Artificial intelligence digital archive management optimization method

Abstract

The invention discloses an artificial intelligence digital archive management optimization method. The optimization method comprises the following specific steps of authority optimization management and control, version updating optimization management and control, log optimization management and control and data storage optimization management and control. Through authority management, version control, log optimization and data storage mode optimization, the problems that data are covered and mistakenly deleted in the management and use process of the digital archives can be effectively avoided, retrieval of the stored data is more accurate, and meanwhile the safety of the digital archives is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer management, and more specifically to an artificial intelligence digital archive management optimization method. Background Art

[0002] Digital archive management is a new form of archive information that has emerged with the development of computer technology, scanning technology, database technology, and storage technology. It converts archive resources on various carriers into digitized information data, stores them in a digital form, accesses and connects them in a networked form, and manages them using a computer system to form an archive information database, facilitating the management and use of information resources.

[0003] The existing digital archive management system has the following problems during its application:

[0004] The archive management system uses shared storage to manage files, but there is no corresponding control mechanism for file versions, which often leads to multiple versions of the same file, making it impossible for the system to accurately track the latest file modifications. Files are easily overwritten or stored repeatedly, causing confusion in the archive files and affecting the security of the archive files. Summary of the Invention

[0005] The present invention discloses an artificial intelligence digital archive management optimization method, the main purpose of which is to overcome the above-mentioned deficiencies and shortcomings of the prior art.

[0006] The technical solution adopted in the present invention is as follows:

[0007] An artificial intelligence digital archive management optimization method, the optimization method comprising the following specific steps:

[0008] S1: Optimized control of permissions: Set read permissions, edit permissions, and approval permissions for digital archive files. Read permissions limit members to only viewing files, edit permissions limit members to allowing file modification requests, and approval permissions limit members to overwriting or deleting archive files.

[0009] S2: Version update optimization and control: This includes version update and modification and regular archiving of versions. Version update and modification means that managers modify the digital archive file version and submit an approval request. If approved, the version number is updated and replaced; otherwise, the original version number is retained. Regular archiving means that managers regularly archive versions and clean up redundant versions.

[0010] S3: Log optimization and control: Log management records the access, modification, and deletion operations of system files, and audits the security and compliance of access and modification;

[0011] S4: Optimized management and control of data storage: Store frequently accessed data versions in high-performance cloud storage, and transfer infrequently accessed data versions to low-cost archive storage. At the same time, use data compression tools to package and compress historical versions to optimize data storage space.

[0012] Furthermore, in step S1, the member with read permission is a common system visitor, and the member with edit permission and approval permission is a system administrator.

[0013] Furthermore, the editing permission sets collaborative editing permission, and the same file is only modified by a single visitor.

[0014] Furthermore, in step S3, the log management includes:

[0015] S31: Define a unified log format and level to facilitate system analysis and troubleshooting;

[0016] S32: Adjust the log level according to the business scenario to reduce problem logs and lower performance consumption;

[0017] S33: Split log files regularly to prevent a single log file from becoming too large.

[0018] S34: Keep regular key logs and delete and clean up expired log files;

[0019] S35: Filter abnormal logs in real time to reduce the storage of invalid log data;

[0020] S36: Archive logs by date to facilitate log tracing and query.

[0021] Through the above description and explanation of the present invention, the advantages of the present invention compared with the prior art are:

[0022] By optimizing permission management, version control, log optimization, and data storage methods, this solution can effectively avoid data overwriting and accidental deletion during the management and use of digital archives, make the retrieval of stored data more accurate, and improve the security of digital archive files. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0024] The specific embodiments of the present invention will be further described and illustrated below with reference to the accompanying drawings.

[0025] like Figure 1As shown, an artificial intelligence digital archive management optimization method includes the following specific steps:

[0026] S1: Optimized management and control of permissions: Set the read permission, edit permission and approval permission of digital archive files. The read permission is used to limit the members of this section to only view the files, and the edit permission is used to limit the members of this section to allow modification requests for files; the approval permission is used to limit the members of this section to overwrite or delete archive files; the members of the read permission are ordinary system visitors, and the members of the edit permission and approval permission are system administrators. The edit permission sets collaborative editing permissions, and the same file is only limited to a single visitor for modification.

[0027] S2: Version update optimization and control: This includes version update and modification and regular archiving of versions. Version update and modification means that managers modify the digital archive file version and submit an approval request. If approved, the version number is updated and replaced; otherwise, the original version number is retained. Regular archiving means that managers regularly archive versions and clean up redundant versions.

[0028] S3: Log optimization and control: Log management records the access, modification, and deletion operations of system files, and audits the security and compliance of access and modification;

[0029] Log management includes:

[0030] S31: Define a unified log format and level to facilitate system analysis and troubleshooting;

[0031] S32: Adjust the log level according to the business scenario to reduce problem logs and lower performance consumption;

[0032] S33: Split log files regularly to prevent a single log file from becoming too large.

[0033] S34: Keep regular key logs and delete and clean up expired log files;

[0034] S35: Filter abnormal logs in real time to reduce the storage of invalid log data;

[0035] S36: Archive logs by date to facilitate log tracing and query.

[0036] S4: Optimized management and control of data storage: Store frequently accessed data versions in high-performance cloud storage, and transfer infrequently accessed data versions to low-cost archive storage. At the same time, use data compression tools to package and compress historical versions to optimize data storage space.

[0037] By optimizing permission management, version control, log optimization, and data storage methods, this solution can effectively avoid data overwriting and accidental deletion during the management and use of digital archives, make the retrieval of stored data more accurate, and improve the security of digital archive files.

Claims

1. An artificial intelligence digital archive management optimization method, characterized by: The optimization method comprises the following specific steps: S1: Optimized control of permissions: Set read permissions, edit permissions, and approval permissions for digital archive files. Read permissions limit members to only viewing files, edit permissions limit members to allowing file modification requests, and approval permissions limit members to overwriting or deleting archive files. S2: Version update optimization and control: This includes version update and modification and regular archiving of versions. Version update and modification means that managers modify the digital archive file version and submit an approval request. If approved, the version number is updated and replaced; otherwise, the original version number is retained. Regular archiving means that managers regularly archive versions and clean up redundant versions. S3: Log optimization and control: Log management records the access, modification, and deletion operations of system files, and audits the security and compliance of access and modification; S4: Optimized management and control of data storage: Store frequently accessed data versions in high-performance cloud storage, and transfer infrequently accessed data versions to low-cost archive storage. At the same time, use data compression tools to package and compress historical versions to optimize data storage space.

2. The artificial intelligence digital archive management optimization method according to claim 1, characterized in that: In step S1, the member with read permission is a common system visitor, and the member with edit permission and approval permission is a system administrator.

3. The artificial intelligence digital archive management optimization method according to claim 1, characterized in that: The editing permissions set collaborative editing permissions, and the same file is only limited to a single visitor for modification.

4. The artificial intelligence digital archive management optimization method according to claim 1, characterized in that: In step S3, log management includes: S31: Define a unified log format and level to facilitate system analysis and troubleshooting; S32: Adjust the log level according to the business scenario to reduce problem logs and lower performance consumption; S33: Split log files regularly to prevent a single log file from becoming too large. S34: Keep regular key logs and delete and clean up expired log files; S35: Filter abnormal logs in real time to reduce the storage of invalid log data; S36: Archive logs by date to facilitate log tracing and query.

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