Database maintenance method and system

Abstract

The invention discloses a database maintenance method and system, and relates to the technical field of databases. The risk library is provided with the CPU occupancy rate risk value, the CPU temperature risk value, the memory utilization rate risk value and the hardware fault, so that the comprehensiveness during detection can be ensured, the maintenance pertinence can be improved, and the database maintenance efficiency can be improved; the maintenance scheme comprises data verification and monitoring, data quality and cleaning, database optimization, database upgrading and log management, the accuracy and stability of the scheme can be guaranteed, intervention of database engineers is not needed, dependence on the professional technical level of workers is reduced, and therefore the labor cost and the operation cost of the database are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of databases, and in particular to a database maintenance method and a system thereof. Background Art

[0002] A database is a warehouse built on a computer storage device that organizes, stores and manages data according to data structures. In simple terms, it can be regarded as an electronic filing cabinet - a place to store electronic files, where users can add, intercept, update, delete and other operations on the data in the files. In the daily work of economic management, it is often necessary to put certain relevant data into such a "warehouse" and process them accordingly according to management needs.

[0003] In traditional technology, the database maintenance process still needs to be completed manually. The current database maintenance method is to set up a database administrator and maintain the database through the intervention of the database administrator. However, the current manual database maintenance method has the problem of low database maintenance efficiency. For this reason, we propose a database maintenance method and system. Summary of the invention

[0004] The purpose of the present invention is to solve the problems mentioned in the above background technology. The present invention provides a database maintenance method and system.

[0005] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0006] A database maintenance method comprises the following steps:

[0007] Build a risk library and set the risk values ​​for CPU usage, CPU temperature, memory usage, and hardware failures;

[0008] Data collection, obtaining real-time operation data of the database;

[0009] Compare data values, comparing the collected data with the thresholds built into the risk library;

[0010] Determine whether the data is within the threshold, if so, continue data collection, if not, proceed to the next step;

[0011] Develop maintenance plans and formulate corresponding solutions based on the judgment results.

[0012] Furthermore, the method further comprises: a fault alarm, and issuing a corresponding alarm based on the determination result.

[0013] Furthermore, the CPU occupancy risk value is divided into three levels: low risk (less than 50%), medium risk (50% to 85%), and high risk (greater than 85%);

[0014] The CPU temperature risk value is divided into three levels: low risk (less than 30°C), medium risk (31-69°C), and high risk (70°C);

[0015] The memory usage risk value is divided into three levels: low risk (less than 55%), medium risk (56% to 88%), and high risk (greater than 88%).

[0016] The hardware failure is divided into power failure and hard disk failure.

[0017] Furthermore, the data collection includes collecting the real-time values ​​of CPU occupancy, CPU temperature and memory usage of the database.

[0018] Furthermore, the formulation of the maintenance plan includes the following steps:

[0019] Data verification and monitoring: by setting up intelligent tools or programs to monitor the quality of data and ensure the accuracy, consistency and completeness of data;

[0020] Data quality and cleaning: clean the data regularly to remove duplicates, fix errors, and update outdated data to ensure data accuracy and consistency;

[0021] Database optimization, including index optimization, query optimization, and table structure optimization;

[0022] Database upgrades, including software version updates, hardware upgrades, and configuration adjustments;

[0023] Log management records database operations and error information. Log checks include occupied space checks and checks after errors.

[0024] Furthermore, the data verification and monitoring includes the following steps:

[0025] Completeness check: check whether the required layers of vector data are complete;

[0026] Check the accuracy of attribute data, check the area summary, prepare the output database quality inspection record sheet, and conduct a summary check on the area;

[0027] Logical consistency check: check whether the logical relationship between data is consistent;

[0028] Edge check: Check the edge of the data at the boundary to ensure that the data is continuous and seamless at the boundary without overlap or missing.

[0029] The results are saved and reported, the inspection results are saved to the specified path, and a database quality inspection record table is generated.

[0030] Furthermore, the data quality and cleaning includes the following steps:

[0031] Data review and understanding: Understand the structure, fields and characteristics of the data set, and understand the source and collection method of the data;

[0032] Handling missing values: handling gaps in data by removing, filling, or predicting missing values;

[0033] Handling duplicate values: Identify and delete duplicate data records to ensure data uniqueness;

[0034] Dealing with outliers: Identify and deal with outliers in the data to reduce their impact on the analysis results;

[0035] Data standardization and normalization: converting data with different characteristics into a unified scale.

[0036] A database maintenance system includes: a data acquisition unit, a data comparison unit, an alarm unit and a command execution unit, wherein the data acquisition unit is used to periodically collect real-time dynamic data of the database, the data comparison unit is used to compare the data collected by the data acquisition unit with a preset threshold, the alarm unit is used to issue an alarm for the result of the data comparison unit, and the command execution unit is used to execute the formulated maintenance plan.

[0037] An electronic device comprises a memory and a processor; wherein the memory is used to store instructions, and the instructions are used to execute any of the methods described above.

[0038] The beneficial effects of the present invention are as follows:

[0039] 1. The risk library of the present invention sets CPU occupancy risk value, CPU temperature risk value, memory usage risk value, and hardware failure, which can ensure the comprehensiveness of detection, thereby improving the pertinence of maintenance and further improving the efficiency of database maintenance.

[0040] 2. The maintenance solution of the present invention includes data verification and monitoring, data quality and cleaning, database optimization, database upgrade and log management, which can ensure the accuracy and stability of the solution without the intervention of database engineers, reducing the dependence on manual professional technical level, thereby reducing labor costs and database operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a work flow chart of the present invention;

[0042] Figure 2 It is a work flow chart for formulating a maintenance plan in the present invention;

[0043] Figure 3 It is a work flow chart of the maintenance system in the present invention. DETAILED DESCRIPTION

[0044] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0045] See also Figure 1 - Figure 3 The present invention provides a database maintenance method, comprising the following steps:

[0046] Build a risk library and set the risk values ​​for CPU usage, CPU temperature, memory usage, and hardware failures;

[0047] Data collection, obtaining real-time operation data of the database;

[0048] Compare data values, comparing the collected data with the thresholds built into the risk library;

[0049] Determine whether the data is within the threshold, if so, continue data collection, if not, proceed to the next step;

[0050] Develop maintenance plans and formulate corresponding solutions based on the judgment results.

[0051] In this embodiment, preferably, the method further includes: a fault alarm, and issuing a corresponding alarm based on the determination result; the issued alarm facilitates the staff to perform corresponding maintenance.

[0052] In this embodiment, preferably, the CPU occupancy risk value is divided into three levels: low risk (less than 50%), medium risk (50% to 85%), and high risk (greater than 85%);

[0053] The CPU temperature risk value is divided into three levels: low risk (less than 30°C), medium risk (31-69°C), and high risk (70°C);

[0054] The memory usage risk value is divided into three levels: low risk (less than 55%), medium risk (56% to 88%), and high risk (greater than 88%).

[0055] Hardware failure is divided into power failure and hard disk failure.

[0056] In this embodiment, preferably, data collection includes collecting real-time values ​​of CPU occupancy, CPU temperature and memory usage of the database.

[0057] In this embodiment, preferably, formulating a maintenance plan includes the following steps:

[0058] Data verification and monitoring: by setting up intelligent tools or programs, we can supervise the quality of data and ensure the accuracy, consistency and completeness of data; we can monitor the operation status and performance indicators of the database in real time to promptly discover and solve potential problems.

[0059] Data quality and cleaning: clean the data regularly to remove duplicates, fix errors, and update outdated data to ensure data accuracy and consistency;

[0060] Database optimization, including index optimization, query optimization, and table structure optimization;

[0061] Database upgrades, including software version updates, hardware upgrades, and configuration adjustments, can reduce database load and improve database response speed.

[0062] Log management records database operations and error information. Log checks include occupied space checks and checks after errors, making it easy to troubleshoot and resolve issues.

[0063] In this embodiment, preferably, data verification and monitoring include the following steps:

[0064] Completeness check: check whether the required layers of vector data are complete; it can ensure the comprehensiveness of the data.

[0065] Check the accuracy of attribute data, check the area summary, prepare the output database quality inspection record sheet, and perform a summary check on the area; this can ensure the accuracy of the data.

[0066] Logical consistency check: check whether the logical relationship between data is consistent; ensure that the data is logically consistent with expectations and has no contradictions or errors.

[0067] Edge check: Check the edge condition of the data at the boundary to ensure that the data is continuous and seamless at the boundary without overlap or missing.

[0068] The results are saved and reported, the inspection results are saved to the specified path, and a database quality inspection record table is generated.

[0069] In this embodiment, preferably, data quality and cleaning include the following steps:

[0070] Data review and understanding: Understand the structure, fields and characteristics of the data set, and understand the source and collection method of the data;

[0071] Handling missing values: handling gaps in data by removing, filling, or predicting missing values;

[0072] Handling duplicate values: Identify and delete duplicate data records to ensure data uniqueness;

[0073] Dealing with outliers: Identify and deal with outliers in the data to reduce their impact on the analysis results;

[0074] Data standardization and normalization: converting data with different characteristics into a unified scale.

[0075] The working principle and use process of the present invention:

[0076] Build a risk library and set the CPU occupancy risk value, CPU temperature risk value, memory usage risk value, and hardware failure; the CPU occupancy risk value is divided into three levels: low risk (less than 50%), medium risk (50%-85%), and high risk (greater than 85%); the CPU temperature risk value is divided into three levels: low risk (less than 30°C), medium risk (31-69°C), and high risk (70°C); the memory usage risk value is divided into three levels: low risk (less than 55%), medium risk (56%-88%), and high risk (greater than 88%); hardware failure is divided into power failure and hard disk failure.

[0077] Data collection, obtaining real-time database operation data; data collection includes collecting the database's real-time CPU occupancy, CPU temperature, and memory usage values.

[0078] Compare data values, comparing the collected data with the thresholds built into the risk library;

[0079] Determine whether the data is within the threshold, if so, continue data collection, if not, proceed to the next step;

[0080] Develop maintenance plans and formulate corresponding solutions based on the judgment results.

[0081] Developing a maintenance plan includes the following steps:

[0082] Data verification and monitoring: by setting up intelligent tools or programs, we can supervise the quality of data and ensure the accuracy, consistency and completeness of data; we can monitor the operation status and performance indicators of the database in real time to promptly discover and solve potential problems.

[0083] Data verification and monitoring includes the following steps:

[0084] Completeness check: check whether the required layers of vector data are complete; it can ensure the comprehensiveness of the data.

[0085] Check the accuracy of attribute data, check the area summary, prepare the output database quality inspection record sheet, and perform a summary check on the area; this can ensure the accuracy of the data.

[0086] Logical consistency check: check whether the logical relationship between data is consistent; ensure that the data is logically consistent with expectations and has no contradictions or errors.

[0087] Edge check: Check the edge condition of the data at the boundary to ensure that the data is continuous and seamless at the boundary without overlap or missing.

[0088] The results are saved and reported, the inspection results are saved to the specified path, and a database quality inspection record table is generated.

[0089] Data quality and cleaning: clean the data regularly to remove duplicates, fix errors, and update outdated data to ensure data accuracy and consistency.

[0090] Data quality and cleaning includes the following steps:

[0091] Data review and understanding: Understand the structure, fields and characteristics of the data set, and understand the source and collection method of the data;

[0092] Handling missing values: handling gaps in data by removing, filling, or predicting missing values;

[0093] Handling duplicate values: Identify and delete duplicate data records to ensure data uniqueness;

[0094] Dealing with outliers: Identify and deal with outliers in the data to reduce their impact on the analysis results;

[0095] Data standardization and normalization: converting data with different characteristics into a unified scale.

[0096] Database optimization, including index optimization, query optimization, and table structure optimization;

[0097] Database upgrades, including software version updates, hardware upgrades, and configuration adjustments, can reduce database load and improve database response speed.

[0098] Log management records database operations and error information. Log checks include occupied space checks and checks after errors, making it easy to troubleshoot and resolve issues.

[0099] Fault alarm: Based on the judgment result, a corresponding alarm is issued; the issued alarm is helpful for the staff to perform corresponding maintenance.

[0100] A database maintenance system includes: a data acquisition unit, a data comparison unit, an alarm unit and a command execution unit, wherein the data acquisition unit is used to periodically collect real-time dynamic data of the database, the data comparison unit is used to compare the data collected by the data acquisition unit with a preset threshold, the alarm unit is used to issue an alarm for the result of the data comparison unit, and the command execution unit is used to execute the formulated maintenance plan.

[0101] An electronic device comprises a memory and a processor; wherein the memory is used to store instructions, and the instructions are used to execute any one of the above methods.

[0102] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A database maintenance method, characterized in that: The steps include: Build a risk library and set the risk values ​​for CPU usage, CPU temperature, memory usage, and hardware failures; Data collection, obtaining real-time operation data of the database; Compare data values, comparing the collected data with the thresholds built into the risk library; Determine whether the data is within the threshold, if so, continue data collection, if not, proceed to the next step; Develop maintenance plans and formulate corresponding solutions based on the judgment results.

2. A database maintenance method according to claim 1, characterized in that: The method further includes: a fault alarm, and issuing a corresponding alarm based on the determination result.

3. A database maintenance method according to claim 1, characterized in that: The CPU occupancy risk value is divided into three levels: low risk (less than 50%), medium risk (50% to 85%), and high risk (greater than 85%); The CPU temperature risk value is divided into three levels: low risk (less than 30°C), medium risk (31-69°C), and high risk (70°C); The memory usage risk value is divided into three levels: low risk (less than 55%), medium risk (56% to 88%), and high risk (greater than 88%). The hardware failure is divided into power failure and hard disk failure.

4. A database maintenance method according to claim 1, characterized in that: The data collection includes collecting the real-time CPU occupancy rate, CPU temperature and memory usage rate values ​​of the database.

5. A database maintenance method according to claim 1, characterized in that: The maintenance plan comprises the following steps: Data verification and monitoring: by setting up intelligent tools or programs to monitor the quality of data and ensure the accuracy, consistency and completeness of data; Data quality and cleaning: clean the data regularly to remove duplicates, fix errors, and update outdated data to ensure data accuracy and consistency; Database optimization, including index optimization, query optimization, and table structure optimization; Database upgrades, including software version updates, hardware upgrades, and configuration adjustments; Log management records database operations and error information. Log checks include occupied space checks and checks after errors.

6. A database maintenance method according to claim 5, characterized in that: The data verification and monitoring includes the following steps: Completeness check: check whether the required layers of vector data are complete; Check the accuracy of attribute data, check the area summary, prepare the output database quality inspection record sheet, and conduct a summary check on the area; Logical consistency check: check whether the logical relationship between data is consistent; Edge check: Check the edge of the data at the boundary to ensure that the data is continuous and seamless at the boundary without overlap or missing. The results are saved and reported, the inspection results are saved to the specified path, and a database quality inspection record table is generated.

7. A database maintenance method according to claim 5, characterized in that: The data quality and cleaning includes the following steps: Data review and understanding: Understand the structure, fields and characteristics of the data set, and understand the source and collection method of the data; Handling missing values: handling gaps in data by removing, filling, or predicting missing values; Handling duplicate values: Identify and delete duplicate data records to ensure data uniqueness; Dealing with outliers: Identify and deal with outliers in the data to reduce their impact on the analysis results; Data standardization and normalization: converting data with different characteristics into a unified scale.

8. A database maintenance system, characterized in that: include: A data acquisition unit, a data comparison unit, an alarm unit and a command execution unit, wherein the data acquisition unit is used to periodically collect real-time dynamic data of a database, the data comparison unit is used to compare the data collected by the data acquisition unit with a preset threshold, the alarm unit is used to issue an alarm for the result of the data comparison unit, and the command execution unit is used to execute the formulated maintenance plan.

9. An electronic device, characterized in that: The method comprises a memory and a processor; wherein the memory is used to store instructions, and the instructions are used to execute the method according to any one of claims 1 to 7.

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