Enterprise digitalization-oriented data asset whole life cycle management method and system

Abstract

The application discloses a data asset whole life cycle management method and system for enterprise digitization, belongs to the technical field of data management, and comprises the following steps: performing management judgment processing on target enterprise digitization asset data, constructing a management judgment constraint model, and generating a management judgment result of the target enterprise digitization asset data; continuously monitoring a compliance state of the target enterprise digitization asset data in the established management judgment constraint model to obtain a target enterprise digitization asset derived data rule matching degree measurement value; dynamically adjusting a boundary range of the management judgment constraint model according to the target enterprise digitization asset derived data rule matching degree measurement value, generating a management applicable level corresponding to the target enterprise digitization asset derived data, adjusting the execution strength of a management action according to the management applicable level of the derived data, and simultaneously optimizing the management rule to realize whole process management of data from generation, derivation, rule evolution to re-management.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a method and system for the full lifecycle governance of data assets for enterprise digitalization. Background Technology

[0002] As enterprises deepen their digital transformation, the types and scale of data assets generated and accumulated within enterprises are numerous and constantly evolving in business processes, departmental divisions, and project implementation. To ensure data quality, compliance, and the reliability of business decisions, enterprises need to establish a governance system covering the entire data lifecycle to systematically manage data collection, storage, processing, use, and archiving.

[0003] Existing technologies typically bind the lifecycle governance status directly to a specific data entity and manage that entity continuously as the governance object. When original data is split into multiple derived data entities during its lifecycle, the original data entity no longer exists, and its lifecycle governance status cannot be naturally mapped or inherited to the derived data entities. Because existing governance technologies lack a clear descriptive mechanism for the relationship between data splitting and governance status inheritance, the governance system can only treat derived data as new, independent governance objects, thus losing the governance context and compliance prerequisites formed by the source data in historical stages during the governance judgment process. Existing governance technologies typically manage governance rules using a static judgment model based on the currently effective rule set, that is, assessing data assets for compliance only based on governance rules effective at the current point in time. When governance rules change in different years or cycles, the governance system cannot make a comprehensive judgment by combining the generation time of the derived data, the historical governance status of the source data, and the applicable governance rules at that time.

[0004] For example, the Chinese invention patent announcement CN120494988A discloses a data-governed intelligent dynamic management system for the entire lifecycle of fixed assets. The system includes: a data acquisition module to acquire data throughout the entire lifecycle of fixed assets; an information entropy enhancement module to generate fixed asset management data; a dynamic management module to establish a dynamic management model for fixed assets; a real-time monitoring module to dynamically adjust maintenance plans and resource allocation for fixed assets; a mobile inventory module to intelligently correct mobile inventory data with errors exceeding a threshold; an automatic reconciliation module to store the optimization results in a fixed asset history database; and a decision support module to seamlessly connect and collaboratively optimize the dynamic management, mobile inventory, and automatic reconciliation processes of fixed assets. This invention can achieve seamless connection and collaborative optimization of the dynamic management, mobile inventory, and automatic reconciliation processes of fixed assets.

[0005] The above-mentioned technology has at least the following technical problems: In the full lifecycle governance of enterprise data assets, data assets generate multiple derived data entities as actual needs arise due to business segmentation, regional division, or project evolution. Existing governance technologies typically bind a single data entity with a comprehensive description of its compliance, rule matching status, risk level, structural and semantic integrity at a given point in time, as well as the execution of governance actions triggered by these states. This prevents derived data from inheriting the compliance judgments, rule matching levels, and governance action records of the original data, necessitating reassessment and execution of governance actions. This not only increases system overhead but may also lead to inconsistencies in governance strategies or rule coverage blind spots. Furthermore, enterprise governance rules are adjusted or updated over time. Existing technologies generally rely solely on currently effective rules for compliance judgments, lacking consideration for the historical evolution of rules. This makes it impossible to accurately reconstruct whether derived data conformed to the true state of the effective rules at the time of its generation. Summary of the Invention

[0006] To address the technical problems existing in the current technology, embodiments of the present invention provide a method for the full lifecycle governance of data assets for enterprise digitalization, the technical solution of which is as follows: Governance judgment processing is performed on the target enterprise's digital asset data, and a governance judgment constraint model is constructed. The governance judgment constraint model is used to carry the governance rules and determine the status of digital assets, and to generate the governance judgment result of the target enterprise's digital asset data.

[0007] Under the constraints of the governance judgment constraint model, the target enterprise's digital asset data is continuously governed and mapped by rules to form target enterprise digital asset derived data that represents its compliance status. The compliance status of the target enterprise's digital asset derived data within the established governance judgment constraint model is continuously monitored to obtain the rule matching metric value of the target enterprise's digital asset derived data.

[0008] The boundary range of the governance judgment constraint model is dynamically adjusted based on the matching metric value of the derived data rules of the target enterprise's digital assets, and the governance applicability level corresponding to the derived data of the target enterprise's digital assets is generated. The execution intensity of the governance actions is adjusted according to the governance applicability level of the derived data.

[0009] When governance rules are updated, the governance judgment constraint model is updated, and optimization suggestions are proposed for the governance rules. The governance judgment results and the governance judgment constraint model conditions are then sent out as executable governance strategies.

[0010] A second aspect of this invention also provides a data asset lifecycle governance system for enterprise digitalization, comprising: The governance judgment and constraint model construction module performs governance judgment processing on the target enterprise's digital asset data, constructs a governance judgment and constraint model, which serves as a rule data mapping space to carry governance rules and determine the status of digital assets, and generates the governance judgment results of the target enterprise's digital asset data. The derived data adaptability assessment and rule matching measurement module is used to continuously process and map the target enterprise's digital asset data under the constraints of the governance judgment constraint model, forming target enterprise digital asset derived data to characterize its compliance status. The module continuously monitors the compliance status of the target enterprise's digital asset derived data within the established governance judgment constraint model and obtains the rule matching measurement value of the target enterprise's digital asset derived data. The governance judgment constraint model adaptive adjustment module is used to dynamically adjust the boundary range of the governance judgment constraint model according to the matching metric value of the target enterprise's digital asset derived data rules, and generate the governance applicability level corresponding to the target enterprise's digital asset derived data, and adjust the execution intensity of governance actions according to the governance applicability level of the derived data; The governance strategy generation and execution control module is used to update the governance judgment constraint model when the governance rules are updated, and at the same time, it proposes optimization suggestions for the governance rules and sends the governance judgment results and governance judgment constraint model conditions into an executable governance strategy.

[0011] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: 1. The data asset lifecycle governance method for enterprise digitalization provided by this invention performs initial governance judgments on enterprise digital asset data and constructs a governance judgment constraint model, providing a quantifiable benchmark for the compliance status of the data. Subsequently, it continuously monitors the rule matching metrics of data evolution and derived data, enabling the governance system to dynamically adjust the boundary of the constraint model and generate the governance applicability level of derived data. This allows for flexible control of the execution intensity of governance actions. When governance rules are updated, it can determine whether the rule change breaks through the constraints of the original constraint model and trigger re-governance when necessary. At the same time, it generates rule optimization suggestions and deploys the governance judgment results and constraint model conditions as executable strategies. This achieves full-process management of data from generation, derivation, rule evolution to re-governance, ensuring the continuous compliance and controllability of data assets throughout their lifecycle. Furthermore, through the implementation of governance results and the generation of optimization suggestions, it helps to improve the use value and management efficiency of data assets, providing strong support for enterprises to establish a closed loop of data trustworthiness and value release in digital transformation.

[0012] 2. This invention first generates governance judgment results and constructs corresponding governance judgment constraint models to provide a quantitative benchmark for the compliance status of data. Furthermore, when data generates derived data, it obtains the structural and semantic information of the derived data to obtain the structural consistency factor and rule matching metric value of the derived data. Based on these indicators, it dynamically adjusts the boundaries of the governance judgment constraint model or maintains the original governance conclusion, thereby achieving continuous compliance monitoring and flexible governance of data during its generation, evolution, and derivation. This enables enterprises to maintain the traceability, quantifiability, and dynamic adaptability of governance throughout the entire data lifecycle, ensuring not only the compliance and integrity of data assets but also providing a reliable foundation for the subsequent release of data usage value and strategy optimization, supporting closed-loop management of data governance in enterprise digital transformation.

[0013] 3. This invention obtains the corresponding governance judgment constraint model boundary adjustment amount and dynamically shrinks the constraint boundary of the governance judgment constraint model based on this adjustment amount. At the same time, it generates a governance applicability level based on the performance of derived data in the rule matching dimension, and associates the first, second and third applicability levels with the execution intensity of governance actions. This enables flexible control over the compliance of derived data and governance strategies, allowing data governance to maintain dynamic adaptability and refined control during data evolution and derivation. It not only ensures the continuous compliance and rule applicability of data assets throughout their entire lifecycle, but also automatically adjusts the governance intensity according to the data status, realizing the elastic execution of governance strategies and closed-loop management throughout the entire lifecycle. This provides reliable data asset protection and value release support for enterprise digital transformation.

[0014] 4. This invention obtains the rule constraint parameters before and after the change when the governance rules are updated, maps the new rules to rule constraint feature vectors, and aligns them with the original constraint boundary parameters in the original governance judgment constraint model. This allows the determination of whether the original governance judgment result is still valid, whether it has failed in the rule matching dimension, and whether it triggers re-governance of the data. At the same time, it statistically analyzes the compliance duration and the number of re-governance triggers within the constraint model to determine whether to propose optimization suggestions for the governance rules. This ensures that the data remains controllable and compliant during the rule evolution process, providing reliable data asset management and continuous governance capabilities for enterprise digital transformation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1A flowchart illustrating the data asset lifecycle governance method for enterprise digitalization provided in this application embodiment; Figure 2 A flowchart of data governance judgment and derived data processing provided for embodiments of this application; Figure 3 The graph showing the evolution of the boundary of the governance judgment constraint model over time, as provided in the embodiments of this application; Figure 4 A graph showing the change of the boundary of the governance judgment constraint model as a function of the number of data derivations provided in the embodiments of this application; Figure 5 A flowchart illustrating the governance rule update and re-governance triggering process provided in this application embodiment; Figure 6 This is a schematic diagram of system modules provided in an embodiment of this application. Detailed Implementation

[0017] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0018] To facilitate understanding of the embodiments of this application, the following points will be explained first: First, in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the preceding and following related objects, but it does not exclude the possibility of indicating an "and" relationship; the specific meaning can be understood in context. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a and b and c. Here, a, b, and c can be single or multiple.

[0019] Second, the use of prefixes such as "first" and "second" in this application is solely for the purpose of distinguishing and describing different things belonging to the same category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no chronological, size, or priority relationship between them.

[0020] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0021] like Figure 1The diagram shown is a flowchart of a data asset lifecycle governance method for enterprise digitalization provided in this application embodiment. The method includes the following steps: Governance judgment processing is performed on the target enterprise's digital asset data, and a governance judgment constraint model is constructed. This model serves as a rule data mapping space to carry governance rules and determine the status of digital assets, generating governance judgment results for the target enterprise's digital asset data. The governance judgment constraint model is a computable constraint model composed of a set of data structure constraints, a set of data semantic constraints, and a set of governance rule constraints, denoted as G=(S, M, R), where S represents the set of data structure constraints, M represents the set of data semantic constraints, and R represents the set of governance rule constraints.

[0022] Under the constraints of the governance judgment constraint model, the target enterprise's digital asset data is continuously governed and mapped by rules to form target enterprise digital asset derived data that represents its compliance status. The compliance status of the target enterprise's digital asset derived data within the established governance judgment constraint model is continuously monitored to obtain the rule matching metric value of the target enterprise's digital asset derived data.

[0023] The boundary range of the governance judgment constraint model is dynamically adjusted based on the matching metric value of the derived data rules of the target enterprise's digital assets, and the governance applicability level corresponding to the derived data of the target enterprise's digital assets is generated. The execution intensity of the governance actions is adjusted according to the governance applicability level of the derived data.

[0024] When governance rules are updated, the governance judgment constraint model is updated, and optimization suggestions are proposed for the governance rules. The governance judgment results and the governance judgment constraint model conditions are then sent out as executable governance strategies.

[0025] like Figure 2 As shown, Figure 2 This application provides a flowchart for data governance judgment and derived data processing. It obtains a copy of the target enterprise's digital asset data and the data structure and semantic information at the current judgment time. Based on the currently effective governance rules, it constructs a governance judgment constraint model and generates an initial governance judgment result. When derived data is generated during the data's lifecycle, the system obtains the structure and semantic information of the derived data and maps it to the original governance judgment constraint model. By calculating the structure consistency factor and rule matching degree, it determines whether the derived data still conforms to the applicable scope of the original governance rules. If not, the governance judgment constraint model is dynamically adjusted, and the execution intensity of governance actions is adjusted according to the governance applicability level of the derived data, thereby maintaining the governance effectiveness and compliance of the derived data throughout its lifecycle.

[0026] like Figure 3 As shown, Figure 3The graph illustrates the evolution of the governance judgment constraint model boundary over time, showcasing the stability changes of governance rules throughout the governance evolution cycle. The horizontal axis represents the time evolution cycle or governance evaluation cycle after the governance judgment is formed, with the indicated value range from the initial judgment time T0 to T4 after multiple governance rule updates. The vertical axis represents the boundary strength of the governance judgment constraint model, characterizing the effective constraint range of the original governance judgment in the current governance environment. As the rules evolve, the boundary strength of the governance judgment constraint model gradually decreases, reflecting the gradual depletion of its security coverage margin for data assets as the rule constraints are continuously refined, tightened, or adapted to new business scenarios. When the boundary strength consistently exceeds the minimum effective threshold, it indicates that the existing governance judgment constraint model remains effective, and the existing governance judgment results can continue to be used. However, when the boundary strength gradually approaches the threshold, the judgment scenario of whether the change in the constraint in the judgment condition dimension exceeds the boundary threshold is used to trigger whether the governance judgment constraint model needs to be re-evaluated or a re-governance process needs to be initiated. The governance judgment process is performed on the target enterprise's digital asset data, and a governance judgment constraint model is constructed. The specific process is as follows: Based on the collected copy of the target enterprise's digital asset data, the target enterprise's digital asset data is obtained, and the data structure status information, data semantic description information, and the set of governance rules that are currently in effect at the current judgment time are collected. These are used as the basic building elements of the governance judgment environment and are uniformly modeled and processed. The data structure status information is then processed into structural feature vectorization.

[0027] It should be noted that an asset data copy is a governance-use mapping copy or snapshot data formed at a specific judgment time or within a judgment period for the target enterprise's digital asset data. Its data content is semantically consistent with the original asset data, but it is isolated from the original asset data in terms of physical storage location, access permissions, and lifecycle. It is used to support governance judgment, feature extraction, and rule mapping processing, thereby avoiding the governance process from directly affecting the original asset data in the business system.

[0028] It should be noted that the governance judgment establishment environment is the set of input conditions and operating context used to construct the governance judgment constraint model. The establishment environment emphasizes the conditional state at the current judgment moment, while the constraint model emphasizes the rule judgment space formed under these conditions. The two are not entirely the same, but there is a clear generative relationship: the governance judgment establishment environment is a governance judgment context environment jointly composed of asset data copies, data structure state information, data semantic description information, and the set of currently effective governance rules; while the governance judgment constraint model is a computable and adjustable judgment domain space formed in this establishment environment through unified modeling and rule mapping. In this application, the governance judgment constraint model is jointly composed of a set of data structure constraints, a set of data semantic constraints, and a set of governance rule constraints, used to form a rule data mapping space for making governance judgments on the target enterprise's digital asset data.

[0029] Based on the governance judgment environment, and according to the set of governance rules currently in effect, the target enterprise's digital asset data copy is subjected to compliance verification according to the judgment granularity defined by the rules, and the governance judgment result of the target enterprise's digital asset data is generated.

[0030] By binding and encapsulating the data structure status information, data semantic description information, and governance rule set used in the governance judgment execution process with the corresponding governance judgment results, a governance judgment constraint model corresponding to the governance judgment results of the target enterprise's digital asset data is constructed.

[0031] It should be noted that for non-numerical judgment conditions, the proportion of newly added, deleted, or rearranged constraints to the original number of conditions is calculated through logical expression structure parsing to form an equivalent structural change coefficient. Data structure status information includes field composition, field hierarchy, whether field splitting or merging exists, and data type configuration; data semantic description information includes the data's purpose of use at the business level, applicable business scenarios, and corresponding data asset classification tags; simultaneously, the set of governance rules in effect at the current judgment time is determined, and the rule identifier, rule version, and applicable scope of each governance rule are obtained; based on the rule's start / stop status, effective time interval, and applicable object constraints, a subset of rules actually participating in the governance judgment at this judgment time is selected.

[0032] It should be noted that when making governance judgments, the system will automatically exclude data governance rules that are not yet in effect, have expired, or are not applicable to the semantic scope of the target enterprise's digital asset data copies, taking into account the state at the time of the judgment. Only governance rules that are binding on the current target enterprise's digital asset data will be retained. The resulting set of rules constitutes the rule environment for this governance judgment. Data governance rules refer to the set of rules that an enterprise predefines during the entire lifecycle management of digital assets to constrain data structure, field types, semantic identifiers, usage purposes, and compliance requirements. The resulting set of rules constitutes the rule environment for this governance judgment.

[0033] It should be noted that the data structure status information includes at least the set of fields of the target enterprise's digital asset data copy, the field data types, the field hierarchy, and whether the fields were generated by splitting or merging; the data semantic description information includes at least the business object identifier, business scenario identifier, and data usage purpose identifier associated with the target enterprise's digital asset data copy; and the governance rule set includes at least the governance rule identifier, rule version information, and rule scope information currently involved in this governance judgment.

[0034] It should be noted that compliance verification of the target enterprise's digital asset data copy according to the defined granularity of judgments first involves parsing the granularity description information corresponding to each governance rule based on the currently effective set of governance rules. Judgment granularity is the smallest quantifiable unit scale corresponding to the actual operation of a governance rule in the data space. Judgment granularity includes at least one or more of the following: dataset level, data table level, field level, or record level. Based on the judgment granularity, the target enterprise's digital asset data copy is logically decomposed, mapping the data copy to several judgment units that correspond one-to-one with the governance rules. Corresponding data structure status information and data semantic description information are loaded for each judgment unit. For example, when the judgment granularity is at the dataset level, the governance rule targets the overall compliance of the entire data asset in terms of overall structure, overall use, or overall compliance status. When the judgment granularity is at the data table level, the governance rule only applies to a specific data table, used to determine whether the table's structure, field configuration, or table-level semantics meet governance requirements. When the judgment granularity is refined to the field or record level, the governance rule further limits compliance to the type, value range, anonymization status, or content compliance of a single data record.

[0035] It should be noted that the specific steps of the unified modeling process are as follows: The data structure state information is processed into structural feature vectors, encoding the number of fields, the distribution of field types, the depth of field hierarchy, and the field derivation relationships into structural feature vectors. These structural feature vectors are then used as the structural benchmark in the governance judgment constraint model to represent the constraints of the original data structure. Simultaneously, the data semantic description information is processed into semantic label normalization, mapping business objects, business scenarios, and usage purposes into a standardized set of semantic labels. This set of semantic labels is recorded as the semantic benchmark in the governance judgment constraint model to limit the applicable semantic scope of governance judgments. Furthermore, the governance system normalizes and encodes the set of governance rules according to rule identifiers and rule versions, forming a rule benchmark set to represent the rule environment upon which this governance judgment depends. When constructing the data structure constraint set, the target data field set, field type information, field hierarchy relationship, and field splitting status collected at the judgment time are recorded as the baseline structure state, and this structure state is represented as a structural baseline model that can be used for subsequent comparisons. When constructing the data semantic constraint set, the business semantic tags, usage purpose identifiers, and applicable business scenario identifiers associated with the target data at the judgment time are collected to form a semantic boundary description used to limit the scope of application of the governance conclusion. The governance rule identifiers, rule versions, and rule application scopes actually involved in this governance judgment are associated and recorded, thereby clarifying the rule environment on which this governance judgment depends. The above-mentioned data structure constraint set, data semantic constraint set, and governance rule constraint set together constitute the initial form of the governance judgment constraint model.

[0036] The governance assessment results generated from the target enterprise's digital asset data include: Based on the predefined compliance conditions, constraint logic, and verification methods in the governance rules, the target enterprise's digital asset data is mapped to various judgment units according to the granularity of judgment, and verification is performed on each judgment unit.

[0037] After completing the verification of each judgment unit, the verification results within the scope of the same governance rule are aggregated within the rule to generate the rule-level judgment result corresponding to the governance rule. Then, according to the result synthesis logic preset in the governance strategy, the judgment results of multiple governance rules are comprehensively analyzed to generate the governance judgment result of the target enterprise's digital asset data.

[0038] It's important to note that in data governance, data quality management, or compliance auditing systems, a governance rule typically doesn't correspond to just one judgment result. Instead, it's broken down into multiple executable verification items or units, such as structural integrity checks, field value range checks, semantic consistency checks, and permission status checks. Each verification unit outputs a unit-level verification result after execution, commonly in the form of a Boolean value (pass / fail), a numerical deviation indicator (deviation rate, missing rate), or a status code. Intra-rule aggregation, under the same governance rule, combines these unit-level verification results into a rule-level result according to predefined rules. The implementation is usually based on the aggregation logic pre-configured in the rule template. For example, if the governance rule is defined as "all verification units must pass", then the logical AND method is used. If any unit fails, the rule is judged as failing. If the rule is defined as "satisfying the main verification unit is sufficient", then primary and secondary identifiers are set for different verification units. As long as the main verification passes, the rule is considered to be valid, and the secondary verification only affects the risk level. If the rule is a numerical rule, the deviation values ​​output by multiple verification units are averaged, weighted averaged, or the maximum value is selected to generate the overall deviation metric value of the rule.

[0039] It should be noted that the comprehensive analysis of the judgment results of multiple governance rules is based on the result synthesis logic preset in the governance strategy. These governance rules typically operate in parallel, with each rule independently producing a rule-level judgment result. By statistically analyzing the number or proportion of rules that failed or are considered high-risk, it is determined whether this number exceeds the threshold set in the governance strategy. This value is then used as the quantitative judgment result for that governance rule, and further mapped to pass / fail or risk level. The overall governance judgment result undergoes a status change.

[0040] It should be noted that mapping the target enterprise's digital asset data to various judgment units is specifically based on the judgment granularity. It involves actually segmenting and generating computable objects from the target data copy. For example, when the judgment granularity of a governance rule is defined as field-level, each specific field instance in the target data copy that meets the rule's scope is identified, and each field instance is treated as an independent judgment unit. Compliance verification calculations are performed on its field type, value constraints, desensitization status, or semantic tags. Similarly, when the judgment granularity is at the data table level or dataset level, the judgment units generated by the system correspond to specific data table objects or specific dataset objects, respectively.

[0041] like Figure 4 As shown, Figure 4This graph illustrates the change in the boundary of the governance judgment constraint model as the number of data derivations occurs. It characterizes the changing applicability of the governance judgment constraint model from the perspective of the processing levels of derived data. The horizontal axis represents the processing levels experienced by the data asset throughout its lifecycle, with a suggested value range from the original data D0 to the derived data D4 after multiple splits and processing. The vertical axis represents the boundary strength of the governance judgment constraint model, characterizing the applicability of the original governance judgment to the derived data, with a suggested value range of 0 to 1. The broken line in the graph represents the gradual evolution of the boundary of the original governance judgment constraint model in the derivation dimension as the data asset is continuously derived, and its data structure, semantic purpose, and governance applicability conditions change. The horizontal dashed line represents the minimum applicable threshold at which the original governance judgment can be inherited by the derived data. When the boundary strength of the governance judgment constraint model is higher than this threshold, it indicates that the derived data can still inherit the original governance judgment to a certain extent; when the boundary strength decreases and approaches or falls below this threshold, it indicates that the governance applicability relationship between the derived data and the original data is gradually weakening, requiring a reassessment of the derived data.

[0042] Constructing a governance judgment constraint model corresponding to the governance judgment results of the target enterprise's digital asset data also includes: When derived data is generated from the target enterprise's digital asset data, the data structure status information and data semantic description information corresponding to the derived data at the current moment are obtained, and the derived data is mapped to the governance dimension space corresponding to the original governance judgment and constraint model. By comparing the field sets of the derived data and the original target enterprise's digital asset data, the percentage change in the number of fields, the percentage of fields retained, and the percentage of field type consistency are statistically analyzed to obtain the structural consistency factor of the target enterprise's digital asset derived data.

[0043] It should be noted that the governance dimension space refers to the multi-dimensional governance judgment coordinate space abstracted from the set of governance rules during the construction of the original governance judgment constraint model. Each dimension corresponds to a type of governance constraint or judgment element, which is used to uniformly express and map the state of digital assets in terms of structure, semantics, compliance, and usage scenarios. It is the logical representation space used for governance judgment and rule matching calculation. Derived data is mapped into this space in order to determine whether its state in each governance dimension still falls within the effective range defined by the original governance judgment constraint model.

[0044] It should be noted that the field quantity change ratio is based on the total number of fields in the original target enterprise's digital asset data. It is the percentage difference between the total number of fields in the derived data and the total number of fields in the original data. The calculation method is the absolute value of the difference between the number of fields in the derived data and the number of fields in the original data divided by the number of fields in the original data. It is used to reflect the degree of change in the structural scale of the derived data. The field retention ratio is based on the field set of the original target enterprise's digital asset data. It is the percentage of the original fields that are still retained in the derived data relative to the total number of fields in the original data. It is used to measure the degree of inheritance of the original data structure in the derivation process. The field type consistency ratio is the percentage of the number of fields in the derived data whose data type is consistent with the corresponding field data type in the original target enterprise's digital asset data, provided that the field names or field identifiers match. It is used to characterize the degree of consistency of the derived data in terms of field semantics and structural constraints.

[0045] It should be noted that the target enterprise's digital asset derived data structure consistency factor is a comprehensive measurement result after uniformly normalizing the aforementioned field quantity change ratio, field retention ratio, and field type consistency ratio. Specifically, each ratio indicator is first standardized to ensure consistent value ranges. Then, weights are assigned to each ratio indicator according to preset governance strategies or structural sensitivity configurations. A weighted calculation method is used to generate a structure consistency factor that reflects the overall consistency between the derived data structure and the original data structure. The higher the factor value, the stronger the structural inheritance of the derived data from the original asset data. In one embodiment, the weights corresponding to the field quantity change ratio, field retention ratio, and field type consistency ratio can be set equally.

[0046] The similarity between the semantic identifier of the purpose of use of the derived data and the semantic identifier recorded in the original judgment is used to determine whether the derived data still meets the applicable scope of the governance rules recorded in the original governance judgment constraint model. The proportion of the number of governance rules that can continue to be applied to the number of the original rule set is also counted to obtain the rule matching metric of the derived data of the target enterprise's digital assets.

[0047] It should be noted that semantic identifier similarity is based on semantic identifier information such as usage purpose, business scenario, or compliance tags recorded at the time of governance judgment between derived data and the original target enterprise's digital asset data. This information is obtained through text similarity calculation or vectorized semantic matching. In specific implementation, the semantic identifiers can be converted into vector representations, and the similarity between the current usage purpose of the derived data and the semantic identifiers recorded at the time of the original governance judgment can be measured by using cosine similarity, edit distance, or the similarity score output by a pre-trained semantic model. This determines whether the derived data still falls within the applicable scope of the corresponding governance rules in the original governance judgment constraint model.

[0048] If the similarity between the structural consistency factor and the semantic identifier of the purpose of use of the derived data of the target enterprise's digital assets and the semantic identifier recorded in the original judgment is lower than the corresponding structural consistency factor threshold and semantic identifier similarity threshold stored in the database, then the boundary range of the governance judgment constraint model will be dynamically adjusted according to the rule matching metric value of the derived data of the target enterprise's digital assets; otherwise, the original governance judgment result will be determined to continue to be valid.

[0049] It should be noted that the field retention ratio represents the proportion of original fields retained in the derived data, while the field type consistency ratio represents the degree of consistency of corresponding fields in terms of data type and constraints. Similarity calculations are performed on the business tag set, usage purpose identifier, and semantic identifier recorded in the original judgment of the derived data. This similarity calculation involves extracting the semantic tag set of the derived data and the semantic tag set of the original data, calculating their overlap rate (the number of intersections between the derived data tags and the original data tags divided by the total number of original data tags). Simultaneously, the usage identifier of the derived data is matched one by one with the usage identifier of the original data, and the proportion of successfully matched usage identifiers to the total number of original usage identifiers is calculated, yielding the usage identifier matching rate. Finally, the semantic tag overlap rate and the usage identifier matching rate are averaged according to preset weights to obtain the overall similarity between the semantic identifier of the derived data's usage purpose and the semantic identifier recorded in the original judgment. This average is used to determine whether the derived data still conforms to the applicable scope of the original governance rules. In one embodiment, the weights corresponding to the semantic tag overlap rate and the usage identifier matching rate can be set with equal weights.

[0050] It should be noted that derived data from the target enterprise's digital asset data refers to data forms created by structural or usage changes to the original enterprise's digital asset data without altering the original business system's operational logic. Specific forms include, but are not limited to, subsets formed by splitting the original data, simplified data formed by trimming the original data fields, new field structures formed by merging or reorganizing multiple fields, and usage-derived data formed by expanding or transferring the original business purpose to other business scenarios while maintaining the data content largely unchanged. When semantic similarity is high, it indicates that the derived data remains within the business semantics assumed by the original governance rules, and the relevant governance rules still have a basis for continued applicability based on semantic premises. When semantic similarity decreases, it indicates that the business purpose of the derived data has undergone a substantial change, requiring a reassessment of whether the original governance rules are still applicable.

[0051] The boundary range of the governance judgment constraint model is dynamically adjusted based on the matching metrics of the data derived from the target enterprise's digital assets. The specific process is as follows: The target enterprise's digital asset derived data rule matching metric is compared with the pre-configured rule matching threshold range in the database to obtain the governance judgment constraint model boundary adjustment amount corresponding to the target enterprise's digital asset derived data rule matching metric. The constraint boundary of the governance judgment constraint model is then shrunk based on the governance judgment constraint model boundary adjustment amount.

[0052] It should be noted that the rule matching threshold interval refers to a set of pre-configured numerical intervals in the system database according to different governance stability states for the rule matching metric of the derived data of the target enterprise's digital assets. This set is used to characterize the degree of matching between the derived data and the original governance rule set. The rule matching threshold interval typically consists of multiple continuous or discrete numerical intervals, each corresponding to a governance stability state or risk state, and establishing a correspondence with the adjustment strategy of the constraint boundary in the governance judgment constraint model. After obtaining the rule matching metric of the derived data of the target enterprise's digital assets, the system performs interval assignment judgment between the rule matching metric and the pre-configured rule matching threshold intervals in the database to determine the threshold interval into which the metric currently falls. Subsequently, based on the pre-associated boundary adjustment coefficient or adjustment ratio of the threshold interval, the system searches for the corresponding boundary adjustment amount of the governance judgment constraint model in a pre-configured one-to-one correspondence mapping table between rule matching threshold intervals and governance judgment constraint model boundary adjustment amounts. The found boundary adjustment amount is used as the basis for shrinking or adjusting the constraint boundaries of the governance judgment constraint model. Specifically, the closer the rule matching metric is to the high matching threshold interval, the smaller the corresponding boundary adjustment amount, and the relatively stable the constraint boundaries of the governance judgment constraint model remain. Conversely, the closer the rule matching metric is to the low matching threshold interval, the larger the corresponding boundary adjustment amount, which triggers the shrinking process of the governance judgment constraint model's constraint boundaries. Finally, based on the calculated governance judgment constraint model boundary adjustment amount, the system proportionally shrinks the constraint boundaries corresponding to each governance dimension in the original governance judgment constraint model, so that the effective range of the governance judgment constraint model dynamically shrinks as the degree of rule matching in the derived data decreases, thereby achieving adaptive tightening of governance judgment conditions.

[0053] It should be noted that the specific process of shrinking the constraint boundaries of the governance judgment constraint model based on the adjustment amount of the governance judgment constraint model boundary involves, after generating the rule matching metric value of the target enterprise's digital asset derived data, the governance system inputs the rule matching metric value into a pre-configured constraint model boundary adjustment mapping table for matching queries. This mapping table establishes the correspondence between the rule matching metric value range and the constraint model boundary adjustment amount, which is defined as a constraint range shrinkage ratio or a constraint threshold offset. Based on the queried constraint model boundary adjustment amounts, including the data structure consistency allowable range adjustment amount, the semantic similarity allowable lower limit adjustment amount, and the rule application ratio lower limit adjustment amount, the constraint boundaries of each governance dimension in the original governance judgment constraint model are simultaneously shrunk. The constraint boundaries of each governance dimension, including the data structure consistency allowable range, the semantic similarity allowable lower limit, and the rule application ratio lower limit, are adjusted. In one embodiment, the data structure consistency allowable range, the semantic similarity allowable lower limit, and the rule application ratio lower limit are set with equal-proportional shrinkage.

[0054] It should be noted that after shrinking the governance judgment constraint model based on the constraint model boundary adjustment, a determination is made as to whether the derived data still falls within the shrunken governance judgment constraint model. If the derived data no longer satisfies the constraints of the shrunken constraint model in any governance dimension, the original governance judgment constraint model is deemed invalid, and the derived data is marked as a data object requiring re-governance. After the constraint model failure determination, a re-governance process is automatically triggered, incorporating the derived data back into the governance judgment execution step. The data structure state information, data semantic description information, and effective governance rule set corresponding to the current judgment time are re-acquired, and a new governance judgment result and corresponding governance judgment constraint model are generated, thereby achieving automatic updating and closed-loop maintenance of governance judgments in data evolution scenarios.

[0055] The specific process for generating the governance applicability level corresponding to the derived data of the target enterprise's digital assets is as follows: Based on the rule matching metric value of derived data derived from the target enterprise's digital assets, the governance applicability level of the derived data is obtained. The governance applicability level of the derived data includes the first applicability level, the second applicability level, and the third applicability level. The execution intensity of governance actions is adjusted according to the governance applicability level of the derived data.

[0056] It should be noted that, based on the rule matching metric value of the derived data of the target enterprise's digital assets, the metric value is subjected to interval mapping processing according to a pre-configured governance applicability level threshold range to determine the governance applicability level corresponding to the derived data. The range of the rule matching metric value is divided into multiple consecutive intervals that increase sequentially from low to high. These intervals have clear magnitude relationships and do not overlap. When the rule matching metric value falls into the highest matching interval, the derived data is determined to belong to the first applicability level; when it falls into the medium matching interval, it is determined to belong to the second applicability level; and when it falls into the lowest matching interval, it is determined to belong to the third applicability level. The first, second, and third applicability levels correspond to high, medium, and low matching states between the derived data and the original governance rule set, respectively. Furthermore, the rule matching metric value range corresponding to the first applicability level is larger than that corresponding to the second applicability level, and the rule matching metric value range corresponding to the second applicability level is larger than that corresponding to the third applicability level. After determining the governance applicability level of derived data, governance actions of different intensities are triggered on the derived data according to the preset correspondence between different governance applicability levels and the intensity of governance action execution. The first applicability level corresponds to the lowest execution intensity or only status recording; the second applicability level corresponds to the medium execution intensity or triggers prompt or verification-type governance actions; and the third applicability level corresponds to the highest execution intensity or triggers restriction, blocking, or forced rectification-type governance actions, thereby achieving hierarchical governance control based on rule matching metrics.

[0057] It should be noted that the governance applicability level of derived data is obtained by matching the comprehensive deviation metric with the pre-configured governance applicability level mapping rule. Based on the mapping result, the applicability level of the original governance judgment to the derived data is determined. The governance applicability level is used to characterize the applicability strength of the original governance judgment in the current derived data scenario.

[0058] It should be noted that the specific process of adjusting the execution intensity of governance actions based on the governance applicability level of derived data is as follows: When the comprehensive deviation metric is at the first applicability level, the original governance judgment is determined to be fully applicable to the derived data, and the original governance rule set and execution strategy are automatically inherited, with only the derivation relationship recorded for auditing; when the comprehensive deviation metric is at the second applicability level, the original governance judgment is determined to be partially applicable to the derived data, and the scope of use or governance requirements of the derived data are restricted. Based on the effective rule subset after the constraint model is shrunk, a derived governance rule set is recombined, and the data usage permissions, circulation scope, or responsibility entity constraints are adjusted simultaneously; when the comprehensive deviation metric is at the third applicability level or the derived data no longer meets the constraint conditions of the shrunk governance judgment constraint model, the original governance judgment is determined to be low-applicability or inapplicable to the derived data, and a re-governance or manual review process is triggered accordingly.

[0059] like Figure 5 As shown, Figure 5 The flowchart for governance rule update and re-governance triggering provided in this application embodiment describes how, when a governance rule is updated, the system obtains the constraint parameters of the governance rule before and after the change, maps the changed rule to a rule constraint feature vector and aligns it with the original constraint boundary, calculates the constraint change of each judgment condition, and compares it with a preset boundary threshold. If the constraint change does not exceed the threshold, the original governance judgment result is maintained and the rule version evolution information is recorded; if it exceeds the threshold, the re-governance process is triggered to re-evaluate the target enterprise's digital asset data and generate governance rule optimization suggestions when necessary, thereby ensuring the continuous effectiveness and historical traceability of the governance judgment result during the rule evolution process. When governance rules are updated, the governance judgment constraint model is updated. The specific process is as follows: When governance rules are updated, the rule constraint parameters of the governance rules before and after the change are obtained. The changed governance rules are mapped into rule constraint feature vectors according to the corresponding constraint objects, applicable scope, judgment conditions and constraint strengths. The vectors are then aligned one by one with the original constraint boundary parameters of the corresponding rules in the original governance judgment constraint model. The differences between the upper and lower limits of the value range of each judgment condition in the governance rule and the allowable missing proportion are calculated. The differences in the value range and the allowable missing proportion of each judgment condition in the governance rule are obtained.

[0060] It should be noted that the rule constraint parameters in governance rules refer to the various constraint conditions explicitly defined at the rule definition level, including the value range corresponding to the judgment condition, constraint threshold, allowable missing ratio, and constraint strength identifier, etc., used to describe the semantic and logical constraint requirements of the governance rules. The constraint boundary parameters in the original governance judgment constraint model, on the other hand, are the boundary representations formed when the aforementioned rule constraint parameters are mapped and solidified into the boundary of the judgment space during the construction of the governance judgment constraint model. The two are one-to-one corresponding in terms of constraint objects and constraint meanings, differing only in their representation and storage medium. Therefore, when governance rules are updated, the two types of unrelated parameters are not compared; instead, the rule constraint parameters extracted from the changed governance rules are aligned and compared with the corresponding constraint boundary parameters formed in the original governance judgment constraint model based on the governance rules before the change. In the specific difference calculation process, the system uses the constraint boundary parameters recorded in the original governance judgment constraint model as a benchmark, and compares the rule constraint parameters of the corresponding judgment conditions in the changed governance rules one by one. It then calculates the difference between the lower limit and upper limit of the value range for each judgment condition, as well as the difference in the allowable missing proportion. These differences represent the magnitude of change of the changed rule constraint parameters relative to the original constraint boundary parameters. The obtained value range difference and allowable missing proportion difference accurately reflect the degree of change in constraint strength and scope of application before and after the governance rule update, and provide a quantifiable basis for subsequent adjustments to the boundary of the governance judgment constraint model and rule optimization.

[0061] Based on the differences in the values ​​of each judgment condition in the governance rule and the differences in the allowable missing proportions, combined with the boundary range allowed by the corresponding judgment condition in the original governance judgment constraint model, the constraint changes introduced when the governance rule is updated are normalized to obtain the constraint changes of the governance rule in each judgment condition dimension, and the constraint changes of the governance rule in each judgment condition dimension are mapped to the rule matching dimension of the governance judgment constraint model.

[0062] The changes in the constraints of the governance rules on each judgment condition dimension are compared with the preset rule constraint boundary thresholds in the governance judgment constraint model to determine whether the update of the governance rules is still within the range of changes allowed by the original constraint model.

[0063] It should be noted that the rule constraint parameters of governance rules refer to the specific set of parameters defined for each governance rule in the process of digital asset governance, which are used to constrain data attributes and behaviors. These parameters include the applicable objects of the rule, judgment conditions, value range, field type requirements, allowable missing ratio, constraint strength, scope of application, and conditions for triggering governance actions. They are used to clarify the constraint requirements of the rule on data assets in terms of structure, semantics, compliance, and usage scenarios. They are quantifiable and operable representations of the rule at the logical and execution levels.

[0064] It should be noted that the changes in the constraints of the governance rules on the judgment condition dimension are derived from the comparison results of the judgment condition threshold parameters in the old and new governance rules. These include, but are not limited to, the effective range reduction ratio formed after the changes in the upper and lower limits of the allowed range of field values, and the acceptable missing space change ratio formed after the changes in the upper limit of the allowed missing proportion of the field. All changes are expressed in the form of proportions to reflect the extent to which the acceptable space of data is reduced or expanded at the judgment condition level before and after the rule update.

[0065] It should be noted that the pre-defined rule constraint boundary thresholds in the governance judgment constraint model also exist in the form of explicit numerical parameters. These thresholds are the maximum permissible boundary of change determined when constructing the governance judgment constraint model, based on the judgment condition thresholds of the governance rule at the original judgment time. They characterize the allowable range of changes in the rule judgment conditions without affecting the validity of the original governance judgment conclusion. For example, for judgment conditions involving value ranges, this boundary threshold corresponds to the maximum reduction ratio allowed by the original rule's allowed range while maintaining the validity of the governance conclusion; for judgment conditions involving permissible missing proportions, this boundary threshold corresponds to the maximum acceptable adjustment range of the permissible missing proportion during rule evolution.

[0066] The specific process for determining whether the updated governance rules are still within the allowable range of the original constraint model is as follows: When the change in the constraint of the governance rule in the judgment condition dimension does not exceed the corresponding boundary threshold stored in the database, the original governance judgment result is maintained and the rule version evolution information is recorded.

[0067] It should be noted that rule version evolution information is a process record used to describe the version change trajectory of the same governance rule at different points in time. This information includes at least the version identifier of the governance rule, its effective time, its expiration time, the corresponding rule constraint parameter summary, and a record of the amount of constraint change compared to the previous version. When the amount of constraint change of the governance rule in the judgment condition dimension does not exceed the corresponding boundary threshold stored in the database, the system maintains the original governance judgment result as valid and writes the change relationship between the current rule version and the historical version, the inheritance of judgment results, and the state of not triggering re-governance into the rule version evolution information for subsequent auditing, backtracking, or rule stability analysis.

[0068] When the amount of change in the constraints of the governance rules in the judgment condition dimension exceeds the corresponding boundary threshold stored in the database, the original governance judgment constraint model is determined to be invalid in the rule matching dimension, and a re-governance process for the target enterprise's digital asset data is triggered.

[0069] It should be noted that the specific process for determining whether the updated governance rule is still within the allowable range of the original constraint model is as follows: During the comparison, the change in the judgment condition constraint calculated after the governance rule update is matched one by one with the corresponding rule constraint boundary threshold recorded in the governance judgment constraint model. The change ratio of the same judgment condition under the same physical meaning is numerically compared. When the change in constraint is within the numerical range defined by the rule constraint boundary threshold, it is determined that the change in the judgment condition dimension of the governance rule has not exceeded the constraint boundary of the original governance judgment constraint model, and the original governance judgment result still holds under the current rule version. When the change in constraint exceeds the corresponding rule constraint boundary threshold, that is, the rule update has caused the data acceptable space allowed by the judgment condition to change numerically beyond the allowable range of the original governance judgment constraint model. For example, the reduction ratio of the effective value interval after the rule update exceeds the maximum reduction ratio allowed by the original constraint model, or the adjustment range of the allowable missing ratio exceeds the maximum acceptable change range predefined in the original constraint model. The updated judgment rules have caused a substantial change in the premises for the original governance judgment to be valid, thus determining that the original governance judgment constraint model is invalid in the rule matching dimension, and triggering a re-governance process for the target enterprise's digital asset data.

[0070] It should be noted that when the amount of constraint change introduced by the governance rule update exceeds the corresponding rule constraint boundary threshold in the governance judgment constraint model, and the original governance judgment constraint model is deemed invalid in the rule matching dimension, the target enterprise's digital asset data is first marked as being in a state of rule change pending re-governance, and the governance state inheritance relationship generated based on the original governance judgment result is frozen to prevent the invalid judgment from being further propagated during data flow. Subsequently, using the updated set of governance rules as the current effective rule environment, the data structure state information and data semantic description information corresponding to the target enterprise's digital asset data at the re-governance moment are re-acquired, and the original governance judgment constraint model is decoupled based on the data state information, releasing its constraint binding in the rule matching dimension, and the governance judgment process is re-executed on the copy of the target enterprise's digital asset data.

[0071] The specific process for making optimization suggestions for governance rules is as follows: Statistically analyze the compliance duration and re-governance trigger count of the target enterprise's digital asset data in the current governance judgment constraint model. If the compliance duration of the target enterprise's digital asset data in the current governance judgment constraint model is lower than or equal to the compliance duration stored in the database, and the re-governance trigger count is higher than or equal to the re-governance trigger count stored in the database, then optimization suggestions for governance rules are made; otherwise, no optimization suggestions for governance rules are required.

[0072] It should be noted that the compliance duration refers to the length of time that the target enterprise's digital asset data remains within the current governance judgment constraint model without triggering a governance judgment failure or re-governance operation. This time is measured from the moment the most recent governance judgment was established or re-governance was completed until a governance judgment failure, rule inapplicability, or re-governance trigger event occurs. It is used to reflect the stable compliance level of the digital asset under the established governance judgment constraint model. The re-governance trigger count refers to the cumulative number of times that governance judgments or governance actions are re-executed on the target enterprise's digital asset data due to reasons such as a decrease in the derived data rule matching metric, shrinkage of the governance judgment constraint model boundary, or changes in rule constraints during the effective period of the same governance rule or the same governance judgment constraint model. This count is used to characterize the frequency and adaptability of disturbances in the actual operation of the governance rules.

[0073] It should be noted that the compliance duration in the current governance judgment constraint model is based on the currently effective governance judgment constraint model. It continuously records the length of time that the data has maintained a compliant state without breaking the boundary of the constraint model since the most recent governance judgment was established, forming the corresponding compliance duration indicator. The number of re-governance triggers is the cumulative statistics of the number of re-governance processes triggered by structural changes, semantic changes or governance rule updates during the life cycle of the data, forming the re-governance trigger count indicator.

[0074] It should be noted that suggestions for optimizing governance rules include appropriately relaxing the threshold range of corresponding judgment conditions, expanding the acceptable business scenarios or usage types within the scope of rule application, or merging overly granular judgments into higher-level judgment units to reduce unnecessary re-governance frequency and thus improve governance stability. By using compliance duration and the number of re-governance triggers as objective feedback signals of governance rule effectiveness, the system can continuously optimize the constraint strength, scope of application, and judgment granularity of governance rules without relying on subjective experience. This allows data asset governance strategies to continuously adjust themselves as data and business evolve, ultimately achieving a closed-loop governance system for the entire lifecycle of enterprise digital scenarios.

[0075] like Figure 6 As shown, Figure 6 The module diagram of the data asset lifecycle governance system for enterprise digitalization provided in this application embodiment includes: a governance judgment and constraint model construction module, which performs governance judgment processing on the target enterprise's digital asset data, constructs a governance judgment constraint model, and the governance judgment constraint model is used to carry the rule data mapping space for governance rules and to determine the status of digital assets, and generates the governance judgment result of the target enterprise's digital asset data.

[0076] The derived data adaptability assessment and rule matching measurement module is used to continuously process and map the target enterprise's digital asset data under the constraints of the governance judgment constraint model, forming target enterprise digital asset derived data to represent its compliance status. The module continuously monitors the compliance status of the target enterprise's digital asset derived data within the established governance judgment constraint model and obtains the rule matching measurement value of the target enterprise's digital asset derived data.

[0077] The governance judgment constraint model adaptive adjustment module is used to dynamically adjust the boundary range of the governance judgment constraint model according to the matching metric value of the target enterprise's digital asset derived data rules, and generate the governance applicability level corresponding to the target enterprise's digital asset derived data, and adjust the execution intensity of governance actions according to the governance applicability level of the derived data; The governance strategy generation and execution control module is used to update the governance judgment constraint model when the governance rules are updated, and at the same time, it proposes optimization suggestions for the governance rules and sends the governance judgment results and governance judgment constraint model conditions into an executable governance strategy.

[0078] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary cases and components described herein may be configured differently from those described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

[0079] The various operations of the example methods described herein can be performed at least in part by an algorithm. This algorithm can be contained in program code or instructions stored in memory (e.g., the aforementioned non-transitory computer-readable storage medium). Such an algorithm may include a machine learning algorithm. In some embodiments, the machine learning algorithm may not be explicitly programmed into the computer to perform the function, but can learn from training data to create a predictive model that performs the function.

[0080] The various operations of the example methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0081] Similarly, the methods described herein can be implemented at least in part by a processor, where one or more specific processors are examples of hardware. For example, at least some operations of a method can be performed by one or more processors or an engine implemented by a processor. Furthermore, one or more processors can also be operated to support the performance of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations can be performed by a set of computers (as an example of a machine including processors), where these operations are accessible via a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application programming interfaces (APIs)).

[0082] The performance of certain operations can be distributed across processors, residing not only within a single machine but also deployed across multiple machines. In some example embodiments, the processor or processor-implemented engine may reside in a single geographic location (e.g., within a home environment, office environment, or server cluster). In other example embodiments, the processor or processor-implemented engine may be distributed across multiple geographic locations.

[0083] In this specification, multiple instances may implement components, operations, or structures described as single instances. Although individual operations of one or more methods are shown and described as separate operations, one or more of the separate operations may be performed simultaneously and do not need to be performed in the order shown. Structures and functions presented as separate components in the example configuration may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of this document.

[0084] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, by the term "invention," and are used for convenience only and are not intended to limit the scope of this application to any single disclosure or concept, should more than one disclosure or concept be disclosed in fact.

[0085] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A data asset lifecycle governance method for enterprise digitalization, characterized in that: Includes the following steps: Governance judgment processing is performed on the target enterprise's digital asset data, and a governance judgment constraint model is constructed. The governance judgment constraint model is used to carry the governance rules and determine the status of digital assets, and to generate the governance judgment result of the target enterprise's digital asset data. The governance judgment constraint model is a computable constraint model composed of a set of data structure constraints, a set of data semantic constraints, and a set of governance rule constraints, denoted as G=(S, M, R), where S represents the set of data structure constraints, M represents the set of data semantic constraints, and R represents the set of governance rule constraints. Under the constraints of the governance judgment constraint model, the target enterprise's digital asset data is continuously governed and mapped by rules to form target enterprise digital asset derived data that represents its compliance status. The compliance status of the target enterprise's digital asset derived data within the established governance judgment constraint model is continuously monitored to obtain the rule matching metric value of the target enterprise's digital asset derived data. The similarity between the semantic identifier of the purpose of use of the derived data and the semantic identifier recorded in the original judgment is used to determine whether the derived data still meets the applicable scope of the governance rules recorded in the original governance judgment constraint model, and the proportion of the number of governance rules that can continue to be applied to the number of the original rule set is calculated to obtain the rule matching metric value of the derived data of the target enterprise's digital assets. Semantic identifier similarity is obtained through text similarity calculation or vectorized semantic matching based on the semantic identifier information of the purpose of use, business scenario or compliance label recorded at the time of governance judgment of derived data and original target enterprise digital asset data; The boundary range of the governance judgment constraint model is dynamically adjusted based on the matching metric value of the derived data rules of the target enterprise's digital assets, and the governance applicability level corresponding to the derived data of the target enterprise's digital assets is generated. The execution intensity of the governance actions is adjusted according to the governance applicability level of the derived data. When governance rules are updated, the governance judgment constraint model is updated, and optimization suggestions are proposed for the governance rules. The governance judgment results and the governance judgment constraint model conditions are then sent out as executable governance strategies.

2. The data asset lifecycle governance method for enterprise digitalization as described in claim 1, characterized in that: The specific process of performing governance judgment processing on the target enterprise's digital asset data and constructing a governance judgment constraint model is as follows: Based on the collected copy of the target enterprise's digital asset data, the target enterprise's digital asset data is obtained. The data structure status information, data semantic description information, and the set of governance rules that are currently in effect are collected at the current judgment time. These are used as the basic building elements of the governance judgment environment for unified modeling and processing. The data structure status information is processed into structural feature vectorization. Based on the governance judgment establishment environment, and according to the set of governance rules currently in effect, the target enterprise's digital asset data copy is subjected to compliance verification according to the judgment granularity defined by the rules, and the governance judgment result of the target enterprise's digital asset data is generated. By binding and encapsulating the data structure status information, data semantic description information, and governance rule set used in the governance judgment execution process with the corresponding governance judgment results, a governance judgment constraint model corresponding to the governance judgment results of the target enterprise's digital asset data is constructed.

3. The data asset lifecycle governance method for enterprise digitalization as described in claim 1, characterized in that: The governance judgment results for generating the target enterprise's digital asset data include: Based on the predefined compliance conditions, constraint logic and verification methods in the governance rules, the target enterprise's digital asset data is mapped into various judgment units according to the granularity of judgment, and verification is performed on each judgment unit. After completing the verification of each judgment unit, the verification results within the scope of the same governance rule are aggregated within the rule to generate the rule-level judgment result corresponding to the governance rule. Then, according to the result synthesis logic preset in the governance strategy, the judgment results of multiple governance rules are comprehensively analyzed to generate the governance judgment result of the target enterprise's digital asset data.

4. The data asset lifecycle governance method for enterprise digitalization as described in claim 2, characterized in that: The governance judgment constraint model corresponding to the governance judgment result of the target enterprise's digital asset data also includes: When the target enterprise's digital asset data generates derived data, the data structure status information and data semantic description information corresponding to the derived data at the current moment are obtained, and the derived data is mapped to the governance dimension space corresponding to the original governance judgment and constraint model. By comparing the field sets of derived data with those of the original target enterprise's digital asset data, and statistically analyzing the percentage change in the number of fields, the percentage of fields retained, and the percentage of field type consistency, the consistency factor of the structure of the target enterprise's digital asset derived data is obtained. If the similarity between the structural consistency factor and the semantic identifier of the purpose of use of the derived data of the target enterprise's digital assets and the semantic identifier recorded in the original judgment is lower than the corresponding structural consistency factor threshold and semantic identifier similarity threshold stored in the database, then the boundary range of the governance judgment constraint model will be dynamically adjusted according to the rule matching metric value of the derived data of the target enterprise's digital assets; otherwise, the original governance judgment result will be determined to continue to be valid.

5. The data asset lifecycle governance method for enterprise digitalization as described in claim 4, characterized in that: The process of dynamically adjusting the boundary range of the governance judgment constraint model based on the matching metric value of the target enterprise's digital asset derived data rules is as follows: The target enterprise's digital asset derived data rule matching metric is compared with the pre-configured rule matching threshold range in the database to obtain the governance judgment constraint model boundary adjustment amount corresponding to the target enterprise's digital asset derived data rule matching metric. Based on the boundary adjustment amount, the allowable range of data structure consistency, the allowable lower limit of semantic similarity, and the lower limit of rule application ratio are adjusted proportionally.

6. The data asset lifecycle governance method for enterprise digitalization as described in claim 4, characterized in that: The specific process for generating the governance applicability level corresponding to the derived data of the target enterprise's digital assets is as follows: Based on the rule matching metric value of derived data derived from the target enterprise's digital assets, the governance applicability level of the derived data is obtained. The governance applicability level of the derived data includes the first applicability level, the second applicability level, and the third applicability level. The execution intensity of governance actions is adjusted according to the governance applicability level of the derived data.

7. The data asset lifecycle governance method for enterprise digitalization as described in claim 1, characterized in that: The process of updating the governance judgment constraint model when the governance rules are updated is as follows: When governance rules are updated, the rule constraint parameters of the governance rules before and after the change are obtained. The changed governance rules are mapped into rule constraint feature vectors according to the corresponding constraint objects, scope of application, judgment conditions and constraint strength. The vectors are then aligned with the original constraint boundary parameters of the corresponding rules in the original governance judgment constraint model. The differences between the upper and lower limits of the value range of each judgment condition in the governance rules and the allowable missing ratio are calculated relative to the original judgment conditions. The differences in the value range and allowable missing ratio of each judgment condition in the governance rules are obtained. Based on the difference in values ​​of each judgment condition in the governance rule and the difference in the allowable missing proportion, combined with the boundary range allowed by the corresponding judgment condition in the original governance judgment constraint model, the constraint change introduced when the governance rule is updated is normalized to obtain the constraint change of the governance rule in each judgment condition dimension, and the constraint change of the governance rule in each judgment condition dimension is mapped to the rule matching dimension of the governance judgment constraint model. The changes in the constraints of the governance rules on each judgment condition dimension are compared with the preset rule constraint boundary thresholds in the governance judgment constraint model to determine whether the update of the governance rules is still within the range of changes allowed by the original constraint model.

8. The data asset lifecycle governance method for enterprise digitalization as described in claim 7, characterized in that: The specific process for determining whether the updated governance rules are still within the range of changes allowed by the original constraint model is as follows: When the change in the constraint of the governance rule in the judgment condition dimension does not exceed the corresponding boundary threshold stored in the database, the original governance judgment result is maintained and the rule version evolution information is recorded. When the amount of change in the constraints of the governance rules in the judgment condition dimension exceeds the corresponding boundary threshold stored in the database, the original governance judgment constraint model is determined to be invalid in the rule matching dimension, and a re-governance process for the target enterprise's digital asset data is triggered.

9. The data asset lifecycle governance method for enterprise digitalization as described in claim 1, characterized in that: The specific process for proposing optimization suggestions for governance rules is as follows: The system statistically analyzes the compliance duration and re-governance trigger count of the target enterprise's digital asset data in the current governance judgment constraint model. If the compliance duration of the target enterprise's digital asset data in the current governance judgment constraint model is lower than or equal to the compliance duration stored in the database, and the re-governance trigger count is higher than or equal to the re-governance trigger count stored in the database, then optimization suggestions are made for the governance rules; otherwise, no optimization suggestions are needed for the governance rules.

10. A system applying the data asset lifecycle governance method for enterprise digitalization as described in any one of claims 1-9, comprising: The governance judgment and constraint model construction module performs governance judgment processing on the target enterprise's digital asset data, constructs a governance judgment and constraint model, which is used to carry the rule data mapping space for governance rules and to determine the status of digital assets, and generates the governance judgment result of the target enterprise's digital asset data; The derived data adaptability assessment and rule matching measurement module is used to continuously process and map the target enterprise's digital asset data under the constraints of the governance judgment constraint model, forming target enterprise digital asset derived data to characterize its compliance status. The module continuously monitors the compliance status of the target enterprise's digital asset derived data within the established governance judgment constraint model and obtains the rule matching measurement value of the target enterprise's digital asset derived data. The governance judgment constraint model adaptive adjustment module is used to dynamically adjust the boundary range of the governance judgment constraint model according to the matching metric value of the target enterprise's digital asset derived data rules, and generate the governance applicability level corresponding to the target enterprise's digital asset derived data, and adjust the execution intensity of governance actions according to the governance applicability level of the derived data; The governance strategy generation and execution control module is used to update the governance judgment constraint model when the governance rules are updated, and at the same time, it proposes optimization suggestions for the governance rules and sends the governance judgment results and governance judgment constraint model conditions into an executable governance strategy.

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