An intelligent database inspection method and system based on risk assessment algorithm

Abstract

The present invention relates to an intelligent database inspection method and system based on a risk assessment algorithm. By introducing nonlinear combinations and dynamic weight adjustments, it can not only more accurately reflect the complex interactions of risk factors, but also flexibly adjust the scoring strategy according to the actual situation, thereby significantly improving the accuracy and practicality of the model. At the same time, by combining confidence interval reasoning with risk preference mapping, the personalization and reliability of the scoring results are further enhanced. Special inspections are carried out on configuration library systems at medium and high risks to monitor the health status of the database system more deeply and comprehensively, and targeted optimization and adjustments are made to abnormal inspection items. At the same time, scheduled inspection tasks can be carried out for low-risk configuration libraries, and the health status of the configuration libraries can be monitored during idle time.

Description

Technical Field

[0001] The present invention relates to an intelligent database inspection method and system based on a risk assessment algorithm, and belongs to the field of intelligent operation and maintenance of databases. Background Art

[0002] With the rapid development of big data, the database system, as the core tool for data management in enterprises, its security and performance will directly affect the stability of the system and user experience. Traditional database operation and maintenance is mostly undertaken by operation and maintenance personnel. Faced with large-scale data, there are problems such as large operation and maintenance workload and low efficiency, and it is difficult to cope with faster and more efficient work needs. The intelligent inspection system realizes data analysis and database risk assessment through automatic inspection, which greatly reduces the workload of operation and maintenance personnel and provides important support for the efficient and stable operation of the database.

[0003] The existing intelligent inspection system regularly conducts all-round inspections on the database's performance indicators, resource utilization, and security configuration according to preset rules, and generates inspection reports. Compared with traditional operation and maintenance work, the intelligent inspection system can improve the efficiency and accuracy of massive data inspections. However, the existing intelligent inspection system has obvious deficiencies in its ability to adapt to complex database environments, mainly in the following two aspects:

[0004] (1) Most existing intelligent inspection systems pre-set fixed inspection processes and lack the ability to adjust the implementation of inspection tasks. They do not make sufficient adjustments and use the current status of the database, historical data, and the latest monitoring information, resulting in a waste of system resources and reduced inspection efficiency.

[0005] (2) Existing intelligent inspection systems rely on simple linear weighted models for risk assessment, which are insufficient to capture the nonlinear relationships between different monitoring items and cannot fully reflect the interactions between the various monitoring items. Traditional risk scoring models lack an adaptive adjustment mechanism, and the scoring results often deviate from the actual situation, resulting in inaccurate risk assessment.

[0006] In summary, the existing intelligent inspection system has problems such as a single inspection method, lack of dynamic adjustment capabilities, and overly simple risk assessment models, making it difficult to apply in a complex database environment. The existing risk scoring model is too simple when dealing with complex risk factors and cannot effectively capture the nonlinear interaction between variables, resulting in a lack of depth and accuracy in the scoring results. In addition, the traditional risk scoring model has poor adaptability when facing risks from different databases. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is the system flow chart.

[0008] Figure 2 This is the algorithm flow chart. Summary of the invention

[0009] In order to solve the technical problems existing in the prior art, the present invention proposes an intelligent database inspection method based on a risk assessment algorithm, which comprises the following steps:

[0010] (1) Fuzzy processing: The monitoring data of each inspection item is used as the input variable and fuzzy processed. The fuzzy membership function is used to calculate the fuzzy membership of each input variable to obtain the membership degree of the variable in the fuzzy set;

[0011] (2) Nonlinear fuzzy reasoning: Calculate the membership of each fuzzy inspection item rule based on the membership of the fuzzy set and the input variable;

[0012] (3) Confidence interval reasoning: used to express the credible range of the results and evaluate the reliability of the reasoning;

[0013] (4) Risk score calculation: Calculate the risk score and obtain the risk level of the inspection item based on the final score;

[0014] Among them, T-norm operation is used to construct a dynamic membership function to dynamically adjust the risk interaction weights between inspection items.

[0015] Furthermore, in step (2), the fuzzy membership of each inspection item is calculated Where X = {x1, x2, ..., x i ,...,x n}, is the input variable, T represents the T-norm operation, is the fuzzy membership function, which represents the input variable x i In the fuzzy set A i The degree of membership in .

[0016] Furthermore, in steps (3) and (4), the inference result y j The confidence interval for Confidence Interval Mapping Calculate the final risk score Where α is the integral adjustment parameter, is the corresponding risk value found inside the confidence interval, w j is the risk term y j The weight of .

[0017] Based on the above method, the present invention proposes an intelligent database inspection system based on risk assessment algorithm, which includes the following five parts:

[0018] (1) Fast inspection module: Automatically adjust the priority and execution order of inspection items by using historical inspection data and current database status; dynamically predict potential risk points during the inspection task execution and provide feedback to the system;

[0019] (2) Special inspection module: Based on the usage patterns, historical issues and inspection results of the database, the system uses an intelligent template recommendation algorithm to recommend the inspection template that best suits the current scenario, helping users select the most effective inspection strategy. The system also identifies potential issues and generates in-depth inspection reports by correlating and analyzing historical data, configuration items and external environments.

[0020] (3) Scheduled inspection module: The system uses a scheduling optimization algorithm to intelligently adjust the time and frequency of inspection tasks based on the database workload, performance indicators, and historical trends to ensure that the impact of inspection tasks on database operations is minimized;

[0021] (4) Inspection configuration module: Users manually configure the basic information of the single database inspection item configuration, configure different inspection templates in the template configuration; use the risk scoring model to determine the risk score of the configuration database;

[0022] (5) Inspection History Module: The system analyzes the results of historical inspection tasks to optimize and adjust the rules and configurations of future inspection tasks.

[0023] The feature is that: in the inspection configuration module, the following steps are specifically included:

[0024] 4.1 Fuzzy processing: Take the monitoring data of each inspection item as input variable and perform fuzzy processing. Calculate the fuzzy membership of each input variable through the fuzzy membership function to obtain the membership degree of the variable in the fuzzy set;

[0025] 4.2 Nonlinear fuzzy reasoning: According to the membership of fuzzy sets and input variables, the membership of each fuzzy inspection item rule is calculated;

[0026] 4.3 Confidence interval reasoning: used to express the credible range of the results and evaluate the reliability of the reasoning;

[0027] 4.4 Risk score calculation: Calculate the risk score and obtain the risk level of the inspection item based on the final score;

[0028] The inspection configuration module uses T-norm operation to construct a dynamic membership function and dynamically adjust the risk interaction weights between inspection items.

[0029] Furthermore, in step 4.2, the fuzzy membership of each inspection item is calculated Where X = {x1, x2, ..., x i ,...,x n}, is the input variable, T represents the T-norm operation, is the fuzzy membership function, which represents the input variable x i In the fuzzy set A i The degree of membership in .

[0030] Furthermore, in steps 4.3 and 4.4, the inference result y j The confidence interval for Confidence Interval Mapping Calculate the final risk score Where α is the integral adjustment parameter, is the corresponding risk value found inside the confidence interval, w j is the risk term y j The weight of .

[0031] The existing technology usually regards the membership function as a single static value, which cannot adapt to the obvious differences in risk characteristics in different time periods in the database inspection system; in addition, in view of the independence of variables in traditional technology, the misjudgment of composite risk items caused by the interaction between multiple variables is not considered. The present invention introduces T-norm operation to construct a dynamic membership function and dynamically adjust the risk interaction weights between inspection items. In view of the mutual influence between multiple risk items in a complex environment, the existing technology does not consider the coupling between tasks, which easily leads to scoring deviations. The present invention introduces an integral adjustment mechanism and a confidence interval mapping function to expand the risk score from a single value to a dynamic interval.

[0032] By introducing nonlinear combinations and dynamic weight adjustments, the present invention can not only more accurately reflect the complex interactions of risk factors, but also flexibly adjust the scoring strategy according to the actual situation, thereby significantly improving the accuracy and practicality of the model. At the same time, by combining confidence interval reasoning with risk preference mapping, the personalization and reliability of the scoring results are further enhanced. Special inspections are carried out on configuration library systems at medium and high risks to monitor the health status of the database system more deeply and comprehensively, and targeted optimization and adjustments are made to abnormal inspection items. At the same time, scheduled inspection tasks can be carried out for low-risk configuration libraries, and the health status of the configuration libraries can be monitored during idle time. DETAILED DESCRIPTION

[0033] Example 1

[0034] In view of the problems of the existing database inspection system in terms of risk assessment accuracy, flexibility and insufficient processing ability for complex risk factors, the present invention proposes an intelligent database inspection system based on risk assessment algorithm. The system flow chart is shown in Figure 1 .Depend on Figure 1 It can be seen that the system includes:

[0035] 1. Rapid inspection module

[0036] The fast inspection module is mainly used to perform fast inspection tasks on the managed database. By calling the preset inspection rules, users can quickly inspect the health status of the database system and feedback the inspection results to the operation and maintenance personnel.

[0037] Functionality:

[0038] Task creation: Users can manually fill in the inspection task name. By default, the current timestamp is used as the task name. Select the inspection type as "Quick Inspection" and fill in the email address to receive the results.

[0039] Database selection: Users can select one or more databases to be inspected.

[0040] Inspection execution: The system performs a comprehensive inspection on the selected database based on the preset inspection items (divided into three categories: resources, status, and security), and displays the inspection results on the task details page.

[0041] Result display: After the inspection task is completed, the system displays the execution results of each inspection item and sends the report via email. If an abnormality is detected, the user can directly click on the abnormal item to view detailed information.

[0042] 2. Special inspection module

[0043] The special inspection module allows users to select pre-configured inspection templates to perform targeted in-depth inspections on the database. This module is particularly suitable for scenarios where specific inspection tasks need to be performed regularly.

[0044] Functionality:

[0045] Template selection: When creating an inspection task, the user selects a special inspection type and chooses a suitable inspection template (such as performance optimization, security audit, etc.) from the template library provided by the system.

[0046] Task configuration: Fill in the task name, select a template, and set the email address to receive the report.

[0047] Inspection execution: The system performs an in-depth inspection on the selected database according to the inspection items preset in the template and generates a detailed inspection report.

[0048] ·Report sending: After the inspection is completed, the system automatically generates a report and sends it to the specified mailbox. The report will describe in detail the execution status of the inspection items and the problems found.

[0049] 3. Scheduled inspection module

[0050] The scheduled inspection module is used to create and manage regular inspection tasks. Users can preset inspection plans, and the system will automatically inspect the database at the set time and frequency, and automatically generate and send inspection reports.

[0051] Functionality:

[0052] · Schedule creation: Users can set the name of the scheduled inspection task, the inspection period (such as daily, weekly, monthly), the inspection frequency (such as once a day), the database to be inspected and the trigger time, and specify the email address to receive the report.

[0053] Task scheduling: The system regularly triggers corresponding inspection tasks and executes inspection rules according to the inspection plan set by the user.

[0054] Plan management: Users can view, edit or delete the created scheduled inspection plans in the plan list, and support manual triggering of plans for immediate execution.

[0055] 4. Inspection configuration module

[0056] The inspection configuration module configures the configuration of the configuration library and inspection items, supports the calculation of the comprehensive score of the system inspection results to estimate the risk score of the configuration library, and optimizes the special inspection plan for the configuration library with medium and high risks.

[0057] Functionality:

[0058] Single database inspection item configuration: Configure database information, including database alias, database type, connection address, database name, application name, and product version.

[0059] Template configuration: This module is only used to batch modify database inspection configuration information, including template name, database type and description. The template also includes multiple inspection item configurations.

[0060] Inspection item configuration: basic information configuration of multiple inspection items, including inspection item name, database type, inspection item description, level, condition, threshold, unit, and inspection item category.

[0061] Risk scoring model: Set the risk scoring model for inspection items and optimize the special inspection model according to the risk level of the scoring results.

[0062] Among them, the existing risk scoring model is too simple when dealing with complex risk factors and cannot effectively capture the nonlinear interaction relationship between variables, resulting in a lack of depth and accuracy in the scoring results. In addition, the traditional risk scoring model has poor adaptability when facing risks from different databases. By introducing nonlinear combinations and dynamic weight adjustments, the present invention can not only more accurately reflect the complex interactions of risk factors, but also flexibly adjust the scoring strategy according to actual situations, thereby significantly improving the accuracy and practicality of the model. At the same time, by combining confidence interval reasoning with risk preference mapping, the personalization and reliability of the scoring results are further enhanced. The algorithm includes the following steps:

[0063] (1) Fuzzy processing

[0064] The monitoring data of each inspection item is used as the input variable X={x1,x2,...,x i ,...,x n} to perform fuzzy processing, through the fuzzy membership function Calculate for each input variable x i The fuzzy membership of the variable x i In the fuzzy set A i The fuzzy set includes low risk, medium risk and high risk.

[0065] (2) Nonlinear fuzzy reasoning

[0066] According to the membership of fuzzy sets and input variables, the membership of each fuzzy inspection item rule is calculated as Among them, the input variable X={x1,x2,...,x i ,...,x n}, T represents T-norm operation, is the fuzzy membership function, which represents the input variable x i In the fuzzy set A i The degree of membership in .

[0067] (3) Confidence interval reasoning

[0068] The inference result y j With confidence interval The present invention defines a nonlinear risk preference function f r (·) to represent the risk tendency, and the final confidence interval is mapped to

[0069] (4) Risk score calculation

[0070] Finally, the risk score is calculated as α is the integral adjustment parameter, w j represents the risk term y j The risk level of the inspection item is obtained based on the final score.

[0071] The health score of the system is deducted according to the corresponding level of high, medium and low risk. For systems with too low health scores, special inspections are carried out to monitor the health status of the database system more deeply and comprehensively, and to optimize and adjust abnormal inspection items in a targeted manner. At the same time, scheduled inspection tasks can be carried out for low-risk configuration libraries, and the health status of the configuration library can be monitored during idle time.

[0072] 5. Inspection history module

[0073] The inspection history module provides a historical record query function for inspection tasks. Users can view the details of completed inspection tasks, historical reports, and exception handling records.

[0074] Functionality:

[0075] Task record: The system records all executed inspection tasks in chronological order, including task name, execution time, inspection type, result summary, etc.

[0076] ·View details: Users can click on the task name to view specific inspection details, including the execution results, abnormal conditions, handling suggestions and risk scores of each inspection item.

[0077] ·Report management: The system provides report download and email sending record query functions. Users can view and download sent historical reports to facilitate long-term monitoring and comparative analysis.

[0078] The algorithm flow chart is as follows Figure 2 shown.

[0079] Example 2

[0080] First, improve the inspection configuration module of the intelligent inspection system, including single database inspection item configuration, template configuration, inspection item configuration and risk assessment model configuration.

[0081] (1) Single database inspection item configuration:

[0082] Database alias Database Type Connection Address Database Name Application Name Product Version 11_DN5 openGauss 192.168.**.*** dcpdb Default apps 2.0.0

[0083] (2) Template configuration:

[0084] In the template configuration section, each template can be configured with multiple inspection item configurations, including the Test template.

[0085] Template Name Database Type describe Test openGauss test0508

[0086] (3) Inspection item configuration

[0087]

[0088] (4) The configuration of the risk scoring model is shown above

[0089] Next, in the intelligent inspection system, you can perform quick inspections on single database inspection items based on user selection or perform special inspections according to template configuration.

[0090] For special inspections, users can select a template configured in the system, which includes multiple inspection item indicators. Specifically, during the special inspection process, the system uses a risk assessment model to determine the health of the database based on the inspection results of each inspection item indicator, such as the inspection item of dynamic memory usage. The specific process is as follows:

[0091] Assume there are 10 inspection items X = {x1, x2, ..., x 10}, the fuzzy set of each inspection item is {'high', 'medium', 'low'}, and the fuzzy rule for a should be:

[0092] Rule 1: IF x1 is “high” AND x2 memory usage is “high” THEN risk is “high”.

[0093] Rule 2: IF x1 is “Medium” AND x3 is “Low” THEN risk is “Medium”.

[0094] …

[0095] The membership values ​​of each fuzzy rule calculated according to the fuzzy set and fuzzy rules are: 0.6, 0.7, 0.8, 0.5, 0.3, 0.4, 0.2, 0.1, 0.6, 0.5;

[0096] Assume that the calculated risk score is

[0097]

[0098] If y∈[0.0,0.4], it is low risk; if y∈[0.4,0.7], it is medium risk; if y∈[0.7,1.0], it is high risk.

[0099] Therefore, the risk score of the inspection database is medium risk. The user can start a special inspection task to conduct a more in-depth inspection of the database and implement corresponding improvement measures based on the suggestions in the inspection report. At the same time, for low-risk configuration libraries, scheduled inspection tasks can be performed to monitor the health status of the configuration library during idle time.

[0100] The units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions divided into various modules. Of course, when implementing this application, the functions of each module can be implemented in the same or more software and / or hardware, or the modules that implement the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0101] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0102] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0103] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0104] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0105] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. An intelligent database inspection method based on a risk assessment algorithm, the method comprising the following steps: (1) Fuzzy processing: The monitoring data of each inspection item is used as the input variable and fuzzy processed. The fuzzy membership function is used to calculate the fuzzy membership of each input variable to obtain the membership degree of the variable in the fuzzy set; (2) Nonlinear fuzzy reasoning: Calculate the membership of each fuzzy inspection item rule based on the membership of the fuzzy set and the input variable; (3) Confidence interval reasoning: used to express the credible range of the results and evaluate the reliability of the reasoning; (4) Risk score calculation: Calculate the risk score and obtain the risk level of the inspection item based on the final score; Its characteristics are as follows: a dynamic membership function is constructed by T-norm operation to dynamically adjust the risk interaction weights between inspection items; integral adjustment and confidence interval mapping functions are introduced to expand the risk score from a single value to a dynamic interval.

2. The intelligent database inspection method based on risk assessment algorithm as claimed in claim 1, characterized in that: In step (2), the fuzzy membership of each inspection item is calculated Where X = {x1, x2, ..., x i ,...,x n }, is the input variable, T represents the T-norm operation, is the fuzzy membership function, which represents the input variable x i In the fuzzy set A i The degree of membership in .

3. The intelligent database inspection method based on risk assessment algorithm as claimed in claim 1, characterized in that: In steps (3) and (4), the inference result y j The confidence interval for Confidence Interval Mapping Calculate the final risk score Where α is the integral adjustment parameter, is the corresponding risk value found inside the confidence interval, w j is the risk term y j The weight of .

4. An intelligent database inspection system based on risk assessment algorithm, including the following five parts: (1) Fast inspection module: Automatically adjust the priority and execution order of inspection items by using historical inspection data and current database status; dynamically predict potential risk points during the inspection task execution and provide feedback to the system; (2) Special inspection module: Based on the usage patterns, historical issues and inspection results of the database, the system uses an intelligent template recommendation algorithm to recommend the inspection template that best suits the current scenario, helping users select the most effective inspection strategy. Identify potential problems and generate in-depth inspection reports by correlating and analyzing historical data, configuration items, and external environments; (3) Scheduled inspection module: The system uses a scheduling optimization algorithm to intelligently adjust the time and frequency of inspection tasks based on the database workload, performance indicators, and historical trends to ensure that the impact of inspection tasks on database operations is minimized; (4) Inspection configuration module: Users manually configure the basic information of the single database inspection item configuration, configure different inspection templates in the template configuration; use the risk scoring model to determine the risk score of the configuration database; (5) Inspection History Module: The system analyzes the results of historical inspection tasks to optimize and adjust the rules and configurations of future inspection tasks. This is characterized by: in the inspection configuration module, The following steps are involved: 4.1 Fuzzy processing: Take the monitoring data of each inspection item as input variable and perform fuzzy processing. Calculate the fuzzy membership of each input variable through the fuzzy membership function to obtain the membership degree of the variable in the fuzzy set; 4.2 Nonlinear fuzzy reasoning: According to the membership of fuzzy sets and input variables, the membership of each fuzzy inspection item rule is calculated; 4.3 Confidence interval reasoning: used to express the credible range of the results and evaluate the reliability of the reasoning; 4.4 Risk score calculation: Calculate the risk score and obtain the risk level of the inspection item based on the final score; The inspection configuration module uses T-norm calculation to construct a dynamic membership function and dynamically adjust the risk interaction weights between inspection items; The risk score is expanded from a single value to a dynamic interval by introducing integral adjustment and confidence interval mapping functions.

5. The intelligent database inspection system based on risk assessment algorithm as claimed in claim 4, characterized in that: In step 4.2, the fuzzy membership of each inspection item is calculated Where X = {x1, x2, ..., x i ,...,x n } is the input variable, T represents T-norm operation, is the fuzzy membership function, which represents the input variable x i In the fuzzy set A i The degree of membership in .

6. The intelligent database inspection system based on risk assessment algorithm as claimed in claim 4, characterized in that: In steps 4.3 and 4.4, the inference result y j The confidence interval for Confidence Interval Mapping Calculate the final risk score Where α is the integral adjustment parameter, is the corresponding risk value found inside the confidence interval, w j is the risk term y j The weight of .

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