Configuration database update processing method, storage medium, and electronic device

Abstract

The application discloses an updating method of a configuration database, a storage medium and an electronic device. The method comprises the following steps: performing first updating on inventory data in the configuration database based on a preset data preservation mode; calling a preset verification algorithm to perform data reconciliation on the inventory data and a third-party data source, obtaining difference data between the inventory data and the third-party data source, and a processing suggestion of the difference data; performing second updating on the inventory data based on the processing suggestion, and obtaining an updated configuration database; dividing data in the configuration database into small-class data and large-class data, obtaining a first score corresponding to the small-class data, and determining a second score corresponding to the large-class data; and determining a score in the configuration database based on the first score and the second score. The application solves the technical problem that the data maintenance and preservation cost is high, the quality evaluation is not unified, and it is difficult to obtain customer recognition due to the lack of effective data preservation and scoring mechanism in the related art.

Description

Technical Field

[0001] This application relates to the field of data processing, and more specifically, to a method for updating a configuration database, a storage medium, and an electronic device. Background Technology

[0002] The Configuration Database (CMDB) integrates all IT resources and relationships related to application systems (covering application systems, databases, middleware, storage, networks, servers, etc.), breaking down data silos and solving cross-disciplinary data sharing issues for all data center operations and maintenance tools. It provides fundamental data support for intelligent operations and maintenance, serving as the cornerstone of the operations and maintenance system and the starting point for digital construction. The data quality and service of the configuration database are crucial, directly impacting the construction and effectiveness of the overall operations and maintenance system.

[0003] Due to the diverse types and complex quantities of configuration data, maintaining and preserving this data to ensure its reliability and trustworthiness is a significant challenge in the construction of configuration databases within the industry. The main reasons are as follows: First, the lack of a long-term mechanism leads to high costs for data maintenance and preservation; second, even after data preservation and remediation, the quality remains difficult to maintain; and third, the absence of evaluation and quantitative standards for data quality makes it difficult to gain user acceptance.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method for updating a configuration database, a storage medium, and an electronic device to at least solve the technical problems in related technologies, such as high data maintenance and preservation costs, inconsistent quality evaluation, and difficulty in gaining customer acceptance due to the lack of an effective data preservation and scoring mechanism.

[0006] According to one aspect of the embodiments of this application, a method for updating a configuration database is provided, comprising: updating existing data in the configuration database once based on a preset data preservation method; calling a preset verification algorithm to reconcile the existing data with a third-party data source to obtain difference data between the existing data and the third-party data source, and processing suggestions for the difference data; updating the existing data a second time based on the processing suggestions to obtain an updated configuration database; dividing the data in the configuration database into sub-categories and major categories, wherein the data granularity of the major categories is larger than that of the sub-categories; obtaining a first score corresponding to the sub-categories and determining a second score corresponding to the major categories; determining a score in the configuration database based on the first score and the second score, wherein the score is used to indicate the data quality of the configuration database.

[0007] Optionally, obtaining the first score corresponding to the sub-category data includes: obtaining the record accuracy of the sub-category data and obtaining the attribute accuracy of the sub-category data; determining the product of the record accuracy, attribute accuracy and preset coefficient as the first score, wherein the record accuracy is used to indicate the score of the sub-category data in the configuration database where no abnormal records have occurred and the update is normal, and the attribute accuracy is used to indicate the proportion of correct attributes in the total attributes in the sub-category data, wherein the higher the proportion, the higher the attribute accuracy.

[0008] Optionally, obtaining the recording accuracy of sub-category data includes: determining the record existence probability to be 0 when the sub-category data has not triggered the call to the preset verification algorithm or has not passed verification by the third-party data source; determining the record existence probability to be 1 when the sub-category data triggers the call to the preset verification algorithm and passes verification by the third-party data source; determining the update accuracy probability based on the production status and update time of the sub-category data, wherein the production status includes: not in production, offline, and in production; when the production status is not in production or offline, if the not-in production time or offline time is less than a preset time, the update accuracy probability is determined to be 1, and if it exceeds the preset time, the predetermined value is decreased for each first predetermined time period exceeding the preset time until the update accuracy probability is 0; when the production status is in production, determining the self-discovery update cycle, if the self-discovery update cycle is less than a predetermined cycle, the update accuracy probability is determined to be 1, and if it exceeds the predetermined cycle, the predetermined value is decreased for each second predetermined time period exceeding the predetermined time until the update accuracy probability is 0; determining the product of the record existence probability and the update accuracy probability as the record accuracy.

[0009] Optionally, obtaining the attribute accuracy of the subclass data includes: determining the credibility and verification attribute values ​​corresponding to different verification algorithms, and obtaining the target number of verification algorithms that did not make verification errors after verifying the subclass data; determining the product of each credibility and its corresponding target number, and obtaining the sum of all products; obtaining the sum of the verification attribute values, and determining the sum of the products and the sum of the verification attribute values ​​as the attribute accuracy.

[0010] Optionally, determining the second score corresponding to the major data category includes: obtaining the data coverage score of the major data category and determining the data quality score corresponding to the major data category; determining the weights corresponding to the coverage score and the data quality score respectively, and the second score is the sum of the products of the coverage score, the data quality score and their respective weights, wherein the data coverage score is used to indicate the degree of coverage of the major data category compared to the third-party data source, and the data quality score is used to indicate the overall quality of the major data category.

[0011] Optionally, obtaining the data coverage score for the major data categories includes: determining the data coverage of the third-party data source; if the data coverage of the third-party data source is greater than the preset coverage, obtaining all data included in the third-party data source and the existing data in the configuration database; determining the intersection of all data and the existing data; determining the first proportion of the intersection data to all data as the data coverage rate, and determining the second proportion of the intersection data to the existing data as the data reliability rate; and determining the product of the data coverage rate and the data reliability rate as the data coverage score.

[0012] Optionally, obtaining the data coverage score for the major data categories includes: determining the data coverage of the third-party data source; if the data coverage of the third-party data source is less than the preset coverage, obtaining all data included in the third-party data source and the existing data in the configuration database; determining the intersection data of all data and the existing data, as well as redundant data; obtaining the first data size, second data size, third data size, and fourth data size corresponding to all data, existing data, intersection data, and redundant data, respectively; obtaining the first difference between the second data size and the fourth data size; adding the first data size to the second data size and subtracting the third data size and the fourth data size to obtain the second difference; determining the ratio of the first difference to the second difference; multiplying this ratio by a preset confidence level to obtain the data coverage rate; determining the ratio of the first difference to the second data size as the data confidence rate; and determining the product of the data coverage rate and the data confidence rate as the data coverage score.

[0013] Optionally, the preset verification algorithm includes a technical verification algorithm. The preset verification algorithm is called to reconcile the existing data with the third-party data source, including: analyzing the verification records of the existing data in historical periods to construct a verification rule system with multiple different dimensions; and reconciling the data with the third-party data source based on the verification rule system at predetermined intervals to identify data problems in the existing data.

[0014] Optionally, the method further includes: receiving a verification instruction input by the target object, and modifying the existing data based on the verification instruction.

[0015] Optionally, preset data preservation methods include: self-discovery updates and process-linked updates. Self-discovery updates include updating data in the configuration database by collecting data through commands, application programming interfaces, and scripts. Process-linked updates include linking the configuration database with the execution flow of each project and updating the configuration database based on changes in data at each node in the execution flow.

[0016] According to another aspect of the embodiments of this application, an update processing apparatus for a configuration database is also provided, comprising: a first update module, configured to update the existing data in the configuration database once based on a preset data preservation method; a reconciliation module, configured to call a preset verification algorithm to reconcile the existing data with a third-party data source, to obtain the difference data between the existing data and the third-party data source, and a processing suggestion for the difference data; a second update module, configured to update the existing data a second time based on the processing suggestion, to obtain an updated configuration database; a partitioning module, configured to partition the data in the configuration database into minor categories and major categories, wherein the data granularity of the major categories is larger than that of the minor categories; an acquisition module, configured to acquire a first score corresponding to the minor categories and determine a second score corresponding to the major categories; and a determination module, configured to determine the score in the configuration database based on the first score and the second score, wherein the score is used to indicate the data quality of the configuration database.

[0017] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any configuration database update processing method.

[0018] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement any configuration database update processing method.

[0019] In this embodiment, a configuration database is obtained by performing a first update based on a preset data preservation method, followed by a second update of the existing data based on a preset verification algorithm. The data in the configuration database is then divided into sub-categories and major categories. Finally, the data quality of the configuration database is evaluated based on these sub-categories and major categories. The process involves: firstly updating the existing data in the configuration database using the preset data preservation method; reconciling the existing data with a third-party data source using the preset verification algorithm to obtain the differences between the existing data and the third-party data source, along with processing suggestions for these differences; performing a second update of the existing data based on these processing suggestions to obtain the updated configuration database; and finally, dividing the data in the configuration database into sub-categories. The system uses a combination of large-category and small-category data, where the granularity of large-category data is larger than that of small-category data. It obtains a first score for the small-category data and determines a second score for the large-category data. Based on the first and second scores, it determines the score in the configuration database, which indicates the data quality of the configuration database. This achieves the goal of real-time data updates and data quality evaluation based on the data classification results in the configuration database. This reduces data preservation and maintenance costs, and provides a more objective evaluation of data quality based on quantifiable standards. Furthermore, it solves the technical problems in related technologies where the lack of an effective data preservation and scoring mechanism leads to high data maintenance and preservation costs, inconsistent quality evaluation, and difficulty in gaining customer acceptance. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a flowchart illustrating an optional configuration database update method according to an embodiment of this application;

[0022] Figure 2 This is a schematic diagram illustrating the relationship between CIs in the embodiments of this application;

[0023] Figure 3 This is a flowchart illustrating the overall method of the data preservation and update closed-loop mechanism in the embodiments of this application;

[0024] Figure 4 This is a schematic flowchart of a database update process in one embodiment of this application;

[0025] Figure 5 This is a schematic diagram of a storage device data model in one embodiment of this application;

[0026] Figure 6 This is a schematic diagram of the result of the full attribute verification in this embodiment of the application;

[0027] Figure 7 This is a schematic diagram of the process for generating processing suggestions in an embodiment of this application;

[0028] Figure 8 This is a schematic diagram comparing the effects of data capability and data quality before and after data optimization on a logic server in one embodiment of this application;

[0029] Figure 9 This is a flowchart illustrating the configuration of data scoring principles in one embodiment of this application;

[0030] Figure 10 This is a schematic diagram of the data quality scoring evaluation process in one embodiment of this application;

[0031] Figure 11 This is a schematic diagram illustrating CI class and relation class as major data quality evaluation dimensions in the embodiments of this application;

[0032] Figure 12 This is a schematic diagram illustrating the effect of precisely verifying the configuration library in one embodiment of this application, where the third-party data source supports exact matching.

[0033] Figure 13 This is a schematic diagram illustrating the effect of supplementing and verifying the configuration library based on the third-party data source when the data coverage of the third-party data source is incomplete in this embodiment of the application.

[0034] Figure 14 This is a schematic diagram of an optional configuration database update processing apparatus according to an embodiment of this application. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] To facilitate a better understanding of the embodiments of this application by those skilled in the art, the technical terms or some nouns that may be involved in this application are explained as follows:

[0038] Data Consistency: Data consistency is a key aspect of evaluating data quality. Generally, consistency refers to whether the same information subject is identical across different datasets. Data Integrity: This describes the degree of missing data information. Missing data can be categorized into missing data records and missing field information records. Data Accuracy: This assesses whether the information in the data records is abnormal or erroneous, such as incorrect, missing, or null field values. Data Timeliness: This refers to the availability of data when needed. Data timeliness is directly related to an enterprise's data processing speed and efficiency, and is a key indicator affecting business processes. Data Validity: This describes the degree to which data conforms to predetermined syntax rules and whether it meets its definitions, such as data type, format, and value range.

[0039] A Configuration Management Database (CMDB) is a logical database that contains information about the entire lifecycle of configuration items and the relationships between them (including physical relationships, real-time communication relationships, non-real-time communication relationships, and dependencies). The CMDB stores and manages various configuration information for devices within an enterprise's IT architecture. It is closely linked to all service support and delivery processes, supporting the operation of these processes, maximizing the value of configuration information, and ensuring data accuracy through these processes.

[0040] According to an embodiment of this application, an embodiment of a configuration database update processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0041] Figure 1 This is a configuration database update processing method according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0042] Step S102: Update the existing data in the configuration database once based on the preset data preservation method;

[0043] It's important to note that configuration data in the configuration database primarily refers to Configuration Items (CIs), Configuration Item Attributes (CI Attributes), and Configuration Item Relationships (CI Relationships). A CI refers to a type of configuration data (such as a server), and CI attributes refer to the configuration information contained within that data, such as the server hostname, number of CPUs, and memory. CI relationships refer to the relationships between different CIs, including those related to running on, containing, accessing, and connecting to them. Figure 2 As shown.

[0044] Optionally, the configuration data in the Configuration Database (CMDB) is not static; it changes with the operating status of the system and servers. Identifying these changes and updating the configuration database promptly to ensure accuracy remains a significant challenge in the industry. Common methods for keeping configuration data up-to-date include:

[0045] 1. Configuration Data Self-Discovery: By installing a self-discovery agent, a data collection script runs automatically on the server at regular intervals to collect information such as OS, software, and IP. The collection script is written using SHELL, API (Application Programming Interface) and other methods for each type of CI (such as database, operating system, middleware), while also being compatible with different versions. This achieves the effect of automatically keeping configuration data fresh and maintaining it. The self-discovery mechanism can not only effectively reduce maintenance costs, but also ensure data accuracy.

[0046] 2. Process: Link with existing resources or change processes. For example, when applying for server resources, once the resource application process is completed, add the relevant CI and relationship records in the configuration library.

[0047] 3. Manual maintenance: This refers to manually logging into the configuration database to maintain the relevant data, including adding and updating data.

[0048] Step S104: Call the preset verification algorithm to reconcile the existing data with the third-party data source, obtain the difference data between the existing data and the third-party data source, and the processing suggestions for the difference data;

[0049] Step S106: Based on the processing suggestions, the existing data is updated a second time to obtain the updated configuration database;

[0050] Step S108: Divide the data in the configuration database into small categories and large categories, wherein the data granularity of the large categories is larger than that of the small categories.

[0051] The technical solution in step S108 ensures that during the verification of existing data, data model analysis and optimization are performed according to the data classification and grading method, and the model can support data identification, maintainability and verification.

[0052] Step S110: Obtain the first score corresponding to the sub-category data and determine the second score corresponding to the major category data;

[0053] Step S112: Determine the score in the configuration database based on the first score and the second score, wherein the score is used to indicate the data quality of the configuration database.

[0054] Traditional data preservation methods, limited by technology or management, struggle to achieve full recording and attribute coverage of configuration data, resulting in high preservation costs or insufficient preservation, and compromised data quality. To address this issue, this application incorporates two additional methods—existing data verification and data validation—to optimize existing preservation methods. By combining these three approaches, a closed-loop data preservation and update mechanism is established, ensuring long-term preservation and quality maintenance of configuration data.

[0055] The overall approach of the data preservation and update closed-loop mechanism is as follows: First, a full data verification of existing configuration data is performed to ensure its accuracy. Then, a data classification and grading method is used to promote full record and attribute coverage of the preservation mechanism and increase the proportion of automated maintenance. Finally, new verification is added. By establishing a verification rule system and a data reconciliation mechanism, the data preservation results are verified to ensure that the preservation mechanism achieves its expected results and supports continuous monitoring of the quality of preserved data, such as... Figure 3 As shown.

[0056] In this configuration database update process, an initial update is performed based on a preset data preservation method. A second update is then performed on the existing data using a preset verification algorithm to obtain the configuration database. Next, the data in the configuration database is divided into sub-categories and major categories. Finally, the data quality of the configuration database is evaluated based on these sub-categories and major categories. The process involves updating the existing data in the configuration database using the preset data preservation method; reconciling the existing data with a third-party data source using the preset verification algorithm to obtain the differences between the existing data and the third-party data source, along with suggested handling methods for these differences; performing a second update on the existing data based on these handling suggestions to obtain the updated configuration database; and finally, dividing the data in the configuration database into sub-categories and major categories. The system categorizes data into small and large categories, with the large category data having a larger granularity than the small category data. It obtains the first score for each small category and determines the second score for each large category. Based on the first and second scores, it determines the score in the configuration database. This score indicates the data quality in the configuration database, achieving real-time data updates and evaluating data quality based on the data classification results in the configuration database. This reduces data preservation and maintenance costs, and provides a more objective evaluation of data quality based on quantifiable standards. Furthermore, it solves the technical problems of high data maintenance and preservation costs, inconsistent quality evaluation, and difficulty in gaining customer acceptance caused by the lack of effective data preservation and scoring mechanisms in related technologies.

[0057] In some embodiments of this application, obtaining the first score corresponding to the sub-category data can be achieved through the following steps: obtaining the record accuracy of the sub-category data and obtaining the attribute accuracy of the sub-category data; determining the product of the record accuracy, attribute accuracy, and a preset coefficient as the first score, wherein the record accuracy is used to indicate that no abnormal records have occurred in the configuration database for the sub-category data and the score for updating normally, and the attribute accuracy is used to indicate the proportion of correct attributes in the total attributes in the sub-category data, wherein the higher the proportion, the higher the attribute accuracy.

[0058] In an optional embodiment of this application, obtaining the recording accuracy of sub-category data includes: determining the record existence probability to be 0 when the sub-category data has not triggered the invocation of the preset verification algorithm or has not passed verification by a third-party data source; determining the record existence probability to be 1 when the sub-category data triggers the invocation of the preset verification algorithm and passes verification by a third-party data source; determining the update accuracy probability based on the production status and update time of the sub-category data, wherein the production status includes: not in production, offline, and in production; when the production status is not in production or offline, if the not-in production time or offline time is less than a preset time, the update accuracy probability is determined to be 1, and if it exceeds the preset time, the predetermined value is decreased for each first predetermined time period exceeding the preset time until the update accuracy probability is 0; when the production status is in production, determining the self-discovery update cycle, if the self-discovery update cycle is less than a predetermined cycle, the update accuracy probability is determined to be 1, and if it exceeds the predetermined cycle, the predetermined value is decreased for each second predetermined time period exceeding the predetermined time until the update accuracy probability is 0; and determining the product of the record existence probability and the update accuracy probability as the record accuracy.

[0059] As an optional implementation method, the attribute accuracy of subclass data can be obtained in the following way: Specifically, the credibility and verification attribute values ​​corresponding to different verification algorithms can be determined, and the target number of verification algorithms that have not made verification errors after verifying the subclass data can be obtained; the product of each credibility and its corresponding target number can be determined, and the sum of all products can be obtained; the sum of the verification attribute values ​​can be obtained, and the sum of the product and the sum of the verification attribute values ​​can be determined as the attribute accuracy.

[0060] In some embodiments of this application, determining the second score corresponding to a major category of data includes: obtaining a data coverage score for the major category of data and determining a data quality score corresponding to the major category of data; determining the weights corresponding to the coverage score and the data quality score respectively, and the second score is based on the sum of the products of the coverage score, the data quality score and their respective weights, wherein the data coverage score is used to indicate the degree of coverage of the major category of data compared to a third-party data source, and the data quality score is used to indicate the overall quality of the major category of data.

[0061] In an optional embodiment of this application, the data coverage score of a large category of data can be obtained in the following way: Specifically, the data coverage of a third-party data source can be determined. If the data coverage of the third-party data source is greater than a preset coverage, all data included in the third-party data source and the existing data of the configuration database are obtained, and the intersection data of all data and existing data is determined. The first proportion of the intersection data to all data is determined as the data coverage, and the second proportion of the intersection data to existing data is determined as the data reliability. The product of the data coverage and the data reliability is determined as the data coverage score.

[0062] As an optional implementation, obtaining the data coverage score for a large category of data can be achieved by determining the data coverage of a third-party data source. If the data coverage of the third-party data source is less than a preset coverage, the following steps are taken: First, obtain all data included in the third-party data source, as well as the existing data in the configuration database. Second, determine the intersection of all data and the existing data, and identify redundant data. Third, obtain the first, second, third, and fourth data sizes corresponding to all data, existing data, intersection data, and redundant data, respectively. Fourth, obtain the first difference between the second and fourth data sizes. Fifth, add the first and second data sizes together and subtract the third and fourth data sizes to obtain the second difference. Sixth, determine the ratio of the first difference to the second difference and multiply this ratio by a preset confidence level to obtain the data coverage rate. Finally, determine the ratio of the first difference to the second data size as the data confidence rate, and determine the product of the data coverage rate and the data confidence rate as the data coverage score.

[0063] It should be noted that the preset verification algorithm includes a technical verification algorithm. The preset verification algorithm is called to reconcile the existing data with the third-party data source. This includes: analyzing the verification records of the existing data in historical periods to construct a verification rule system with multiple different dimensions; and reconciling the data with the third-party data source based on the verification rule system at predetermined intervals to identify data problems in the existing data.

[0064] To facilitate receiving manual verification from users, as an optional embodiment, the method further includes: receiving verification instructions input by the target object, and modifying the existing data based on the verification instructions.

[0065] It should be noted that the preset data preservation methods include: self-discovery updates and process-linked updates. Self-discovery updates include updating the data in the configuration database by collecting data through commands, application programming interfaces, and scripts. Process-linked updates include linking the configuration database with the execution flow of each project and updating the configuration database based on changes in the data at each node in the execution flow.

[0066] Figure 4 This is a schematic flowchart of a database update processing method in one embodiment of this application, as shown below. Figure 4 As shown, the above update process can be divided into three stages: existing data verification, mechanism optimization, and verification. Existing data verification is used for model analysis and optimization, followed by data verification and repair to summarize the issues requiring verification. Mechanism optimization involves optimizing the data using an inbound preservation mechanism and a verification mechanism. Verification is used for phased verification.

[0067] In terms of data modeling, CI categories, attributes, and relationships are categorized and analyzed based on data usage scenarios. This includes analyzing whether the data possesses unique identification conditions, whether the data is maintainable, and whether the data has undergone sufficient verification. Based on these dimensions, optimizations are made to address identified issues, including removing irrelevant attributes to improve data fill rate, and considering adding or improving maintenance and verification methods in subsequent data preservation mechanisms and verification processes. This aims to better support mechanisms and ensure comprehensive verification coverage based on the data model. Figure 5 This is a schematic diagram of a storage device data model in one embodiment of this application, as shown below. Figure 5 As shown.

[0068] After completing the model analysis and clarifying the data verification methods, data verification and repair can begin. By introducing third-party data sources (multi-source data) or establishing relevant rules, a comprehensive review of existing data is necessary to uncover hidden data issues. Third-party data sources also involve various categories. If the data from a third-party data source is accurate, a precise verification is performed; otherwise, a supplementary verification is conducted. For attributes not covered by third-party data sources, relevant rules are established for verification, ensuring full record and attribute verification to identify as many data problems as possible. Figure 6 This is a schematic diagram of the result of the full attribute verification in this embodiment of the application, as shown below. Figure 6 As shown.

[0069] In the process of mechanism optimization, for the problems discovered, we will promote the optimization or coverage of the data preservation and maintenance mechanism to achieve a complete solution, ensure the accuracy of incremental data, and at the same time build a verification mechanism to strengthen the proactive inspection of data quality, thereby promoting data preservation, updates and accuracy.

[0070] To improve data freshness and accuracy, a comprehensive record-keeping and attribute-covering preservation mechanism should be constructed. The table below shows the optimization ideas for data freshness.

[0071]

[0072] It's important to note that the verification mechanism is a step added to the data preservation process in this application. Verification not only checks whether the preservation measures have achieved the expected results, but it's also a means of data preservation itself. Problems discovered during verification trigger data preservation and updates, creating a closed-loop preservation system. Verification includes two types: technical verification, which proactively verifies data from multiple dimensions using technical means such as verification rules; and user verification, which involves specific user groups using the data and providing feedback on usage issues. Technical verification is prioritized to resolve the vast majority of problems, while user verification serves to verify and supplement the results of technical verification. These two methods fully guarantee data preservation and accuracy, improving the user data usage experience. The following sections will elaborate on technical verification and user verification respectively.

[0073] Specifically, technical verification involves summarizing and refining verification methods and experiences encountered during the process of verifying existing data, and constructing data verification methods. These methods mainly include two categories: building a verification rule system and conducting data reconciliation with third-party data sources. The innovative aspect of supplementing traditional data preservation methods with technical verification is that it enables the discovery of data problems and simultaneously drives data preservation.

[0074] 1) Construct a verification rule system to proactively discover data issues from multiple angles and dimensions.

[0075] During the verification of existing data, issues such as data duplication, inconsistency, and non-standardization were discovered. These issues were summarized into rules, addressing multiple dimensions including data uniqueness, consistency, standardization, and reasonableness, and striving to cover all record attributes. This proactive approach can largely identify and correct data problems, improving data accuracy. Furthermore, these data dimensions and rule templates are universal and can be promoted for similar types of configuration data. For example, uniqueness and attribute integrity rules can be configured for all CI records to ensure data non-duplication and attribute fill rates. For equipment data, the integrity of the relationship with the server rack must be checked, and enumerated values ​​for equipment brands, names, etc., are checked to improve data standardization. The storage device rule system is shown in the table below.

[0076]

[0077] 2) Establishing a regular data reconciliation mechanism with third-party data sources can more effectively guarantee data accuracy.

[0078] The selected third-party data sources can be either comprehensive and accurate, or accurate but not comprehensive. For comprehensive and accurate data sources, the configuration library performs precise verification at the record and attribute levels. For the other type of data source, only supplementary verification at the record and attribute levels is performed.

[0079] It performs regular, fully automated reconciliation with third-party data, and also supports automatic handling or generation of handling suggestions for verification data issues. Figure 7 This is a flowchart illustrating the process of generating processing suggestions in this embodiment, such as... Figure 7 As shown.

[0080] Specifically, user verification involves specific users defining verification cases and participating in data verification. Similarly, discovering data issues triggers data updates to keep the data fresh. User participation can address shortcomings in technical verification, such as a lack of experience, and expand existing data verification dimensions. Furthermore, user verification methods drive the improvement of technical verification techniques, enhancing the timeliness of issue detection.

[0081] The final step, data verification, is essentially the actual execution of the verification mechanism. Verification is divided into two phases. The first phase occurs after data reconciliation is completed, but the data preservation and verification mechanism is still under development. At this stage, the main focus is on ensuring the quality of existing data. The second phase, after the data preservation and verification mechanism has been optimized, involves three months of close verification. If the data problem rate is found to be below 5%, this phase of data verification is successfully completed, and routine monitoring is implemented to maintain data quality in the long term.

[0082] By employing a closed-loop preservation mechanism, configuration data maintenance capabilities and data quality can be effectively improved. In practice, using logical servers (Linux, Windows, etc.), the closed-loop preservation mechanism has demonstrated outstanding results in real-world applications. Figure 8 This is a schematic diagram comparing the effects of data capability and data quality before and after data optimization on a logic server, according to one embodiment of this application. Figure 8 It can be seen that both data capabilities and data quality have been significantly improved before and after optimization.

[0083] To better demonstrate the data freshness capability and effectiveness, and to better support the tracking of data freshness, this application has for the first time implemented a configuration data quality evaluation and quantification standard, namely configuration data quality scoring, which improves the visualization and intelligence level of configuration data freshness. Configuration data quality scoring is also a major highlight of this application compared with traditional methods.

[0084] To ensure the objectivity, rationality, and usability of the scoring, the scoring objects, scoring methods, and result interpretations must be clearly defined when developing the scoring scheme. Figure 9 This is a flowchart illustrating the configuration of data scoring principles in one embodiment of this application, as shown below. Figure 9 As shown, this process mainly includes three steps: defining the scoring object, defining the scoring method, and interpreting the scoring results. The following is a detailed introduction to the scoring algorithm:

[0085] According to the scoring approach, both broad data categories (CI category and CI relationship category) and detailed data (CI record and relationship record) need to be scored. The scoring must consider the accuracy of preservation methods and differences in verification levels to scientifically quantify data quality. For CI records, record accuracy is determined by both record-level accuracy and attribute accuracy. Record accuracy primarily ensures the existence and normal updating of records; attribute accuracy is mainly judged by the proportion of correct attributes in the total attributes. The scoring algorithm considers the different requirements for attributes and update frequency in consumption scenarios based on the data's CI status (not in production, in production, in production). For differences in the accuracy of preservation methods, different levels of reliability are assigned differentiated values, thus more objectively and scientifically reflecting data quality through scoring.

[0086] Figure 10 This is a schematic diagram of the data quality scoring evaluation process in one embodiment of this application, as shown below. Figure 10 As shown, data quality score = record accuracy rate Attribute accuracy 100.

[0087] To verify the technical and business feasibility of the algorithm, a scoring test was conducted on Linux data, and business verification was performed. Details are as follows:

[0088] A-CC-APP01 is a Linux data record, and the server has an auto-discovery agent installed. This record contains 50 attributes in total, of which 46 are auto-discovery attributes (CPU, CORE, memory, etc.), 2 are automatically maintained process attributes (CI status, CI responsible person), and 2 are manually entered process attributes (management hostname, purpose). The scoring for this data record is as follows:

[0089] 1) Record accuracy: The record can be accurately verified with the third-party data source VCENNER, and the verification results match (record existence rate = 1); the record is self-discovered data, and the self-discovery time was updated within two weeks (update timeliness rate = 1); record existence rate = 1.

[0090] 2) Attribute accuracy

[0091] Of the self-discovered attributes, 20 can be verified from the third-party data source VCENTER, and the results match (credibility 1); of the 24 self-discovered attributes, 2 are automatically maintained by the process, and were verified according to the verification rules, with the results passing (credibility 0.8); 2 self-discovered attributes cannot be verified by any means (operating system bitness, version information) (credibility 0.6); 2 manually verified attributes were verified according to the verification rules, and the results passing (credibility 0.6); Attribute accuracy = (20 + 26) 0.8+2 0.6+2 (0.6) / 50 = 0.86, therefore the score is 86 points. The business verification results are as follows: The data center platform group server administrator participated in this verification. The business feedback indicated that the records were managed and running normally under their name. Some attributes were verified through the management platform VCENTER, and the reliability of this part of the data is absolutely guaranteed. For attributes outside the management platform, the business side had no other means to strengthen verification. This part of the data is based on the configuration library results. Currently, no problems have been found in data usage, but potential data problems may not have been discovered. The score needs to be adjusted based on experience. The business verification result is passed.

[0092] like Figure 11As shown, CI and relational data are considered major data categories. Their quality evaluation includes not only the overall quality of all data within these categories (the average score of all data in that category), but also the overall data coverage. Therefore, their quality scores must include both of these dimensions.

[0093] The data coverage algorithm is as follows: 1) When the third-party data source supports exact matching:

[0094] Data coverage scoring needs to take into account both data coverage and the accuracy of the data in the database; therefore, data score = data coverage. Data inventory accuracy 100.

[0095] Figure 12 This is a schematic diagram illustrating the effect of precise verification of the configuration library in one embodiment of this application, where the third-party data source supports exact matching. Figure 12 As shown,

[0096] A represents data from a third-party data source, and this type of data is based on the third-party data source; B represents configured inventory data; C represents data that exists jointly with both the third-party data source and the configuration library; where data coverage = C / A; data reliability = C / B.

[0097] Coverage rating = C C / AB 100.

[0098] To verify the reasonableness of the scoring, a trial calculation and verification were performed using the LINUX class (virtual machine), and the results are as follows:

[0099] The Linux class contains 19,077 virtual machine data records (B), the third-party data source (VCENTER) contains 18,796 data records (A), the total number of matching records is 18,563 (C), the configuration library has 233 missing data records (AC), and the configuration library has 514 redundant data records (BC); Linux coverage score = (18,563 18563) / (19077) 18796) 100 = 96 points

[0100] The business verification results are as follows: The data center platform group server administrator participated in this business verification. He believed that the LINUX virtual machine data in the configuration library had good coverage during normal use, and he approved the data score. The verification was passed.

[0101] Figure 13This is a schematic diagram illustrating the effect of supplementing and verifying the configuration database based on the third-party data source when the data coverage of the third-party data source is incomplete. In this case, the third-party data source can only supplement and verify the configuration database; that is, if the data in the third-party data source exists, the data in the configuration database must exist, and vice versa. Since the third-party data source cannot perform precise verification, the reliability of this verification method is empirically set to 0.8. The detailed algorithm is as follows: Figure 13 As shown:

[0102] A represents data from a third-party data source, and the total amount of data in this configuration library is A+BCD; B represents the configuration inventory data.

[0103] C represents data that coexists with third-party data sources and configuration libraries; D represents redundant data (e.g., duplicate data) discovered through configuration and verification rules.

[0104] Data coverage = [(BD) / (A + BCD)] 0.8; Data credibility = (BD) / B; Coverage score = 100 (BD) (BD) / [(A+BCD)] B]; To verify the reasonableness of the scoring, a trial calculation and verification were performed using the LINUX class (virtual machine), and the results are as follows:

[0105] There are a total of 4399 physical machine data records in the LINUX class (B), a total of 4229 data records in the third-party data source (CAMA) (A), a total of 4133 matching records (C), 266 missing data records in the configuration library (AC), and 0 redundant data records in the configuration library that pass the uniqueness verification rule (D).

[0106] Data coverage = [(4399-0) / (4399+4229-4133-0)] 0.8 = 0.77; Data credibility = (4399-0) / 4399 = 1; Coverage score = 77 points.

[0107] The business verification results are as follows: Besides the configuration library and CAMA, there are no other means to strengthen the verification of LINUX physical machines. Based on data usage experience, the data coverage of the configuration library is relatively accurate. However, occasionally, some special servers are found to have missing data records due to the administrator not submitting the application process when applying for resources, or the process execution being irregular. The scoring results are accepted, and the verification is passed.

[0108] In summary, the Linux coverage score is (96). 19077+77 (4299) / (19077 + 4299) = 93 points;

[0109] The average score for the LINUX category data records is 81; therefore, the overall score for the LINUX category is 93. 0.5+81 0.5 = 87 points

[0110] The following table explains the scoring service and its function in the embodiments of this application, as shown in the table below:

[0111]

[0112] By configuring scores, not only can the quality of configuration data be improved, enhancing the visualization and intelligence of data update and preservation, but the configuration scores also intuitively present the capabilities of the preservation mechanism and its implementation effect, playing a significant role in enabling configuration data to achieve long-term preservation and updates.

[0113] Figure 14 This is a configuration database update processing apparatus according to an embodiment of this application, such as... Figure 14 As shown, the update processing apparatus includes:

[0114] The first update module 140 is used to update the existing data in the configuration database once based on a preset data preservation method;

[0115] The reconciliation module 142 is used to call a preset verification algorithm to reconcile the existing data with the third-party data source, obtain the difference data between the existing data and the third-party data source, and the processing suggestions for the difference data.

[0116] The second update module 144 is used to perform a second update on the existing data based on the processing suggestions to obtain the updated configuration database.

[0117] The partitioning module 146 is used to divide the data in the configuration database into small categories and large categories, wherein the data granularity of the large categories is larger than that of the small categories.

[0118] Module 148 is used to obtain the first score corresponding to the sub-category data and determine the second score corresponding to the major category data.

[0119] The determination module 150 is used to determine the score in the configuration database based on the first score and the second score, wherein the score is used to indicate the data quality of the configuration database.

[0120] In this update processing device, the first update module 140 is used to update the existing data in the configuration database once based on a preset data preservation method; the reconciliation module 142 is used to call a preset verification algorithm to reconcile the existing data with the third-party data source, obtain the difference data between the existing data and the third-party data source, and the processing suggestions for the difference data; the second update module 144 is used to update the existing data a second time based on the processing suggestions, to obtain the updated configuration database; the partitioning module 146 is used to partition the data in the configuration database into small categories and large categories, wherein the data granularity of the large categories is larger than that of the small categories; and the acquisition module 148. The first module is used to obtain the first score corresponding to the sub-category of data and determine the second score corresponding to the sub-category of data. The second module is used to determine the score in the configuration database based on the first score and the second score. The score is used to indicate the data quality of the configuration database. This achieves the purpose of real-time data updates and evaluation of data quality based on the data classification results in the configuration database. This reduces data preservation and maintenance costs and provides a more objective evaluation of data quality based on quantifiable standards. It also solves the technical problems in related technologies, such as high data maintenance and preservation costs, inconsistent quality evaluation, and difficulty in gaining customer recognition, caused by the lack of an effective data preservation and scoring mechanism.

[0121] Based on the above-mentioned embodiments, it can be seen that this application has the following beneficial effects;

[0122] 1. This application innovatively proposes a closed-loop data preservation mechanism, combining traditional preservation methods with practical experience and positive reinforcement. This mechanism flexibly utilizes traditional preservation methods to improve business efficiency, while incorporating newly added steps for verifying existing data and data validation. This not only effectively supplements traditional preservation mechanisms but also ensures their practical implementation and effectiveness. 2. This application is the first to digitize configuration data quality, establishes standards for data quality evaluation, and, through data classification and grading, comprehensively considers the impact of differences in preservation and validation methods on data quality. A self-developed data quality scoring algorithm has been developed to achieve data quality scoring. The scoring intuitively provides information on data preservation and execution capabilities, providing digital evidence for data quality visualization and situational awareness. 3. This application summarizes and creates a method for updating and preserving configuration data, while also quantitatively evaluating the quality of preserved data. It is a complete data update and preservation mechanism that effectively addresses the shortcomings of traditional update and preservation methods and has been fully validated in practice.

[0123] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any configuration database update processing method.

[0124] Specifically, the aforementioned storage medium is used to store program instructions for the following functions, thereby implementing the following functions:

[0125] The existing data in the configuration database is updated once based on a preset data preservation method; a preset verification algorithm is called to reconcile the existing data with the third-party data source to obtain the difference data between the existing data and the third-party data source, as well as the processing suggestions for the difference data; the existing data is updated a second time based on the processing suggestions to obtain the updated configuration database; the data in the configuration database is divided into minor categories and major categories, where the data granularity of the major categories is larger than that of the minor categories; the first score corresponding to the minor categories is obtained, and the second score corresponding to the major categories is determined; the score in the configuration database is determined based on the first score and the second score, where the score is used to indicate the data quality of the configuration database.

[0126] Optionally, in this embodiment, the storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of the storage medium include electrical connections based on 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 fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0127] An electronic device is provided according to an embodiment of the present application, the electronic device comprising: 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 configuration database update processing method of any of the above.

[0128] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0129] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0130] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0133] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0135] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for updating a configuration database, characterized in that, include: The existing data in the configuration database is updated once based on the preset data preservation method; A preset verification algorithm is invoked to reconcile the existing data with the third-party data source, thereby obtaining the difference data between the existing data and the third-party data source, as well as processing suggestions for the difference data; Based on the processing suggestions, the existing data is updated a second time to obtain the updated configuration database; The data in the configuration database is divided into minor categories and major categories, wherein the data granularity of the major categories is larger than that of the minor categories. Obtaining the first score corresponding to the sub-category of data includes: obtaining the record accuracy of the sub-category of data and obtaining the attribute accuracy of the sub-category of data; determining the product of the record accuracy, attribute accuracy, and a preset coefficient as the first score, wherein the record accuracy is used to indicate the score of the sub-category of data in the configuration database where no abnormal records have occurred and updates are normal, and the attribute accuracy is used to indicate the proportion of correct attributes in the total attributes of the sub-category of data, the higher the proportion, the higher the attribute accuracy; Determining the second score corresponding to the major category of data includes: obtaining the data coverage score of the major category of data and determining the data quality score corresponding to the major category of data; determining the weights corresponding to the coverage score and the data quality score respectively; and determining the sum of the products of the coverage score, the data quality score and their respective weights as the second score, wherein the data coverage score is used to indicate the degree of coverage of the major category of data compared to the third-party data source, and the data quality score is used to indicate the overall quality of the major category of data; The score in the configuration database is determined based on the first score and the second score, wherein the score is used to indicate the data quality of the configuration database.

2. The update processing method according to claim 1, characterized in that, Obtaining the recording accuracy of the aforementioned sub-category of data includes: If it is determined that the sub-category of data has not triggered the invocation of the preset verification algorithm or has failed the verification by the third-party data source, the probability of the record existing is determined to be 0; if the sub-category of data has triggered the invocation of the preset verification algorithm and has passed the verification by the third-party data source, the probability of the record existing is determined to be 1. The probability of accurate updates is determined based on the production status and update time of the aforementioned sub-category data, wherein the production status includes: not yet in production, in the process of being taken offline, and already in production; In the production status of "not in production" or "offline", if the time of not in production or not offline is less than a preset time, the update accuracy probability is determined to be 1. If it exceeds the preset time, the preset value is decreased for each first predetermined time period exceeding the preset time until the update accuracy probability is 0. When the production status is "in production", a self-discovery update cycle is determined. If the self-discovery update cycle is less than a predetermined cycle, the update accuracy probability is determined to be 1. If it exceeds the predetermined cycle, the predetermined value is decreased for each second predetermined time period exceeding the predetermined cycle until the update accuracy probability is 0. The product of the probability of the record's existence and the probability of accurate update is determined as the accuracy of the record.

3. The update processing method according to claim 1, characterized in that, Obtaining the attribute accuracy of the subclass data includes: Determine the credibility and verification attribute values ​​corresponding to different verification algorithms, and obtain the target number of verification algorithms that did not make verification errors after verifying the subclass data; Determine the product of each confidence level and the corresponding number of targets, and obtain the sum of all products; Obtain the sum of the verified attribute values, and determine the sum of the product and the sum of the verified attribute values ​​as the accuracy of the attribute.

4. The update processing method according to claim 1, characterized in that, Obtain the data coverage score for the aforementioned major category of data, including: Determine the data coverage of the third-party data source. If the data coverage of the third-party data source is greater than the preset coverage, obtain all the data included in the third-party data source and the existing data of the configuration database, and determine the intersection data of the all data and the existing data. The first proportion of the intersection data to all data is determined as the data coverage rate, and the second proportion of the intersection data to the existing data is determined as the data reliability rate. The product of the data coverage rate and the data reliability rate is determined as the data coverage score.

5. The update processing method according to claim 1, characterized in that, Obtain the data coverage score for the aforementioned major category of data, including: Determine the data coverage of the third-party data source. If the data coverage of the third-party data source is less than a preset coverage, obtain all the data included in the third-party data source and the existing data of the configuration database; determine the intersection data of the all data and the existing data, as well as the redundant data. The first data size, second data size, third data size, and fourth data size corresponding to the all data, the existing data, the intersection data, and the redundant data are obtained respectively. Obtain the first difference between the second data size and the fourth data size; Add the first data size to the second data size, and subtract the third data size and the fourth data size to obtain the second difference. Determine the ratio of the first difference to the second difference, and multiply the ratio by a preset confidence level to obtain the data coverage rate. The ratio of the first difference to the second data size is determined as the data confidence rate, and the product of the data coverage rate and the data confidence rate is determined as the data coverage score.

6. The update processing method according to claim 1, characterized in that, The preset verification algorithm includes a technical verification algorithm. Calling the preset verification algorithm reconciles the existing data with third-party data sources, including: Based on the analysis of the verification records of the existing data in the historical period, a verification rule system including multiple different dimensions is constructed. At predetermined intervals, data reconciliation is performed between the verification rule system and the third-party data source to identify data problems in the existing data.

7. The update processing method according to claim 6, characterized in that, The method further includes: Receive verification instructions from the target object, and modify the existing data based on the verification instructions.

8. The update processing method according to any one of claims 1 to 7, characterized in that, The preset data preservation method includes: self-discovery update and process-linked update. The self-discovery update includes updating the data in the configuration database by collecting data through commands, application programming interfaces and scripts. The process-linked update includes linking the configuration database with the execution flow of each project and updating the configuration database based on the changes in data at each node in the execution flow.

9. An update processing apparatus for a configuration database, characterized in that, include: The first update module is used to update the existing data in the configuration database once based on a preset data preservation method; The reconciliation module is used to call a preset verification algorithm to reconcile the existing data with the third-party data source, obtain the difference data between the existing data and the third-party data source, and the processing suggestions for the difference data; The second update module is used to perform a second update on the existing data based on the processing suggestions to obtain the updated configuration database. The partitioning module is used to divide the data in the configuration database into minor categories and major categories, wherein the data granularity of the major categories is larger than that of the minor categories. The acquisition module is used to acquire a first score corresponding to the sub-category data and determine a second score corresponding to the major category data. The acquisition process of the first score includes: acquiring the record accuracy of the sub-category data and acquiring the attribute accuracy of the sub-category data; determining the product of the record accuracy, attribute accuracy, and a preset coefficient as the first score, wherein the record accuracy indicates the score of the sub-category data in the configuration database where no abnormal records have occurred and updates are normal, and the attribute accuracy indicates the proportion of correct attributes in the total attributes of the sub-category data; the higher the proportion, the higher the attribute accuracy. The determination process of the second score includes: acquiring the data coverage score of the major category data and determining the data quality score corresponding to the major category data; determining the weights corresponding to the coverage score and the data quality score respectively; and determining the sum of the products of the coverage score, the data quality score, and their respective weights as the second score, wherein the data coverage score indicates the degree of coverage of the major category data compared to the third-party data source, and the data quality score indicates the overall quality of the major category data. A determining module is configured to determine a score in the configuration database based on the first score and the second score, wherein the score is used to indicate the data quality of the configuration database.

10. A non-volatile storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to perform the configuration database update processing method according to any one of claims 1 to 8.

11. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the configuration database update processing method as described in any one of claims 1 to 8.

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