Data issuing and receiving method and system based on maturity

Abstract

The invention belongs to the technical field of data management and processing, and discloses a maturity-based data issuing and receiving method and system. The method comprises the following steps: setting a plurality of evaluation dimensions including accuracy, integrity, consistency, timeliness and reliability of data, setting a weight and a specific evaluation index for each evaluation dimension, and performing data maturity evaluation on the data according to each evaluation dimension by adopting a mode of combining a machine learning algorithm and manual auditing; making a data issuing strategy according to the evaluated maturity level; and making a data receiving strategy. According to the method provided by the invention, the data utilization efficiency is improved, the receiver is ensured to obtain the data with enough quality and value at proper time, and the resource waste is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of data management and processing, and in particular to a maturity-based data distribution and reception method and system. Background Art

[0002] In today's digital age, data has become an important asset for enterprises and organizations. However, the value and availability of data often depend on its maturity. Traditional data management methods usually lack effective assessment and targeted processing of data maturity, resulting in inefficiency, unstable quality, and insufficient security in the process of data issuance and receipt. Summary of the invention

[0003] The present invention aims to provide a data distribution and reception method and system based on maturity, which can realize efficient, accurate and safe data circulation through accurate evaluation and intelligent processing of data maturity.

[0004] The technical solution of the present invention is as follows: A method for issuing and receiving data based on maturity, comprising the following steps:

[0005] Step 1: Data maturity assessment:

[0006] Step 1.1: Set multiple evaluation dimensions, including data accuracy, completeness, consistency, timeliness and reliability;

[0007] Step 1.2: Set weights and specific evaluation indicators for each evaluation dimension;

[0008] Step 1.3: Use a combination of machine learning algorithms and manual review to assess the maturity of the data based on each assessment dimension, and divide the maturity into primary, intermediate, advanced, and special levels;

[0009] Step 2: Develop a data release strategy based on the assessed maturity level:

[0010] Data at the primary maturity level is only released to specific internal testers or development teams for preliminary verification and improvement of the data;

[0011] Intermediate maturity data is distributed to a limited number of people in relevant business departments for small-scale trials and feedback;

[0012] Advanced maturity data is widely distributed within an enterprise or organization to support daily business operations;

[0013] Data of the extra-high maturity level may be considered for external sharing or sales on the basis of meeting internal needs.

[0014] The accuracy is weighted at 60%, the integrity is weighted at 10%, the consistency is weighted at 10%, the timeliness is weighted at 5%, and the reliability is weighted at 15%.

[0015] Furthermore, it also includes a data receiving strategy; the data receiving strategy process is:

[0016] The recipient makes a request to receive data, including information on the type, purpose and expected maturity of the data required;

[0017] The system determines whether to approve the request based on the recipient’s authority and maturity;

[0018] If approved, the data sender will release the data to the recipient in the predetermined manner and format and record the relevant operation log.

[0019] The maturity levels are classified according to the following criteria:

[0020] A maturity level of less than 60 points is considered primary maturity;

[0021] The maturity level is greater than or equal to 60 points, and less than 80 points is intermediate maturity;

[0022] The maturity level is greater than or equal to 80 points, and less than 90 points is advanced maturity;

[0023] The maturity is greater than or equal to 90 points, and less than 100 points is extra maturity.

[0024] The machine learning algorithm is a linear regression algorithm or a logistic regression algorithm.

[0025] A maturity-based data issuance and reception system, comprising:

[0026] Data acquisition module: responsible for collecting data from different data sources;

[0027] Maturity assessment module: Use the set assessment indicators and assessment weights to conduct maturity assessment on data;

[0028] Policy management module: formulate and manage data distribution and reception strategies;

[0029] Permission control module: ensures that only legitimate users or systems can make requests and receive data based on issuance and reception policies;

[0030] Data distribution module: distributes data to legitimate recipients according to the receiving strategy;

[0031] Logging module: records all data issuance and receipt operations for easy auditing and tracing.

[0032] The beneficial effects of the present invention are as follows: data maturity is formulated, and data is judged and transmitted by maturity level, so as to realize efficient use of data. By improving data utilization efficiency, it is ensured that the recipient obtains data with sufficient quality and value at the right time, thus avoiding waste of resources. DETAILED DESCRIPTION

[0033] The maturity of data is closely related to the industry. This solution can be applied to industries such as ordnance, military equipment, heavy industry, and shipbuilding.

[0034] Data Maturity Assessment:

[0035] 1. Set multiple evaluation dimensions, including data accuracy, completeness, consistency, timeliness and reliability.

[0036] 2. Set weights and specific evaluation indicators for each evaluation dimension. The accuracy is weighted at 60%, the integrity is weighted at 10%, the consistency is weighted at 10%, the timeliness is weighted at 5%, and the reliability is weighted at 15%.

[0037] 3. Use a combination of machine learning algorithms and manual review to assess the maturity of the data and divide the maturity into different levels such as primary, intermediate, advanced and special. The work content of each level of maturity is shown in Table 1.

[0038] The maturity levels are classified according to the following criteria:

[0039] A maturity level of less than 60 points is considered primary maturity;

[0040] The maturity level is greater than or equal to 60 points, and less than 80 points is intermediate maturity;

[0041] The maturity level is greater than or equal to 80 points, and less than 90 points is advanced maturity;

[0042] The maturity is greater than or equal to 90 points, and less than 100 points is extra maturity.

[0043] Table 1 Work content at different maturity levels

[0044]

[0045] Data release strategy:

[0046] 1. Data at the primary maturity level is only released to specific internal testers or development teams for preliminary verification and improvement of the data.

[0047] 2. Data at the intermediate maturity level can be distributed to a limited number of personnel in relevant business departments for small-scale trials and feedback.

[0048] 3. Data at an advanced maturity level can be widely distributed within an enterprise or organization to support daily business operations.

[0049] 4. Special maturity data can be considered for external sharing or sales on the basis of meeting internal needs.

[0050] Data receiving process:

[0051] 1. The recipient submits a request to receive data, including information such as the type, purpose and expected maturity of the data required.

[0052] 2. The system determines whether to approve the request based on the recipient's authority and the maturity of the data.

[0053] 3. If approved, the data will be released to the recipient in the predetermined manner and format, and the relevant operation log will be recorded.

[0054] Data distribution and reception system architecture based on maturity:

[0055] 1. Data acquisition module: responsible for collecting data from different data sources.

[0056] 2. Maturity assessment module: Use the set assessment criteria and algorithms to conduct maturity assessment on data.

[0057] 3. Policy management module: formulate and manage strategies for data issuance and reception.

[0058] 4. Permission control module: ensures that only legitimate users or systems can make requests and receive data.

[0059] 5. Data distribution module: distributes data to legitimate recipients according to the strategy.

[0060] 6. Logging module: records all data issuance and receipt operations for easy auditing and tracing.

[0061] The key points of the present invention are to establish a comprehensive and detailed data maturity assessment standard system; develop efficient automated assessment tools to improve assessment efficiency and accuracy; and design intelligent matching algorithms to achieve accurate docking of data issuance and reception requests.

[0062] For example, during the product development process, a company accurately released data based on data maturity, shortening the R&D cycle by 20%.

[0063] At the same time, the present invention ensures data quality: through maturity assessment and targeted distribution, the dissemination and use of low-quality data are reduced.

[0064] Statistics show that business errors caused by data quality issues are reduced by 40%.

[0065] In addition, the present invention enhances data security: strict authority control and issuance strategy effectively prevent improper leakage of sensitive data, and successfully reduces the incidence of data security accidents.

[0066] 1. Data generation / update;

[0067] Description: This is the starting point of the data life cycle, including the creation of new data and the modification of existing data.

[0068] Implementation method: Generate new data through data entry system, sensor collection, API interface call, user input, etc.; update existing data through database update, file modification, data cleaning and conversion, etc.

[0069] 2. Maturity Assessment

[0070] Description: Evaluate data quality based on preset evaluation criteria (such as accuracy, completeness, consistency, timeliness, security, etc.).

[0071] Implementation method: Use data quality tools or custom scripts to check whether the data meets the standards and generate evaluation reports.

[0072] 3. Maturity determination

[0073] Description: Match the assessment results with the preset maturity levels to determine the maturity level of the data.

[0074] Implementation: Categorize data into different maturity levels (e.g., elementary, intermediate, advanced) based on the scores or ratings in the assessment report.

[0075] 4. Distribution strategy determination

[0076] Description: Determine the data release strategy based on the maturity level of the data.

[0077] Implementation method: Establish a set of rules to specify to which objects data of different maturity levels can be released, the scope of release, whether additional security measures are required, etc.

[0078] 5. Receiver Request

[0079] Description: The recipient (such as other departments, partners, customers, etc.) makes a request to receive data.

[0080] Implementation method: Submit data requests through online forms, emails, API requests, etc., and specify the specific content, purpose and receipt method of the required data.

[0081] 6. Permission Verification

[0082] Description: Verify that the recipient has permission to receive data at this maturity level.

[0083] Implementation: Check the recipient’s identity, role, contractual agreement, etc. to confirm whether they have the right to access the requested data.

[0084] 7. Data Release

[0085] Description: If the permission verification is passed, the data will be released to the recipient according to the release policy.

[0086] Implementation method: Send data to the recipient through data sharing platforms, file transfer, API interfaces, etc. to ensure the security and compliance of data transmission.

[0087] 8. Receipt Confirmation

[0088] Description: The receiver confirms receipt of the data and provides feedback on the receipt status.

[0089] Implementation method: The recipient confirms data receipt through online confirmation, email reply, API response, etc., and reports any reception problems or abnormalities.

[0090] 9. Data Usage and Monitoring

[0091] Description: The receiver uses the data and monitors the usage of the data.

[0092] Implementation method: The recipient uses data according to business needs and establishes a data usage monitoring mechanism to ensure the compliance, security and efficiency of data usage. Monitoring may include data access logs, usage frequency, abnormal behavior detection, etc.

Claims

1. A method for issuing and receiving data based on maturity, characterized in that: The steps include: Step 1: Data maturity assessment: Step 1.1: Set multiple evaluation dimensions, including data accuracy, completeness, consistency, timeliness and reliability; Step 1.2: Set weights and specific evaluation indicators for each evaluation dimension; Step 1.3: Use a combination of machine learning algorithms and manual review to assess the maturity of the data based on each assessment dimension, and divide the maturity into primary, intermediate, advanced, and special levels; Step 2: Develop a data release strategy based on the assessed maturity level: Data at the primary maturity level is only released to specific internal testers or development teams for preliminary verification and improvement of the data; Data at the intermediate maturity level are distributed to a limited number of people in relevant business departments for small-scale trials and feedback; Advanced maturity data is widely distributed within an enterprise or organization to support daily business operations; Data at the extra-maturity level may be considered for external sharing or sales on the basis of meeting internal needs.

2. The method for issuing and receiving data based on maturity according to claim 1, characterized in that: The accuracy is weighted at 60%, the integrity is weighted at 10%, the consistency is weighted at 10%, the timeliness is weighted at 5%, and the reliability is weighted at 15%.

3. The method for issuing and receiving data based on maturity according to claim 1 or 2, characterized in that: It also includes a data receiving strategy; the data receiving strategy process is: The recipient makes a request to receive data, including information on the type, purpose and expected maturity of the data required; The system determines whether to approve the request based on the recipient’s authority and maturity; If approved, the data sender will release the data to the recipient in the predetermined manner and format and record the relevant operation log.

4. The method for issuing and receiving data based on maturity according to claim 1 or 2, characterized in that: The maturity levels are classified according to the following criteria: A maturity level of less than 60 points is considered primary maturity; The maturity level is greater than or equal to 60 points, and less than 80 points is intermediate maturity; The maturity level is greater than or equal to 80 points, and less than 90 points is advanced maturity; The maturity is greater than or equal to 90 points, and less than 100 points is extra maturity.

5. The method for issuing and receiving data based on maturity according to claim 4, characterized in that: The machine learning algorithm is a linear regression algorithm or a logistic regression algorithm.

6. A maturity-based data distribution and reception system, characterized in that: The maturity-based data issuance and reception system includes: Data acquisition module: responsible for collecting data from different data sources; Maturity assessment module: Use the set assessment indicators and assessment weights to conduct maturity assessment on data; Policy management module: formulate and manage data distribution and reception strategies; Permission control module: ensures that only legitimate users or systems can make requests and receive data based on issuance and reception policies; Data distribution module: distributes data to legitimate recipients according to the receiving strategy; Logging module: records all data issuance and receipt operations for easy auditing and tracing.

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