A database management and security analysis method

Abstract

The present invention proposes a database management and security analysis method, which includes: defining objectives and constraints, wherein the objective is to minimize the comprehensive security risk of all data storage units in the database system, and the constraints include storage capacity constraints and performance overhead constraints; performing sensitivity analysis on key parameters in the database architecture to evaluate the degree of influence of changes in key parameters on the objectives and constraints; optimizing and adjusting the key parameters of the database architecture based on the sensitivity analysis results to achieve the minimum security risk while satisfying the actual constraints. The database management and security analysis method proposed in the present invention optimizes the key parameters of the database architecture through systematic optimization steps and precise calculations, thereby achieving the minimum security risk while satisfying the constraints of storage capacity and performance overhead.

Description

Technical Field

[0001] The present invention relates to the technical field of database management, and in particular to a database management and security analysis method. Background Art

[0002] Database systems have become a core component of modern enterprise information architecture. Databases not only carry a large amount of business data, but are also responsible for data storage, retrieval, processing and protection. With the continuous increase in data volume and the increasing value of data, the security and performance of database systems are also facing challenges. How to ensure data security while maintaining efficient operation of the database has become an urgent problem to be solved in the current database management field.

[0003] At present, a series of technologies and methods have been developed in the field of database management and security analysis. For example, database security is guaranteed through access control, encryption technology, audit logs, etc.; database performance is improved through index optimization, query rewriting, cache strategy and other technologies; in addition, there are some special management tools and security analysis solutions for specific database systems (such as relational databases, NoSQL databases, etc.).

[0004] Although the existing technology has made certain achievements in the development of database management and security analysis, there are still some defects as follows: the existing technology often focuses on a single security risk point, such as access control, data encryption, etc., but lacks a comprehensive assessment of the comprehensive security risks of all data storage units in the database system, which leads to the neglect of certain potential security risks in the optimization process; when optimizing database performance or security, the existing technology often fails to fully consider actual constraints such as storage capacity and performance overhead, which may lead to problems such as insufficient storage capacity and performance degradation in actual applications of the optimization solution; when optimizing the key parameters of the database architecture, the existing technology often lacks scientific basis and guidance, which leads to the optimization process may be relatively blind and arbitrary, and cannot ensure the optimization of the optimization effect. Summary of the invention

[0005] Based on this, the purpose of the present invention is to propose a database management and security analysis method to solve the above-mentioned problems.

[0006] A database management and security analysis method according to the present invention comprises:

[0007] Define objectives and constraints, where the objective is to minimize the comprehensive security risk of all data storage units in the database system, and the constraints include storage capacity constraints and performance overhead constraints;

[0008] Conduct sensitivity analysis on key parameters in the database architecture to assess the impact of changes in key parameters on objectives and constraints;

[0009] Based on the results of sensitivity analysis, key parameters of the database architecture are optimized and adjusted to minimize security risks while meeting actual constraints.

[0010] Furthermore, the step of performing sensitivity analysis on key parameters in the database architecture to evaluate the impact of changes in key parameters on objectives and constraints includes:

[0011] Integrate the security risks of each data storage unit in the database, construct a security risk matrix, and calculate the partial derivatives of the security risk matrix with respect to key parameters to obtain the partial derivative matrix of the objective function to reflect the response of the objective function to changes in key parameters;

[0012] The partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameters are calculated respectively to obtain the partial derivative matrix of the storage capacity constraint function and the partial derivative matrix of the performance constraint function, so as to respectively reflect the responses of the storage capacity constraint function and the performance constraint function to the changes of the key parameters;

[0013] A sub-weight matrix is ​​constructed to represent the association weights between configuration options and data storage units in different key parameter directions.

[0014] Furthermore, the step of calculating the partial derivative of the security risk matrix with respect to the key parameter includes:

[0015] Decomposing the security risk matrix into a sum of a series of rank-tensors, each of which is composed of the outer product of three vectors;

[0016] The decomposed vector is differentiated, and the partial derivative of the vector is converted into the partial derivative of the objective function with respect to the key parameters through tensor operations and the chain rule.

[0017] Furthermore, the steps of respectively calculating the partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameters include:

[0018] The partial derivatives of the storage capacity constraint function and the performance constraint function with respect to key parameters are calculated using the derivation rules of calculus.

[0019] Furthermore, before the step of integrating the security risks of each data storage unit in the database and constructing a security risk matrix, the step further includes:

[0020] Assess the security risk of each data storage unit.

[0021] Furthermore, before the step of respectively calculating the partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameters to obtain the partial derivative matrix of the storage capacity constraint function and the partial derivative matrix of the performance constraint function, the step further includes:

[0022] Evaluate the impact of each data storage unit on storage capacity and performance.

[0023] Furthermore, the step of optimizing and adjusting the key parameters of the database architecture based on the sensitivity analysis results to minimize the security risks while satisfying the actual constraints includes:

[0024] Based on the partial derivative matrix of the objective function, the partial derivative matrix of the storage capacity constraint function, the partial derivative matrix of the performance constraint function and the sub-weight matrix, the key parameters in the database architecture are optimized and adjusted to minimize security risks while satisfying the constraints of storage capacity and performance overhead.

[0025] Furthermore, the step of optimizing and adjusting key parameters in the database architecture based on the partial derivative matrix of the objective function, the partial derivative matrix of the storage capacity constraint function, the partial derivative matrix of the performance constraint function, and the sub-weight matrix to minimize security risks while satisfying the constraints of storage capacity and performance overhead includes:

[0026] According to the objective function and the constraints, a Lagrangian function is constructed, and the Lagrangian function is: , where λ and μ are Lagrange multipliers, is the objective function, i and j are used to distinguish different constraints. represents the i-th storage capacity constraint function, represents the jth performance constraint function;

[0027] According to the partial derivative matrix of the objective function , the partial derivative matrix of the storage capacity constraint function , the partial derivative matrix of the performance constraint function and the sub-weight matrix, calculate the gradient of the Lagrangian function;

[0028] For the current temperature T and the current solution x, generate a new candidate solution ;

[0029] Calculate the objective function value of the new candidate solution x′ and the Lagrangian function value , and determine whether the new candidate solution x′ satisfies the constraints, that is, and ;

[0030] According to the preset acceptance criteria, decide whether to accept the new solution as the current solution;

[0031] According to the preset cooling schedule, the temperature T is lowered;

[0032] Until the temperature drops to the target minimum level, the simulated annealing process ends and the final solution x is output. ∗ As the optimized key parameter value.

[0033] In summary, according to the above-mentioned database management and security analysis method, the goal is to minimize the comprehensive security risks of all data storage units in the database system, and reasonably set storage capacity and performance overhead as constraints; by conducting in-depth sensitivity analysis on key parameters in the database architecture, the degree of influence of parameter changes on the goals and constraints is accurately evaluated, thereby providing a scientific basis for subsequent optimization and adjustment; based on the sensitivity analysis results, the present invention optimizes and adjusts key parameters, which not only minimizes security risks, but also ensures that the optimization scheme can meet the constraints of storage capacity and performance overhead in practical applications, thereby improving the overall performance and security of the database, and providing a strong guarantee for the safe and efficient operation of the database. Therefore, the database management and security analysis method proposed in the present invention optimizes the key parameters of the database architecture through systematic optimization steps and precise calculations, which minimizes security risks while meeting the constraints of storage capacity and performance overhead.

[0034] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or will be learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0036] Figure 1 A flowchart of a database management and security analysis method according to an embodiment of the present invention;

[0037] Figure 2 Another process implementation diagram of a database management and security analysis method according to an embodiment of the present invention;

[0038] Figure 3 A system block diagram of a database management and security analysis device according to an embodiment of the present invention;

[0039] Figure 4 Another system block diagram of a database management and security analysis device according to an embodiment of the present invention; DETAILED DESCRIPTION

[0040] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0041] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0043] See also Figure 1 , an embodiment of the present invention provides a database management and security analysis method, the method comprising steps S101 to S103:

[0044] S101, defining objectives and constraints, wherein the objective is to minimize the comprehensive security risk of all data storage units in the database system, and the constraints include storage capacity constraints and performance overhead constraints.

[0045] Understandably, by clearly defining the goals and constraints, a clear direction and framework is provided for database management and security analysis. The goal of minimizing the comprehensive security risk ensures the security of the database system, while the setting of constraints ensures the feasibility and practicality of the optimization plan.

[0046] The core goal is to minimize the comprehensive security risks of all data storage units in the database system (such as data leakage, data tampering, unauthorized access, and other risks) so that resources and energy can be concentrated on solving the key issue of database security. By minimizing these risks, the data security in the database can be ensured, thereby protecting the core assets of the enterprise.

[0047] By setting storage capacity constraints and performance cost constraints, we can ensure that the optimization process does not sacrifice the basic functions and performance of the database.

[0048] Since the database system needs to store a large amount of data, and storage resources are limited, during the optimization process, it is necessary to ensure that the storage capacity limit of the database is not exceeded. The storage capacity constraint ensures the feasibility of the optimization plan and avoids system crashes or performance degradation caused by insufficient storage capacity.

[0049] The performance of the database system is critical to the business operations of an enterprise. Although optimization work aims to improve security, it must not be at the expense of performance. Therefore, performance overhead constraints ensure that the optimization solution will not impose excessive performance burden on the database system while improving security.

[0050] S102, performing sensitivity analysis on key parameters in the database architecture to evaluate the impact of changes in key parameters on objectives and constraints.

[0051] It is understandable that by performing sensitivity analysis on key parameters in the database architecture (such as database configuration, storage structure, access control policy settings, etc.), it is possible to deeply evaluate the impact of changes in these parameters on objectives and constraints, so as to identify which parameters have a significant impact on security risks, storage capacity and performance overhead. The sensitivity analysis results obtained can provide scientific guidance for subsequent database parameter management, so that security risks can be more effectively reduced when adjusting parameters, while avoiding excessive impact on storage capacity and performance.

[0052] S103, optimizing and adjusting key parameters of the database architecture based on the sensitivity analysis results to minimize security risks while satisfying actual constraints.

[0053] Understandably, based on the sensitivity analysis results, the key parameters of the database architecture are optimized and adjusted to minimize security risks, thereby improving the overall security of the database and reducing the occurrence of security risks such as data leakage and tampering. During the optimization and adjustment process, the present invention also fully considers the storage capacity constraints and performance overhead constraints to ensure the feasibility of the optimization scheme in practical applications, so as to avoid problems such as insufficient storage capacity and performance degradation that may occur during the optimization process. Therefore, through the optimization and adjustment of the present invention, not only the security risks are reduced, but also the overall performance of the database is improved to a certain extent. For example, by reasonably adjusting key parameters such as cache size and index strategy, the query speed and response time of the database can be improved.

[0054] Based on step S101 to step S103, the goal is to minimize the comprehensive security risk of all data storage units in the database system, and reasonably set storage capacity and performance overhead as constraints; by conducting in-depth sensitivity analysis on key parameters in the database architecture, the degree of influence of parameter changes on the goals and constraints is accurately evaluated, thereby providing a scientific basis for subsequent optimization and adjustment; based on the sensitivity analysis results, the present invention optimizes and adjusts key parameters, which not only minimizes security risks, but also ensures that the optimization scheme can meet the constraints of storage capacity and performance overhead in practical applications, thereby improving the overall performance and security of the database, and providing a strong guarantee for the safe and efficient operation of the database. Therefore, the database management and security analysis method proposed by the present invention optimizes the key parameters of the database architecture through systematic optimization steps and precise calculations, which minimizes security risks while meeting the constraints of storage capacity and performance overhead.

[0055] The following is a further detailed introduction to a database management and security analysis method according to an embodiment of the present invention:

[0056] Further optional, such as Figure 2 As shown, in step S102, the step of performing sensitivity analysis on key parameters in the database architecture to evaluate the degree of influence of changes in key parameters on objectives and constraints includes steps S201 to S203:

[0057] S201, integrating the security risks of each data storage unit in the database, constructing a security risk matrix, and calculating the partial derivative of the security risk matrix with respect to key parameters to obtain a partial derivative matrix of the objective function to reflect the response of the objective function to changes in the key parameters;

[0058] S202, respectively calculating partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameters, and obtaining a partial derivative matrix of the storage capacity constraint function and a partial derivative matrix of the performance constraint function, so as to respectively reflect the responses of the storage capacity constraint function and the performance constraint function to the changes of the key parameters;

[0059] S203: construct a sub-weight matrix to represent the association weights between the configuration options and the data storage units in different key parameter directions.

[0060] Based on steps S201 to S203, a sensitivity analysis is performed to evaluate the degree of impact of changes in key parameters in the database architecture on the objectives (minimization of security risks) and constraints (storage capacity and performance). First, the security risks of each data storage unit in the database are integrated to form a security risk matrix. Each element of this matrix represents the security risk level of the corresponding data storage unit. By integrating security risks, the overall security status of the database can be more comprehensively understood; next, the partial derivatives of the security risk matrix with respect to key parameters are calculated to obtain the partial derivative matrix of the objective function. This partial derivative matrix reflects the response of the objective function (i.e., security risk) to changes in key parameters. By analyzing the partial derivative matrix, the system can identify which key parameters have the greatest impact on security risks; in addition to security risks, the constraints of storage capacity and performance overhead also need to be considered. Therefore, The partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameters are calculated respectively to obtain the corresponding partial derivative matrices. These partial derivative matrices reflect the response of the storage capacity and performance constraint functions to the changes in the key parameters, which helps the system understand how to ensure that the constraints are met when adjusting the key parameters. Finally, a sub-weight matrix is ​​constructed to represent the association weights between the configuration options and the data storage units in different key parameter directions. This matrix reflects the degree of association between different key parameters and the data storage units. Through the sub-weight matrix, the system can have a deeper understanding of how the key parameters affect the security risks and performance of the data storage units, thereby providing more precise guidance for optimization and adjustment.

[0061] In the specific implementation, the partial derivative matrix of the objective function is: , where f is the objective function (safety risk matrix) and x is the key parameter vector.

[0062] Partial derivative matrix of storage capacity constraint function: ,in, is the i-th storage capacity constraint function.

[0063] Partial derivative matrix of performance constraint function: ,in, is the jth performance constraint function.

[0064] Sub-weight matrix: ,in, Represents the association weight between the i-th data storage unit and the j-th key parameter.

[0065] Further optionally, in step S201, the step of calculating the partial derivative of the security risk matrix with respect to the key parameter includes:

[0066] Decomposing the security risk matrix into a sum of a series of rank-tensors, each of which is composed of the outer product of three vectors;

[0067] The decomposed vector is differentiated, and the partial derivative of the vector is converted into the partial derivative of the objective function with respect to the key parameters through tensor operations and the chain rule.

[0068] Based on this step, the characteristics of tensor decomposition are used to decompose the complex security risk matrix into simpler tensor factors, thereby simplifying the calculation process of partial derivatives, which is more efficient than directly calculating the partial derivatives of the original matrix, especially when dealing with large-scale data and complex models. These factors can represent different risk factors or parameters. By analyzing these factors, we can have a deeper understanding of the composition and change rules of the security risk matrix, enhance the interpretability of the model, and better capture the inherent structure and correlation in the security risk matrix, so that more factors and information can be considered when calculating partial derivatives to improve the accuracy of the calculation results. This is very important for understanding how the security risk matrix changes with changes in key parameters. Specifically, the security risk matrix is ​​decomposed into the sum of a series of rank-tensors, each rank-tensor is composed of the outer product of three vectors, and a high-order tensor can be decomposed into the sum of a series of rank-tensors through CP decomposition. Then, each vector is differentiated, and the partial derivatives of the vector are converted into partial derivatives of the objective function (i.e., the safety risk matrix) with respect to key parameters through tensor operations and the chain rule. The algebraic operation properties of tensors, such as outer products and inner products, are used to process and simplify the derivative expressions. The chain rule is used to link the derivatives of the vector to the derivatives of the original safety risk matrix.

[0069] Further optionally, in step S202, the step of respectively calculating the partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameter includes:

[0070] The partial derivatives of the storage capacity constraint function and the performance constraint function with respect to key parameters are calculated using the derivation rules of calculus.

[0071] Further optionally, before step S201, the step of integrating the security risks of each data storage unit in the database to construct a security risk matrix also includes:

[0072] Assess the security risk of each data storage unit.

[0073] Based on this step, in order to assess the security risk of each data storage unit, we can first determine the scope of the data storage unit to be assessed, including which databases, tables, fields, etc., and then analyze the various potential threats that the data storage unit may face, such as unauthorized access, data leakage, data tampering, service interruption, etc., and assess the impact of each potential threat on the data storage unit, including data integrity, confidentiality, and loss of availability, as well as possible financial losses, reputation damage, legal liability, etc., and then further assess the probability and frequency of each potential threat to determine its likelihood and severity. It is also necessary to assess the effectiveness and integrity of the existing security measures of the data storage unit, such as access control, data encryption, backup and recovery plans, etc. During the assessment process, industry standards and best practices, such as ISO 27001, must be followed to ensure the consistency and comparability of the assessment process. Then, each potential threat is quantitatively scored based on risk impact and probability. Since security risks are dynamic, it is necessary to regularly monitor and update risk assessment results to reflect the latest security threats and environmental changes. After assessing the security risks of each data storage unit, these risks are integrated into the security risk matrix.

[0074] Further optionally, before step S202, the step of respectively calculating the partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameters to obtain the partial derivative matrix of the storage capacity constraint function and the partial derivative matrix of the performance constraint function also includes:

[0075] Evaluate the impact of each data storage unit on storage capacity and performance.

[0076] Based on this step, when evaluating the impact of each data storage unit on the overall storage capacity, specifically, it is necessary to analyze the data volume of each data storage unit, including the current data volume and the expected future growth volume, and also consider the impact of storage optimization technologies such as data compression and deduplication on the storage requirements of each unit. It is also necessary to evaluate the impact of different storage media (such as SSD, HDD) on storage capacity, including read and write speeds, lifespan and cost.

[0077] When evaluating the impact of each data storage unit on the overall performance, specifically, it is necessary to measure the read and write operation frequency and response time of each data storage unit, analyze data access patterns, such as random access, sequential access, and their impact on performance, consider concurrent access volume, and the system's performance under high concurrency, and evaluate the performance improvement effects of caching strategies, indexing strategies, etc.

[0078] During the specific evaluation implementation, historical data and monitoring tools can be used to analyze the usage and performance of each data storage unit, and then data modeling can be performed to predict future storage needs and performance trends.

[0079] The purpose of evaluating the impact of each data storage unit on storage capacity and performance is to understand how each data storage unit affects the overall storage capacity and performance, so that the subsequent calculation of partial derivatives can more accurately reflect the impact of each parameter on system constraints.

[0080] Further optionally, in step S103, the step of optimizing and adjusting the key parameters of the database architecture based on the sensitivity analysis result to minimize the security risk while satisfying the actual constraint conditions includes:

[0081] Based on the partial derivative matrix of the objective function, the partial derivative matrix of the storage capacity constraint function, the partial derivative matrix of the performance constraint function and the sub-weight matrix, the key parameters in the database architecture are optimized and adjusted to minimize security risks while satisfying the constraints of storage capacity and performance overhead.

[0082] Based on this step, the embodiment of the present invention is a complex multi-objective optimization problem involving multiple aspects such as security risk, storage capacity and performance overhead. By using the partial derivative matrix and the sub-weight matrix, accurate optimization adjustment of key parameters in the database architecture can be achieved, thereby minimizing security risks while meeting actual constraints, thereby improving the security and overall performance of the database.

[0083] Specifically, the partial derivative matrix of the objective function reflects the rate of change of the objective function (security risk) relative to each key parameter in the database architecture. Based on the partial derivative matrix, it is possible to identify which parameters have the greatest impact on the security risk, so that these parameters can be adjusted preferentially to minimize the security risk.

[0084] The partial derivative matrix of the storage capacity constraint function represents the changes in the storage capacity constraint conditions relative to each key parameter. During the optimization process, it is necessary to ensure that the adjusted parameters do not violate the storage capacity constraints. The partial derivative matrix can be used to determine how to keep the storage capacity within an acceptable range when adjusting the parameters.

[0085] The partial derivative matrix of the performance constraint function reflects the changes in performance overhead (such as response time, throughput, etc.) relative to key parameters. While pursuing the minimization of security risks, it is necessary to ensure that the performance of the database will not be seriously affected. The partial derivative matrix can maintain performance stability during the optimization process.

[0086] The sub-weight matrix represents the relative importance of each objective when weighing multiple objectives, such as security risk, storage capacity, performance overhead, etc. By adjusting the sub-weight matrix, flexible trade-offs between different objectives can be achieved during the optimization process, thereby finding the best solution that meets all constraints.

[0087] Based on the above partial derivative matrix and sub-weight matrix, an optimization algorithm is used to adjust the key parameters in the database architecture to minimize security risks while meeting the storage capacity and performance overhead constraints. During the optimization process, the parameter values ​​are continuously adjusted through iterative calculations until a parameter combination that meets all constraints and minimizes security risks is found.

[0088] Further optionally, the step of optimizing and adjusting key parameters of the database architecture based on the sensitivity analysis result to minimize security risks while satisfying actual constraints includes:

[0089] According to the objective function and the constraints, a Lagrangian function is constructed, and the Lagrangian function is: , where λ and μ are Lagrange multipliers, is the objective function, i and j are used to distinguish different constraints. represents the i-th storage capacity constraint function, represents the jth performance constraint function;

[0090] According to the partial derivative matrix of the objective function , the partial derivative matrix of the storage capacity constraint function , the partial derivative matrix of the performance constraint function and the sub-weight matrix, calculate the gradient of the Lagrangian function;

[0091] For the current temperature T and the current solution x, generate a new candidate solution ;

[0092] Calculate the objective function value of the new candidate solution x′ and the Lagrangian function value , and determine whether the new candidate solution x′ satisfies the constraints, that is, and ;

[0093] According to the preset acceptance criteria, decide whether to accept the new solution as the current solution;

[0094] According to the preset cooling schedule, the temperature T is lowered;

[0095] Until the temperature drops to the target minimum level, the simulated annealing process ends and the final solution x is output. ∗ As the optimized key parameter value.

[0096] Based on this step, according to the objective function (security risk) and constraints (storage capacity, performance overhead), a Lagrangian function is constructed to transform the original constrained optimization problem into an unconstrained optimization problem, where λ and μ in the Lagrangian function are Lagrangian multipliers, which are used to balance the relationship between the objective function and the constraints. During the optimization process, the gradient of the Lagrangian function is calculated using the partial derivative matrix of the objective function, the partial derivative matrix of the storage capacity constraint function, the partial derivative matrix of the performance constraint function, and the sub-weight matrix to provide directional information for the optimization algorithm. The simulated annealing algorithm is used for optimization and solution, which can maintain the diversity of solutions during the optimization process and avoid falling into local optimality. The final solution output is used as the optimized key parameter value. This final solution is the optimal solution (or approximate optimal solution) that minimizes the security risk while satisfying all constraints.

[0097] This embodiment optimizes and adjusts the key parameters in the database architecture by constructing a Lagrangian function and using a simulated annealing algorithm to minimize security risks while ensuring that the actual constraints of storage capacity and performance overhead are met. This optimization scheme has the advantages of global optimization, flexibility, and practicality, and can effectively improve the security and performance of the database. Among them, through the global search capability of the simulated annealing algorithm, it is possible to find a global optimal solution (or approximate optimal solution) that satisfies all constraints and minimizes security risks. Both the Lagrangian function and the simulated annealing algorithm have high flexibility and can adapt to different objective functions, constraints, and optimization requirements.

[0098] For further information, see Figure 3 The embodiment of the present invention further provides a database management and security analysis device, the device comprising:

[0099] Definition module: used to define objectives and constraints. The objective is to minimize the comprehensive security risk of all data storage units in the database system. Constraints include storage capacity constraints and performance overhead constraints.

[0100] Analysis module: used to perform sensitivity analysis on key parameters in the database architecture to evaluate the impact of changes in key parameters on objectives and constraints;

[0101] Optimization and Adjustment Module: It is used to optimize and adjust the key parameters of the database architecture based on the results of sensitivity analysis to minimize security risks while meeting actual constraints.

[0102] Further optional, such as Figure 4 As shown, the above analysis module includes:

[0103] The first partial derivative matrix module is used to integrate the security risks of each data storage unit in the database, construct a security risk matrix, and calculate the partial derivatives of the security risk matrix with respect to key parameters to obtain the partial derivative matrix of the objective function to reflect the response of the objective function to changes in key parameters;

[0104] The second partial derivative matrix module is used to calculate the partial derivatives of the storage capacity constraint function and the performance constraint function with respect to the key parameters, and obtain the partial derivative matrix of the storage capacity constraint function and the partial derivative matrix of the performance constraint function, so as to respectively reflect the response of the storage capacity constraint function and the performance constraint function to the change of the key parameters;

[0105] Sub-weight matrix module: used to construct a sub-weight matrix to represent the association weights between configuration options and data storage units in different key parameter directions.

[0106] Further optionally, the first partial derivative matrix module is further used for:

[0107] Decomposing the security risk matrix into a sum of a series of rank-tensors, each of which is composed of the outer product of three vectors;

[0108] The decomposed vector is differentiated, and the partial derivative of the vector is converted into the partial derivative of the objective function with respect to the key parameters through tensor operations and the chain rule.

[0109] Further optionally, the second partial derivative matrix module is further used for:

[0110] The partial derivatives of the storage capacity constraint function and the performance constraint function with respect to key parameters are calculated using the derivation rules of calculus.

[0111] Further optionally, the first partial derivative matrix module is further used for:

[0112] Assess the security risk of each data storage unit.

[0113] Further optionally, the second partial derivative matrix module is further used for:

[0114] Evaluate the impact of each data storage unit on storage capacity and performance.

[0115] Further optionally, the above optimization and adjustment module is also used for:

[0116] Based on the partial derivative matrix of the objective function, the partial derivative matrix of the storage capacity constraint function, the partial derivative matrix of the performance constraint function and the sub-weight matrix, the key parameters in the database architecture are optimized and adjusted to minimize security risks while satisfying the constraints of storage capacity and performance overhead.

[0117] Further optionally, the above optimization and adjustment module is also used for:

[0118] According to the objective function and the constraints, a Lagrangian function is constructed, and the Lagrangian function is: , where λ and μ are Lagrange multipliers, is the objective function, i and j are used to distinguish different constraints. represents the i-th storage capacity constraint function, represents the jth performance constraint function;

[0119] According to the partial derivative matrix of the objective function , the partial derivative matrix of the storage capacity constraint function , the partial derivative matrix of the performance constraint function and the sub-weight matrix, calculate the gradient of the Lagrangian function;

[0120] For the current temperature T and the current solution x, generate a new candidate solution ;

[0121] Calculate the objective function value of the new candidate solution x′ and the Lagrangian function value , and determine whether the new candidate solution x′ satisfies the constraints, that is, and ;

[0122] According to the preset acceptance criteria, decide whether to accept the new solution as the current solution;

[0123] According to the preset cooling schedule, the temperature T is lowered;

[0124] Until the temperature drops to the target minimum level, the simulated annealing process ends and the final solution x is output. ∗ As the optimized key parameter value.

[0125] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A database management and security analysis method, characterized in that: The method comprises: Define objectives and constraints, where the objective is to minimize the comprehensive security risk of all data storage units in the database system, and the constraints include storage capacity constraints and performance overhead constraints; Conduct sensitivity analysis on key parameters in the database architecture to assess the impact of changes in key parameters on objectives and constraints, including: Integrate the security risks of each data storage unit in the database, construct a security risk matrix, and calculate the partial derivatives of the security risk matrix with respect to key parameters to obtain the partial derivative matrix of the objective function to reflect the response of the objective function to changes in key parameters; The partial derivatives of the storage capacity constraint function and the performance cost constraint function with respect to the key parameters are calculated respectively, and the partial derivative matrix of the storage capacity constraint function and the partial derivative matrix of the performance cost constraint function are obtained to respectively reflect the responses of the storage capacity constraint function and the performance cost constraint function to the changes of the key parameters; Constructing a sub-weight matrix to represent the association weights between configuration options and data storage units in different key parameter directions; Based on the sensitivity analysis results, key parameters of the database architecture are optimized and adjusted to minimize security risks while meeting actual constraints, including: Construct the Lagrangian function according to the objective function and constraints; Calculate the gradient of the Lagrangian function according to the partial derivative matrix of the objective function, the partial derivative matrix of the storage capacity constraint function, the partial derivative matrix of the performance cost constraint function, and the sub-weight matrix; For the current temperature and current solution, generate a new candidate solution; Calculate the objective function value and Lagrangian function value of the new candidate solution, and determine whether the new candidate solution satisfies the constraints; According to the preset acceptance criteria, decide whether to accept the new candidate solution as the current solution; Reduce the temperature according to the preset cooling schedule; Until the temperature drops to the target minimum level, the simulated annealing process ends and the final solution is output as the optimized key parameter value.

2. The database management and security analysis method according to claim 1, characterized in that: The step of calculating the partial derivative of the safety risk matrix with respect to key parameters comprises: Decomposing the security risk matrix into a sum of a series of rank-tensors, each of which is composed of the outer product of three vectors; The decomposed vector is differentiated, and the partial derivative of the vector is converted into the partial derivative of the objective function with respect to the key parameters through tensor operations and the chain rule.

3. The database management and security analysis method according to claim 1, characterized in that: The steps of respectively calculating the partial derivatives of the storage capacity constraint function and the performance cost constraint function with respect to the key parameters include: The derivation rules of calculus are used to calculate the partial derivatives of the storage capacity constraint function and the performance cost constraint function with respect to key parameters.

4. The database management and security analysis method according to claim 1, characterized in that: Before the step of integrating the security risks of each data storage unit in the database to construct a security risk matrix, the following steps are also included: Assess the security risk of each data storage unit.

5. The database management and security analysis method according to claim 1, characterized in that: The step of respectively calculating the partial derivatives of the storage capacity constraint function and the performance cost constraint function with respect to the key parameters to obtain the partial derivative matrix of the storage capacity constraint function and the partial derivative matrix of the performance cost constraint function also includes: Evaluate the impact of each data storage unit on storage capacity and performance.

6. The database management and security analysis method according to claim 1, characterized in that: In the step of constructing the Lagrangian function according to the objective function and the constraints: The Lagrangian function is: , where λ and μ are Lagrange multipliers, is the objective function, i and j are used to distinguish different constraints. represents the i-th storage capacity constraint function, represents the j-th performance cost constraint function.

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