Payment transaction data processing method

Abstract

The invention discloses a payment transaction data processing method, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining payment transaction information of a user, and carrying out the data cleaning of the payment transaction information, and obtaining updated payment transaction information; s2, the updated payment transaction information is imported into a MySQL database; s3, constructing an engine index statement for updating the payment transaction information; s4, generating an encryption key for the updated payment transaction information according to the engine index statement corresponding to the updated payment transaction information; and S5, completing encryption by using the encryption key for updating the payment transaction information. The encryption key is generated according to the feature parameters of the payment transaction information, sensitive data can be protected more effectively, and unauthorized access and leakage are prevented.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a payment transaction data processing method. Background Art

[0002] With the development of computer technology, more and more technologies are applied in the field of payment transactions, and the security requirements for privacy information such as user payment transaction data are becoming higher and higher. Therefore, it is necessary to encrypt sensitive fields in the database (such as user ID number, transaction data, payment data and business data, etc.) into ciphertext fields. However, the amount of sensitive fields in the database is huge, which easily leads to problems such as low data encryption accuracy and lack of targeting. Therefore, how to improve the encryption security of payment transaction information has become an urgent problem to be solved. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a payment transaction data processing method.

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

[0005] S1. Obtain the user's payment transaction information, and perform data cleansing on the payment transaction information to obtain updated payment transaction information;

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

[0007] S3. In the MySQL database, an engine index statement is constructed for updating payment transaction information according to the user's account type;

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

[0009] S5. Complete encryption using the encryption key of the updated payment transaction information.

[0010] In S1, the user's payment transaction information may include any data of the user's payment transaction, such as fields 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 number of connections and the maximum number of connections of 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 for updating payment transaction information according to the information proportion value and the user's access weight;

[0015] S34, generating a B-Tree index according to the message matrix;

[0016] S35. Based on the B-Tree index, an engine index statement is constructed for updating the payment transaction information.

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

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

[0019] ; In the formula, Connection max Indicates the maximum number of connections to the MySQL database. now Indicates the maximum number of connections to the MySQL database, and ε indicates the value corresponding to the user's account type.

[0020] If the user's account type is a guest, ε is small; if the user's account type is an administrator, ε is large. The specific size of ε can be set manually. The maximum number of connections refers to the maximum number of client connections that can be connected to the MySQL database at the same time. The default maximum number of connections for the MySQL database is 151, but it can also be modified.

[0021] Furthermore, in S32, the calculation formula of 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 buffer pool size, 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. The size of the buffer pool has an important impact on database performance, so it is involved in the calculation of the information ratio. The buffer pool hit rate refers to the ratio of the number of requests that successfully read data from the buffer pool to the total number of read requests.

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

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

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

[0027] The beneficial effect of the above further scheme is: in the present 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 structure, and each node has multiple child nodes to form a balanced tree, making it possible to perform efficient search operations on large-scale data sets. The B-Tree index usually includes a root node, internal nodes, and leaf nodes. The root node is the entrance to the tree, from which the search starts; the internal node contains the index key value, which is used to guide the direction of the search; the leaf node contains a pointer to the actual data or the data itself. Therefore, the present invention adopts the B-Tree index as a type to participate in the generation of index statements.

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

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

[0030] S42: Combine the index encryption identifier and the identifier corresponding to the index name of the engine index statement to obtain an encryption key.

[0031] The beneficial effect of the above further scheme is: in the present invention, in S31, the index prefix length refers to the number of prefix characters covered by the column value of the engine index statement, and the filling factor is used to weigh 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 for distinguishing different indexes in the database. Therefore, the present invention combines the above characteristic parameters of the engine index statement to generate a specific encryption key for the statement.

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

[0033] ; In the formula, l represents the index prefix length of the engine index statement, β represents the filling factor of the engine index statement, Indicates rounding up.

[0034] The beneficial effects of the present invention are: the present invention generates targeted engine index statements for payment transaction information in the MySQL database, and effectively combines the data situation of the payment transaction information in the database, which can significantly improve the encryption performance; the present invention generates encryption keys according to the characteristic parameters of the payment transaction information, which can more effectively protect sensitive data and prevent unauthorized access and leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a flowchart of a method for processing payment transaction data. DETAILED DESCRIPTION

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

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

[0038] S1. Obtain the user's payment transaction information, and perform data cleansing on the payment transaction information to obtain updated payment transaction information;

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

[0040] S3. In the MySQL database, an engine index statement is constructed for updating payment transaction information according to the user's account type;

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

[0042] S5. Complete encryption using the encryption key of the updated payment transaction information.

[0043] In S1, the user's payment transaction information may include any data of the user's payment transaction, such as fields such as user ID, user name, account balance, order ID, product information, and order amount.

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

[0045] S31. Calculate the user's access weight based on the current number of connections and the maximum number of connections of 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 for updating payment transaction information according to the information proportion value and the user's access weight;

[0048] S34, generating a B-Tree index according to the message matrix;

[0049] S35. Based on the B-Tree index, an engine index statement is constructed for updating the payment transaction information.

[0050] In the present invention, in S35, a CREATE INDEX index command can be used to construct an engine index statement for updating payment transaction information. As an index type, a B-Tree index can affect the storage structure and query performance of the index.

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

[0052] ; In the formula, Connection max Indicates the maximum number of connections to the MySQL database. now Indicates the maximum number of connections to the MySQL database, and ε indicates the value corresponding to the user's account type.

[0053] If the user's account type is a guest, ε is small; if the user's account type is an administrator, ε is large. The specific size of ε can be set manually. The maximum number of connections refers to the maximum number of client connections that can be connected to the MySQL database at the same time. The default maximum number of connections for the MySQL database is 151, but it can also be modified.

[0054] In the embodiment of the present invention, in S32, the calculation formula of 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 buffer pool size, 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. The size of the buffer pool has an important impact on database performance, so it is involved in the calculation of the information ratio. The buffer pool hit rate refers to the ratio of the number of requests that successfully read data from the buffer pool to the total number of read requests.

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

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

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

[0060] In the present 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 structure, and each node has multiple child nodes to form a balanced tree, making it possible to perform efficient search operations on large-scale data sets. The B-Tree index usually includes a root node, internal nodes, and leaf nodes. The root node is the entrance to the tree, from which the search begins; the internal node contains the index key value, which is used to guide the direction of the search; the leaf node contains a pointer to the actual data or the data itself. Therefore, the present invention uses the B-Tree index as a type to participate in the generation of index statements.

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

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

[0063] S42: Combine the index encryption identifier and the identifier corresponding to the index name of the engine index statement to obtain an encryption key.

[0064] In the present invention, in S31, the index prefix length refers to the number of prefix characters covered by the column value of the engine index statement, and the fill factor is used to weigh 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 to distinguish different indexes in the database. Therefore, the present invention combines the above-mentioned characteristic parameters of the engine index statement to generate a specific encryption key for the statement.

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

[0066] ; In the formula, l represents the index prefix length of the engine index statement, β represents the filling factor of the engine index statement, Indicates rounding up.

[0067] 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 payment transaction data processing method, characterized in that: The following steps are involved: S1. Obtain the user's payment transaction information, and perform data cleansing on the payment transaction information to obtain updated payment transaction information; S2. Import the updated payment transaction information into the MySQL database; S3. In the MySQL database, an engine index statement is constructed for updating payment transaction information according to the user's account type; S4. Generate an encryption key for updating the payment transaction information according to the engine index statement corresponding to the updated payment transaction information; S5. Complete encryption using the encryption key of the updated payment transaction information.

2. The payment transaction data processing method according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31. Calculate the user's access weight based on the current number of connections and the maximum number of connections of 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 for updating payment transaction information according to the information proportion value and the user's access weight; S34, generating a B-Tree index according to the message matrix; S35. Based on the B-Tree index, an engine index statement is constructed for updating the payment transaction information.

3. The payment transaction data processing method according to claim 2, characterized in that: In S31, the calculation formula of the user's access weight γ is: ; In the formula, Connection max Indicates the maximum number of connections to the MySQL database. now Indicates the maximum number of connections to the MySQL database, and ε indicates the value corresponding to the user's account type.

4. The payment transaction data processing method according to claim 2, characterized in that: In S32, the calculation formula of 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 buffer pool size, L represents the character length of the updated payment transaction information, and e represents the exponent.

5. The payment transaction data processing method according to claim 2, characterized in that: In S33, the expression of the message matrix J for updating the payment transaction information is: ; In the formula, γ represents the user's access weight, and α represents the information proportion value.

6. The payment transaction data processing method according to claim 2, characterized in that: In the above S34, the row and column values ​​of the message matrix are rounded up as the index key value of the internal node, and the updated payment transaction information is used as the leaf node to generate a B-Tree index.

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

8. The payment transaction data processing method according to claim 7, characterized in that: In S41, the expression of the index encryption identifier δ is: ; In the formula, l represents the index prefix length of the engine index statement, β represents the filling factor of the engine index statement, Indicates rounding up.

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