A method for processing payment transaction data

Abstract

This invention discloses a payment transaction data processing method, belonging to the field of data processing technology, including the following steps: S1, obtaining the user's payment transaction information and cleaning the information to obtain updated payment transaction information; S2, importing the updated payment transaction information into a MySQL database; S3, constructing an engine index statement for the updated payment transaction information; S4, generating an encryption key for the updated payment transaction information based on the corresponding engine index statement; S5, completing encryption using the encryption key. This invention generates an encryption key based on the characteristic parameters of the payment transaction information, which can more effectively protect sensitive data and prevent unauthorized access and leakage.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and specifically relates to a method for processing payment transaction data. Background Technology

[0002] With the development of computer technology, more and more technologies are being applied in the field of payment transactions. As a result, the security requirements for sensitive information such as user payment transaction data are also increasing. Therefore, it is necessary to encrypt sensitive fields in the database (such as user identification numbers, transaction data, payment data, and business data) into ciphertext fields. However, the sheer volume of sensitive data in databases can easily lead to problems such as low encryption accuracy and lack of specificity. Therefore, improving the encryption security of payment transaction information has become an urgent problem to be solved. Summary of the Invention

[0003] To address the above problems, this invention proposes a method for processing payment transaction data.

[0004] The technical solution of the present invention is: a payment transaction data processing method comprising the following steps:

[0005] S1. Obtain the user's payment transaction information, clean the payment transaction information, and obtain updated payment transaction information;

[0006] S2. Import the updated payment transaction information into the MySQL database;

[0007] S3. In the MySQL database, construct engine index statements based on the user's account type to update payment transaction information;

[0008] S4. Generate an encryption key for the updated payment transaction information based on the engine index statement corresponding to the updated payment transaction information;

[0009] S5. Use the encryption key to update the payment transaction information to complete the encryption.

[0010] In S1, a user's payment transaction information can include any data related to the user's payment transaction, such as user ID, user name, account balance, order ID, product information, and order amount.

[0011] Furthermore, S3 includes the following sub-steps:

[0012] S31. Calculate the user's access weight based on the current and maximum number of connections to the MySQL database and the user's account type;

[0013] S32. Calculate the information proportion value by using the character length of the updated payment transaction information;

[0014] S33. Generate a message matrix that updates payment transaction information based on the information proportion value and the user's access weight;

[0015] S34. Generate a B-Tree index based on the message matrix;

[0016] S35. Based on the B-Tree index, construct engine index statements for updating payment transaction information.

[0017] The beneficial effect of the above-mentioned further solution is that, in this invention, in S35, the CREATE INDEX index command can be used to construct an engine index statement for updating payment transaction information. The B-Tree index, as an index type, can affect the index's storage structure and query performance.

[0018] Furthermore, in S31, the formula for calculating the user's access weight γ is:

[0019] In the formula, Connection max This indicates the maximum number of connections to a MySQL database. now This represents the maximum number of connections to the MySQL database, where ε represents the value corresponding to the user's account type.

[0020] If a user's account type is guest, ε will be smaller; if the user's account type is administrator, ε will be larger. The specific value of ε can be set manually. The maximum number of connections refers to the maximum number of client connections that can simultaneously connect to the MySQL database. The default maximum number of connections for the MySQL database is 151, but this can be modified.

[0021] Furthermore, in S32, the formula for calculating the information proportion value α is:

[0022] In the formula, HIT represents the hit rate of the buffer pool in the MySQL database, N represents the size of the buffer pool, L represents the character length of the updated payment transaction information, and e represents the exponent.

[0023] The buffer pool is used to cache data and indexes. Its size has a significant impact on database performance and is therefore included in the calculation of the information hit rate. The buffer pool hit rate refers to the ratio of the number of requests that successfully retrieve data from the buffer pool to the total number of read requests.

[0024] Furthermore, in S33, the expression for the message matrix J that updates the payment transaction information is:

[0025] In the formula, γ represents the user's access weight, and α represents the information proportion.

[0026] Furthermore, in S34, the row and column values ​​of the message matrix are rounded up to the nearest integer and used as the index key values ​​of the internal nodes. The updated payment transaction information is used as the leaf node to generate the B-Tree index.

[0027] The beneficial effects of the above-mentioned further solutions are as follows: In this invention, the B-Tree index is a balanced tree structure that can be used to organize and store data in a table. It organizes data in a tree-like structure, with each node having multiple child nodes, forming a balanced tree, making efficient lookup operations possible on large-scale datasets. A B-Tree index typically includes a root node, internal nodes, and leaf nodes. The root node is the entry point to the tree, from which the search begins; internal nodes contain index keys to guide the search direction; and leaf nodes contain pointers to the actual data or the data itself. Therefore, this invention uses the B-Tree index as the type to participate in the generation of index statements.

[0028] Furthermore, S4 includes the following sub-steps:

[0029] S41. Calculate the index encryption identifier based on the index prefix length and fill factor of the engine index statement;

[0030] S42. Combine the index encryption identifier with the identifier corresponding to the index name in the engine index statement to obtain the encryption key.

[0031] The beneficial effects of the above-mentioned further scheme are as follows: In this invention, in S31, the index prefix length refers to the number of prefix characters covered by the column values ​​of the engine index statement, and the fill factor is used to balance the query performance of the index statement. In S32, the engine index statement itself has an index name, and the index name has a unique identifier used to distinguish different indexes in the database. Therefore, this invention combines the above-mentioned characteristic parameters of the engine index statement to generate a specific encryption key for the statement.

[0032] Furthermore, in S41, the expression for the index encryption identifier δ is:

[0033] In the formula, l represents the index prefix length of the engine index statement, and β represents the fill factor of the engine index statement. This indicates rounding up to the nearest integer.

[0034] The beneficial effects of this invention are: This invention generates targeted engine index statements for payment transaction information in the MySQL database and effectively combines the data situation of payment transaction information in the database, which can significantly improve encryption performance; This invention generates encryption keys based on the characteristic parameters of payment transaction information, which can more effectively protect sensitive data and prevent unauthorized access and leakage. Attached Figure Description

[0035] Figure 1 A flowchart for the payment transaction data processing method. Detailed Implementation

[0036] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0037] like Figure 1 As shown, the present invention provides a payment transaction data processing method, including the following steps:

[0038] S1. Obtain the user's payment transaction information, clean the payment transaction information, and obtain updated payment transaction information;

[0039] S2. Import the updated payment transaction information into the MySQL database;

[0040] S3. In the MySQL database, construct engine index statements based on the user's account type to update payment transaction information;

[0041] S4. Generate an encryption key for the updated payment transaction information based on the engine index statement corresponding to the updated payment transaction information;

[0042] S5. Use the encryption key to update the payment transaction information to complete the encryption.

[0043] In S1, a user's payment transaction information can include any data related to the user's payment transaction, such as user ID, user name, account balance, order ID, product information, and order amount.

[0044] In this embodiment of the invention, S3 includes the following sub-steps:

[0045] S31. Calculate the user's access weight based on the current and maximum number of connections to the MySQL database and the user's account type;

[0046] S32. Calculate the information proportion value by using the character length of the updated payment transaction information;

[0047] S33. Generate a message matrix that updates payment transaction information based on the information proportion value and the user's access weight;

[0048] S34. Generate a B-Tree index based on the message matrix;

[0049] S35. Based on the B-Tree index, construct engine index statements for updating payment transaction information.

[0050] In this invention, in step S35, the CREATE INDEX command can be used to construct an engine index statement for updating payment transaction information. The B-Tree index, as an index type, can affect the index's storage structure and query performance.

[0051] In this embodiment of the invention, in S31, the formula for calculating the user's access weight γ is:

[0052] In the formula, Connection max This indicates the maximum number of connections to a MySQL database. now This represents the maximum number of connections to the MySQL database, where ε represents the value corresponding to the user's account type.

[0053] If a user's account type is guest, ε will be smaller; if the user's account type is administrator, ε will be larger. The specific value of ε can be set manually. The maximum number of connections refers to the maximum number of client connections that can simultaneously connect to the MySQL database. The default maximum number of connections for the MySQL database is 151, but this can be modified.

[0054] In this embodiment of the invention, in S32, the formula for calculating the information proportion value α is:

[0055] In the formula, HIT represents the hit rate of the buffer pool in the MySQL database, N represents the size of the buffer pool, L represents the character length of the updated payment transaction information, and e represents the exponent.

[0056] The buffer pool is used to cache data and indexes. Its size has a significant impact on database performance and is therefore included in the calculation of the information hit rate. The buffer pool hit rate refers to the ratio of the number of requests that successfully retrieve data from the buffer pool to the total number of read requests.

[0057] In this embodiment of the invention, in S33, the expression for the message matrix J that updates the payment transaction information is:

[0058] In the formula, γ represents the user's access weight, and α represents the information proportion.

[0059] In this embodiment of the invention, in S34, the row and column values ​​of the message matrix are rounded up to be used as the index key values ​​of the internal nodes, and the updated payment transaction information is used as the leaf nodes to generate a B-Tree index.

[0060] In this invention, a B-Tree index is a balanced tree structure used to organize and store data in a table. It organizes data in a tree-like structure, with each node having multiple child nodes, forming a balanced tree, making efficient lookup operations possible on large-scale datasets. A B-Tree index typically includes a root node, internal nodes, and leaf nodes. The root node is the entry point to the tree, from which the search begins; internal nodes contain index keys to guide the search direction; and leaf nodes contain pointers to the actual data or the data itself. Therefore, this invention uses a B-Tree index as the type to participate in the generation of index statements.

[0061] In this embodiment of the invention, S4 includes the following sub-steps:

[0062] S41. Calculate the index encryption identifier based on the index prefix length and fill factor of the engine index statement;

[0063] S42. Combine the index encryption identifier with the identifier corresponding to the index name in the engine index statement to obtain the encryption key.

[0064] In this invention, in S31, the index prefix length refers to the number of prefix characters covered by the column values ​​of the engine index statement, and the fill factor is used to balance the query performance of the index statement. In S32, the engine index statement itself has an index name, and the index name has a unique identifier used to distinguish different indexes in the database. Therefore, this invention combines the above-mentioned characteristic parameters of the engine index statement to generate a specific encryption key for the statement.

[0065] In this embodiment of the invention, in S41, the expression for the index encryption identifier δ is:

[0066] In the formula, l represents the index prefix length of the engine index statement, and β represents the fill factor of the engine index statement. This indicates rounding up to the nearest integer.

[0067] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for processing payment transaction data, characterized in that, Includes the following steps: S1. Obtain the user's payment transaction information, clean the payment transaction information, and obtain updated payment transaction information; S2. Import the updated payment transaction information into the MySQL database; S3. In the MySQL database, construct engine index statements based on the user's account type to update payment transaction information; S4. Generate an encryption key for the updated payment transaction information based on the engine index statement corresponding to the updated payment transaction information; S5. Complete encryption using the encryption key for updating payment transaction information; S3 includes the following sub-steps: S31. Calculate the user's access weight based on the current and maximum number of connections to the MySQL database and the user's account type; S32. Calculate the information proportion value by using the character length of the updated payment transaction information; S33. Generate a message matrix that updates payment transaction information based on the information proportion value and the user's access weight; S34. Generate a B-Tree index based on the message matrix; S35. Based on the B-Tree index, construct engine index statements for updating payment transaction information; In step S31, the formula for calculating the user's access weight γ is as follows: In the formula, Connection max This indicates the maximum number of connections to a MySQL database. now This represents the current number of connections to the MySQL database, where ε represents the value corresponding to the user's account type. In step S32, the formula for calculating the information proportion value α is: In the formula, HIT represents the hit rate of the buffer pool in the MySQL database, N represents the size of the buffer pool, L represents the character length of the updated payment transaction information, and e represents the exponent. In step S33, the expression for the message matrix J that updates the payment transaction information is: In the formula, γ represents the user's access weight, and α represents the information proportion. In step S34, the row and column values ​​of the message matrix are rounded up to the nearest integer and used as the index key values ​​of the internal nodes. The updated payment transaction information is used as the leaf nodes to generate a B-Tree index.

2. The payment transaction data processing method according to claim 1, characterized in that, S4 includes the following sub-steps: S41. Calculate the index encryption identifier based on the index prefix length and fill factor of the engine index statement; S42. Combine the index encryption identifier with the identifier corresponding to the index name in the engine index statement to obtain the encryption key.

3. The payment transaction data processing method according to claim 2, characterized in that, In step S41, the expression for the index encryption identifier δ is: In the formula, l represents the index prefix length of the engine index statement, and β represents the fill factor of the engine index statement. This indicates rounding up to the nearest integer.

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