of the business process correlation coefficient, represents the time synchronization correlation coefficient weight. At the same time, + + =1.

[0103] Indicates the proportion of secondary data that comes from primary data, It reflects the extent to which the secondary data is dependent on the primary data during the generation process. For example, if 70% of the data items in the secondary data are directly obtained from the primary data after some processing, then =0.7.

[0104] Indicates the data update trigger ratio (the value range is 0 to 1, if the primary data change must trigger the secondary data update, it is 1, if not triggered, it is 0). Indicates the probability of triggering the update of secondary data when primary data changes. It can be calculated based on historical government update data. For example, if the primary data A is updated n times, and m of them trigger the update of the secondary data B, then the trigger ratio is If primary data changes rarely trigger secondary data updates, then The value is relatively small, close to 0.

[0105] Indicates the ratio of the secondary data to the primary data business refinement. It reflects the degree of refinement of primary data by secondary data in the business process, such as the proportion of business explanation and supplement of primary data by secondary data. If secondary data can refine the business of primary data by 60%, then =0.6.

[0106] Indicates the frequency of business co-use, that is, the frequency of primary data and secondary data being used simultaneously in government business processes. If primary data and secondary data are always used together in many business scenarios, then The value of is 1.

[0107] Indicates the importance of the business (the value range is 0 to 1, determined according to the importance of the primary data and secondary data to the business when they are used simultaneously in the business scenario). That is, it is determined according to the importance of the primary data and secondary data to the business when they are used simultaneously in the business scenario. The value range is 0 to 1. When the simultaneous use of both is very important to the business, close to 1; if the importance is low, The value of is smaller.

[0108] t represents the average update time interval of the secondary data after the primary data is updated, that is, the average length of time from the update of the primary data to the corresponding update of the secondary data.

[0109] The maximum allowed update time interval is T. T is determined based on factors such as business requirements and system performance. Secondary data should be updated within this time range.

[0110] For example, in an urban traffic management system, real-time traffic flow data, major traffic accident information, etc. can be identified as primary data. These data are crucial for the transportation department to take timely diversion measures and formulate traffic policies, so a higher collection frequency can be set, such as collecting once every 15 minutes. Data such as the number of parking spaces and road maintenance progress in a certain area can be used as secondary data. These data have a certain auxiliary role in traffic management, but their importance is relatively low, and they can be set to be collected once every hour. In this way, the traffic management department can grasp the traffic conditions in a timely manner and make scientific decisions, while also making rational use of resources to ensure that the data in the government database remains valid at all times.

[0111] This embodiment simplifies the complexity of data management and improves work efficiency through automatic query and synchronous update mechanisms. At the same time, the frequency of secondary data collection is dynamically adjusted based on the frequency update formula, which not only ensures the timeliness of data, but also avoids waste of resources. The introduction of functions such as scheduled tasks and monitoring mechanisms further enhances the reliability and security of data management. This embodiment provides a strong technical guarantee for the effective management of government data and scientific decision-making.

[0112] Corresponding to the government metadata management method in the above embodiment, Figure 2 This is a structural block diagram of a government metadata management system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The government metadata management system 20 includes: a data classification module 21, an access module 22 and a data management module 23.

[0113] The data classification module 21 is used to generate government metadata based on government data, and determine the target data category of the government data based on the government metadata. The target data category includes internal data and external data.

[0114] The access module 22 is used to determine the access rights of government data based on the target data category. The access rights include full access, partial access and prohibited access.

[0115] The data management module 23 is used to manage government data based on access rights and government metadata. Management operations include data collection and data query.

[0116] In one embodiment of the present disclosure, the access module 22 is specifically configured to determine the access rights to the government data based on the user role in response to the target data category being internal data.

[0117] In response to the target data category of the government data being external data, the access rights of the government data are determined based on the access type of the government data, which includes public data, restricted data, and non-public data.

[0118] In one embodiment of the present disclosure, the data management module 23 is specifically used to determine the data blood relationship between government data.

[0119] Manage government data based on access rights, data lineage and government metadata.

[0120] In one embodiment of the present disclosure, the data classification module 21 is specifically used to determine the type of government data based on characteristic information of the government data.

[0121] Generate government metadata based on government data types.

[0122] In one embodiment of the present disclosure, the government data type includes basic information type, business management type and decision support type. The data classification module 21 is further used to generate first government metadata based on the characteristic information of the government data and the first metadata template in response to the government data type being the basic information type.

[0123] In response to the government data type being a business management type, second government metadata is generated based on characteristic information of the government data and a second metadata template.

[0124] In response to the government affairs data type being a decision support type, third government affairs metadata is generated based on characteristic information of the government affairs data and a third metadata template.

[0125] In one embodiment of the present disclosure, the data management module 23 is specifically used to determine the target data level of the government data based on the government metadata. The target data level includes primary data and secondary data. The priority of primary data is higher than that of secondary data.

[0126] Determine the frequency of data collection based on the target data level.

[0127] The government affairs data is collected based on the data collection frequency to obtain the government affairs collection data.

[0128] The government affairs database is updated based on the government affairs collection data. The government affairs database stores multiple government affairs data tables, and one government affairs data table stores one type of government affairs data.

[0129] In one embodiment of the present disclosure, the data management module 23 is further specifically used to determine the data collection frequency of government data based on government metadata.

[0130] The target data level in response to government affairs data is primary data.

[0131] The data collection frequency of the secondary data is calculated based on the data collection frequency of the primary data.

[0132] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0133] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0134] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0135] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0136] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the government metadata management method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.

[0137] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0138] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0139] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0143] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for managing government metadata, characterized in that: include: generating government metadata based on the government data, and determining a target data category of the government data based on the government metadata; The target data categories include internal data and external data; Determining access rights to the government data based on the target data category; The access rights include full access, partial access and prohibited access; The determining the access rights to the government data based on the target data category includes: In response to the target data category being internal data, determining access rights to the government data based on a user role; In response to the target data category of the government data being external data, determining the access rights of the government data based on the access type of the government data; the access type includes public data, restricted data, and non-public data; Performing management operations on the government data based on the access rights and the government metadata; the management operations include data collection and data query; Performing management operations on the government data based on the government metadata includes: Determine the target data level of the government data based on the government metadata; the target data level includes primary data and secondary data; the priority of the primary data is higher than the priority of the secondary data; determining a data collection frequency based on the target data level; Collecting the government affairs data based on the data collection frequency to obtain government affairs collection data; Based on the government affairs collection data, the government affairs database is updated; the government affairs database stores a plurality of government affairs data tables, and one government affairs data table stores one type of government affairs data; The step of determining the data collection frequency based on the target data level includes: Determining a data collection frequency of the government data based on the government metadata; In response to the target data level of the government data being primary data; Calculating the data collection frequency of the secondary data based on the data collection frequency of the primary data includes: Calculate the data collection frequency of the secondary data based on the data collection frequency and frequency update formula of the primary data; The frequency update formula is: in, Indicates the collection frequency after the secondary data is updated, Indicates the initial collection frequency of secondary data, that is, the original collection frequency of secondary data without considering the impact of primary data changes. Indicates the collection frequency after the first-level data is updated. Indicates the initial collection frequency of the first-level data, that is, the regular collection frequency of the first-level data initially set. Represents the weight of the correlation coefficient of data sources, represents the weight of the business process correlation coefficient, represents the time synchronization correlation coefficient weight, + + =1, Indicates the proportion of secondary data that comes from primary data, Indicates the data update trigger ratio, that is, when the primary data changes, the possibility of triggering the secondary data update. Indicates the ratio of the secondary data to the primary data business refinement. Indicates the frequency of business co-use, that is, the frequency of primary data and secondary data being used simultaneously in the government business process. Indicates business importance, which is determined based on the importance of primary data and secondary data to the business when used simultaneously in a business scenario. The value range is 0 to 1, t represents the average update time interval of the secondary data after the primary data is updated, and T represents the maximum allowed update time interval.

2. The government metadata management method according to claim 1, characterized in that: The performing management operations on the government data based on the access permission and the government metadata includes: Determine the data blood relationship between the government data; The government data is managed based on the access rights, the data lineage relationship and the government metadata.

3. The government metadata management method according to claim 1, characterized in that: Generating government metadata based on government data includes: Determining the type of government affairs data based on the characteristic information of the government affairs data; Generate government metadata based on the government data type.

4. The government metadata management method according to claim 3, characterized in that: The government data types include basic information, business management and decision support; The generating government metadata based on the government data type includes: In response to the government data type being a basic information type, generating first government metadata based on characteristic information of the government data and a first metadata template; In response to the government data type being a business management type, generating second government metadata based on the characteristic information of the government data and a second metadata template; In response to the government affairs data type being a decision support type, third government affairs metadata is generated based on characteristic information of the government affairs data and a third metadata template.

5. A government metadata management system, characterized in that: include: A data classification module, used to generate government metadata based on government data, and determine a target data category of the government data based on the government metadata; The target data categories include internal data and external data; An access module, used to determine the access rights of the government data based on the target data category; the access rights include full access, partial access and prohibited access; An access module, specifically configured to determine access rights to the government data based on a user role in response to the target data category being internal data; In response to the target data category of the government data being external data, determining access rights to the government data based on an access type of the government data; The access type includes public data, restricted data and non-public data; A data management module, used to perform management operations on the government data based on the access rights and the government metadata; the management operations include data collection and data query; The data management module is specifically used to determine the target data level of the government data based on the government metadata; the target data level includes primary data and secondary data; the priority of the primary data is higher than the priority of the secondary data; determining a data collection frequency based on the target data level; Collecting the government affairs data based on the data collection frequency to obtain government affairs collection data; Based on the government affairs collection data, the government affairs database is updated; the government affairs database stores a plurality of government affairs data tables, and one government affairs data table stores one type of government affairs data; The data management module is further specifically used to determine the data collection frequency of the government data based on the government metadata; In response to the target data level of the government data being primary data; Calculating the data collection frequency of the secondary data based on the data collection frequency of the primary data includes: Calculate the data collection frequency of the secondary data based on the data collection frequency and frequency update formula of the primary data; The frequency update formula is: in, Indicates the collection frequency after the secondary data is updated, Indicates the initial collection frequency of secondary data, that is, the original collection frequency of secondary data without considering the impact of primary data changes. Indicates the collection frequency after the first-level data is updated. Indicates the initial collection frequency of the first-level data, that is, the regular collection frequency of the first-level data initially set. Represents the weight of the correlation coefficient of data sources, represents the weight of the business process correlation coefficient, represents the time synchronization correlation coefficient weight, + + =1, Indicates the proportion of secondary data that comes from primary data, Indicates the data update trigger ratio, that is, when the primary data changes, the possibility of triggering the secondary data update. Indicates the ratio of the secondary data to the primary data business refinement. Indicates the frequency of business co-use, that is, the frequency of primary data and secondary data being used simultaneously in the government business process. Indicates business importance, which is determined based on the importance of primary data and secondary data to the business when used simultaneously in a business scenario. The value range is 0 to 1, t represents the average update time interval of the secondary data after the primary data is updated, and T represents the maximum allowed update time interval.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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