Database analysis method based on open source technology

Abstract

The invention discloses a database analysis method based on an open source technology, and relates to the technical field of database analysis, and the database analysis method comprises the following steps: S01, constructing data relation mapping of a target database, and classifying data; step S02, acquiring an internal safety index; s03, acquiring an external security index of the database; step S04, based on the internal security index and the external security index, acquiring a database overall security index, and corresponding to a corresponding security level; and S05, taking different levels of security protection measures based on different security levels. According to the database analysis method based on the open source technology, the database analysis technology based on the open source technology is provided, the database safety is analyzed in detail, a visual and digital basis is provided for safety judgment of the database, safety protection of the database can be enhanced in time, and the safety of the database is improved. And the security of various data in the database is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of database analysis, and in particular to a database analysis method based on open source technology. Background Art

[0002] Open source technology is a decentralized production model that allows anyone to modify and share technology; a database is a warehouse used to organize, store, and manage data; nowadays, with the popularization of the Internet, the world today is an Internet world full of data, filled with a large amount of data, and there are many sources of data, such as travel records, consumption records, browsed web pages, sent messages, etc., including text data, image data, music data, and sound data. In order to facilitate the management of data in the Internet world, the concept of database management system has been derived, that is, database. Users can add, query, update, and delete data in the database; therefore, the database stores a large amount of data, including sensitive data and ordinary data, so the security of the database is particularly important.

[0003] In the prior art, no targeted analysis is conducted on the security of the database. For example, a method for database management and security analysis is disclosed in a public document with publication number CN117540434B. The database is encrypted by setting an encryption method to improve the security of the data, and a security audit tool is established. The security audit tool is used to monitor all operations on the database management end and generate an audit log based on the operations. The audit log cannot be modified. Only basic security analysis is performed through the security audit tool, and no detailed analysis is conducted on the security of the database. Therefore, the security of the encrypted database cannot be obtained, and a visual digital basis cannot be provided for improving the security of the database.

[0004] Therefore, it is urgent to improve this shortcoming. The present invention studies and improves the existing technology and its shortcomings, and provides a database analysis method based on open source technology. Summary of the invention

[0005] The purpose of the present invention is to provide a database analysis method based on open source technology to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a database analysis method based on open source technology, comprising the following steps:

[0007] Step S01: collect data in the target database, build a data relationship mapping of the target database, and classify the data;

[0008] Step S02: Perform data security risk assessment on different types of data based on the risk assessment matrix to obtain an internal security index;

[0009] Step S03: Based on the vulnerability data and protection data of the database, obtain the external security index of the database;

[0010] Step S04: Based on the internal security index and the external security index of step S02 and step S03, the overall security index of the database is obtained, and the corresponding security level is obtained;

[0011] Step S05: Taking different levels of security protection measures based on the different security levels in step S04.

[0012] Furthermore, the specific method of constructing the data relationship mapping of the target database in step S01 is:

[0013] Step S11: Divide the data in the database into sensitive data and common data, and record the sensitive data as x(1) and the common data as x(2);

[0014] Step S12: preprocessing the data in the database using wavelet analysis;

[0015] The functional expression of the wavelet function is: Where x is the data to be preprocessed, and x is either x(1) or x(2); a and b are both constants, ranging from [0 to +∞];

[0016] When the data is x(t), the expression after wavelet function transformation is: Where, t = 1, 2; WT x (a,b) represents the wavelet function of the data x(t), a means the scale factor, b means the time shift, ψ a,b (t) is expressed as wavelet basis; WT x (a, b) is understood as: under different wavelet basis function conditions, x(t) is denoised and adapted to the requirements of different wavelet basis functions;

[0017] Step S13: extracting key features of the data based on a genetic algorithm; since the data in the database is relatively large, if all features of the data are extracted, the amount of calculation is very large and the accuracy of the data features will be reduced. Therefore, only extracting the key features of the data can speed up the feature extraction efficiency and improve the feature extraction accuracy;

[0018] Step S14: construct a data relationship mapping of the target database based on the extracted sensitive data features and general data features; map the sensitive data, general data and corresponding data features one by one.

[0019] Furthermore, the key features of the data extracted based on the genetic algorithm in step S13 are selected by searching for features with representative performance, the importance of the features is evaluated according to the evaluation criteria, and repeated iterations are performed again until the optimal data feature subset is generated; the specific steps include:

[0020] Step S21: Set the population size to N and the initial population to Among them, X(t) is the initial population corresponding to the t category data, that is, the feature set; are the features contained in the feature set, i=1, 2, 3...N; t=1, 2; when t=1, the data for feature extraction is sensitive data, and when t=2, the data for feature extraction is common data;

[0021] Step S22: Calculate the individual fitness value of the Tth generation according to the fitness function, denoted as The fitness function formula is expressed as: Among them, c is the number of feature subsets of the category, Represented as the subset mean vector in category t, represents the average vector of the feature set, Nt represents the number of features in category t, Represents the vector of feature j in category t; when t=1, it represents sensitive data, and when t=2, it represents common data; by calculating the individual fitness value, the specific characteristics of sensitive data and common data in the database are obtained;

[0022] Step S23: Estimate the probability of data feature selection, the calculation formula is: in, Features The corresponding weight value;

[0023] Step S24: Based on the calculation results in step S23, two individuals are selected from the updated population for crossover recombination, and the individual gene positions are mutated to obtain a new generation of population. The individual corresponding to the maximum fitness value is extracted and recorded as The extracted individuals are the features currently judged to be of high importance;

[0024] Step S25: Compared with the fitness threshold, if If it is greater than or equal to the threshold, the iterative process is terminated, and the top k features ranked by fitness are output to form the optimal feature subset. Otherwise, step S22 is continued. The value of k can be set by technicians in this field according to actual needs, and there is no specific limitation on the value of k. Repeat the iteration until the optimal data feature subset is selected.

[0025] Furthermore, the data is classified in step S01 as follows: based on the data relationship mapping of the constructed target database, all data are classified, and after the data features are extracted, the data that meets the sensitive data feature subset is marked as sensitive data, and the data that meets the common data feature subset is marked as common data, and the sensitive data is represented by a set as follows: x(1)={x(1)1,x(1)2,x(1)3,…,x(1) n};x(2)={x(2)1,x(2)2,x(2)3,…,x(2) m}.

[0026] Furthermore, the risk assessment matrix is ​​a structured method for determining the risk level of potential hazards. It considers two factors: severity of consequences and frequency of occurrence. Severity of consequences refers to how much impact a certain data may have on the database, while frequency of occurrence refers to how high the probability is that a certain data may be at risk. By combining these two factors, the risk assessment matrix provides a comprehensive method to evaluate and compare different risk data and determine what prevention and control measures should be taken to reduce their risks. These factors are divided into different levels, and the risk size of each data is determined according to their characteristics, and the risk level of each data is quantified;

[0027] The main risk influencing factors in the risk assessment matrix are hardware failure frequency, network attack frequency, encryption key update frequency and encryption level; the higher the level, the more serious the consequences.

[0028] Furthermore, the calculation formula of the internal safety index is: Among them, A in is the internal security index, A1 is the security factor of sensitive data, A2 is the security factor of common data, k1 and k2 are corresponding proportional factors, satisfying k1>1.5k2; the specific values ​​of k1 and k2 can be set by those skilled in the art, and no specific limitation is made to these specific values;

[0029] Among them, S g is the number of sensitive data with high risk level, S z is the number of data with medium risk level in sensitive data, S d is the number of data with low risk level in sensitive data, λ1, λ2, and λ3 are the corresponding proportional coefficients, satisfying λ1>λ2>λ3; no specific value is limited;

[0030] Among them, S g ′ is the number of data with high risk level in general data, S z′ is the number of data with medium risk level in general data, S d ′ is the number of data with low risk level in general data, λ1′, λ2′, and λ3′ are corresponding proportional coefficients, satisfying λ1′>λ2′>λ3′; no specific limitation is made on the specific value.

[0031] Furthermore, the step S03 uses the security vulnerability index CVE to analyze the vulnerability situation, and the calculation formula is: CVE=Q×C×G×F, where Q represents the number of vulnerabilities, C represents the severity of the vulnerabilities, G represents the degree of vulnerability to attacks, and F represents the impact range of the vulnerabilities.

[0032] Furthermore, the calculation results of the security vulnerability index are divided as follows:

[0033] 1. When CVE∈[0, 0.3], it indicates a low risk. At this time, the security vulnerability index is low, and the potential risk or damage may be small. At this time, some simple measures can be taken, such as updating software and patching, to fix the vulnerability and reduce the risk.

[0034] 2. When CVE∈[0.3, 0.65], it indicates that there is a medium risk. At this time, the security vulnerability index is medium, and there is a certain degree of potential risk or damage. At this time, it is necessary to strengthen the monitoring and protection of the system, conduct security inspections and assessments regularly, and discover and repair vulnerabilities in a timely manner;

[0035] 3. When CVE∈[0.65, 0.9], it indicates a high risk. At this time, the security vulnerability index is high, and the potential risk or damage is large. At this time, it is necessary to take immediate action to fix the vulnerability and strengthen security measures, such as access control, firewalls, etc., to reduce the damage;

[0036] 4. When CVE∈[0.9, 1.0], it indicates that there is a serious risk. At this time, the security vulnerability index is very high, and the potential risk or damage is very large. At this time, emergency measures need to be taken as soon as possible to repair the vulnerability, and relevant professionals should be notified immediately to assist in handling it.

[0037] Furthermore, the calculation formula of the external safety index is: Among them, A out is the external security index, R is the protection range, E is the number of protection software and programs, and η is other influencing factors, which can be the protection level or other protection influencing factors.

[0038] Furthermore, the calculation formula of the overall security index of the database is: Among them, A′ is the overall security index of the database;

[0039] When A′>YU1, the security level is determined to be high; there may be potential risks at this time, and the database needs to be further monitored;

[0040] When YU2≤A′≤YU1, the security level is determined to be medium. At this time, measures need to be taken to investigate security issues in the database and strengthen security measures to enhance database security protection;

[0041] When A′<YU3, the security level is judged to be low, and measures need to be taken as soon as possible to check and repair the security issues in the database;

[0042] YU1 is the first judgment threshold, YU2 is the second judgment threshold, and YU3 is the third judgment threshold; YU1 satisfies YU1≥90%, YU2 satisfies 70%≤YU2<90%, and YU3 satisfies YU3<70%; the specific values ​​of YU1, YU2 and YU3 are set by technicians in this field, and no specific limitation is made on these values.

[0043] The present invention provides a database analysis method based on open source technology, which has the following beneficial effects:

[0044] The present invention is provided with step S02 and step S03, and by constructing a data relationship mapping of the target data, the data is classified, and sensitive data is distinguished from general data. In step S02, data security risk assessment is performed on data of different categories based on the risk assessment matrix to obtain an internal security index; in step S03, the external security index of the database is obtained based on the vulnerability data and protection data of the database; and the overall security index of the database is calculated based on the internal security index and the external security index; analysis is performed from two aspects, namely, internal data security and external overall vulnerability security, and a comprehensive analysis is performed in combination with the database data itself and the database environment, which is more comprehensive and detailed, provides a visual and digital basis for the security of the database, can take corresponding protection measures according to the security level of the database, and can clarify the effect of improving the security of the database, thereby improving the security of various types of data in the database. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of the steps of a database analysis method based on open source technology of the present invention;

[0046] Figure 2 The present invention provides a risk level comparison table for a database analysis method based on open source technology. DETAILED DESCRIPTION

[0047] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0048] like Figure 1-Figure 2 As shown, a database analysis method based on open source technology includes the following steps:

[0049] Step S01: collect data in the target database, build a data relationship mapping of the target database, and classify the data;

[0050] Step S02: Perform data security risk assessment on different types of data based on the risk assessment matrix to obtain an internal security index;

[0051] Step S03: Based on the vulnerability data and protection data of the database, obtain the external security index of the database;

[0052] Step S04: Based on the internal security index and the external security index of step S02 and step S03, the overall security index of the database is obtained, and the corresponding security level is obtained;

[0053] Step S05: Taking different levels of security protection measures based on the different security levels in step S04.

[0054] In this embodiment, it needs to be further explained that the specific method of constructing the data relationship mapping of the target database in step S01 is:

[0055] Step S11: Divide the data in the database into sensitive data and common data, and record the sensitive data as x(1) and the common data as x(2);

[0056] Step S12: pre-processing the data in the database using wavelet analysis; the function expression of the wavelet function is: Wherein, x is the data to be preprocessed, and in this embodiment, x can be any one of x(1) or x(2); a and b are both constants, and their value range is [0, +∞]; when the data is x(t), the expression after wavelet function transformation is: Where, t = 1, 2; WT x (a,b) represents the wavelet function of the data x(t), a means the scale factor, b means the time shift, ψ a,b (t) is expressed as wavelet basis; WT x (a, b) can be understood as: under different wavelet basis function conditions, x(t) is denoised and adapted to the requirements of different wavelet basis functions;

[0057] Step S13: extracting key features of the data based on a genetic algorithm; since the data in the database is relatively large, if all features of the data are extracted, the amount of calculation is very large and the accuracy of the data features will be reduced. Therefore, only extracting the key features of the data can speed up the feature extraction efficiency and improve the feature extraction accuracy;

[0058] Step S14: construct a data relationship mapping of the target database based on the extracted sensitive data features and general data features; map the sensitive data, general data and corresponding data features one by one.

[0059] In this embodiment, it should be further explained that the key features of data extracted based on the genetic algorithm in step S13 are selected by searching for features with representative performance, the importance of the features is evaluated according to the evaluation criteria, and repeated iterations are performed again until the optimal data feature subset is generated; the specific steps include:

[0060] Step S21: Set the population size to N and the initial population to Among them, X(t) is the initial population corresponding to the t category data, that is, the feature set; are the features contained in the feature set, i=1, 2, 3...N; t=1, 2; when t=1, the data for feature extraction is sensitive data, and when t=2, the data for feature extraction is common data;

[0061] Step S22: Calculate the individual fitness value of the Tth generation according to the fitness function, denoted as The fitness function formula is expressed as: Among them, c is the number of feature subsets of the category, Represented as the subset mean vector in category t, represents the average vector of the feature set, Nt represents the number of features in category t, Represents the vector of feature j in category t; when t=1, it represents sensitive data, and when t=2, it represents common data; by calculating the individual fitness value, the specific characteristics of sensitive data and common data in the database are obtained;

[0062] Step S23: Estimate the probability of data feature selection, the calculation formula is: in, Features The corresponding weight value;

[0063] Step S24: Based on the calculation results in step S23, two individuals are selected from the updated population for crossover recombination, and the individual gene positions are mutated to obtain a new generation of population. The individual corresponding to the maximum fitness value is extracted and recorded as The extracted individuals are the features currently judged to be of high importance;

[0064] Step S25: Compared with the fitness threshold, if If it is greater than or equal to the threshold, the iterative process is terminated, and the top k features ranked by fitness are output to form the optimal feature subset. Otherwise, step S22 is continued. The value of k can be set by those skilled in the art according to actual needs, and this embodiment does not specifically limit the value of k. Repeat the iteration until the optimal data feature subset is selected.

[0065] In this embodiment, it should be further explained that the method of classifying data in step S01 is: based on the data relationship mapping of the constructed target database, all data are classified, and after the data features are extracted, the data that meets the sensitive data feature subset is marked as sensitive data, and the data that meets the common data feature subset is marked as common data. The sensitive data is represented by a set as follows: x(1)={x(1)1,x(1)2,x(1)3,…,x(1) n};x(2)={x(2)1,x(2)2,x(2)3,…,x(2) m}.

[0066] In this embodiment, it should be further explained that the risk assessment matrix is ​​a structured method for determining the risk level of potential dangers. It considers two factors: the severity of the consequences and the frequency of occurrence. The severity of the consequences refers to how much impact a certain data may have on the database, while the frequency of occurrence refers to how high the probability that a certain data may have a risk is. By combining these two factors, the risk assessment matrix provides a comprehensive method to evaluate and compare different risk data and determine what prevention and control measures should be taken to reduce their risks. These factors are divided into different levels, and the risk size of each data is determined according to their characteristics, and the risk level of each data is quantified;

[0067] The main risk factors in the risk assessment matrix are hardware failure frequency, network attack frequency, encryption key update frequency, and encryption level. The higher the level, the greater the severity of the consequences. The risk level comparison table is as follows: Figure 2 shown.

[0068] In this embodiment, it should be further explained that the calculation formula of the internal safety index is: Among them, A inis the internal security index, A1 is the security factor of sensitive data, A2 is the security factor of common data, k1 and k2 are corresponding proportional factors, satisfying k1>1.5k2; the specific values ​​of k1 and k2 can be set by those skilled in the art, and this embodiment does not specifically limit these specific values;

[0069] Among them, S g is the number of sensitive data with high risk level, S z is the number of data with medium risk level in sensitive data, S d is the number of data with low risk level in sensitive data, λ1, λ2, and λ3 are corresponding proportional coefficients, satisfying λ1>λ2>λ3; this embodiment does not specifically limit the specific values;

[0070] Among them, S g ′ is the number of data with high risk level in general data, S z ′ is the number of data with medium risk level in general data, S d ' is the number of data with low risk level in general data, λ1', λ2', and λ3' are corresponding proportional coefficients, satisfying λ1'>λ2'>λ3'; this embodiment does not make any specific limitation on the specific values.

[0071] In this embodiment, it should be further explained that the step S03 uses the security vulnerability index CVE to analyze the vulnerability situation, and the calculation formula is: CVE=Q×C×G×F, where Q represents the number of vulnerabilities, C represents the severity of the vulnerability, G represents the vulnerability to attack, and F represents the impact range of the vulnerability;

[0072] The following classifications are made based on the calculation results of the security vulnerability index:

[0073] 1. When CVE∈[0, 0.3], it indicates a low risk. At this time, the security vulnerability index is low, and the potential risk or damage may be small. At this time, some simple measures can be taken, such as updating software and patching, to fix the vulnerability and reduce the risk.

[0074] 2. When CVE∈[0.3, 0.65], it indicates that there is a medium risk. At this time, the security vulnerability index is medium, and there is a certain degree of potential risk or damage. At this time, it is necessary to strengthen the monitoring and protection of the system, conduct security inspections and assessments regularly, and discover and repair vulnerabilities in a timely manner;

[0075] 3. When CVE∈[0.65, 0.9], it indicates a high risk. At this time, the security vulnerability index is high, and the potential risk or damage is large. At this time, it is necessary to take immediate action to fix the vulnerability and strengthen security measures, such as access control, firewalls, etc., to reduce the damage;

[0076] 4. When CVE∈[0.9, 1.0], it indicates that there is a serious risk. At this time, the security vulnerability index is very high, and the potential risk or damage is very large. At this time, emergency measures need to be taken as soon as possible to repair the vulnerability, and relevant professionals should be notified immediately to assist in handling;

[0077] The security vulnerability index CVE can be used to evaluate the performance of network security devices, such as firewalls, intrusion detection networks, etc.; in the field of network security, the CVE calculation formula can be used to evaluate the security of network applications, such as Web applications and databases.

[0078] In this embodiment, it should be further explained that the calculation formula of the external safety index is: Among them, A out is the external security index, R is the protection range, E is the number of protection software and programs, and η is other influencing factors, which can be the protection level or other protection influencing factors.

[0079] In this embodiment, it should be further explained that the calculation formula of the overall security index of the database is: Among them, A′ is the overall security index of the database;

[0080] When A′>YU1, the security level is determined to be high; there may be potential risks at this time, and the database needs to be further monitored;

[0081] When YU2≤A′≤YU1, the security level is determined to be medium. At this time, measures need to be taken to investigate security issues in the database and strengthen security measures to enhance database security protection;

[0082] When A′<YU3, the security level is judged to be low, and measures need to be taken as soon as possible to check and repair the security issues in the database;

[0083] YU1 is the first determination threshold, YU2 is the second determination threshold, and YU3 is the third determination threshold; YU1 satisfies YU1≥90%, YU2 satisfies 70%≤YU2<90%, and YU3 satisfies YU3<70%; the specific values ​​of YU1, YU2 and YU3 are set by those skilled in the art, and this embodiment does not make any specific limitation on these values.

[0084] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A database analysis method based on open source technology, characterized in that: The following steps are involved: Step S01: collect data in the target database, build a data relationship mapping of the target database, and classify the data; Step S02: Perform data security risk assessment on different types of data based on the risk assessment matrix to obtain an internal security index; Step S03: Based on the vulnerability data and protection data of the database, obtain the external security index of the database; Step S04: Based on the internal security index and the external security index of step S02 and step S03, the overall security index of the database is obtained, and the corresponding security level is obtained; Step S05: Taking different levels of security protection measures based on the different security levels in step S04.

2. According to the database analysis method based on open source technology in claim 1, it is characterized in that: The specific method of constructing the data relationship mapping of the target database in step S01 is: Step S11: Divide the data in the database into sensitive data and common data, and record the sensitive data as x(1) and the common data as x(2); Step S12: pre-processing the data in the database using wavelet analysis; The functional expression of the wavelet function is: Where x is the data to be preprocessed, and x is either x(1) or x(2); a and b are both constants, ranging from [0 to +∞]; When the data is x(t), the expression after wavelet function transformation is: Where, t = 1, 2; WT x (a,b) represents the wavelet function of the data x(t), a means the scale factor, b means the time shift, ψ a,b (t) is expressed as wavelet basis; WT x (a, b) is understood as: under different wavelet basis function conditions, x(t) is denoised and adapted to the requirements of different wavelet basis functions; Step S13: extracting key features of data based on genetic algorithm; Step S14: constructing a data relationship mapping of the target database based on the extracted sensitive data features and common data features.

3. The database analysis method based on open source technology according to claim 2, characterized in that: In step S13, the key features of the data extracted based on the genetic algorithm are selected by searching for features with representative performance, the importance of the features is evaluated according to the evaluation criteria, and repeated iterations are performed again until the optimal data feature subset is generated; The specific steps include: Step S21: Set the population size to N and the initial population to Among them, X(t) is the initial population corresponding to the t category data, that is, the feature set; are the features contained in the feature set, i=1, 2, 3...N; t=1, 2; when t=1, the data for feature extraction is sensitive data, and when t=2, the data for feature extraction is common data; Step S22: Calculate the individual fitness value of the Tth generation according to the fitness function, denoted as The fitness function formula is expressed as: Among them, c is the number of feature subsets of the category, Represented as the subset mean vector in category t, represents the average vector of the feature set, Nt represents the number of features in category t, Represents the vector of feature j in category t; when t=1, it represents sensitive data, and when t=2, it represents common data; by calculating the individual fitness value, the specific characteristics of sensitive data and common data in the database are obtained; Step S23: Estimate the probability of data feature selection, the calculation formula is: in, Features The corresponding weight value; Step S24: Based on the calculation results in step S23, two individuals are selected from the updated population for crossover recombination, and the individual gene positions are mutated to obtain a new generation of population. The individual corresponding to the maximum fitness value is extracted and recorded as The extracted individuals are the features currently judged to be of high importance; Step S25: Compared with the fitness threshold, if If it is greater than or equal to the threshold, the iterative process is terminated, and the top k features ranked by fitness are output to form the optimal feature subset. Otherwise, step S22 is continued; and repeated iterations are performed until the optimal data feature subset is selected.

4. The database analysis method based on open source technology according to claim 1, characterized in that: The method of classifying data in step S01 is: based on the data relationship mapping of the constructed target database, all data are classified, and after the data features are extracted, the data that meets the sensitive data feature subset is marked as sensitive data, and the data that meets the common data feature subset is marked as common data. The sensitive data is represented by a set: x(1) = {x(1)1, x(1)2, x(1)3, ..., x(1) n };x(2)={x(2)1,x(2)2,x(2)3,…,x(2) m }.

5. The database analysis method based on open source technology according to claim 1, characterized in that: The risk assessment matrix is ​​used to determine the risk level of potential dangers, to evaluate and compare different risk data, and to determine what prevention and control measures should be taken to reduce their risks. The risk factors in the risk assessment matrix are divided into different levels, and the risk size of each data is determined according to their characteristics. The risk level of each data is quantified, and the higher the level, the greater the severity of the consequences.

6. The database analysis method based on open source technology according to claim 1, characterized in that: The calculation formula of the internal safety index is: Among them, A in is the internal security index, A1 is the security factor for sensitive data, A2 is the security factor for common data, k1 and k2 are corresponding proportional factors, satisfying k1>1.5k2; the specific values ​​of k1 and k2 are not limited; Among them, S g is the number of sensitive data with high risk level, S z is the number of data with medium risk level in sensitive data, S d is the number of data with low risk level in sensitive data, λ1, λ2, and λ3 are the corresponding proportional coefficients, satisfying λ1>λ2>λ3; Among them, S g ′ is the number of data with high risk level in general data, S z ′ is the number of data with medium risk level in general data, S d ′ is the number of data with low risk level in general data, λ1′, λ2′, and λ3′ are the corresponding proportional coefficients, satisfying λ1′>λ2′>λ3′.

7. The database analysis method based on open source technology according to claim 1, characterized in that: The step S03 uses the security vulnerability index CVE to analyze the vulnerability situation, and the calculation formula is: CVE=Q×C×G×F, where Q represents the number of vulnerabilities, C represents the severity of the vulnerabilities, G represents the vulnerability of the vulnerabilities, and F represents the impact range of the vulnerabilities.

8. The database analysis method based on open source technology according to claim 7, characterized in that: The calculation results of the security vulnerability index are divided as follows:

1. When CVE∈[0, 0.3], it indicates a low risk. At this time, the security vulnerability index is low, and the potential risk or damage may be small; 2. When CVE∈[0.3, 0.65], it indicates that there is a medium risk. At this time, the security vulnerability index is medium, and there is a certain potential risk or damage degree; 3. When CVE∈[0.65, 0.9], it indicates a high risk. At this time, the security vulnerability index is high, and the potential risk or damage is large; 4. When CVE∈[0.9, 1.0], it indicates that there is a serious risk. At this time, the security vulnerability index is very high and the potential risk or damage is very large.

9. The database analysis method based on open source technology according to claim 1, characterized in that: The calculation formula of the external safety index is: Among them, A out is the external security index, R is the protection range, E is the number of protection software and programs, and η is other influencing factors, which can be the protection level or other protection influencing factors.

10. The database analysis method based on open source technology according to claim 1, characterized in that: The calculation formula of the overall security index of the database is: Among them, A′ is the overall security index of the database; When A′>YU1, the security level is judged to be high, there is a potential risk, and the database needs to be further monitored; When YU2≤A′≤YU1, the security level is determined to be medium, and measures need to be taken to investigate security issues in the database and strengthen security measures to enhance database security protection; When A′<YU3, the security level is judged to be low, and measures need to be taken as soon as possible to check and repair the security issues in the database; YU1 is the first determination threshold, YU2 is the second determination threshold, and YU3 is the third determination threshold; YU1 satisfies YU1≥90%, YU2 satisfies 70%≤YU2<90%, and YU3 satisfies YU3<70%.

Images