Data governance method, apparatus, device, and storage medium

Abstract

The application discloses a data governance method and device, equipment and storage medium, relates to the technical field of data quality governance, and comprises the following steps: acquiring each configuration item in a resource configuration database, wherein the configuration item contains attributes and a relationship between the current configuration item and other configuration items, the attributes and the relationship are respectively bound with check rules, and the attributes and the relationship are respectively provided with basic importance; determining a consumption interface of the attributes and the relationship, and collecting dynamic calling indexes of the consumption interface; determining a quality score weight of the configuration item according to the basic importance of the attributes and the relationship and the dynamic calling indexes of the corresponding consumption interface; weighting the quality score weight according to compliance inspection results and the quality score weight of the attributes and the relationship to obtain a quality score of the configuration item, wherein the compliance inspection results are obtained by performing compliance detection on the check rules corresponding to the attributes and the relationship; and when the quality score is lower than a preset governance threshold, performing differentiated governance on the configuration item. The application improves the data governance efficiency.

Description

Technical Field

[0001] This application relates to the field of data quality governance technology, and in particular to data governance methods, data governance devices, electronic devices and storage media. Background Technology

[0002] In traditional resource configuration data management, the attribute and relationship data of each configuration item are typically stored in different management modules, with their respective validation logic and management strategies independent of each other. However, the frequency and dependence of different attributes and relationships during business consumption vary significantly. Some attributes or relationships, despite being assigned high importance, are rarely consumed in actual business operations, while others, though less important, are critical dependencies for high-frequency business interactions. Under this management model, where static hierarchical classification is consistently disconnected from business consumption frequency, when quality issues arise in resource configuration data, it becomes impossible to distinguish which configuration items have the greatest impact on business and require priority repair. This leads to a large amount of governance resources being invested in low-impact or unaffected configuration items, while high-risk configuration items that truly impact business continuity are delayed. Consequently, the governance effect is severely out of sync with actual business needs, resulting in low data governance efficiency. Summary of the Invention

[0003] The main objective of this application is to provide a data governance method, data governance device, electronic device, and storage medium, aiming to solve the technical problem of low data governance efficiency.

[0004] To achieve the above objectives, this application proposes a data governance method, which includes: Retrieve each configuration item from the resource configuration database, wherein each configuration item includes attributes and relationships between the current configuration item and other configuration items, and each attribute and relationship is bound to a validation rule and has a basic importance set. Determine the consumption interface for the attribute and the relationship, and collect the dynamic call metrics of the consumption interface; The quality score weight of the configuration item is determined based on the basic importance of each attribute and relationship and the dynamic call index of the corresponding consumption interface. The quality score of the configuration item is obtained by weighting the compliance check results of the attribute and the relationship with the quality score weight, wherein the compliance check results are obtained by calling the verification rules corresponding to the attribute and the relationship to perform compliance detection; When the quality score is lower than a preset governance threshold, the configuration item is subject to differentiated governance.

[0005] In one embodiment, after the step of obtaining each configuration item from the resource configuration database, the method further includes: Obtain the filtering rules for each of the configuration items, wherein the filtering rules include filtering conditions based on the attributes of the configuration item itself and filtering conditions based on the attributes of other configuration items associated with the configuration item; A query statement is generated based on the filtering rules, and the target configuration item is selected from each of the configuration items by executing the query statement; The corresponding verification rules are invoked for the target configuration item to perform compliance checks, and the compliance check results are obtained.

[0006] In one embodiment, after the step of obtaining each configuration item from the resource configuration database, the method further includes: Obtain the compliance detection task for the configuration items to be tested from each of the aforementioned configuration items; Obtain the historical quality score, historical anomaly count, and basic importance of each of the configuration items to be detected; The priority score for each compliance testing task is calculated based on the historical quality score, the number of historical anomalies, and the basic importance. The compliance testing tasks are arranged according to the priority scoring order, a task execution queue is generated, and the compliance testing tasks are executed sequentially according to the task execution queue.

[0007] In one embodiment, after the step of generating the task execution queue, the method further includes: Get the number of attributes, the number of relationships, and the number of validation rules bound to the configuration item to be detected for the current detection task; The complexity factor of the configuration item to be detected is calculated based on the number of attributes, the number of relationships, and the number of verification rules. The number of target configuration items in the current detection task is determined based on the filtering rules of the configuration items to be detected; Calculate the number of concurrent threads based on the complexity factor and the number of target configuration items; Create a corresponding number of threads according to the stated number of concurrent threads to perform the compliance checks of the current detection task in parallel.

[0008] In one embodiment, the dynamic invocation metric includes the number of consumers and the frequency of interface calls; the step of determining the quality score weight of the configuration item based on the basic importance of the attribute and the relationship and the dynamic invocation metric of the corresponding consumer interface includes: The dynamic call index is normalized to obtain the normalized dynamic call index; The quality score weight of each configuration item is obtained by weighting and summing the basic importance with the normalized dynamic call index.

[0009] In one embodiment, the step of performing differentiated management on the configuration items includes: Obtain the anomaly type from the compliance check results; When the anomaly type is a manageable anomaly type, automatic repair is performed on the configuration item; When the anomaly type is not a manageable anomaly type, an approval work order is initiated to allow for manual data governance.

[0010] In one embodiment, when the quality score is lower than a preset governance threshold, the method further includes: Identify the abnormal configuration items whose quality scores are lower than a preset governance threshold, and the target consumption interface of the abnormal configuration items; Obtain the target business system to which the target consumption interface belongs; Based on the topological relationship of the abnormal configuration item in the resource configuration database, determine the affected configuration items involved by the abnormal configuration item; A governance notification is generated based on the exception type of the exception configuration item, the target consumption interface, the target business system, and the impact configuration item.

[0011] Furthermore, to achieve the above objectives, this application also proposes a data governance apparatus, which includes: The acquisition module is used to acquire each configuration item in the resource configuration database. The configuration item includes attributes and relationships between the current configuration item and other configuration items. The attributes and relationships are bound to validation rules and have basic importance set. The data collection module is used to determine the consumption interface of the attribute and the relationship, and to collect the dynamic call indicators of the consumption interface; The determination module is used to determine the quality score weight of the configuration item based on the basic importance of the attribute and the relationship and the dynamic call index of the corresponding consumption interface. The weighting module is used to weight the quality score of the configuration item by the compliance check results of the attribute and the relationship and the quality score weight, wherein the compliance check results are obtained by calling the verification rules corresponding to the attribute and the relationship to perform compliance detection; The governance module is used to perform differentiated governance on the configuration items when the quality score is lower than a preset governance threshold.

[0012] In addition, to achieve the above objectives, this application also proposes an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data governance method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the data governance method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the data governance method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: This application assigns a basic importance to the attributes and relationships of each configuration item and collects dynamic call metrics of the consumption interfaces for each attribute and relationship in real time. The process of determining the quality score weight takes into account both basic importance and dynamic call metrics. Basic importance reflects the pre-defined importance of each attribute and relationship in management, while dynamic call metrics reflect the actual consumption frequency of each attribute and relationship in business interactions. The combination of these two factors allows the quality score weight to integrate both static level and dynamic dependency information. Based on this, a weighted calculation is performed using the compliance check results of each attribute and relationship and this weight. The resulting quality score can accurately characterize the actual business impact when different configuration items encounter problems. Therefore, when differentiating the governance of configuration items based on this quality score, governance resources can be prioritized for configuration items with high weight and significant business impact, while those with low weight and weak business dependency can have their governance priority reduced or be handled using a lightweight approach. The technical solution of this application embodiment achieves a precise match between governance resource investment and actual business needs, avoiding the resource mismatch phenomenon in traditional solutions where low-impact issues are over-managed and high-risk issues are delayed due to the separation between static levels and business activity. This significantly improves data governance efficiency and business continuity assurance level. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the data governance method of this application Figure 1 ; Figure 2 A flowchart illustrating an embodiment of the data governance method of this application Figure 2 ; Figure 3 A flowchart illustrating an embodiment of the data governance method of this application Figure 3 ; Figure 4 A flowchart illustrating an embodiment of the data governance method of this application Figure 4 ; Figure 5 A flowchart illustrating an embodiment of the data governance method of this application Figure 5 ; Figure 6 This is a schematic diagram of the module structure of the data governance device of this application; Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the data governance method of this application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] It should be noted that the executing entity in this embodiment can be an electronic device, such as a server, desktop computer, laptop computer, or other computing device with data processing and communication functions; it can also be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, or a cloud server, server cluster, distributed computing platform, etc., capable of realizing the above functions. The following description uses an electronic device as an example to illustrate this embodiment and the subsequent embodiments.

[0022] Based on this, embodiments of this application provide a data governance method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the data governance method of this application.

[0023] In one feasible embodiment, the application scenarios of the technical solution of this application can be: IT operation and maintenance management in the financial industry, resource management of cloud computing platforms, configuration management database operation and maintenance of large enterprises, etc.

[0024] In this embodiment, the data governance method includes steps S1 to S5: Step S1: Obtain each configuration item in the resource configuration database. The configuration item includes attributes and relationships between the current configuration item and other configuration items. The attributes and relationships are bound to validation rules and have basic importance set. It should be noted that a resource configuration database is a database system used to store and manage IT asset configuration information and their relationships, such as a configuration management database (CMDB).

[0025] Optionally, configuration items are digital objects of IT resources managed in the resource configuration database, such as electronic equipment resources related to servers, network devices, database instances, or application systems.

[0026] Optionally, attributes are data fields that describe the characteristics of the configuration item itself, such as IP address, operating system version, number of CPU cores, etc.; relationships are data structures used to characterize the topological connections or dependency associations between the current configuration item and other configuration items, such as "belongs to" relationships or "connected to" relationships.

[0027] Optionally, the validation rules are predefined judgment conditions used to detect whether the attribute values ​​or relationship links of configuration items meet the compliance requirements. For example, the IP address format validation rule is used to detect whether the IP address field conforms to the dotted decimal format, and the relationship validation rule is used to detect whether the dependency link between the current configuration item and other configuration items is complete.

[0028] Optionally, the validation rules adopt a template-based management approach, pre-encapsulating general validation logic into rule templates. Different attributes or relationships of different configuration items directly reference the corresponding rule templates when binding validation rules, eliminating the need for repeated configuration. The binding of rule templates with attributes or relationships supports multi-rule combination validation. Attribute rules are used to perform compliance checks on the value of a single attribute, relationship rules are used to perform compliance checks on the association links between configuration items, and combination rules are used to logically combine multiple attribute rules and / or relationship rules to perform joint checks. Rule types include validity rules, timeliness rules, integrity rules, intra-model consistency rules, and inter-model consistency rules.

[0029] Optionally, the base importance is a quantitative value that is pre-set for each attribute or relationship, reflecting the degree of importance that the attribute or relationship is pre-determined to be at the management level. For example, the IP address attribute of the core trading system is set to a high base importance, while the notes field of the test environment is set to a low base importance.

[0030] Optionally, the electronic device reads each configuration item and its corresponding attribute and relationship data from the resource configuration database through the database connection interface, and loads the verification rules and basic importance settings pre-configured for each attribute and relationship.

[0031] Understandably, since different attributes and relationships have varying degrees of importance in management, setting a basic importance for each attribute and relationship can provide an initial differentiated basis for determining subsequent governance priorities.

[0032] Step S2: Determine the consumption interface for the attribute and the relationship, and collect the dynamic call metrics of the consumption interface; It should be noted that the consumption interface is the API (Application Programming Interface) that provides data access services to the external resource configuration database. External business systems use this interface to obtain attribute data or relationship data from the resource configuration database. Different consumption interfaces establish a mapping relationship with the attributes and relationships they consume when they are applied for. That is, each consumption interface clearly records which configuration items and which attributes or relationships it accesses.

[0033] Optionally, the electronic device determines which consumer interfaces consume each attribute and relationship in the current configuration item based on the interface application record or interface configuration information.

[0034] Optionally, dynamic call metrics are statistical data on the frequency or number of times each consumer interface is called by external business systems within a preset time window.

[0035] Optionally, the electronic device aggregates and statistically analyzes the call logs of each consumer interface according to a preset statistical period (e.g., the most recent hour or the most recent day) through the log collection component or API gateway, and obtains the total number of calls and the number of consumers for each consumer interface within the statistical period, as a dynamic call indicator.

[0036] Optionally, each call to the consumption interface by the external business system indicates that the attribute or relationship corresponding to that call has been consumed by the actual business once. Therefore, the dynamic call metric of the consumption interface can reflect the actual consumption frequency of its corresponding attribute or relationship in business interactions.

[0037] It is understandable that different attributes and relationships in the configuration items are invoked at different frequencies during business consumption. Some attributes or relationships, although set with high basic importance, are rarely consumed in actual business, while other attributes or relationships, although with low basic importance, are key dependencies for high-frequency business interactions. Therefore, by introducing dynamic call metrics for the consumption interface, information reflecting the actual consumption frequency of the business can be provided for determining the quality score weight, based on the basic importance.

[0038] Step S3: Determine the quality score weight of the configuration item based on the basic importance of each attribute and relationship and the dynamic call index of the corresponding consumption interface; It should be noted that the quality score weight is used to measure the proportion of influence of each attribute or relationship in the quality score of the configuration item.

[0039] For each attribute and relationship, the electronic device combines its basic importance with the dynamic call indicators of each consumer interface corresponding to that attribute or relationship to determine the quality score weight of that attribute or relationship.

[0040] Optionally, the quality score weights are determined as follows: obtain the pre-set base importance L for each attribute and relationship, as well as the normalized number of consumers C(t) and the normalized interface call frequency V(t). Substitute these parameters into the weight formula W(t)=α·L+β·C(t)+γ·V(t) to calculate the quality score weights for each attribute and relationship. Here, the base importance L is a fixed value and is pre-set according to the importance of the attribute or relationship. The number of consumers C(t) and the interface call frequency V(t) are normalized dynamic values. α, β, and γ are weight parameters that are dynamically adjusted through the reinforcement learning model.

[0041] Optionally, the electronic device dynamically adjusts α, β, and γ through a reinforcement learning model. During the adjustment process, the number of consumers C(t) and the frequency of interface calls V(t) are normalized using Min-Max Scaling to eliminate the difference in dimensions. The improvement in the score after governance, the time consumed by the governance task, and the fluctuation of the weights are used as the optimization objectives of the multi-objective optimization function to drive the reinforcement learning model to output the values ​​of α, β, and γ that make the overall governance effect optimal.

[0042] Optionally, electronic devices can have quality score thresholds set according to the type of configuration item. Different types correspond to different preset governance thresholds. For example, when a server model configuration item has a quality score below 90%, an alarm and subsequent governance process are triggered to achieve a differentiated governance strategy. For instance, if an attribute has a high basic importance but its corresponding consumer interface is called very infrequently over a period of time, its quality score weight is lowered accordingly; conversely, if an attribute has a medium basic importance but its corresponding consumer interface is called very frequently, its quality score weight is increased accordingly.

[0043] Understandably, by combining basic importance with dynamic call metrics to determine the quality score weight, this weight carries both static management level and dynamic business popularity information. When a quality problem occurs in a certain attribute or relationship, the higher its quality score weight, the greater the impact of the problem on the actual business, and it should be given priority for governance.

[0044] Step S4: The quality score of the configuration item is obtained by weighting the compliance check results of the attribute and the relationship with the quality score weight, wherein the compliance check results are obtained by calling the verification rules corresponding to the attribute and the relationship to perform compliance detection; It should be noted that the compliance check results are obtained by calling the respective bound verification rules to perform compliance checks on each attribute and each relationship.

[0045] Optionally, compliance checks for attributes include: reading the current value of the attribute, calling the validation rules bound to the attribute (such as non-empty validation, format validation, value range validation, etc.) to perform logical judgment on the attribute value, and determining that the attribute is compliant if the requirements of the validation rules are met, and that the attribute is non-compliant if the requirements are not met.

[0046] Optionally, compliance checks for a relationship include: reading the current link data of the relationship, calling the verification rules bound to the relationship (such as relationship integrity verification, relationship consistency verification, etc.) to perform logical judgments on the relationship link, and determining that the relationship is compliant if the requirements of the verification rules are met, and that the relationship is non-compliant if the requirements are not met.

[0047] After the electronic device obtains the compliance check results for each attribute and each relationship, it performs a weighted calculation on the compliance check results of each attribute and each relationship with the corresponding quality score weight determined in step S3. That is, for each attribute or relationship, its compliance check result (e.g., compliance is 1, non-compliance is 0) is multiplied by the corresponding quality score weight, and then the weighted results of all attributes and relationships are summed to obtain the quality score of the configuration item.

[0048] Optionally, the quality score of a configuration item is equal to the sum of the scores of each attribute and each relation; the individual score of an attribute or relation can be obtained by multiplying the quality score weight of that attribute or relation by the corresponding compliance check result, and then multiplying by 100. For example: a configuration item includes attribute A, attribute B, and relation C: attribute A has a quality score weight of 0.3, and the compliance check result is compliant, so its individual score is 0.3 × 1 × 100 = 30; attribute B has a quality score weight of 0.2, and the compliance check result is non-compliant, so its individual score is 0.2 × 0 × 100 = 0; relation C has a quality score weight of 0.5, and the compliance check result is compliant, so its individual score is 0.5 × 1 × 100 = 50; the quality score of the configuration item is 30 + 0 + 50 = 80 points.

[0049] Optionally, in the quality scoring process, in addition to calculating the overall quality score of the configuration item, an independent sub-score is calculated separately using the same quality scoring formula for the attributes and corresponding relationships that are pre-marked as important. This independent sub-score only uses the marked important attributes and their corresponding relationships as the objects of calculation. This independent sub-score and the overall quality score of the configuration item are output simultaneously as parallel indicators to reflect the data quality status of the marked important attributes and their corresponding relationships.

[0050] Understandably, since the quality score weighting already incorporates both basic importance and dynamically invoked metrics, the weighted quality score can accurately reflect the overall data quality level of the configuration item. Furthermore, the attributes or relationships corresponding to high scores have a greater impact on the business, and their quality issues should be addressed first.

[0051] Step S5: When the quality score is lower than the preset governance threshold, differentiated governance is performed on the configuration item.

[0052] It should be noted that the preset governance threshold is a pre-set scoring threshold used to trigger governance actions, and different configuration items can set different preset governance thresholds.

[0053] Optionally, for configuration items carrying core business, a higher preset governance threshold (e.g., 90 points) is set, and governance is triggered once the quality score falls below this threshold; for configuration items carrying non-core business, a lower preset governance threshold (e.g., 70 points) is set, and governance is triggered only when the quality score is severely low.

[0054] The electronic device compares the quality score of a configuration item with the preset governance threshold corresponding to that configuration item. When the quality score is lower than the preset governance threshold, the electronic device performs differentiated governance on that configuration item.

[0055] Optionally, differentiated governance refers to determining the quality score weights and compliance check results of each attribute and relationship based on the quality score of the configuration item, and adopting different governance strategies for different configuration items or different attributes and relationships within the same configuration item.

[0056] Optionally, differentiated governance includes: for attributes or relationships with high quality score weights and non-compliance results, electronic devices mark them as high-priority governance objects and allocate governance resources to them for processing first; for attributes or relationships with low quality score weights and non-compliance results, electronic devices mark them as low-priority governance objects and allocate fewer governance resources or postpone processing.

[0057] Optionally, after determining that the quality score is lower than the preset governance threshold, the electronic device can also trace back the target consumption interface that references the configuration item and the dependent business system through the graph database according to the pre-built interface consumption mapping relationship, and output a governance notification to help governance personnel quickly locate the impact of the problem and perform targeted repairs.

[0058] Understandably, by comparing the quality score with the preset governance threshold, the determination of governance trigger conditions is achieved; while differentiated governance ensures that the order of governance resource investment matches the actual business impact. Governance resources are prioritized for application to configuration items and their attributes or relationships that have the greatest impact on the business, avoiding the resource mismatch phenomenon of low-impact issues occupying too many governance resources and high-risk issues being delayed in processing.

[0059] Optionally, the electronic device leverages multi-dimensional data aggregation and end-to-end traceability capabilities to achieve visualized monitoring and closed-loop management of configuration item quality issues. Specifically, the electronic device generates structured reports based on issue type, configuration item type, and time period. Issue types include attribute errors and missing relationships; configuration item types include servers and databases; and time periods include daily, weekly, and monthly reports. The generated reports can be exported to external files. The electronic device generates dynamic trend charts based on historical quality scoring data, supporting analysis of quality change trends by configuration item type and time dimension, including hourly, daily, and weekly dimensions. The electronic device records rule verification task logs, scoring calculation processes, and governance process execution approval records, enabling traceability and auditing of the entire governance process.

[0060] This embodiment provides a data governance method. By setting basic importance for the attributes and relationships of each configuration item and collecting dynamic call indicators of the consumption interfaces of each attribute and relationship in real time, the determination process of the quality score weight takes into account both basic importance and dynamic call indicators. Basic importance reflects the pre-defined importance of each attribute and relationship in management, while dynamic call indicators reflect the actual consumption frequency of each attribute and relationship in business interactions. The combination of these two factors allows the quality score weight to integrate both static level and dynamic dependency information. Based on this, the quality score is calculated by weighting the compliance check results of each attribute and relationship with this weight. The resulting quality score can accurately characterize the actual business impact when different configuration items have problems. Therefore, when differentiating the governance of configuration items based on this quality score, governance resources can be prioritized for configuration items with high weight and significant business impact, while the governance priority of configuration items with low weight and weak business dependency can be reduced or a lightweight processing method can be adopted. The technical solution of this application embodiment achieves a precise match between governance resource investment and actual business needs, avoiding the resource mismatch phenomenon in traditional solutions where low-impact issues are over-managed and high-risk issues are delayed due to the separation between static levels and business activity. This significantly improves data governance efficiency and business continuity assurance level.

[0061] Based on the above embodiments of this application, in another embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter. In addition, after step S1, the method further includes: Step A11: Obtain the filtering rules for each configuration item, wherein the filtering rules include filtering conditions based on the attributes of the configuration item itself and filtering conditions based on the attributes of other configuration items associated with the configuration item. It should be noted that in the resource configuration database, the importance and business attributes of different configuration items vary significantly. If a full scan of all configuration items is performed for each compliance check, it will consume a large amount of computing resources and time, especially when the number of configuration items reaches hundreds of thousands or even millions, at which point the detection efficiency will drop drastically. Therefore, it is necessary to filter configuration items based on filtering rules to narrow down the scope of compliance checks.

[0062] Filtering rules are pre-defined conditions used to select configuration items that need to be checked for compliance from all configuration items in the resource configuration database.

[0063] The filtering rules include two types of filtering conditions: filtering conditions based on the configuration item's own attributes, which refers to filtering conditions set directly on the data fields of the current configuration item itself; and filtering conditions based on the attributes of other configuration items associated with the configuration item, which refers to filtering conditions that can be set by referencing the attribute fields of other configuration items that are related to this configuration item through the relationship chain between the configuration items.

[0064] Optionally, the electronic device generates a query statement based on filtering rules, and selects the target configuration item from each configuration item by executing the query statement. Through the above filtering mechanism, a flexible choice between full detection and incremental detection is achieved, that is, it is not limited to incremental detection based on the time dimension, but rather to conditional incremental detection based on filter configuration. For example, assuming that compliance detection needs to be performed on configuration items of type "server", a set of filters is configured to determine which server instances need to be included in the detection scope: Filter 1 is a model-intra-condition filter (i.e., a filtering condition based on the configuration item's own attributes), which filters out servers with an operating system type of "Linux" and a deployment environment of "production environment"; Filter 2 is a model-inter-condition filter (i.e., a filtering condition based on the attributes of other configuration items associated with the configuration item), which excludes servers whose associated application system status is "offline" (i.e., filtering by referencing the "running status" field of the application system through the "deployed" or "belongs to" relationship between the server and its application system); only server instances that pass both Filter 1 and Filter 2 will be included in the scope of this compliance detection and participate in the subsequent quality score calculation. Similarly, when it is necessary to filter out database instances whose associated physical machines are not in a "stopped" state, this condition is based on the "deployed on" relationship between the database instance and the physical machine, and uses the "stopped" field of the physical machine for filtering.

[0065] Optionally, filtering rules can be pre-configured by the administrator based on business needs or operation and maintenance strategies, and take effect immediately after the configuration is completed and approved.

[0066] Understandably, by supporting two types of filtering conditions simultaneously, filtering rules can flexibly express complex business filtering logic. They can filter from the dimension of the configuration item itself, or from the dimension of other related configuration items, thereby accurately locating the set of configuration items that need to be checked for compliance.

[0067] Step A12: Generate a query statement based on the filtering rules, and select the target configuration item from each of the configuration items by executing the query statement; It should be noted that after obtaining the filtering rules, the electronic device parses the filtering rules, extracts the filtering conditions based on the attributes of the configuration item itself and the filtering conditions based on the attributes of other configuration items associated with the configuration item, and converts these filtering conditions into executable query statements according to the query syntax of the resource configuration database.

[0068] Optionally, when the filtering criteria only involve the attributes of the configuration item itself, the electronic device generates a Structured Query Language (SQL) query statement; when the filtering criteria involve the attributes of other configuration items associated with the configuration item, the electronic device generates a graph database query statement, such as a Cypher query statement, because it involves topological connection queries between configuration items.

[0069] The electronic device executes the query statement, sending it to the database engine corresponding to the resource configuration database for retrieval. The database engine matches the query against all configuration item data based on the filtering conditions in the query statement, returning the configuration items that simultaneously meet the filtering conditions based on both the configuration item's own attributes and the attributes of other associated configuration items as the query results. The electronic device receives the query results and identifies the selected configuration items as the target configuration items.

[0070] Optionally, the target configuration item refers to the set of configuration items that actually need to be tested for compliance during this compliance testing cycle. The number of target configuration items is usually less than or equal to the total number of configuration items in the resource configuration database.

[0071] Understandably, by executing query statements for filtering, the number of configuration items actually included in the subsequent compliance testing scope is greatly reduced, enabling incremental or conditional testing and effectively alleviating the computational pressure of subsequent compliance testing steps.

[0072] Step A13: Call the corresponding verification rules to perform compliance checks on the target configuration item and obtain the compliance check results.

[0073] It should be noted that after selecting the target configuration item, the electronic device performs compliance checks only on that target configuration item, not on all configuration items. For each configuration item in the target configuration item, the electronic device determines its included attributes and relationships, and then calls the verification rules pre-bound to these attributes and relationships to perform compliance judgments on each attribute value and relationship link of that configuration item.

[0074] Optionally, if a target configuration item contains three attributes and one relationship, the electronic device calls the attribute validation rules bound to each of the three attributes and the relationship validation rules bound to the relationship, respectively, to obtain the individual compliance check results for the three attributes and the individual compliance check result for the relationship. The electronic device summarizes the individual compliance check results for each attribute and each relationship in each target configuration item to obtain the compliance check results for all target configuration items.

[0075] Optionally, the compliance check results include compliance or non-compliance. The non-compliance results record information such as the configuration item identifier, attribute name or relationship type corresponding to the non-compliant attribute or relationship, the configuration item identifier, attribute name or relationship type corresponding to the non-compliant attribute or relationship, the violation check rule identifier, and the rule content.

[0076] Understandably, performing compliance checks only on the target configuration items selected by the filtering rules ensures that the configuration items that need to be focused on are covered, while avoiding the consumption of computing resources on configuration items that do not need to be checked. This significantly improves the execution efficiency of compliance checks, while ensuring that the data used for subsequent quality scoring comes only from the selected configuration items to be checked, making the scoring results more targeted.

[0077] In one possible implementation, after step S1, the method further includes: Step B11: Obtain the compliance detection task for the configuration item to be detected in each of the configuration items; It should be noted that during the daily operation and maintenance of the resource configuration database, periodic compliance checks on configuration items are required to continuously monitor data quality. Each configuration item to be checked corresponds to a compliance check task.

[0078] The configuration items to be tested refer to the configuration items that need to be tested for compliance within the current testing period. Optionally, the configuration items to be tested can be all the configuration items in the resource configuration database, or the target configuration items selected by the aforementioned filtering rules.

[0079] The electronic device obtains each configuration item to be tested and its corresponding compliance testing task. Specifically, the electronic device determines the list of configuration items that need to be tested for compliance within the current testing cycle according to the preset testing plan or task scheduling strategy, and generates a corresponding compliance testing task for each configuration item in the list.

[0080] Optionally, the detection plan can be configured to be triggered periodically by hour, day, or week, or it can be manually triggered by operations and maintenance personnel.

[0081] Understandably, due to the significant differences in data volume and verification rule complexity among different configuration items, the execution time and computing resources required for each compliance detection task are different. Therefore, it is necessary to further distinguish the execution priority of each detection task to ensure that the detection results of key configuration items can be obtained first.

[0082] Step B12: Obtain the historical quality score, historical anomaly count, and basic importance of each configuration item to be detected; It should be noted that before determining the execution order of each compliance testing task, the electronic device needs to acquire the data required for calculating task priorities. Historical quality score refers to the quality score value calculated for the configuration item to be tested in the previous or preceding testing cycle; this value reflects the data quality level of the configuration item at a past point in time.

[0083] The number of historical anomalies refers to the total number of compliance anomalies that occurred in the configuration item to be tested within a preset historical time period (e.g., the past 7 days or the past 30 days). Optionally, the number of historical anomalies can be obtained by statistically analyzing the total number of non-compliant attributes and relationships of the configuration item in each previous testing period.

[0084] Basic importance is a pre-defined quantitative value for a configuration item, reflecting the degree of importance that the configuration item is pre-determined to be at the management level.

[0085] Electronic devices obtain the historical quality score, historical anomaly count, and basic importance of each configuration item to be tested by querying the historical scoring record table, the anomaly statistics table, and the configuration item basic information table.

[0086] Understandably, a lower historical quality score indicates that the data quality of the configuration item has been poor for a long time and needs to be prioritized for testing to confirm whether quality problems still exist. The more historical anomalies there are, the more likely the configuration item is a "high-risk" configuration item with frequent problems and needs to be prioritized for testing. The higher the basic importance, the higher the business value carried by the configuration item and the greater the impact of its data quality problems, and it also needs to be prioritized for testing.

[0087] Step B13: Calculate the priority score of each compliance detection task based on the historical quality score, the number of historical anomalies, and the basic importance. It should be noted that the priority score is a comprehensive score used to quantitatively assess the urgency of each compliance testing task. The higher the priority score, the earlier the task should be executed. For each compliance testing task, the electronic device performs a weighted comprehensive calculation of the three parameters obtained in step B12: the historical quality score, the number of historical anomalies, and the basic importance of the configuration item to be tested. This calculation yields the priority score for the compliance testing task.

[0088] Optionally, the higher the basic importance, the higher the priority score; the lower the historical quality score, the higher the priority score; and the more historical anomalies, the higher the priority score.

[0089] Optionally, the specific formula for calculating the Priority Score is: Priority Score = w1 × Importance + w2 × (100 QualityScore) + w3 × (100 ExceptionCount), where Importance is the importance level of the configuration item, QualityScore is the historical quality score, ExceptionCount is the number of historical exceptions, and w1, w2, and w3 are preset weight coefficients. Specifically, the electronic device multiplies the base importance by the first weight coefficient, multiplies (the difference between the preset maximum score and the historical quality score) by the second weight coefficient, and multiplies (the difference between the preset maximum number of exceptions and the number of historical exceptions) by the third weight coefficient, and then sums them up to obtain the priority score.

[0090] Understandably, through the comprehensive calculation of the above three dimensions, the priority score reflects information on the management importance, historical quality level, and historical stability of the configuration item, enabling the detection task of configuration items with high importance, low historical score, and multiple anomalies to obtain a higher priority score.

[0091] Step B14: Arrange the compliance detection tasks according to the priority scoring order, generate a task execution queue, and execute each compliance detection task sequentially according to the task execution queue.

[0092] It should be noted that the task execution queue is an ordered list of tasks arranged according to the execution order of compliance testing tasks. After calculating the priority scores of all compliance testing tasks, the electronic device sorts the compliance testing tasks in descending order of priority scores, and the sorted task sequence determines the task execution queue.

[0093] Optionally, when two or more compliance testing tasks have the same priority score, they are further sorted according to their basic importance from high to low or according to their creation time from earliest to latest. After the electronic device generates a task execution queue, it retrieves and executes each compliance testing task in the order of the queue. Executing a compliance testing task specifically involves calling the verification rules bound to the configuration item to be tested corresponding to the task to perform compliance testing.

[0094] Optionally, the electronic device executes tasks one by one in the queue order. After the previous task is completed and the compliance check result is output, the next task in the queue will be executed.

[0095] Understandably, generating task execution queues through priority scoring ensures that critical, high-risk, and long-term poor-quality configuration items can complete compliance checks first. Their compliance check results can be prioritized for subsequent quality scoring and governance decisions, avoiding delays in obtaining the test results of important configuration items due to indiscriminate sequential execution, thereby improving the timeliness of the overall governance response.

[0096] In one possible implementation, after step B14, the method further includes: Step C11: Obtain the number of attributes, the number of relationships, and the number of bound verification rules for the configuration item to be detected corresponding to the current detection task; It should be noted that before performing each compliance testing task, electronic devices need to assess the computing resources required for that task.

[0097] The number of attributes refers to the total number of attribute fields contained in the current configuration item to be tested; the number of relationships refers to the total number of topological connections or dependency relationships established between the current configuration item to be tested and other configuration items; the number of bound validation rules refers to the total number of validation rules pre-bound to each attribute and each relationship of the current configuration item to be tested.

[0098] The electronic device obtains the three values ​​mentioned above by reading the metadata information and rule configuration information of the configuration item in the resource configuration database.

[0099] Understandably, the number of attributes, relationships, and bound verification rules vary significantly among different configuration items. For example, a server configuration item may contain dozens of attributes and multiple relationship links, while a simple application configuration item may contain only a few attributes and relationships. The more attributes, relationships, and bound verification rules a configuration item has, the greater the computational workload required to perform a complete compliance check on that configuration item. Therefore, it is necessary to obtain the above three parameters to assess the complexity of the current detection task.

[0100] Step C12: Calculate the complexity factor of the configuration item to be detected based on the number of attributes, the number of relationships, and the number of verification rules; It should be noted that the complexity factor is a comprehensive value used to quantify the complexity of performing compliance checks on the current configuration item to be checked. This value reflects the relative amount of computational work required to perform the current check task.

[0101] The electronic device performs a weighted comprehensive calculation on the number of attributes, the number of relationships, and the number of verification rules obtained in step C11 to obtain the complexity factor of the configuration item to be detected.

[0102] Optionally, the weight coefficients corresponding to the three quantities are preset based on actual operation and maintenance experience; parameters with a higher proportion of computing resource consumption are assigned higher weight coefficients, for example, the number of verification rules usually has a greater impact on computing resources than the number of attributes.

[0103] Optionally, the complexity factor is calculated as follows: multiply the number of attributes by the first preset weight, the number of relations by the second preset weight, and the number of validation rules by the third preset weight, and then sum them up. The sum is then incremented by 1 to obtain the final complexity factor. The increment is to prevent the complexity factor from being zero or too small, which could lead to an abnormal number of threads in subsequent calculations.

[0104] Understandably, the more attributes, relationships, and binding validation rules a configuration item contains, the higher the complexity factor becomes. This means that performing compliance checks on the configuration item requires more computing resources and takes longer, thus necessitating the allocation of more concurrent threads to accelerate the checks.

[0105] Step C13: Determine the number of target configuration items in the current detection task based on the filtering rules of the configuration items to be detected; It should be noted that, as mentioned above, by executing a query statement generated based on filtering rules, the target configuration item can be filtered out from each configuration item.

[0106] In this step, the electronic device determines the actual number of configuration items to be tested for the current compliance testing task based on pre-configured filtering rules. Specifically, the electronic device obtains the filtering rules for the configuration items to be tested corresponding to the current testing task, executes the query statement corresponding to the filtering rules, and counts the number of configuration items that meet all filtering conditions as the target number of configuration items.

[0107] Optionally, when a filtering rule contains multiple filtering conditions, the multiple filtering conditions are logically ANDed, meaning that the target configuration item must satisfy all filtering conditions simultaneously.

[0108] Understandably, the number of target configuration items directly determines the total data volume of the current detection task. The larger the number of target configuration items, the greater the total workload of compliance detection. Therefore, it needs to be used for subsequent calculation of the number of concurrent threads.

[0109] Step C14: Calculate the number of concurrent threads based on the complexity factor and the number of target configuration items; It should be noted that the number of concurrent threads is the number of threads that an electronic device simultaneously starts to perform compliance checks in parallel in order to execute the current compliance check task.

[0110] The electronic device takes the complexity factor and the number of determined target configuration items as input parameters, and calculates the number of concurrent threads applicable to the current detection task according to the preset concurrent thread number calculation strategy.

[0111] Optionally, the logic of this calculation strategy is as follows: the number of concurrent threads is positively correlated with both the complexity factor and the number of target configuration items, that is, the higher the complexity factor or the larger the number of target configuration items, the more concurrent threads are calculated; at the same time, the calculation strategy also sets a maximum thread limit to prevent the number of threads from growing indefinitely and causing the system resources to be exhausted.

[0112] Optionally, the complexity factor is calculated using the formula C = 1 + w4 × f + w5 × r + w6 × ρ, where f is the number of attributes of the current configuration item to be detected, r is the number of relations, ρ is the number of bound validation rules, and w4, w5, and w6 are preset weight coefficients; the concurrent thread count is calculated using the formula Thread Count = min((30 × C × ln(N+1)) / (1+C), Max), where N is the number of target configuration items determined based on the filtering rules, and Max is the preset maximum thread count. For example, the electronic device first uses the complexity factor as a benchmark, combines it with the logarithmic growth value of the number of target configuration items for comprehensive calculation, and then takes the smaller value between the calculated result and the preset maximum thread count as the final determined concurrent thread count.

[0113] Understandably, introducing the target configuration item number in a logarithmic form slows down the growth rate of the number of threads when the target configuration item number is extremely large, thus avoiding excessive expansion of the number of threads due to a surge in the number of configuration items. By setting a maximum thread limit, excessive concurrency is prevented from causing system resource contention or crashes, ensuring the stable operation of electronic devices under high load.

[0114] Step C15: Create a corresponding number of threads according to the number of concurrent threads to perform the compliance detection of the current detection task in parallel.

[0115] It should be noted that electronic devices create the calculated number of concurrent threads. A thread is the smallest unit of execution that an operating system can schedule; multiple threads can concurrently execute different subtasks within the same process.

[0116] The electronic device allocates all target configuration items in the current compliance detection task to each thread according to the number of concurrent threads created, so that each thread can perform compliance detection in parallel by calling the corresponding verification rules for its assigned target configuration items.

[0117] Optionally, the allocation method can be equal distribution, where the total number of target configuration items is divided by the number of concurrent threads to obtain the number of configuration items each thread is responsible for checking; or a dynamic allocation method can be used, where a thread continues to retrieve new configuration items from the task queue after completing the checking of the currently allocated configuration items, until all target configuration items have been checked. Each thread outputs the compliance check sub-results for each attribute and relationship in the target configuration items it is responsible for. The electronic device merges and summarizes the sub-results output by all threads to form the complete compliance check result for the current detection task.

[0118] Understandably, by using concurrent threads to execute compliance checks in parallel, the multi-core processor resources of electronic devices are fully utilized. The check tasks that originally needed to be executed serially are split into multiple sub-tasks to be executed in parallel, which significantly shortens the total time for large-scale configuration item compliance checks and improves the overall detection efficiency of the data governance system. At the same time, the number of threads is dynamically calculated based on the complexity factor and the number of target configuration items, avoiding the problem of resource waste caused by a fixed number of threads when the number of configuration items is small or insufficient processing capacity when the number of configuration items is extremely large.

[0119] Based on the above embodiments of this application, in another embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter. Based on this, the dynamic call indicators include the number of consumers and the frequency of interface calls; step S3 includes: Step S31: Normalize the dynamic call index to obtain the normalized dynamic call index. It should be noted that, as mentioned earlier, dynamic call metrics are statistical data on how many times each consumer interface is called by external business systems within a preset time window. Specifically, dynamic call metrics include the number of consumers and the frequency of interface calls.

[0120] The number of consumers refers to the number of external business systems corresponding to the consumption interface that consumes the current attribute or relationship within a preset time window. This number reflects the breadth of dependency of the current attribute or relationship at the business level, that is, how many independent business systems depend on the data of this attribute or relationship. The interface call frequency refers to the total number of times each consumption interface is called within a preset time window. This number reflects the access density of the current attribute or relationship at the business level, that is, how frequently the data of this attribute or relationship is obtained by business systems.

[0121] Since the number of consumers and the frequency of API calls usually differ in magnitude, for example, the number of consumers may be in the single digits to dozens, while the frequency of API calls may be as high as hundreds of thousands or even more. If the two are directly weighted and calculated, the higher frequency of API calls will dominate the calculation result, causing the information carried by the lower number of consumers to be overwhelmed.

[0122] Therefore, electronic devices need to normalize both the number of consumers and the frequency of interface calls, mapping them to a unified numerical range. Normalization refers to the mathematical transformation process of scaling the original values ​​proportionally to a preset numerical range.

[0123] Optionally, the electronic device adopts the Min-Max normalization method, that is, for the number of consumers or the frequency of interface calls, the maximum and minimum values ​​of the corresponding indicators in all configuration items are obtained, the indicator value of the current configuration item is subtracted from the minimum value and then divided by the difference between the maximum and minimum values, so that the normalization result falls within the range of 0 to 1.

[0124] The electronic device performs the above normalization process on the number of consumers and the frequency of interface calls respectively, to obtain the normalized number of consumers and the normalized frequency of interface calls. The two together constitute the normalized dynamic call index.

[0125] Understandably, normalization eliminates the dimensional difference between the number of consumers and the frequency of API calls, allowing both to participate in subsequent calculations on the same numerical scale, while utilizing information from both dimensions.

[0126] Step S32: The basic importance is weighted and summed with the normalized dynamic call index to obtain the quality score weight of each configuration item.

[0127] It should be noted that the basic importance is a quantitative value that is pre-set for each attribute or relationship, reflecting the degree of importance that attribute or relationship is pre-determined at the management level. Its numerical range can be the same as the numerical range of the normalized dynamic call index (e.g., both are in the range of 0 to 1), or they can be set separately.

[0128] The normalized dynamic call metrics include the normalized number of consumers and the normalized frequency of API calls.

[0129] For each attribute and relationship, the electronic device performs a weighted summation of the basic importance and the normalized dynamic call metric. Specifically, the electronic device multiplies the basic importance by a first weighting coefficient, the normalized number of consumers by a second weighting coefficient, and the normalized interface call frequency by a third weighting coefficient. The products of these three factors are then added together to obtain the quality score weight for that attribute or relationship. The first, second, and third weighting coefficients are pre-set parameters used to adjust the proportions of basic importance, number of consumers, and interface call frequency in the quality score weight. The specific values ​​of these three weighting coefficients can be dynamically adjusted according to actual operation and maintenance strategies.

[0130] Optionally, the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient can be adjusted based on the improvement in score after governance, the time required for governance tasks, and the volatility of weights.

[0131] Understandably, the quality score weight is a comprehensive reflection of the basic importance, the normalized number of consumers, and the normalized frequency of interface calls. It includes both pre-set static management importance information and dynamic business popularity information such as the breadth of actual business dependencies and access density, so that the quality score calculated based on this quality score weight can accurately reflect the actual business impact of each attribute and relationship when quality problems occur.

[0132] Based on the above embodiments of this application, in another embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter. Based on this, step S5 includes: Step S51: Obtain the anomaly type in the compliance check results; It should be noted that, as mentioned above, the compliance check results are the data obtained after calling the corresponding verification rules for each attribute and relationship to perform compliance checks. When the detection result of a certain attribute or relationship does not meet the corresponding verification rules, it is determined that the attribute or relationship is abnormal.

[0133] The anomaly type in the compliance check results refers to the classification identifier used to characterize which type of quality problem was specifically triggered by the current non-compliant attribute or relationship.

[0134] Optionally, exception types (also called problem types) may include validity exceptions, indicating that the attribute value does not conform to the preset valid format, such as an incorrect IP address format; timeliness exceptions, indicating that the attribute value has not been updated within the preset time window; completeness exceptions, indicating that required attributes or required relationship links are missing; intra-model consistency exceptions, indicating logical contradictions between different attributes within the same configuration item; and inter-model consistency exceptions, indicating that the attributes or relationships of the current configuration item are inconsistent with the corresponding information of other associated configuration items.

[0135] Electronic devices can parse the classification identifier of the anomaly type corresponding to each non-compliant attribute or relationship from the compliance check results.

[0136] It is understandable that different types of anomalies require fundamentally different repair methods. For example, formatting issues may be corrected by automated scripts, while data source errors require manual intervention to confirm the source data. Therefore, it is necessary to first obtain the anomaly type to determine which governance method to use subsequently.

[0137] Step S52: When the anomaly type is a manageable anomaly type, perform automated repair on the configuration item; It should be noted that manageable anomaly types are predefined categories of anomalies that can be directly repaired by electronic devices through automated operations without human intervention. The electronic device determines whether the acquired anomaly type belongs to the predefined set of manageable anomaly types.

[0138] Optionally, compliance check results are presented as a non-compliant configuration item instance report. This report records the anomaly type, the type of configuration item executed, the name of the attribute or relationship where the anomaly occurred and its corresponding field, the relationship chain in which the anomaly occurred, and the identifier of the failed validation rule. The issue classification rule engine determines the specific governance method based on judgment factors such as anomaly type, the type of configuration item involved, fields, chain, validation rule, and data source.

[0139] Optionally, manageable exception types may include program execution timeout exceptions, non-standard data structure format exceptions, and timeliness or data authentication exceptions that can be confirmed by comparing source data snapshots.

[0140] When an electronic device determines that the anomaly type is manageable, it triggers an automated repair process. Automated repair refers to the process by which the electronic device calls a preset external management system interface, allowing the management system to automatically perform specific repair operations on the problematic data.

[0141] Optionally, the specific operations for automated repair vary depending on the type of anomaly: For program execution timeout anomalies, the electronic device directly triggers a retry mechanism to re-execute compliance checks; for format non-standard anomalies, timeliness anomalies, or data authentication anomalies, the electronic device calls an external management system. The management system compares the non-compliant configuration item data with the latest data from the source data snapshot or data source. After confirming the specific cause of the anomaly, it rebuilds or re-manages the problematic configuration item attributes. For example, it automatically corrects incorrectly formatted IP address fields to the correct format, automatically rebuilds missing relationship links, or re-synchronizes and updates outdated attribute values.

[0142] Understandably, for manageable anomalies, automated repair processes can quickly complete repairs without human intervention, significantly shortening the problem response cycle and reducing the inefficiency caused by manual repairs.

[0143] Step S53: When the anomaly type is not a manageable anomaly type, initiate an approval work order to perform data management manually.

[0144] It should be noted that when the electronic device determines that the acquired anomaly type is not a manageable anomaly type, it means that the current anomaly cannot be directly repaired through automated operation and requires manual intervention.

[0145] An approval work order is an electronic work order used to record details of abnormal issues and drive the execution of manual remediation processes. When an electronic device initiates an approval work order, it generates work order content based on the abnormal details recorded in the compliance check results. This content includes the issue configuration item identifier, the name of the abnormal attribute or relationship, the abnormal type, the time of the abnormality, and a suggested remediation plan. The electronic device then sends this work order to the corresponding approval stage according to preset approval process rules.

[0146] Optionally, in the manual governance process, the electronic device first forwards the approval work order to the operations and maintenance personnel, who then confirm whether the anomaly is caused by an error in the rule template configuration. If the anomaly is confirmed to be caused by an error in the rule template configuration, the operations and maintenance personnel modify the rule template configuration and re-execute the compliance check. Once the check passes, the current governance process ends. If the operations and maintenance personnel confirm that the anomaly is not a configuration issue, the work order is further forwarded to the person in charge of the data source, who then confirms and repairs the data source. After the repair is completed, the electronic device re-calls the verification rule corresponding to the configuration item to perform a compliance check. The work order can only be closed after the repair result is confirmed to meet the compliance requirements.

[0147] Understandably, for complex issues that cannot be automatically repaired, the approval work order mechanism has enabled the standardized flow of manual governance processes, allowing abnormal issues to be confirmed and verified at each level, preventing issues from being missed or closed without sufficient verification. At the same time, the records of approval work orders also provide a complete operation log for subsequent auditing and traceability.

[0148] In one feasible implementation, when the quality score is lower than a preset governance threshold, the method further includes: Step D51: Determine the abnormal configuration items whose quality scores are lower than the preset governance threshold, and the target consumption interface of the abnormal configuration items; It should be noted that when an electronic device determines that the quality score of the current configuration item is lower than the preset governance threshold, it indicates that there is a problem with the data quality of the configuration item, and it is necessary to further determine which specific configuration items triggered this condition.

[0149] Optionally, abnormal configuration items refer to configuration items whose quality scores are lower than the preset governance threshold.

[0150] Optionally, the target consumption interface refers to a pre-determined and recorded consumption interface that has a consumption association with the attributes and relationships in the abnormal configuration item. Optionally, when a user applies for a consumption interface, the electronic device parses the fields, association links, and filtering conditions contained in the interface definition, dynamically generates a corresponding graph database query template based on the parsing results, and supports parameterized queries. It identifies the application system to which the user belongs based on the user's account information and binds the interface with the corresponding attributes and relationships, constructing a mapping relationship between the interface and attributes, relationships, and topology. For example, when the interface application specifies the type of configuration item to be queried, the depth of the query path, and the range of returned data, the electronic device generates a corresponding graph database query template based on the above information. After generating the query template, the electronic device uses a graph database parser to parse the query template, extracting the configuration item node types, the relationship types between configuration items, and the attribute fields involved. Based on the path information in the query template, it extracts the topological hierarchy relationships between configuration items, constructs the interface's topological dependency graph for subsequent link propagation analysis, thereby constructing a complete mapping between the interface and attributes, relationships, and topology.

[0151] When the quality score of a configuration item is lower than the preset governance threshold, the electronic device performs a reverse query through the graph database, locates the consumption interface that consumes the problematic attributes or relational data in the abnormal configuration item according to the pre-built mapping relationship, analyzes the topological propagation path of the problematic data using the path query algorithm of the graph database, extracts the affected configuration items according to the propagation path, and feeds back the located consumption interface and the affected configuration items to the governance notification.

[0152] As mentioned earlier, external business systems obtain attribute or relational data from the resource configuration database by calling consumption interfaces. Different consumption interfaces establish mapping relationships with the consumed attributes and relationships at the time of application. Therefore, when a configuration item is identified as an abnormal configuration item, the electronic device queries the consumption interfaces corresponding to each attribute and relationship in the abnormal configuration item according to the pre-stored interface consumption mapping relationship, and determines all the queried consumption interfaces as the target consumption interfaces. In this application, the consumption interfaces and systems affected by the quality problem are limited to those that have already consumed resource configuration database data; the impact propagation refers to the propagation of the data with the quality problem to downstream configuration items and systems that rely on this interface to obtain data through the impact on the interface query chain.

[0153] Understandably, the data quality issues of abnormal configuration items do not only affect the configuration item itself. Since the consumption interface provides data access services to the outside world, any interface that consumes the problematic attributes or relational data in the abnormal configuration item may be indirectly affected. Therefore, it is necessary to first identify the abnormal configuration item and the target consumption interface associated with it.

[0154] Step D52: Obtain the target business system to which the target consumption interface belongs; It should be noted that the target business system refers to the external business system to which the target consumption interface belongs, that is, the business system that consumes resources and configures database data through this interface.

[0155] After determining the target consumption interface, the electronic device queries the business system to which each target consumption interface belongs, and then identifies all the queryed business systems as the target business system.

[0156] It is understandable that the consumption interface itself does not exist independently; its data consumption behavior ultimately serves the business system to which it belongs. Identifying the target business system to which the target consumption interface belongs clarifies which business systems may be affected by actual business operations due to consuming data from abnormal configuration items.

[0157] Step D53: Based on the topological relationship of the abnormal configuration item in the resource configuration database, determine the affected configuration items involved by the abnormal configuration item; It should be noted that the configuration items in the resource configuration database are interconnected through relationships to form a topology. For example, the server configuration item is connected to the application system configuration item through the "deployed" relationship, and the application system configuration item is connected to the business domain configuration item through the "belongs to" relationship.

[0158] When a configuration item's data has a quality problem, due to the topological connections between configuration items, the problem may propagate through the relationship links, causing other configuration items directly or indirectly related to it to also be affected by the data they consume.

[0159] Affected configuration items refer to other configuration items in the resource configuration database topology that are connected to the abnormal configuration items through one or more relationship links, and whose data quality may be affected by the problems of the abnormal configuration items.

[0160] The electronic device performs a path traversal query, starting from the abnormal configuration item, based on a pre-built resource configuration database topology graph (e.g., configuration item nodes and relationship edges stored in the graph database) to find other configuration item nodes that are associated with the abnormal configuration item through relationship links.

[0161] Optionally, the electronic device performs a breadth-first traversal or a depth-first traversal, and within a preset maximum traversal depth (e.g., within 3 hops), identifies all configuration item nodes that can be reached from the abnormal configuration item via the relationship link as the affected configuration item.

[0162] Understandably, the ultimate goal of resource configuration data governance is to ensure the normal operation of various business systems. Problems with abnormal configuration items not only affect themselves, but may also spread to upstream and downstream related configuration items through the topology. Therefore, it is necessary to identify all affected configuration items based on the topology relationship in order to assess the scope of the problem's impact.

[0163] Step D54: Generate a governance notification based on the exception type of the exception configuration item, the target consumption interface, the target business system, and the impact configuration item.

[0164] It should be noted that a governance notification is an electronic message generated and output by an electronic device to inform the relevant governance personnel of the details and scope of the abnormal configuration item issue.

[0165] The electronic device integrates and summarizes the identified abnormal configuration items and their abnormal types, the identified target consumer interfaces, the identified target business systems, and the identified affected configuration items to generate a governance notification.

[0166] Optionally, the governance notification should include at least the following information: the identifier of the abnormal configuration item (such as the configuration item name or ID), the specific name of the abnormal attribute or relationship, the classification of the abnormal type (such as validity abnormality, integrity abnormality, etc.), the list of target consumption interfaces and the list of target business systems to which they belong, the list of affected configuration items and a description of the topological path between them and the abnormal configuration items.

[0167] Optionally, the electronic device also obtains the system contact person of the target business system, the owner (OWNER) of the abnormal configuration item, and the data provider's responsible person based on the interface application record or configuration item management information, and writes the above contact information into the governance notification. After generating the governance notification, the electronic device sends the governance notification to the above contacts according to the preset notification policy.

[0168] Understandably, by generating governance notifications containing complete information such as anomaly details, consumption interface, the business system to which the issue belongs, and affected configuration items, governance personnel can grasp the specific location of the problem, the scope of business impact, and the topology propagation path without performing additional queries. This allows them to quickly formulate targeted remediation plans and accurately notify all relevant parties, avoiding the remediation omissions or notification spread issues caused by unclear impact scope in traditional solutions.

[0169] For example, to help understand the implementation process of the data governance method obtained by combining this embodiment with the above embodiments, please refer to... Figure 2In this embodiment, the governance object is configuration items, which include attributes and relationships. Attributes describe the characteristic data fields of the configuration item itself, and relationships represent the topological association between the current configuration item and other configuration items. Attributes and relationships are each bound to a verification rule and have corresponding quality score weights. In the actual governance process, after obtaining each configuration item, the consumption interface of the attribute and relationship is first determined, and the dynamic call indicators of the consumption interface (including the number of consumers and the frequency of interface calls) are collected. Then, based on the pre-set basic importance of each attribute and relationship, a weighted sum is performed with the normalized dynamic call indicators to determine the quality score weight of the configuration item. Next, the verification rules bound to each attribute and relationship are called to perform compliance checks, and the compliance check results are obtained. The compliance check results are then weighted and calculated with the corresponding quality score weights to finally obtain the quality score of the configuration item. When the quality score is lower than the preset governance threshold, differentiated governance is performed on the configuration item, thereby achieving precise matching between governance resources and actual business activity.

[0170] For example, to help understand the implementation process of the data governance method obtained by combining this embodiment with the above embodiments, please refer to... Figure 3 The system retrieves compliance testing tasks for all configuration items to be tested within the current testing period. For each configuration item, it obtains its corresponding historical quality score, historical anomaly count, and basic importance. The historical quality score reflects the past data quality level of the configuration item, the historical anomaly count reflects the frequency of its occurrence, and the basic importance reflects the pre-determined importance of the configuration item at the management level. These historical quality scores, historical anomaly counts, and basic importance are used as input parameters for weighted comprehensive calculation to obtain the priority score for each compliance testing task. The compliance testing tasks are then arranged in descending order of their calculated priority scores, generating a task execution queue. Subsequently, the compliance testing tasks are executed sequentially according to the order of this task execution queue. This process ensures that configuration items with high importance, low historical quality, and frequent anomalies are prioritized for testing, thereby improving the timeliness and accuracy of the overall data governance response.

[0171] For example, to help understand the implementation process of the data governance method obtained by combining this embodiment with the above embodiments, please refer to... Figure 4First, compliance checks are performed based on the verification rules bound to the attributes and relationships of each configuration item, resulting in compliance check results. Next, the quality score weight is determined by combining the pre-set basic importance of each attribute and relationship with the dynamic call indicators of the corresponding consumption interface. The compliance check results are then weighted and calculated to obtain the quality score of the configuration item. When the quality score is lower than a preset governance threshold, impact analysis and governance notification are triggered. This involves tracing back through pre-built interface consumption mapping relationships and topology relationships to determine the target consumption interface, the target business system to which the abnormal configuration item belongs, and the affected configuration items, and generating a governance notification based on the anomaly type. Subsequently, the quality process begins, performing differentiated governance based on the anomaly type. For manageable anomaly types, automatic repair is performed; for unmanageable types, an approval work order is initiated for manual data governance, followed by re-verification after repair. Finally, trend analysis is performed, generating a dynamic trend chart based on historical quality score data and recording rule verification task logs, score calculation processes, and governance process execution approval records. This enables traceability and auditing of the entire governance process, ensuring accurate allocation of governance resources and continuously improving data governance efficiency and business continuity assurance.

[0172] For example, to help understand the implementation process of the data governance method obtained by combining this embodiment with the above embodiments, please refer to... Figure 5 When the quality score of a configuration item falls below a preset governance threshold, an impact analysis is first triggered. By tracing back the target consumption interface, the business system to which the abnormal configuration item belongs, and the affected related configuration items through a pre-built mapping relationship and topology link, a governance notification is generated accordingly. The governance notification is pushed directly to the consumer to inform the relevant business system of the current data risk. On the other hand, it is differentiated according to the type of anomaly: if the anomaly type is a manageable anomaly type, the automatic governance process is directly executed (such as repair through automated scripts or management systems); if the anomaly type is not a manageable anomaly type, it is transferred to the manual governance process (such as initiating an approval work order and transferring it to the relevant responsible person for data source repair). The governance process ends after both complete the corresponding repair actions.

[0173] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the data governance method of this application. Any simple modifications based on this technical concept, such as the interaction and combination of various embodiments, are all within the protection scope of this application.

[0174] This application also provides a data governance device, please refer to... Figure 6 The data governance device includes: The acquisition module 10 is used to acquire each configuration item in the resource configuration database. The configuration item includes attributes and relationships between the current configuration item and other configuration items. The attributes and relationships are bound to validation rules and are each set with a basic importance. The data acquisition module 20 is used to determine the consumption interface of the attribute and the relationship, and to collect the dynamic call indicators of the consumption interface; The determination module 30 is used to determine the quality score weight of the configuration item based on the basic importance of the attribute and the relationship and the dynamic call index of the corresponding consumption interface. The weighting module 40 is used to weight the quality score of the configuration item by the compliance check results of the attribute and the relationship and the quality score weight, wherein the compliance check results are obtained by calling the verification rules corresponding to the attribute and the relationship to perform compliance detection; The governance module 50 is used to perform differentiated governance on the configuration item when the quality score is lower than a preset governance threshold.

[0175] Optionally, the acquisition module 10 is used to acquire the filtering rules for each of the configuration items, wherein the filtering rules include filtering conditions based on the attributes of the configuration item itself and filtering conditions based on the attributes of other configuration items associated with the configuration item. A query statement is generated based on the filtering rules, and the target configuration item is selected from each of the configuration items by executing the query statement; The corresponding verification rules are invoked for the target configuration item to perform compliance checks, and the compliance check results are obtained.

[0176] Optionally, the acquisition module 10 is used to acquire the compliance detection task of the configuration item to be detected in each of the configuration items; Obtain the historical quality score, historical anomaly count, and basic importance of each of the configuration items to be detected; The priority score for each compliance testing task is calculated based on the historical quality score, the number of historical anomalies, and the basic importance. The compliance testing tasks are arranged according to the priority scoring order, a task execution queue is generated, and the compliance testing tasks are executed sequentially according to the task execution queue.

[0177] Optionally, the acquisition module 10 is used to acquire the number of attributes, the number of relationships, and the number of bound verification rules of the configuration item to be detected corresponding to the current detection task; The complexity factor of the configuration item to be detected is calculated based on the number of attributes, the number of relationships, and the number of verification rules. The number of target configuration items in the current detection task is determined based on the filtering rules of the configuration items to be detected; Calculate the number of concurrent threads based on the complexity factor and the number of target configuration items; Create a corresponding number of threads according to the stated number of concurrent threads to perform the compliance checks of the current detection task in parallel.

[0178] Optionally, the dynamic call metric includes the number of consumers and the frequency of interface calls; the determining module 30 is used to normalize the dynamic call metric to obtain the normalized dynamic call metric. The quality score weight of each configuration item is obtained by weighting and summing the basic importance with the normalized dynamic call index.

[0179] Optionally, the governance module 50 is used to obtain the anomaly type in the compliance check results; When the anomaly type is a manageable anomaly type, automatic repair is performed on the configuration item; When the anomaly type is not a manageable anomaly type, an approval work order is initiated to allow for manual data governance.

[0180] Optionally, the governance module 50 is used to determine abnormal configuration items whose quality scores are lower than a preset governance threshold, and the target consumption interface of the abnormal configuration items; Obtain the target business system to which the target consumption interface belongs; Based on the topological relationship of the abnormal configuration item in the resource configuration database, determine the affected configuration items involved by the abnormal configuration item; A governance notification is generated based on the exception type of the exception configuration item, the target consumption interface, the target business system, and the impact configuration item.

[0181] The data governance apparatus provided in this application, employing the data governance method described in the above embodiments, can solve the technical problem of low data governance efficiency. Compared with the prior art, the beneficial effects of the data governance apparatus provided in this application are the same as those of the data governance method described in the above embodiments, and other technical features in the data governance apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0182] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the data governance method described in the first embodiment above.

[0183] The following is for reference. Figure 7 It shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of this application. Figure 7 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0184] like Figure 7 As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0185] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0186] The electronic device provided in this application, employing the data governance method described in the above embodiments, can solve the technical problem of low data governance efficiency. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the data governance method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0187] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0188] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0189] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the data governance method described in the above embodiments.

[0190] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0191] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0192] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0193] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0194] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0195] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described data governance method, thereby solving the technical problem of low data governance efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the data governance method provided in the above embodiments, and will not be repeated here.

[0196] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data governance method described above.

[0197] The computer program product provided in this application can solve the technical problem of low data governance efficiency. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the data governance method provided in the above embodiments, and will not be repeated here.

[0198] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A data governance method, characterized in that, The data governance methods include: Retrieve each configuration item from the resource configuration database, wherein each configuration item includes attributes and relationships between the current configuration item and other configuration items, and each attribute and relationship is bound to a validation rule and has a basic importance set. Determine the consumption interface for the attribute and the relationship, and collect the dynamic call metrics of the consumption interface; The quality score weight of the configuration item is determined based on the basic importance of each attribute and relationship and the dynamic call index of the corresponding consumption interface. The quality score of the configuration item is obtained by weighting the compliance check results of the attribute and the relationship with the quality score weight, wherein the compliance check results are obtained by calling the verification rules corresponding to the attribute and the relationship to perform compliance detection; When the quality score is lower than a preset governance threshold, the configuration item is subject to differentiated governance.

2. The method as described in claim 1, characterized in that, Following the step of obtaining each configuration item from the resource configuration database, the method further includes: Obtain the filtering rules for each of the configuration items, wherein the filtering rules include filtering conditions based on the attributes of the configuration item itself and filtering conditions based on the attributes of other configuration items associated with the configuration item; A query statement is generated based on the filtering rules, and the target configuration item is selected from each of the configuration items by executing the query statement; The corresponding verification rules are invoked for the target configuration item to perform compliance checks, and the compliance check results are obtained.

3. The method as described in claim 1, characterized in that, Following the step of obtaining each configuration item from the resource configuration database, the method further includes: Obtain the compliance detection task for the configuration items to be tested from each of the aforementioned configuration items; Obtain the historical quality score, historical anomaly count, and basic importance of each of the configuration items to be detected; The priority score for each compliance testing task is calculated based on the historical quality score, the number of historical anomalies, and the basic importance. The compliance testing tasks are arranged according to the priority scoring order, a task execution queue is generated, and the compliance testing tasks are executed sequentially according to the task execution queue.

4. The method as described in claim 3, characterized in that, Following the step of generating the task execution queue, the following is also included: Get the number of attributes, the number of relationships, and the number of validation rules bound to the configuration item to be detected for the current detection task; The complexity factor of the configuration item to be detected is calculated based on the number of attributes, the number of relationships, and the number of verification rules. The number of target configuration items in the current detection task is determined based on the filtering rules of the configuration items to be detected; Calculate the number of concurrent threads based on the complexity factor and the number of target configuration items; Create a corresponding number of threads according to the stated number of concurrent threads to perform the compliance checks of the current detection task in parallel.

5. The method as described in claim 1, characterized in that, The dynamic invocation metrics include the number of consumers and the frequency of interface calls; the step of determining the quality score weight of the configuration item based on the basic importance of the attribute and the relationship and the dynamic invocation metrics of the corresponding consumer interface includes: The dynamic call index is normalized to obtain the normalized dynamic call index; The quality score weight of each configuration item is obtained by weighting and summing the basic importance with the normalized dynamic call index.

6. The method as described in claim 1, characterized in that, The steps for differentiating the configuration items include: Obtain the anomaly type from the compliance check results; When the anomaly type is a manageable anomaly type, automatic repair is performed on the configuration item; When the anomaly type is not a manageable anomaly type, an approval work order is initiated to allow for manual data governance.

7. The method as described in claim 1, characterized in that, When the quality score is lower than a preset governance threshold, the method further includes: Identify the abnormal configuration items whose quality scores are lower than a preset governance threshold, and the target consumption interface of the abnormal configuration items; Obtain the target business system to which the target consumption interface belongs; Based on the topological relationship of the abnormal configuration item in the resource configuration database, determine the affected configuration items involved by the abnormal configuration item; A governance notification is generated based on the exception type of the exception configuration item, the target consumption interface, the target business system, and the impact configuration item.

8. A data governance device, characterized in that, The data governance device includes: The acquisition module is used to acquire each configuration item in the resource configuration database. The configuration item includes attributes and relationships between the current configuration item and other configuration items. The attributes and relationships are bound to validation rules and have basic importance set. The data collection module is used to determine the consumption interface of the attribute and the relationship, and to collect the dynamic call indicators of the consumption interface; The determination module is used to determine the quality score weight of the configuration item based on the basic importance of the attribute and the relationship and the dynamic call index of the corresponding consumption interface. The weighting module is used to weight the quality score of the configuration item by the compliance check results of the attribute and the relationship and the quality score weight, wherein the compliance check results are obtained by calling the verification rules corresponding to the attribute and the relationship to perform compliance detection; The governance module is used to perform differentiated governance on the configuration items when the quality score is lower than a preset governance threshold.

9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data governance method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the data governance method as described in any one of claims 1 to 7.

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