A data security management method based on artificial intelligence

Abstract

The present invention discloses a data security management method based on artificial intelligence, which belongs to the field of data processing technology and includes the following steps: S1, calculating the storage management requirements for the data to be managed according to the database storing the data to be managed; S2, performing a read operation on the data to be managed and generating a multidimensional data set for the data to be managed; S3, generating a secure encryption key using the storage management requirements and the multidimensional data set of the data to be managed; S4, encrypting the data to be managed using the secure encryption key to complete security management. The present invention generates encryption keys by combining storage management requirements and multidimensional data sets, which can ensure the complexity and uniqueness of the keys, and can dynamically adjust the generation strategy of the encryption keys according to changes in storage management requirements, taking into account the flexibility and scalability of security management.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a data security management method based on artificial intelligence. Background Art

[0002] As an important part of the information technology field, with the popularization of the Internet and the rapid development of technology, network security threats are becoming increasingly complex and diverse, and the requirements for network security management systems are becoming higher and higher. At present, most companies upload data to the database of the cloud platform for management to reduce enterprise management costs. However, security incidents such as data leakage, theft and tampering occur frequently, causing huge economic losses and reputation damage to companies. Traditional data security management methods often have problems such as low efficiency and insufficient protection when facing large-scale and complex data sets. Therefore, it is particularly important to develop an efficient and intelligent data security management method. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a data security management method based on artificial intelligence.

[0004] The technical solution of the present invention is: a data security management method based on artificial intelligence comprises the following steps:

[0005] S1. Calculate storage management requirements for the data to be managed according to the database storing the data to be managed;

[0006] S2. Read the data to be managed and generate a multidimensional data set for the data to be managed;

[0007] S3, using the storage management requirements of the data to be managed and the multidimensional data set to generate a secure encryption key;

[0008] S4. Use the security encryption key to encrypt the data to be managed to complete security management.

[0009] Furthermore, S1 includes the following sub-steps:

[0010] S11, determining whether the data to be managed is located in the temporary database of the SQL Server database, if so, proceeding to S12, otherwise proceeding to S13;

[0011] S12, using the file group of the temporary database to calculate the transient stored procedure value for the data to be managed, transferring the data to be managed to the SQL Server database, and entering S13;

[0012] S13. Calculate storage management requirements for the data to be managed using the port number of the SQL Server database.

[0013] The beneficial effect of the above further solution is that: in the present invention, by judging whether the data to be managed is located in the temporary database of the SQL Server database, the data can be classified and processed to obtain a suitable transient stored procedure value. The data in the temporary database is transferred to the SQL Server database, and the transient stored procedure value or constant is used to participate in the calculation of the storage management demand, so as to ensure that the database can run continuously and stably.

[0014] In S11, tempdb is a temporary database in SQL Server, which is used to store data such as temporary tables and temporary stored procedures. In S13, the database port number is the digital identifier corresponding to the network interface where the database service listens and receives client requests. Different database systems have different default port numbers, ranging from 0 to 65535. You can use the INSERT INTO...SELECT statement to transfer data from a table in tempdb to a table in the target database.

[0015] Furthermore, in S12, the transient storage process value S of the data to be managed moment The calculation formula is:

[0016] ;

[0017] In the formula, C represents the size of the PRIMARY file group in the temporary database, n represents the size of the data to be managed, and e represents the exponent.

[0018] A PRIMARY filegroup can contain one or more data files. The number and size of data files can be configured according to actual needs. Each database must have a PRIMARY filegroup.

[0019] Furthermore, in S13, the calculation formula of the storage management requirement Q of the data to be managed is:

[0020] ;

[0021] ;

[0022] In the formula, S moment represents the transient stored procedure value of the data to be managed, c represents a constant, D represents the management weight, PORT represents the port number of the SQL Server database, t represents the threshold of the blocking process in the SQL Server database, and N MAX Indicates the maximum memory usage of the SQL Server database, N MIN Indicates the minimum memory usage of the SQL Server database, n indicates the size of the data to be managed, and β indicates the default value of the DEFAULT constraint corresponding to the data to be managed.

[0023] When a process of managing data is blocked for more than this threshold, SQL Server database will generate a blocked process report to help diagnose and solve performance problems, that is, the threshold of the blocked process. Reasonable setting of the maximum memory usage of SQL Server database can avoid system overload and ensure that the operating system and other applications have sufficient memory resources. The minimum memory usage of SQL Server helps prevent the operating system from frequently adjusting memory allocation due to insufficient memory, thereby affecting performance. DEFAULT constraint is a powerful tool in SQL for setting column default values.

[0024] Furthermore, S2 includes the following sub-steps:

[0025] S21, using the shared lock of the SQL Server database to read the data to be managed, and obtain the corresponding index data;

[0026] S22. Calculate the index ratio according to the shared lock and the fill factor of the index data;

[0027] S23. Generate a corresponding multidimensional data set for the data to be managed according to the index ratio.

[0028] The beneficial effect of the above further scheme is: in the present invention, in S21, the shared lock is usually used for read operations, allowing a transaction to read a record, while other transactions can also read this record, but cannot modify it. Through the read operation of the shared lock, the corresponding index data can be obtained; index data is one of the key means of optimizing database performance, which can significantly speed up the query speed of data and improve the response performance of the database. The fill factor is an important parameter for controlling the filling degree of the index page. By reasonably setting the fill factor, the storage efficiency of the index can be optimized and the generation of page splits and fragments can be reduced. By generating a multidimensional data set, key generation can be supported.

[0029] Furthermore, in S22, the calculation formula of the index ratio α is:

[0030] ;

[0031] In the formula, max(·) represents the maximum value function, min(·) represents the minimum value function, rand(0,1) represents a random number generated between 0 and 1, g represents the granularity of the shared lock, and f represents the fill factor of the index data.

[0032] The granularity of a shared lock refers to the range of resources that are locked. The fill factor of an index specifies how full the index pages are.

[0033] Furthermore, in S23, the index ratio is used as the minimum support of the association rule algorithm, and the association rule algorithm is used to process the data to be managed to obtain a corresponding multidimensional data set.

[0034] The beneficial effect of the above further scheme is: in the present invention, the multidimensional data set is the core result generated after the SSAS (association rule algorithm) processes the data, usually including the data of the fact table and the dimension table, allowing the user to analyze and query the data from multiple angles.

[0035] Furthermore, S3 includes the following sub-steps:

[0036] S31, generating a management data pair according to the measure value of the fact table and the primary key of the dimension table in the multidimensional data set;

[0037] S32. Generate a secure encryption key using management data and storage management requirements.

[0038] Furthermore, in S31, the expression of the management data pair M is:

[0039] ;

[0040] In the formula, F represents the measurement value of the fact table, T represents the primary key of the dimension table, Indicates rounding up.

[0041] The measure value is the core data in the fact table, usually a numeric value. The primary key of the dimension table is used to uniquely identify each row of data in the dimension table, usually a self-increasing integer.

[0042] Furthermore, in S32, the expression of the security encryption key J is:

[0043] ;

[0044] In the formula, F represents the measurement value of the fact table, T represents the primary key of the dimension table, and D represents the management weight. Indicates rounding up, S moment Indicates the transient storage process value of the data to be managed, and c indicates a constant.

[0045] The beneficial effects of the present invention are as follows: the present invention obtains storage management requirements and multidimensional data sets by analyzing in detail the characteristics of the data to be managed and the database to which it belongs, and designs a security management solution that better meets actual needs; by combining storage management requirements and multidimensional data sets to generate encryption keys, the complexity and uniqueness of the keys can be ensured, and the encryption key generation strategy can be dynamically adjusted according to changes in storage management requirements, taking into account the flexibility and scalability of security management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure is a flow chart of the data security management method based on artificial intelligence. DETAILED DESCRIPTION

[0047] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0048] like Figure 1 As shown, the present invention provides a data security management method based on artificial intelligence, comprising the following steps:

[0049] S1. Calculate storage management requirements for the data to be managed according to the database storing the data to be managed;

[0050] S2. Read the data to be managed and generate a multidimensional data set for the data to be managed;

[0051] S3, using the storage management requirements of the data to be managed and the multidimensional data set to generate a secure encryption key;

[0052] S4. Use the security encryption key to encrypt the data to be managed to complete security management.

[0053] In this embodiment of the present invention, S1 includes the following sub-steps:

[0054] S11, determining whether the data to be managed is located in the temporary database of the SQL Server database, if so, proceeding to S12, otherwise proceeding to S13;

[0055] S12, using the file group of the temporary database to calculate the transient stored procedure value for the data to be managed, transferring the data to be managed to the SQL Server database, and entering S13;

[0056] S13. Calculate storage management requirements for the data to be managed using the port number of the SQL Server database.

[0057] In the present invention, by judging whether the data to be managed is located in the temporary database of the SQL Server database, the data can be classified and processed to obtain a suitable transient stored procedure value. The data in the temporary database is transferred to the SQL Server database, and the transient stored procedure value or constant is used to participate in the calculation of storage management requirements to ensure that the database can run continuously and stably.

[0058] In S11, tempdb is a temporary database in SQL Server, which is used to store data such as temporary tables and temporary stored procedures. In S13, the database port number is the digital identifier corresponding to the network interface where the database service listens and receives client requests. Different database systems have different default port numbers, ranging from 0 to 65535. You can use the INSERT INTO...SELECT statement to transfer data from a table in tempdb to a table in the target database.

[0059] In the embodiment of the present invention, in S12, the transient storage process value S of the data to be managed moment The calculation formula is:

[0060] ;

[0061] In the formula, C represents the size of the PRIMARY file group in the temporary database, n represents the size of the data to be managed, and e represents the exponent.

[0062] A PRIMARY filegroup can contain one or more data files. The number and size of data files can be configured according to actual needs. Each database must have a PRIMARY filegroup.

[0063] In the embodiment of the present invention, in S13, the calculation formula of the storage management demand Q of the data to be managed is:

[0064] ;

[0065] ;

[0066] In the formula, S moment represents the transient stored procedure value of the data to be managed, c represents a constant, D represents the management weight, PORT represents the port number of the SQL Server database, t represents the threshold of the blocking process in the SQL Server database, and N MAX Indicates the maximum memory usage of the SQL Server database, N MIN Indicates the minimum memory usage of the SQL Server database, n indicates the size of the data to be managed, and β indicates the default value of the DEFAULT constraint corresponding to the data to be managed.

[0067] When a process of managing data is blocked for more than this threshold, SQL Server database will generate a blocked process report to help diagnose and solve performance problems, that is, the threshold of the blocked process. Reasonable setting of the maximum memory usage of SQL Server database can avoid system overload and ensure that the operating system and other applications have sufficient memory resources. The minimum memory usage of SQL Server helps prevent the operating system from frequently adjusting memory allocation due to insufficient memory, thereby affecting performance. DEFAULT constraint is a powerful tool in SQL for setting column default values.

[0068] In this embodiment of the present invention, S2 includes the following sub-steps:

[0069] S21, using the shared lock of the SQL Server database to read the data to be managed, and obtain the corresponding index data;

[0070] S22. Calculate the index ratio according to the shared lock and the fill factor of the index data;

[0071] S23. Generate a corresponding multidimensional data set for the data to be managed according to the index ratio.

[0072] In the present invention, in S21, the shared lock is usually used for read operations, allowing one transaction to read a record, while other transactions can also read this record, but cannot modify it. Through the read operation of the shared lock, the corresponding index data can be obtained; index data is one of the key means of optimizing database performance, which can significantly speed up the query speed of data and improve the response performance of the database. The fill factor is an important parameter for controlling the filling degree of the index page. By reasonably setting the fill factor, the storage efficiency of the index can be optimized and the generation of page splits and fragments can be reduced. By generating a multidimensional data set, key generation can be supported.

[0073] In the embodiment of the present invention, in S22, the calculation formula of the index ratio α is:

[0074] ;

[0075] In the formula, max(·) represents the maximum value function, min(·) represents the minimum value function, rand(0,1) represents a random number generated between 0 and 1, g represents the granularity of the shared lock, and f represents the fill factor of the index data.

[0076] The granularity of a shared lock refers to the range of resources that are locked. The fill factor of an index specifies how full the index pages are.

[0077] In the embodiment of the present invention, in S23, the index ratio is used as the minimum support of the association rule algorithm, and the association rule algorithm is used to process the data to be managed to obtain a corresponding multidimensional data set.

[0078] The beneficial effect of the above further scheme is: in the present invention, the multidimensional data set is the core result generated after the SSAS (association rule algorithm) processes the data, usually including the data of the fact table and the dimension table, allowing the user to analyze and query the data from multiple angles.

[0079] In this embodiment of the present invention, S3 includes the following sub-steps:

[0080] S31, generating a management data pair according to the measure value of the fact table and the primary key of the dimension table in the multidimensional data set;

[0081] S32. Generate a secure encryption key using management data and storage management requirements.

[0082] In the embodiment of the present invention, in S31, the expression of the management data pair M is:

[0083] ;

[0084] In the formula, F represents the measurement value of the fact table, T represents the primary key of the dimension table, Indicates rounding up.

[0085] The measure value is the core data in the fact table, usually a numeric value. The primary key of the dimension table is used to uniquely identify each row of data in the dimension table, usually a self-increasing integer.

[0086] In the embodiment of the present invention, in S32, the expression of the security encryption key J is: ;

[0087] In the formula, F represents the measurement value of the fact table, T represents the primary key of the dimension table, and D represents the management weight. Indicates rounding up, S moment Indicates the transient storage process value of the data to be managed, and c indicates a constant.

[0088] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A data security management method based on artificial intelligence, characterized in that: The following steps are involved: S1. Calculate storage management requirements for the data to be managed according to the database storing the data to be managed; S2. Perform a read operation on the data to be managed and generate a multidimensional data set for the data to be managed; S3, using the storage management requirements of the data to be managed and the multidimensional data set to generate a secure encryption key; S4. Encrypt the data to be managed using a secure encryption key to complete security management; The S1 comprises the following sub-steps: S11, determining whether the data to be managed is located in the temporary database of the SQL Server database, if so, proceeding to S12, otherwise proceeding to S13; S12, using the file group of the temporary database to calculate the transient stored procedure value for the data to be managed, transferring the data to be managed to the SQL Server database, and entering S13; S13, using the port number of the SQL Server database, calculating the storage management requirements for the data to be managed; In S12, the transient storage process value S of the data to be managed moment The calculation formula is: In the formula, C represents the size of the PRIMARY file group in the temporary database, n represents the size of the data to be managed, and e represents the exponent; In S13, the calculation formula of the storage management requirement Q of the data to be managed is: In the formula, S moment represents the transient stored procedure value of the data to be managed, c represents a constant, D represents the management weight, PORT represents the port number of the SQL Server database, t represents the threshold of the blocking process in the SQL Server database, and N MAX Indicates the maximum memory usage of the SQL Server database, N MIN Indicates the minimum memory usage of the SQL Server database, n indicates the size of the data to be managed, and β indicates the default value of the DEFAULT constraint corresponding to the data to be managed; The S2 comprises the following sub-steps: S21, using the shared lock of the SQL Server database to read the data to be managed, and obtain the corresponding index data; S22. Calculate the index ratio according to the shared lock and the fill factor of the index data; S23. Generate a corresponding multidimensional data set for the data to be managed according to the index ratio; In S22, the calculation formula of the index ratio α is: In the formula, max(·) represents the maximum value function, min(·) represents the minimum value function, rand(0,1) represents a random number generated between 0 and 1, g represents the granularity of the shared lock, and f represents the fill factor of the index data; In S23, the index ratio is used as the minimum support of the association rule algorithm, and the association rule algorithm is used to process the management data to obtain a corresponding multidimensional data set; The S3 comprises the following sub-steps: S31, generating a management data pair according to the measure value of the fact table and the primary key of the dimension table in the multidimensional data set; S32, generating a secure encryption key using the management data pair and the storage management requirements; In S31, the expression of the management data pair M is: In the formula, F represents the measurement value of the fact table, T represents the primary key of the dimension table, Indicates rounding up; In S32, the expression of the security encryption key J is: In the formula, F represents the measurement value of the fact table, T represents the primary key of the dimension table, and D represents the management weight. Indicates rounding up, S moment Indicates the transient storage process value of the data to be managed, and c indicates a constant.

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