A standards-based approach to data asset governance

Abstract

The present application proposes a standard-based data asset governance method, including extracting link descriptions and link IDs of a data governance process; accessing a standard library that the data asset process depends on to obtain standard file descriptions and standard file IDs that constrain the data governance process; performing attention analysis on link descriptions and standard file representations to obtain a standard compliance matching graph; constructing a standard metadata model based on the standard compliance matching graph, and encoding the standard metadata model to obtain a standard mapping table to construct a standard data governance rule engine; assigning roles to a data-based governance organizational structure, and establishing a workflow control model between the assigned roles and link descriptions; establishing an object collaborative mapping framework between the workflow control model and the standard data governance rule engine to perform standard data asset governance tasks based on the object collaborative mapping framework.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a standard-based data asset governance method. Background Art

[0002] In the current information age, data has become an important asset of enterprises. Effectively managing these data assets is crucial to improving business efficiency, ensuring data security, and driving intelligent decision-making. Data asset governance, as a key way to achieve this goal, aims to ensure the quality, availability, security, and compliance of data. However, traditional data asset governance methods face many challenges, especially in terms of following industry standards, achieving data standardization, and cross-departmental collaboration. These issues directly affect the effectiveness and efficiency of data governance.

[0003] First, the disconnect between data governance processes and standards is a significant problem. Existing data governance frameworks often lack a systematic approach to ensure that data processing processes strictly follow relevant national standards, industry specifications, or internal corporate standards. This disconnection may result in data governance activities not meeting external regulatory requirements or internal quality standards, increasing compliance risks and data quality issues.

[0004] Secondly, the assessment and monitoring mechanism for standard compliance is insufficient. Even if organizations try to manually compare data governance processes with standard requirements, it is difficult to achieve efficient and accurate matching due to the cumbersome process, strong subjectivity and easy omissions, thus affecting the overall effectiveness and standardization level of data governance.

[0005] Furthermore, poor coordination between data governance activities and organizational structures is also a common problem. In data governance projects involving multiple departments, unclear roles and chaotic workflow control lead to unclear governance responsibilities, slow response, and difficulty in forming an effective synergy to promote the in-depth implementation of data governance. Summary of the invention

[0006] The purpose of this application is to propose a standard-based data asset governance method to solve or alleviate technical problems existing in the prior art.

[0007] The technical solutions provided by the embodiments of this application are as follows:

[0008] A standards-based data asset governance approach, comprising:

[0009] Obtain the data asset governance process definition file and parse it to extract the link description and link ID of the data governance process;

[0010] Access the standard library that the data asset process depends on to obtain the standard file description and standard file ID that constrain the data governance process;

[0011] Performing attention analysis on the link description and the standard document representation to obtain a standard compliance matching graph, wherein in the standard compliance matching graph, the standard document description and the link description having a constraint relationship are associated through the link ID and the standard document ID;

[0012] According to the standard compliance matching graph, a standard metadata model is constructed, and the standard metadata model is encoded to obtain a standard mapping table to construct a standard data governance rule engine;

[0013] Assign roles to the data governance organizational structure and establish a workflow control model between the assigned roles and the link descriptions;

[0014] An object collaborative mapping framework is established between the workflow control model and the standard data governance rule engine to execute standard data asset governance tasks according to the object collaborative mapping framework.

[0015] The standards-based data asset governance approach proposed in this application has the following technical benefits:

[0016] (1) By automatically acquiring and parsing the data asset governance process definition file and accessing the standard library to obtain the relevant standard file description, the data governance process is directly linked to the standard specification. By performing attention analysis on the link description and the standard file description, a standard compliance matching graph is constructed to ensure that each governance link can find the corresponding compliance standard, solve the problem of disconnection between the process and the standard, and enhance the standardization and compliance of data governance activities.

[0017] (2) Using the standard compliance matching graph obtained by attention analysis, this solution further constructs a standard metadata model and encodes a standard mapping table, which is the basis for building a standard data governance rule engine. The rule engine can automatically perform real-time monitoring and evaluation of data governance activities to ensure that the operations of each link meet the pre-set standard requirements, greatly improving the efficiency and accuracy of compliance evaluation and reducing human errors and omissions.

[0018] (3) This solution makes detailed role allocation for the data governance organizational structure, clarifies the responsibilities of different roles in the data governance process, and establishes a workflow control model between role and link descriptions. This clear division of labor and control model helps eliminate problems such as unclear responsibilities and poor coordination, promotes cross-departmental cooperation, speeds up the decision-making process, and improves overall execution.

[0019] (4) By establishing an object collaborative mapping framework between the workflow control model and the standard data governance rule engine, this solution ensures that data governance tasks can be efficiently executed under the guidance of the rule engine based on the role allocation and workflow model in the organizational structure. This collaborative mapping framework can dynamically adjust resource allocation and flexibly respond to various situations that arise during the governance process, further improving the flexibility and response speed of data asset governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Hereinafter, some specific embodiments of the present application will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0021] Figure 1 A flowchart of a standards-based data asset governance method according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0023] Figure 1 The following is a flow chart of a data asset management method based on standards according to an embodiment of the present application. A data asset management method based on standards includes:

[0024] Obtain the data asset governance process definition file and parse it to extract the link description and link ID of the data governance process;

[0025] Access the standard library that the data asset process depends on to obtain the standard file description and standard file ID that constrain the data governance process;

[0026] Performing attention analysis on the link description and the standard document representation to obtain a standard compliance matching graph, wherein in the standard compliance matching graph, the standard document description and the link description having a constraint relationship are associated through the link ID and the standard document ID;

[0027] According to the standard compliance matching graph, a standard metadata model is constructed, and the standard metadata model is encoded to obtain a standard mapping table to construct a standard data governance rule engine;

[0028] Assign roles to the data governance organizational structure and establish a workflow control model between the assigned roles and the link descriptions;

[0029] An object collaborative mapping framework is established between the workflow control model and the standard data governance rule engine to execute standard data asset governance tasks according to the object collaborative mapping framework.

[0030] The standards-based data asset governance approach proposed in this application has the following technical benefits:

[0031] (3) By automatically acquiring and parsing the data asset governance process definition file and accessing the standard library to obtain the relevant standard file description, the data governance process is directly linked to the standard specification. By performing attention analysis on the link description and the standard file description, a standard compliance matching graph is constructed to ensure that each governance link can find the corresponding compliance standard, solve the problem of disconnection between the process and the standard, and enhance the standardization and compliance of data governance activities.

[0032] (4) Using the standard compliance matching graph obtained by attention analysis, this solution further constructs a standard metadata model and encodes and generates a standard mapping table, which is the basis for building a standard data governance rule engine. The rule engine can automatically perform real-time monitoring and evaluation of data governance activities to ensure that the operations of each link meet the pre-set standard requirements, greatly improving the efficiency and accuracy of compliance evaluation and reducing human errors and omissions.

[0033] (3) This solution makes detailed role allocation for the data governance organizational structure, clarifies the responsibilities of different roles in the data governance process, and establishes a workflow control model between role and link descriptions. This clear division of labor and control model helps eliminate problems such as unclear responsibilities and poor coordination, promotes cross-departmental cooperation, speeds up the decision-making process, and improves overall execution.

[0034] (4) By establishing an object collaborative mapping framework between the workflow control model and the standard data governance rule engine, this solution ensures that data governance tasks can be efficiently executed under the guidance of the rule engine based on the role allocation and workflow model in the organizational structure. This collaborative mapping framework can dynamically adjust resource allocation and flexibly respond to various situations that arise during the governance process, further improving the flexibility and response speed of data asset governance.

[0035] Optionally, obtaining a data asset governance process definition file and parsing it to extract a link description and link ID of the data governance process includes:

[0036] Obtain a data asset governance process definition file, and parse the data asset governance process definition file based on the configured parsing library to extract the link description and link ID of the data governance process.

[0037] To this end, combined with the technical processing steps of the specific solution, the above process of obtaining and parsing the data asset governance process definition file to extract the link description and link ID has the following technical benefits:

[0038] (1) Automated file acquisition and parsing mechanisms replace traditional manual reading and input, greatly shortening processing time, while reducing errors caused by human factors and improving the accuracy and efficiency of data processing. This is especially important for large-scale or frequently changing data asset governance processes.

[0039] (2) The configuration-based parsing library design means that the system can flexibly adapt to different process definition file formats. It only needs to adjust the configuration to support the new format without making major changes to the core parsing logic. This not only simplifies system maintenance work, but also ensures the long-term applicability and scalability of the technical solution.

[0040] (3) By parsing and standardizing the extraction link ID and description, a unified identification system and language are established for the data governance process, which facilitates internal communication and external audits. This standardization not only improves the consistency of process management, but also provides a solid foundation for subsequent process optimization, quality control and compliance inspection.

[0041] (4) Clearly record the ID and description of each governance link, enhance the transparency of the process, and make every decision point and operation step in the process traceable and reviewable. This is crucial for internal audits, problem troubleshooting and responsibility tracing, and is conducive to establishing a more reliable and trustworthy data governance system.

[0042] (5) The extracted link information can be further used for data analysis, such as identifying process bottlenecks and evaluating governance effectiveness, providing data support for management and promoting evidence-based decision-making. In addition, this data is also an important basis for achieving process automation and intelligent management, which helps to continuously optimize and adaptively adjust data governance strategies.

[0043] The following is a sample code to implement the above steps:

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[0046] Optionally, the acquiring of the data asset governance process definition file and parsing the data asset governance process definition file based on the configured parsing library to extract the link description and link ID of the data governance process include:

[0047] Start the file monitoring service to obtain the data asset governance process definition file;

[0048] Based on the configured parsing library, the data asset governance process definition file is parsed to determine the format of the data asset governance process definition file, so as to extract the link description and link ID of the data governance process.

[0049] To this end, the solution of dynamically parsing the data asset governance process definition file based on the startup file monitoring service and the configuration-based parsing library has the following technical benefits:

[0050] (1) By starting the file monitoring service, the system can monitor any changes (such as addition, modification or deletion) in the data asset governance process definition files in the specified directory in real time. This dynamic response mechanism ensures the instant update of the process definition without manual intervention, greatly improving the response speed and automation level of data governance activities.

[0051] (2) The configuration-based parsing library can automatically adjust the parsing strategy according to the actual format of the file. This means that even when faced with process definition files of different versions or formats, the system can automatically identify and correctly parse them, improving the flexibility and compatibility of the system and reducing the maintenance costs caused by format changes.

[0052] (3) Real-time analysis of process definition files and automatic extraction of link descriptions and IDs enables the data governance team to quickly understand process details and adjust and optimize governance strategies in a timely manner. Automated extraction reduces human errors, ensures the accuracy of information in the data governance process, and improves the efficiency and quality of the entire data lifecycle management.

[0053] (4) Through centralized management and automatic monitoring of process definitions, the system can more easily implement standardized and normalized management to ensure that all processes follow unified standards and best practices. This is not only conducive to the consistency of internal management, but also facilitates meeting external regulatory requirements and audits.

[0054] (5) In enterprise data governance practices, processes often need to be quickly iterated based on business needs. This solution provides technical support for rapid adaptation to business changes through real-time monitoring and automatic analysis, allowing data governance processes to be adjusted agilely and keep pace with business development.

[0055] To implement the above steps, use the watchdog library to monitor changes in the file system and dynamically select the parsing library to process process definition files of different formats based on the configuration.

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[0058] Optionally, the configuration-based parsing library parses the data asset governance process definition file to determine the format of the data asset governance process definition file to extract the link description and link ID of the data governance process, including:

[0059] Parsing the data asset governance process definition file based on the configured parsing library to determine the format of the data asset governance process definition file;

[0060] According to the format of the data asset governance process definition file, a corresponding data element location tag is matched, wherein the data element location tag includes an XML tag, a JSON key value, or a YAML path;

[0061] Based on the data element positioning tag, call Java's DOM / SAX parser or JavaScript's native JSON object or js-yaml parser, use regular expressions, and match with the data asset governance process definition file to extract the link description and link ID of the data governance process.

[0062] To this end, a configuration-based parsing library is used to automatically identify the format of the data asset governance process definition file, and the solution of accurately extracting the link description and link ID through the data element positioning tag has the following technical benefits:

[0063] (1) The system can automatically identify the format of the process definition file (such as XML, JSON, YAML, etc.) according to the configuration, without the need for manual pre-judgment or conversion of the file format, which improves the adaptability and flexibility of the platform. This design enables the system to seamlessly handle data governance process definitions from different sources or standards, reducing the integration difficulties caused by different formats.

[0064] (2) By matching data element location tags (such as XML tags, JSON key-value paths, and YAML paths), combined with DOM / SAX parsers (for XML), native JavaScript JSON objects, or js-yaml parsers (for JSON / YAML), the system can directly locate the required key information (link ID and description), reducing unnecessary data traversal and improving the efficiency and accuracy of data extraction.

[0065] (3) Using regular expressions to further accurately locate and extract information significantly reduces the potential for human errors in the extraction process compared to completely manual processing or hard-coded logic. At the same time, configurable parsing strategies facilitate future adjustments or expansions, reducing long-term maintenance costs.

[0066] (4) Through a unified data element location label system, a common access interface is established for process definition files of different formats, which promotes the standardization of data governance process definitions and also reserves an extended interface to support more formats or more complex data structures in the future.

[0067] (5) The automated parsing and extraction process speeds up the initialization of the data asset governance process, enabling the data governance team to understand and apply the latest process definitions more quickly, thereby improving the response speed and execution efficiency of the entire data governance work.

[0068] The following provides example code, which provides two example code snippets to show how to extract step descriptions and step IDs based on different formats (XML, JSON, YAML) of data asset governance process definition files.

[0069] XML Parsing: DOM parser is used here to extract information from XML file.

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[0073] Optionally, the accessing of the standard library that the data asset process depends on to obtain the standard file description and standard file ID constraining the data governance process includes:

[0074] Build dynamic query requests based on the API interface configured with a standard file list;

[0075] Obtaining query screening constraint keywords, and adding the query screening constraint keywords to the dynamic query request for vectorization to obtain a content-based recommendation vector;

[0076] The standard library that the data asset process depends on is accessed based on the content-based recommendation vector to obtain the standard file description and standard file ID that constrain the data governance process.

[0077] To this end, the solution dynamically constructs query requests and combines content-based recommendation algorithms to access the standard library that the data asset process relies on, which has the following technical benefits:

[0078] (1) Dynamically generate query requests through the configuration-based standard file list API interface, which can flexibly adapt to the different requirements for standard files in different scenarios and ensure the accuracy and timeliness of the query scope. This dynamism allows the system to adjust query parameters in real time according to the specific requirements of the current data governance process to obtain the most relevant results.

[0079] (2) Introducing query screening constraint keywords and vectorizing them to generate content-based recommendation vectors, this process effectively improves the accuracy of retrieval. Vectorization not only reduces the ambiguity of text understanding, but also enables the system to quickly locate standard documents that are highly relevant to the governance process based on similarity calculations, avoiding interference from irrelevant or low-relevance content and improving retrieval efficiency.

[0080] (4) Accessing the standard library based on content-based recommendation vectors is essentially using machine learning technology to intelligently match standard documents. This process can discover potential best practices or standard guidelines and provide a scientific basis for data governance decisions. The introduction of recommendation algorithms enables the system to mine the most critical standard documents for specific governance processes from a massive standard library, providing strong support for process optimization and compliance verification.

[0081] (5) The standard file descriptions and IDs obtained through this solution help the data governance process follow industry or international standards and ensure the compliance of data processing activities. The implementation of standardization improves the quality and reliability of data assets, reduces legal or policy risks, and enhances the organization's data governance capabilities.

[0082] (6) The extracted standard file ID provides a clear reference for data governance activities, facilitating future audits, reviews, and knowledge transfer. The records of these IDs and description information help build a data governance knowledge base, promote knowledge sharing and experience accumulation, and help organizations continuously improve their data governance capabilities in the long run.

[0083] The following is a sample code to show how to implement the technical solution described above:

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[0086] Optionally, the obtaining of query screening constraint keywords and adding the query screening constraint keywords to the dynamic query request for vectorization to obtain a content-based recommendation vector includes:

[0087] Acquire multiple query screening constraint keywords, perform semantic analysis on each of the query screening constraint keywords, and assign corresponding semantic weights;

[0088] Based on all query screening constraint keywords and their corresponding weights, a weighted keyword vector space is constructed;

[0089] The dynamic query request is mapped into the weighted keyword vector space, so as to add the query screening constraint keyword into the dynamic query request for vectorization to obtain a content-based recommendation vector.

[0090] To this end, deep semantic analysis and weighted processing are used to optimize the query screening and constraint keyword processing process, which has the following technical benefits:

[0091] (1) By obtaining multiple query filter constraint keywords and performing semantic analysis on each keyword, the solution can deeply understand the meaning behind the keywords, rather than just surface text matching. Semantic analysis helps to identify the relationship between keywords, such as synonyms, hyponyms, and hyponyms, thereby improving the breadth and depth of the query.

[0092] (2) Based on the results of semantic analysis, each keyword is assigned a corresponding semantic weight. This step can reflect the importance of the keyword in the specific query context. For example, keywords that are more directly related to the current governance process may be assigned a higher weight, so that the key points can be more accurately highlighted during the search and irrelevant information can be filtered out.

[0093] (3) A weighted keyword vector space is constructed based on all keywords and their weights. This approach converts keywords from simple strings to multidimensional vectors that carry importance information. This representation method not only retains the semantic information of keywords, but also emphasizes their relative importance in the query through weights, thereby enhancing the expressiveness and discrimination of the query.

[0094] (4) Mapping dynamic query requests into the weighted keyword vector space actually enhances the original request, enabling it to more accurately locate highly relevant content when querying the standard library. This vectorization process helps overcome the ambiguity of natural language, reduce noise interference, and achieve effective generation of content-based recommendation vectors.

[0095] The following is a code to implement the above process:

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[0097] The above code first defines a simple semantic analysis function, then performs semantic analysis on a set of sample query filter constraint keywords and assigns weights. Next, a weighted keyword vector space is constructed using TfidfVectorizer, and a sample dynamic query request is vectorized. Finally, the similarity between the query vector and the keyword vector space is calculated to recommend the most relevant keywords for the query.

[0098] Optionally, the accessing the standard library that the data asset process depends on based on the content-based recommendation vector to obtain a standard file description and a standard file ID that constrains the data governance process includes:

[0099] Based on the set adaptation layer, converting the content-based recommendation vector into a standard query subject instruction;

[0100] Based on the standard query subject instruction, the standard library that the data asset process depends on is accessed to obtain the standard file description and standard file ID that constrain the data governance process.

[0101] To this end, this technical solution introduces an adaptation layer between the content-based recommendation vector and the standard library query to achieve the conversion from the recommendation vector to the standard query instruction, thereby effectively obtaining the relevant standard file description and ID from the standard library that the data asset process relies on. The specific technical benefits are as follows:

[0102] (1) As an intermediate conversion component, the adaptation layer enables the system to flexibly respond to the query interfaces and data structures of different standard libraries. Even if the query syntax or protocol of the standard library changes, only the adaptation layer logic needs to be adjusted without modifying the underlying recommendation algorithm or front-end query request, which enhances the compatibility and versatility of the system.

[0103] (2) Converting content-based recommendation vectors into standard query subject instructions actually converts high-dimensional vector information into a query language that the database can directly understand, which helps narrow the query scope and reduce unnecessary data retrieval, thereby improving query efficiency. At the same time, the recommendation vector based on semantic analysis and weight distribution can more accurately reflect the query intent and ensure that the obtained standard documents are highly relevant.

[0104] (3) The entire process is highly automated, and no human intervention is required from the generation of recommendation vectors to the conversion of standard query instructions. This not only reduces labor costs, but also avoids errors caused by human factors. In addition, the standardized query instruction format helps maintain the consistency of query logic, ensuring that each query follows a unified standard, which is convenient for auditing and maintenance.

[0105] (4) By accurately matching standard files, excessive queries and invalid access to the standard library are avoided, the pressure on the standard library server is reduced, and resource usage is optimized. At the same time, the obtained standardized file ID and description information provide a direct basis for the standardized management of the data governance process, facilitating subsequent tracking, review and version control.

[0106] To this end, the following sample code is provided to implement the above steps:

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[0109] Optionally, the setting-based adaptation layer converts the content-based recommendation vector into a standard query subject instruction, including:

[0110] Based on the set adaptation layer, structural analysis is performed on the content-based recommendation vector to extract multiple structural feature keywords;

[0111] Constructing an instruction syntax tree using the extracted multiple structural feature keywords;

[0112] Semantic expansion and context adaptation processing are performed based on the instruction syntax tree to generate a standard query subject instruction.

[0113] To this end, hierarchical operation has the following benefits:

[0114] (1) Structural analysis of content-based recommendation vectors based on the set adaptation layer. This process not only identifies individual keywords, but also deeply understands the structural relationship between them. For example, if the recommendation vector points to "data privacy" and "compliance", structural analysis can identify which subfield or category of data governance these keywords belong to, thereby providing richer contextual information for subsequent processing.

[0115] (2) By constructing the extracted structural feature keywords into a command syntax tree, this method uses the tree data structure in computer science to organize keywords, which not only ensures the logical clarity of the query command, but also makes the query more structured and precise. The command tree can reflect the hierarchical relationship and logical association between keywords. For example, child nodes represent specific conditions, and parent nodes represent broader concepts. The query constructed in this way can more accurately match the documents in the standard library.

[0116] (3) Semantic expansion and context adaptation based on the command syntax tree further improve the intelligence level of the query. This stage may include the use of natural language processing technology (such as word embedding, synonym replacement, hyponym expansion) to enrich the semantic connotation of the query command, ensuring that even if the user's expression is not completely accurate, the system can understand its intention and find the most relevant results. For example, for the keyword "data encryption", the system may automatically expand to related concepts such as "encryption algorithm" and "key management", thereby increasing the coverage and accuracy of the retrieval.

[0117] (4) The final generated standard query subject instruction can more efficiently locate the precise standard file description and its ID in the standard library that the data asset process relies on. This not only speeds up the speed of finding and applying standards in the data governance process, but also ensures that the adopted standards are the most consistent with the current data processing context and compliance requirements, thereby significantly enhancing the legality and security of data processing activities.

[0118] To this end, the following sample code is created to demonstrate the implementation of the above process to complete structural analysis, syntax tree construction, and semantic expansion.

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[0120] Optionally, the step of accessing a standard library that the data asset process depends on based on the standard query subject instruction to obtain a standard file description and a standard file ID constraining the data governance process, including:

[0121] Performing semantic analysis on the standard query subject instruction to extract all query key elements therein;

[0122] Serializing all the query key elements to obtain a multi-element association vector;

[0123] Based on the multi-factor association vector, the standard library on which the data asset process depends is accessed to obtain the standard file description and standard file ID that constrain the data governance process.

[0124] The technical processing steps of the above optional solution have the following technical advantages:

[0125] (1) Conduct in-depth semantic analysis of standard query subject instructions. This step goes beyond simple keyword matching and can understand the intent and context behind the query instruction. For example, for the instruction "Application of data privacy compliance in cloud storage", semantic analysis not only identifies keywords such as "data privacy", "compliance", and "cloud storage", but also understands the logical relationship between them, such as the specific requirements for privacy protection in cloud storage environments. This improvement in the level of understanding makes subsequent retrieval more accurate and avoids information omissions or false positives caused by literal matching.

[0126] (2) All the extracted query key elements are serialized into multi-element association vectors. This process is actually a structural processing of the query elements, which enhances the internal connection and weight distribution between the elements. The association vector can better reflect the relative importance and mutual influence of each element, which helps to perform more complex matching calculations in the standard library, thereby accurately locking the standard files most relevant to the query.

[0127] (3) Based on the multi-element association vector, the standard library that the data asset process relies on is accessed. This step uses advanced indexing technology and intelligent filtering algorithms to quickly locate the standard file descriptions and IDs that meet the conditions in the huge standard file library. This efficient retrieval mechanism greatly shortens the time from query to result acquisition, improves the response speed of the data governance process, and ensures the pertinence and practicality of the obtained standard files, avoiding the tediousness and potential errors of manual screening.

[0128] (4) The ultimate goal of the entire technical processing flow is to provide precise guidance documents for data governance activities. These standard document descriptions and IDs are directly linked to specific governance requirements and operating specifications, guiding data processing personnel on how to implement compliance operations in specific scenarios. Through automated and accurate retrieval, data processing activities are ensured to comply with relevant laws and regulations, industry standards, and internal corporate policies from the outset, effectively reducing compliance risks and improving the quality and security of data management.

[0129] The following is an example code that shows how to perform semantic analysis, serialize key elements, and access the standard library based on a multi-element association vector:

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[0132] Optionally, the performing attention analysis on the link description and the standard file representation to obtain a standard compliance matching graph, wherein in the standard compliance matching graph, the standard file description and the link description having a constraint relationship are associated through the link ID and the standard file ID, including:

[0133] Based on the mined frequent item sets, the similarity between the link description and the standard document expression is inferred to perform attention analysis on the link description and the standard document expression to obtain a standard compliance matching graph, and the standard file description and the link description with a constraint relationship are associated and configured as attributes of nodes in the standard compliance matching graph through the link ID and the standard file ID.

[0134] To this end, a standard compliance matching graph is constructed through attention analysis, aiming to achieve accurate matching of the constraint relationship between the data governance process and the standard document, which has the following technical benefits:

[0135] (1) Through in-depth similarity reasoning between the link description and the standard document expression, the implicit association between the two is mined based on frequent item sets. This analysis goes beyond simple keyword matching and goes deep into the semantic level, improving the accuracy and depth of matching. This helps to identify links and standards that have similar requirements despite different expressions, ensuring that data governance activities strictly comply with relevant regulations.

[0136] (2) By using the attention analysis mechanism, the system can automatically identify and emphasize the most critical and relevant parts of the link description and standard documents, similar to the focus of human attention during reading comprehension. This not only helps to filter out the most critical standard compliance information from a large amount of information, but also optimizes the resource allocation in the matching process, ensuring accurate matching of core elements and reducing noise interference.

[0137] (3) By constructing a standard compliance matching graph, the link ID and the standard document ID are connected through a constraint relationship, which enhances the transparency of the process and facilitates compliance review and risk assessment.

[0138] To this end, the following exemplary code is provided to implement the process of constructing a standard compliance matching graph based on frequent item sets and similarity reasoning:

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[0141] Optionally, the inference of the similarity between the link description and the standard document representation based on the mined frequent item sets, so as to perform attention analysis on the link description and the standard document representation to obtain a standard compliance matching graph, includes:

[0142] Based on the mined frequent item sets, reasoning about the similarity between the link description and the standard document representation to generate a frequent pattern tree;

[0143] Based on the frequent pattern tree, attention analysis is performed on the link description and the standard document representation to generate a standard compliance matching graph.

[0144] To this end, by integrating frequent item set mining and attention mechanism, the accuracy and efficiency of standard compliance matching are effectively improved, which has the following technical benefits:

[0145] (1) By mining frequent item sets in the link description and the standard document representation, we can identify high-frequency keywords or phrases that appear in different descriptions. These frequent items, as feature bases, can not only capture the commonalities between descriptions, but also help reveal the core elements that are critical to standard compliance. This step filters out irrelevant information, allowing the subsequent matching process to focus more on the core content.

[0146] (2) The generated frequent pattern tree not only presents the hierarchical relationship between item sets, but also reveals their occurrence patterns in the description, providing an intuitive structure for understanding and analyzing the complex associations between links and criteria. The tree structure helps the system organize information efficiently, making it more straightforward to find correlations and patterns in large-scale data sets, reducing unnecessary comparisons and computational burdens.

[0147] (3) Based on the frequent pattern tree, an attention analysis mechanism is introduced to provide a more detailed perspective for matching link descriptions with standard document descriptions. The attention mechanism can dynamically assign weights to different description elements to ensure that key information receives higher attention during the matching process. In this way, even when there are large differences in the descriptions, the most critical parts for compliance assessment can be accurately identified and highlighted, thereby improving the flexibility and accuracy of matching.

[0148] (4) The standard compliance matching graph generated through the above steps not only intuitively shows which links are closely related to which specific standards, but also reflects the degree of matching through the weight of the edge, so that reviewers can quickly locate high-risk or non-compliant links. This not only shortens the time for manual review, but also reduces the error rate caused by human negligence, and improves overall work efficiency.

[0149] (5) This technical solution is not only applicable to one-time compliance assessments, but the matching graphs and frequent pattern trees it generates can also serve as the basis for continuous monitoring and improvement. By analyzing historical data, we can further extract the best practices of compliance management, guide the formulation and improvement of future standards, and form a virtuous circle.

[0150] The following is an example code that implements the above technical process:

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[0152] Optionally, the configuring the standard file description and the link description having the constraint relationship as attributes of a node in the standard compliance matching graph by associating the link ID and the standard file ID includes:

[0153] The standard file description and the link description with a constraint relationship are intersected with the standard compliance matching graph through the link ID and the standard file ID, so as to associate the standard file description and the link description with a constraint relationship with the link ID and the standard file ID as attributes of nodes in the standard compliance matching graph.

[0154] The method is adopted to associate the standard file description and the link description with the constraint relationship through the link ID and the standard file ID as the node attribute in the standard compliance matching graph, and uniformly manage and identify the standard file and link description, that is, to assign a unique standard file ID and link ID to each standard file description and link description. The purpose of this is to ensure that the specific description content can be accurately located in a large-scale data set, laying a foundation for subsequent matching and analysis. The constraint relationship between the standard file description and the link description is identified by analyzing or pre-defining rules. These constraints may be reflected in the compliance of standard requirements with actual operation processes, execution guidelines under specific conditions, etc. The identification process may involve natural language processing technology, such as semantic similarity calculation, rule matching, etc., and is completed in an automated or semi-automated manner. The identified description with a constraint relationship is "intersected" with the standard compliance matching graph. The "intersection operation" here can be understood as a mapping or matching process, that is, the link description and the standard file description that meet specific constraints are found in the standard compliance matching graph according to their IDs, and the constraint relationship is added to these nodes as an attribute. This step ensures the effective integration of information so that the matching graph contains not only structural information but also rich semantic associations.

[0155] To this end, the above solution has the following technical effects:

[0156] (1) By directly associating the link ID and standard document ID with the node attributes of the matching graph, the traceability of the entire audit or analysis process is improved. This means that for any standard or link, its position in the matching graph and its related constraint information can be quickly located, increasing the transparency of compliance assessment.

[0157] (2) By using constraint relationships as node attributes, in-depth analysis can be directly performed on specific link-standard pairs during standard compliance assessment, improving the accuracy and efficiency of matching. This helps to quickly identify potential compliance risk points and provides a basis for optimizing processes and reducing violations.

[0158] (3) Standardized data organization and attribute configuration create conditions for the introduction of automated and intelligent analysis tools. For example, algorithms can be developed to automatically detect non-compliant node combinations in matching graphs, or machine learning models can be used to predict compliance issues that may arise in the future, so that preventive measures can be taken in advance.

[0159] To this end, the following example code is provided to build a standard compliance matching graph and add constraint relationship attributes:

[0160]

[0161]

[0162] Optionally, constructing a standard metadata model according to the standard compliance matching graph, and encoding the standard metadata model to obtain a standard mapping table to construct a standard data governance rule engine, including:

[0163] Performing temporal redundancy and structural similarity analysis on the standard compliance matching graph to generate a declarative business logic expression;

[0164] Decoupling the business logic and rules of the declarative business logic expression to build a standard metadata model;

[0165] Encoding the standard metadata model to obtain a standard mapping table, and obtaining a business logic declaration set;

[0166] Based on the business logic declaration set, a standard data governance rule engine is constructed.

[0167] To this end, the solution here has the following technical benefits:

[0168] (1) By analyzing the temporal redundancy and structural similarity of the standard compliance matching graph, the system can identify repeated or similar standard requirements, reduce unnecessary duplication of work, and ensure that each business process and data processing activity follows the latest and most optimized standard specifications. This helps enterprises or organizations maintain high standards of data quality and compliance and avoid increased risks and costs caused by inconsistent standard implementation.

[0169] (2) Convert the analysis results into declarative business logic expressions, making business rules more intuitive, easy to understand, and easy to maintain. Declarative programming allows non-technical personnel to participate in the definition and adjustment of rules, lowering the technical threshold and improving team collaboration efficiency. At the same time, this form of logical expression is easy for automated tools to parse and execute, laying the foundation for subsequent automated data governance.

[0170] (3) By decoupling business logic from rules, business logic or rules can be modified independently without affecting each other, which enhances the flexibility and scalability of the system. This design enables the standard metadata model to better adapt to future changes, such as adding new standards, modifying rules, etc., and reduces the complexity and risk of system upgrades.

[0171] (4) The standard metadata model and standard mapping table constructed provide a unified basic framework for data governance, ensuring the consistency and standardization of data processing. At the same time, the rule engine based on the business logic declaration set can be flexibly configured and adjusted according to the needs of different business scenarios, achieving a good balance between standardization and business personalization needs.

[0172] (5) The standard data governance rule engine can quickly respond to changes in data governance requirements, automatically perform tasks such as data quality checks and compliance verification, timely discover and warn of potential problems, and accelerate the decision-making process. When data governance problems occur, the rule engine can provide detailed violation reports and location information to help quickly find the root cause of the problem and take corrective measures, thereby improving problem-solving efficiency.

[0173] An exemplary code example is provided below to illustrate the implementation of the above process:

[0174]

[0175]

[0176] Optionally, constructing a standard data governance rule engine based on the business logic declaration set includes:

[0177] Extracting the semantics and logical structure of the business logic declaration set, including at least identifying conditional expressions, action instructions, and data flows;

[0178] Based on the semantics and logical structure, set the triggering conditions of the rules, define the execution order and priority of the rules, and build a standard data governance rule engine.

[0179] To this end, the above process of building a standard data governance rule engine has the following technical benefits:

[0180] (1) By extracting the semantics and logical structure from the business logic declaration set, the system can transform complex business requirements into clear and standardized rule expressions. This structured approach makes it easier for business personnel with non-technical backgrounds to understand the logic behind the rules, promotes cross-departmental communication, and reduces maintenance costs.

[0181] (2) Identifying conditional expressions, action instructions, and data flows helps to accurately define each link of data processing. The clear identification of conditional expressions ensures that the rules are triggered only when specific conditions are met, reducing the risk of misoperation; the detailed definition of action instructions ensures the accuracy of data operations; and the clear mapping of data flows supports the effective flow of data at different processing stages, ensuring the efficient execution of data governance.

[0182] (3) Based on the extracted logical structure, the system can intelligently set the triggering conditions, execution order, and priority of the rules. This not only reduces unnecessary computing and resource consumption, but also ensures that key rules are processed first, improving the real-time response capability of data governance. For example, in high-concurrency data processing scenarios, reasonable arrangement of rule priorities can avoid resource competition and ensure the smoothness of core business processes.

[0183] (4) The standardized data governance rule engine effectively monitors and controls data access, use, and storage processes through strict logic verification and rule execution mechanisms, complying with data protection regulations such as GDPR and HIPAA. The rule engine can automatically detect whether data processing activities are compliant and promptly intercept illegal access or operations, reducing the risk of data leakage and violations.

[0184] To this end, the following is a sample code that illustrates how to build a standard data governance rule engine based on a set of business logic declarations:

[0185]

[0186]

[0187] Optionally, the step of allocating roles for the data-based governance organizational structure and establishing a workflow control model between the allocated roles and the link descriptions includes:

[0188] Obtain historical role responsibility data and identify patterns of responsibility similarity to match the data asset governance role map corresponding to the data-based governance organizational structure to assign roles, and based on the set optimal role reachability tree, establish a workflow control model between the assigned roles and the link descriptions.

[0189] To this end, the above solution designs a systematic and intelligent role allocation and workflow control system for the data-based governance organizational structure through historical data analysis, role similarity identification, role graph matching, and the establishment of a workflow control model. It has the following technical benefits:

[0190] (1) By analyzing historical role responsibility data and identifying the patterns and similarities of responsibilities, we can quickly locate role configurations that match the existing data-based governance organizational structure. This approach avoids starting from scratch to design roles, uses historical experience to accelerate the decision-making process, and ensures the rationality and accuracy of role allocation, reducing the subjectivity of human decision-making and improving the efficiency of human resource allocation.

[0191] (2) The matching process between roles and the data asset governance role map ensures that the role definition not only meets the current governance needs of the organization, but also covers all key governance links. This helps to build a comprehensive and balanced governance framework, ensuring that data governance activities cover every key node in the data life cycle, thereby improving overall governance effectiveness.

[0192] (3) The workflow control model established based on the optimal role reachability tree can ensure that the governance tasks of each link are effectively executed and promote smooth collaboration between different roles. By optimizing the workflow path between roles, the delay and misunderstanding of task delivery are reduced, work efficiency is improved, and the coherence and consistency of the data governance process are ensured.

[0193] (4) As the organization develops and governance needs change, the model can be dynamically adjusted based on new responsibility models and historical execution data. The mapping relationship between roles and link descriptions is not static, but can be optimized based on actual execution results and feedback, ensuring that the organizational structure can quickly respond to changes in the external environment and continuously improve the effectiveness and efficiency of data governance.

[0194] (5) Through clear role allocation and workflow control, the governance responsibilities of each link are clearly defined, which is conducive to improving the compliance of data governance. When encountering data problems or compliance reviews, the relevant responsible persons and links can be quickly located, which facilitates problem tracking and responsibility tracing, and enhances the transparency and auditability of data governance.

[0195] To this end, the following is an exemplary code showing how to implement a simplified role assignment and workflow control model building process through Python:

[0196]

[0197]

[0198] Optionally, the acquisition of historical role responsibility data, identification of responsibility similarity patterns, and matching with the data asset governance role map corresponding to the data-based governance organizational structure to perform role allocation includes:

[0199] Obtain historical role responsibility data and perform cluster analysis on it to identify patterns of responsibility similarity;

[0200] Obtaining a demand description vector for data asset governance to map the data asset governance roles corresponding to the data governance organizational structure;

[0201] Based on the trained finite state machine, the pattern of responsibility similarity is matched with the data asset governance role map to perform role allocation.

[0202] To this end, this technical solution integrates cluster analysis, demand vector mapping and finite state machine matching, and has the following technical benefits:

[0203] (1) By clustering and analyzing historical role responsibility data, the system can automatically identify similarity patterns of responsibilities. Compared with manual analysis, this method not only greatly improves efficiency, but also can discover subtle patterns in large data sets, ensuring the scientificity and accuracy of role responsibility division.

[0204] (2) Obtaining the description vector of data asset governance requirements provides a clear direction for role allocation. This demand-oriented strategy ensures that role allocation is closely aligned with actual business needs, avoids resource mismatch, and improves the pertinence and effectiveness of data governance.

[0205] (3) Using a trained finite state machine (FSM) to match the model with the role graph is a flexible and efficient method. FSM can accurately match the responsibility model with the roles in the organizational structure according to the preset state transition rules. This mechanism not only ensures the logical rigor of the matching process, but also can handle complex role dependencies and conflicts, making role allocation more reasonable and efficient.

[0206] (4) The solution can be flexibly adjusted as data governance needs change. The cluster analysis and FSM matching mechanisms support dynamic updates and can self-optimize according to emerging responsibility models and organizational structure adjustments, ensuring the timeliness and adaptability of the role allocation model.

[0207] (5) The entire matching process is based on algorithms and models rather than subjective judgment, so the decision-making process of role allocation is more transparent and easier to audit and trace. This helps to improve trust within the organization and also facilitates review and optimization when problems arise.

[0208] To this end, exemplary codes are provided as follows to implement the basic steps of the above technical solution:

[0209]

[0210]

[0211]

[0212] Optionally, the step of establishing a workflow control model between the assigned roles and the link descriptions based on the set optimal role reachable tree includes:

[0213] Based on the set optimal role reachability tree, analyze the reachability between roles to determine the optimal role collaboration path;

[0214] Based on the optimal role collaboration path, a workflow control model between the assigned roles and the link descriptions is established.

[0215] To this end, through the analysis of the optimal role reachability tree and the establishment of a workflow control model, role collaboration and process control in a data-based governance organization are effectively optimized, with the following technical benefits:

[0216] (1) By analyzing the accessibility between roles and determining the best role collaboration path, unnecessary interactions between roles can be reduced, work overlap can be avoided, and each role’s tasks can be ensured to be clear and directly related to their area of ​​expertise. In this way, team members can focus more on their core responsibilities and improve overall work efficiency.

[0217] (2) The workflow control model established based on the optimal role collaboration path ensures that each step of the data governance process is closely linked to the responsibilities of a specific role. This model can clearly show how each role should participate in an orderly manner in the entire chain from data collection, processing, analysis to decision-making, enhance the logic and coherence of the process, and reduce confusion and misunderstandings during execution.

[0218] (3) Clear division of roles and efficient workflows are conducive to accurate information transmission and rapid decision-making. Each role makes decisions in the areas in which it excels, which reduces information distortion in the decision-making process and improves the accuracy and quality of decisions.

[0219] (4) By analyzing role accessibility and collaboration paths, we can identify bottlenecks and redundancies in resource allocation and make optimization adjustments. This ensures that key links have sufficient resource support while avoiding the accumulation of resources in inefficient areas, thereby maximizing resource utilization.

[0220] (5) The workflow control model clearly defines the responsibilities of each link. Once a delay or error occurs in the process, the problem can be quickly located and corrected in a timely manner. It also enhances the sense of responsibility of individuals and teams.

[0221] To this end, an exemplary code is provided, which uses a network graph (NetworkX library) to represent the reachability between roles and builds a simple workflow control model based on it:

[0222]

[0223]

[0224] In the above code, a directed graph is constructed to simulate the reachability between roles, and then based on this graph and the given link description, the person in charge of each link and the role of the predecessor link are determined. In actual applications, the workflow control model may require more complex logic to handle issues such as dependencies between roles, authority control, and task scheduling, and is usually integrated with a specific business process management system (BPM) or workflow engine to achieve automated workflow control.

[0225] Optionally, the establishing of an object collaborative mapping framework between the workflow control model and the standard data governance rule engine to perform standard data asset governance tasks according to the object collaborative mapping framework includes:

[0226] Based on the data asset governance event-driven logic, the workflow control model and the standard data governance rule engine are synchronized in state and parsed in dependencies to establish an object collaborative mapping framework between the workflow control model and the standard data governance rule engine;

[0227] Decision support analysis is performed based on the object collaborative mapping framework to perform standard data asset governance tasks.

[0228] To this end, by building an object collaborative mapping framework between the workflow control model and the standard data governance rule engine, efficient and automated execution of data asset governance tasks is achieved, which has the following technical benefits:

[0229] (1) The object collaborative mapping framework ensures close coordination between the workflow control model and the rule engine. Through state synchronization and dependency resolution, information flow between different components can be carried out in real time and accurately, promoting seamless collaboration between cross-functional teams and improving the overall collaborative efficiency of data governance work.

[0230] (2) Based on event-driven logic, the system can quickly respond to various events in the data governance process, such as data inflow, rule changes, or anomaly detection, and automatically trigger corresponding rule execution and workflow adjustment. This instant response mechanism significantly shortens the cycle from problem discovery to resolution, and improves the automation and response speed of data governance.

[0231] (3) Through the collaborative mapping framework, in-depth decision support analysis can be conducted before the data governance task is executed, using the logical judgment of the rule engine and the process control advantages of the workflow model, comprehensively considering business rules, compliance requirements and data processing capabilities to ensure the accuracy and rationality of decisions. This helps reduce human decision-making errors and improve the scientificity and effectiveness of governance decisions.

[0232] (4) The object collaborative mapping framework supports dynamic adjustment and expansion. When business needs change or new data governance rules are added, you only need to adjust the mapping relationship or rule engine configuration to quickly adapt to the changes. There is no need to reconstruct the workflow model or rule system on a large scale, thus ensuring the flexibility and sustainability of the data governance process.

[0233] (5) By collaboratively executing standard data asset governance tasks, we ensure that each governance link strictly follows the preset rules and processes, effectively avoiding non-compliant behaviors caused by human operational errors, improving the standardization level of data governance and data quality, and reducing compliance risks.

[0234] To this end, an exemplary code is provided below to implement the object collaborative mapping framework between the workflow control model and the standard data governance rule engine, and to perform data asset governance tasks based on this:

[0235]

[0236]

[0237] The present application embodiment provides a data asset management device based on standards, which includes:

[0238] A first processing unit is used to obtain a data asset governance process definition file and parse it to extract a link description and link ID of the data governance process;

[0239] A second processing unit is used to access a standard library that the data asset process depends on, so as to obtain a standard file description and a standard file ID constraining the data governance process;

[0240] a third processing unit, configured to perform attention analysis on the link description and the standard document representation to obtain a standard compliance matching graph, wherein in the standard compliance matching graph, the standard document description and the link description having a constraint relationship are associated through the link ID and the standard document ID;

[0241] A fourth processing unit is used to construct a standard metadata model according to the standard compliance matching graph, and encode the standard metadata model to obtain a standard mapping table to construct a standard data governance rule engine;

[0242] A fifth processing unit is used to assign roles to the data-based governance organizational structure and establish a workflow control model between the assigned roles and the link descriptions;

[0243] The sixth processing unit is used to establish an object collaborative mapping framework between the workflow control model and the standard data governance rule engine to perform standard data asset governance tasks according to the object collaborative mapping framework.

[0244] For an exemplary description of each unit in the above device, please refer to the above Figure 1 Records of.

[0245] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer executable program, and the processor is used to run the computer executable program to implement the method described in any embodiment of the present application.

[0246] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A standards-based data asset governance method, characterized in that: include: Obtain the data asset governance process definition file and parse it to extract the link description and link ID of the data governance process; Access the standard library that the data asset process depends on to obtain the standard file description and standard file ID that constrain the data governance process; Performing attention analysis on the link description and the standard document representation to obtain a standard compliance matching graph, wherein in the standard compliance matching graph, the standard document description and the link description having a constraint relationship are associated through the link ID and the standard document ID; According to the standard compliance matching graph, a standard metadata model is constructed, and the standard metadata model is encoded to obtain a standard mapping table to construct a standard data governance rule engine; Assign roles to the data governance organizational structure and establish a workflow control model between the assigned roles and the link descriptions; Establishing an object collaborative mapping framework between the workflow control model and the standard data governance rule engine to perform standard data asset governance tasks according to the object collaborative mapping framework; The step of obtaining the data asset governance process definition file and parsing it to extract the link description and link ID of the data governance process includes: Obtain a data asset governance process definition file, and parse the data asset governance process definition file based on the configured parsing library to extract the link description and link ID of the data governance process; The step of obtaining a data asset governance process definition file and parsing the data asset governance process definition file based on a configured parsing library to extract the link description and link ID of the data governance process includes: Start the file monitoring service to obtain the data asset governance process definition file; Based on the configured parsing library, the data asset governance process definition file is parsed to determine the format of the data asset governance process definition file, so as to extract the link description and link ID of the data governance process; The access to the standard library that the data asset process depends on, to obtain the standard file description and standard file ID constraining the data governance process, includes: Build dynamic query requests based on the API interface configured with a standard file list; Obtaining query screening constraint keywords, and adding the query screening constraint keywords to the dynamic query request for vectorization to obtain a content-based recommendation vector; The standard library on which the data asset process depends is accessed based on the content-based recommendation vector to obtain a standard file description and a standard file ID that constrains the data governance process.

2. A standard-based data asset governance method according to claim 1, characterized in that: The configuration-based parsing library parses the data asset governance process definition file to determine the format of the data asset governance process definition file, so as to extract the link description and link ID of the data governance process, including: Parsing the data asset governance process definition file based on the configured parsing library to determine the format of the data asset governance process definition file; According to the format of the data asset governance process definition file, a corresponding data element location tag is matched, wherein the data element location tag includes an XML tag, a JSON key value, or a YAML path; Based on the data element positioning tag, call Java's DOM / SAX parser or JavaScript's native JSON object or js-yaml parser, use regular expressions, and match with the data asset governance process definition file to extract the link description and link ID of the data governance process.

3. A standard-based data asset governance method according to claim 1, characterized in that: The obtaining of query screening constraint keywords and adding the query screening constraint keywords to the dynamic query request for vectorization to obtain a content-based recommendation vector includes: Acquire multiple query screening constraint keywords, perform semantic analysis on each of the query screening constraint keywords, and assign corresponding semantic weights; Based on all query screening constraint keywords and their corresponding weights, a weighted keyword vector space is constructed; The dynamic query request is mapped into the weighted keyword vector space, so as to add the query screening constraint keyword into the dynamic query request for vectorization to obtain a content-based recommendation vector.

4. A standard-based data asset governance method according to claim 1, characterized in that: The accessing the standard library that the data asset process depends on based on the content-based recommendation vector to obtain the standard file description and standard file ID that constrain the data governance process includes: Based on the set adaptation layer, converting the content-based recommendation vector into a standard query subject instruction; Based on the standard query subject instruction, access the standard library that the data asset process depends on to obtain the standard file description and standard file ID that constrain the data governance process; The adaptation layer based on the settings converts the content-based recommendation vector into a standard query subject instruction, including: Based on the set adaptation layer, structural analysis is performed on the content-based recommendation vector to extract multiple structural feature keywords; Constructing an instruction syntax tree using the extracted multiple structural feature keywords; Semantic expansion and context adaptation processing are performed based on the instruction syntax tree to generate a standard query subject instruction.

5. A standard-based data asset governance method according to claim 4, characterized in that: The step of accessing the standard library that the data asset process depends on based on the standard query subject instruction to obtain the standard file description and standard file ID that constrain the data governance process includes: Performing semantic analysis on the standard query subject instruction to extract all query key elements therein; Serializing all the query key elements to obtain a multi-element association vector; Based on the multi-factor association vector, the standard library on which the data asset process depends is accessed to obtain the standard file description and standard file ID that constrain the data governance process.

6. A standard-based data asset governance method according to claim 1, characterized in that: The performing attention analysis on the link description and the standard document representation to obtain a standard compliance matching graph, wherein in the standard compliance matching graph, the standard document description and the link description having a constraint relationship are associated through the link ID and the standard document ID, including: Based on the mined frequent item sets, the similarity between the link description and the standard document expression is inferred to perform attention analysis on the link description and the standard document expression to obtain a standard compliance matching graph, and the standard file description and the link description with a constraint relationship are associated and configured as attributes of nodes in the standard compliance matching graph through the link ID and the standard file ID.

7. A standard-based data asset governance method according to claim 6, characterized in that: The method of inferring the similarity between the link description and the standard document representation based on the mined frequent item sets, performing attention analysis on the link description and the standard document representation, and obtaining a standard compliance matching graph includes: Based on the mined frequent item sets, reasoning about the similarity between the link description and the standard document representation to generate a frequent pattern tree; Based on the frequent pattern tree, attention analysis is performed on the link description and the standard document representation to generate a standard compliance matching graph.

8. A standard-based data asset governance method according to claim 6, characterized in that: The configuring the standard file description and the link description having the constraint relationship as attributes of a node in the standard compliance matching graph through associating the link ID and the standard file ID includes: The standard file description and the link description with the constraint relationship are intersected with the standard compliance matching graph through the link ID and the standard file ID, so as to associate the standard file description and the link description with the constraint relationship with the link ID and the standard file ID as attributes of the nodes in the standard compliance matching graph.

9. A standard-based data asset governance method according to claim 1, characterized in that: The step of constructing a standard metadata model according to the standard compliance matching graph, and encoding the standard metadata model to obtain a standard mapping table to construct a standard data governance rule engine includes: Performing temporal redundancy and structural similarity analysis on the standard compliance matching graph to generate a declarative business logic expression; Decoupling the business logic and rules of the declarative business logic expression to build a standard metadata model; Encoding the standard metadata model to obtain a standard mapping table, and obtaining a business logic declaration set; Based on the business logic declaration set, a standard data governance rule engine is constructed.

10. A standard-based data asset governance method according to claim 1, characterized in that: The allocation of roles for the data-based governance organizational structure and the establishment of a workflow control model between the allocated roles and the link descriptions include: Acquire historical role responsibility data, identify patterns of responsibility similarity, match them with the data asset governance role map corresponding to the data governance organizational structure, and perform role allocation, and establish a workflow control model between the assigned roles and the link descriptions based on the set optimal role reachability tree; wherein, the acquisition of historical role responsibility data, identification of patterns of responsibility similarity, match them with the data asset governance role map corresponding to the data governance organizational structure, and perform role allocation includes: Obtain historical role responsibility data and perform cluster analysis on it to identify patterns of responsibility similarity; Obtaining a demand description vector for data asset governance to map the data asset governance roles corresponding to the data governance organizational structure; Based on the trained finite state machine, the pattern of responsibility similarity is matched with the data asset governance role map to perform role allocation; The step of establishing a workflow control model between the assigned roles and the link descriptions based on the set optimal role reachable tree includes: Based on the set optimal role reachability tree, analyze the reachability between roles to determine the optimal role collaboration path; Based on the optimal role collaboration path, a workflow control model between the assigned roles and the link descriptions is established.

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