Intelligent storage database management method and system

Abstract

The present invention discloses an intelligent storage database management method and system, which relates to the technical field of database data management, including the database collecting data to be stored for preprocessing, automatically classifying and generating intelligent indexes; performing data security detection based on the data to be stored, and implementing security measures according to the detection results; regularly detecting the stored data and formulating data access control strategies. The present invention constructs a data classification model to classify the data to be stored and detects the data security, and implements security processing measures, thereby greatly improving the speed of database data processing, improving storage efficiency, and effectively monitoring data security, improving the security of data storage, and improving the flexibility and efficiency of data storage.

Description

Technical Field

[0001] The present invention relates to the technical field of database data management, and in particular to an intelligent storage database management method and system. Background Art

[0002] In today's era of information explosion, data is an important resource. The efficiency of its collection, storage and management is directly related to the development speed and quality of the information technology field. As a key tool for organizing, storing and managing data, database management systems (DBMS) have developed from simple file storage to complex relational and non-relational databases with the continuous advancement of computer science, and then to today's distributed database systems and cloud database services. This development process is accompanied by the growing demand for data processing speed, storage efficiency, data security and ease of use. Especially in the context of the rapid development of big data, cloud computing and Internet of Things technologies, higher requirements have been put forward for database management methods, which has promoted the development of intelligent database management technology. Intelligent database management technology attempts to achieve automation and intelligence of data management by introducing technologies such as machine learning, data mining and artificial intelligence, so as to improve the efficiency and accuracy of data processing, while reducing labor costs and error rates. Although current database management technology has made significant progress, it still has shortcomings. Existing technologies still rely on a lot of manual intervention in data preprocessing, classification and index generation, which increases the time and labor costs of data processing and increases the risk of errors. In terms of data security detection, static security strategies are often adopted, which are difficult to cope with increasingly complex and changeable security threats. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing intelligent storage database management method and system, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing technology still relies on a large amount of manual intervention in data preprocessing, classification and index generation and often adopts static security strategies in data security detection, which makes it difficult to cope with increasingly complex and changeable security threats.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent storage database management method, which includes: the database collects the data to be stored for preprocessing, automatically classifies and generates an intelligent index; performs data security detection based on the data to be stored, and implements security measures according to the detection results; regularly detects the stored data and formulates a data access control strategy.

[0006] As a preferred solution of the intelligent storage database management method of the present invention, wherein: the database collects the data to be stored for preprocessing means that the database collects the data to be stored to form a data set D, which is represented by Gaussian distribution:

[0007]

[0008] in is the distribution function of the data, is the input value, is the total number of data in data set D, is a Gaussian distribution, and are the mean and variance of the j-th Gaussian distribution, is the weight of the jth Gaussian distribution, and ;

[0009] Use Gaussian smoothing to clean and remove noise from the dataset D:

[0010]

[0011] in is the preprocessed data set, and are the mean and variance of the data in dataset D respectively.

[0012] As a preferred solution of the intelligent storage database management method of the present invention, the automatic classification to generate intelligent index refers to building a data classification model to classify the pre-processed data, including:

[0013] Define the kernel function for:

[0014]

[0015] in and are the i-th data and j-th data in the preprocessed data set, respectively. is the kernel function parameter, is the Euclidean distance between two data points;

[0016] The objective function and constraints are established as follows:

[0017]

[0018]

[0019] in and b are the weight vector and bias term respectively, is the regularization parameter, is the total number of data in the preprocessed dataset, is the slack variable of the i-th data, is the label value of the i-th data;

[0020] Use the dual problem to train the model:

[0021]

[0022]

[0023] in and are the i-th and j-th Lagrange multipliers respectively, is the label value of the jth data;

[0024] Obtain a data classification model through model training:

[0025]

[0026] Use cross-validation and model accuracy to evaluate model performance through the test set and iteratively train the model until there is no obvious loss in the model or the preset number of iterations is reached, then stop training the output data classification model;

[0027] The preprocessed dataset The data is input into the data classification model for automatic classification, and an intelligent index is generated based on the data classification.

[0028] As a preferred solution of the intelligent storage database management method of the present invention, the data security detection based on the data to be stored includes:

[0029] Measuring the integrity of data to be stored for:

[0030]

[0031] in For the dataset The i-th updated version of For hash encryption, is the total number of updated versions, is the frequency of access to the ith version, is the attenuation constant, is the time attenuation coefficient, is the adjustment factor;

[0032] Evaluate the data privacy sensitivity G as:

[0033]

[0034] in Score the sensitivity of the data, is the data access control level, is the number of sensitive information types, For the dataset In the score of the lth privacy attribute, is the total number of privacy attributes, is the weight of the data privacy attribute;

[0035] The external threat score L of the assessment data is:

[0036]

[0037] in is the current time, is the most recent time when the i-th threat occurred, and are the impact coefficient and attenuation rate of the i-th threat, The dataset for the i-th threat The degree of influence, The number of external threats that occurred;

[0038] The data security detection formula is defined as:

[0039]

[0040] in Score the data security test. , as well as They are the weight coefficients of the integrity of the data to be stored, the privacy sensitivity of the data, and the external threat score of the data.

[0041] As a preferred solution of the intelligent storage database management method of the present invention, wherein: the implementation of security measures according to the detection results refers to calculating the external threat score of the stored data and then judging the data security:

[0042] If the data security detection score is 0, it means that the data to be stored has no security threats at all. The database stores the data according to the intelligent index and marks it as safe data. There is no need to perform regular data detection.

[0043] If the data security detection score is not 0, it means that there is a security threat in the data to be stored, and the larger the data security detection score, the more serious the security threat. The database notifies the staff to check the data to be stored. The staff processes the data to be stored to different degrees according to the data security detection score, and re-scores the processed data until the data security detection score is 0, then the data is stored, and a data investigation record is generated for synchronous storage.

[0044] As a preferred solution of the intelligent storage database management method described in the present invention, the periodic detection of stored data refers to the database regularly performing data verification operations on the stored data according to the intelligent index, confirming the integrity and consistency of the data, detecting data damage or tampering, synchronizing the backup data and verifying the integrity and availability of the data backup.

[0045] As a preferred solution of the intelligent storage database management method described in the present invention, wherein: the formulation of data access control strategy refers to the database setting access security measures for stored data, including setting access rights and access passwords, the access passwords and access rights correspond to and are unique, when data access is performed, the database maintains full-process monitoring, and detects data integrity and consistency after the data access is completed, and generates data access records and detection records for synchronous storage.

[0046] Another object of the present invention is to provide an intelligent storage database management system, which comprises:

[0047] A data acquisition module, used to acquire data to be stored and pre-process the data;

[0048] Intelligent classification module, used to build a data classification model to classify the data to be stored and generate data intelligent index;

[0049] A security detection module is used to detect the security of the data to be stored and implement security measures based on the security detection results;

[0050] The storage control module is used to perform regular checks on stored data and formulate data access control policies to maintain monitoring of stored data.

[0051] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned intelligent storage database management method when executing the computer program.

[0052] A computer-readable storage medium stores a computer program, which implements the steps of the above-mentioned intelligent storage database management method when executed by a processor.

[0053] The beneficial effects of the present invention are as follows: the present invention constructs a data classification model to classify the data to be stored and detects the data security, and implements security processing measures, thereby greatly improving the speed at which the database processes data, improving storage efficiency, and effectively monitoring data security, thereby improving the security of data storage, and improving the flexibility and efficiency of data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0055] Figure 1 The figure is a flowchart of the intelligent storage database management method.

[0056] Figure 2 The present invention is a schematic diagram of the data processing flow of the intelligent storage database management method.

[0057] Figure 3 It is a structural diagram of the intelligent storage database management system. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] Example 1

[0062] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and this embodiment provides an intelligent storage database management method, and the intelligent storage database management method includes the following steps:

[0063] S1. The database collects the data to be stored for preprocessing, automatically classifies and generates intelligent indexes;

[0064] Specifically, the database collects the data to be stored and performs preprocessing, which means that the database collects the data to be stored to form a data set D, which is represented by a Gaussian distribution:

[0065]

[0066] in is the distribution function of the data, is the input value, is the total number of data in data set D, is a Gaussian distribution, and are the mean and variance of the j-th Gaussian distribution, is the weight of the jth Gaussian distribution, and ;

[0067] Use Gaussian smoothing to clean and remove noise from the dataset D:

[0068]

[0069] in is the preprocessed data set, and are the mean and variance of the data in dataset D respectively.

[0070] By using Gaussian distribution representation and Gaussian smoothing for data preprocessing, noise and outliers in the data can be effectively removed and the quality of the data can be improved. This is crucial for subsequent data analysis, mining and decision making, because high-quality data can provide more accurate information and support more effective data-driven decisions. The Gaussian smoothing process can not only remove noise, but also highlight the main characteristics and trends of the data, making the distribution of the data more obvious, which is convenient for subsequent data processing and analysis. By preprocessing the data, the details of the original data can be blurred to a certain extent. For data sets containing sensitive information, this is an effective data protection method that can be used for subsequent data processing and avoid direct leakage of sensitive information. The preprocessed data has higher quality and less noise, so it can achieve higher efficiency and faster response speed in database management operations such as storage, index establishment and data retrieval.

[0071] Furthermore, automatic classification to generate intelligent indexes refers to building a data classification model to classify the preprocessed data, including:

[0072] Define the kernel function for:

[0073]

[0074] in and are the i-th data and j-th data in the preprocessed data set, respectively. is the kernel function parameter, is the Euclidean distance between two data points;

[0075] The objective function and constraints are established as follows:

[0076]

[0077]

[0078] in and b are the weight vector and bias term respectively, is the regularization parameter, is the total number of data in the preprocessed dataset, is the slack variable of the i-th data, is the label value of the i-th data;

[0079] Use the dual problem to train the model:

[0080]

[0081]

[0082] in and are the i-th and j-th Lagrange multipliers respectively, is the label value of the jth data;

[0083] Obtain a data classification model through model training:

[0084]

[0085] Use cross-validation and model accuracy to evaluate model performance through the test set and iteratively train the model until there is no obvious loss in the model or the preset number of iterations is reached, then stop training the output data classification model;

[0086] The preprocessed dataset The data is input into the data classification model for automatic classification, and an intelligent index is generated based on the data classification.

[0087] By automatically classifying the preprocessed data and generating intelligent indexes, the retrieval speed of the database can be significantly improved. The intelligent index is optimized according to the classification results of the data, making the data query operation faster and more accurate. Automated data classification reduces the need for manual intervention and improves the efficiency and reliability of database management. In addition, through intelligent indexing, the storage structure of the database can also be optimized, thereby reducing storage costs. The use of cross-validation and model accuracy testing can ensure that the constructed data classification model has good generalization ability, which means that the model can effectively process new and unseen data, ensuring the stability and reliability of the model. The use of kernel functions enables the method to effectively handle nonlinearly separable complex data problems, expanding the application scope of the method. Whether it is text data, image data or other types of complex data, it can be effectively managed by this method. By continuously iterating the training model until the model performance is stable, the present invention can dynamically adapt to data changes and ensure the accuracy of data classification and index generation. This dynamic adaptability is particularly important for processing large-scale dynamically changing data sets.

[0088] S2. Perform data security testing based on the data to be stored and implement security measures based on the test results;

[0089] Specifically, data security testing based on the data to be stored includes:

[0090] Measuring the integrity of data to be stored for:

[0091]

[0092] in For the dataset The i-th updated version, each time new data is stored, the data set is updated to form a new version. For hash encryption, is the total number of updated versions, is the frequency of access to the ith version, is the attenuation constant, is the time attenuation coefficient, is the adjustment factor;

[0093] Evaluate the data privacy sensitivity G as:

[0094]

[0095] in Score the sensitivity of data by manually scoring the sensitivity of data. The data access control level is evaluated by the difficulty of data access. is the number of sensitive information types, For the dataset The score of the lth privacy attribute is evaluated by the privacy level. is the total number of privacy attributes, is the weight of the data privacy attribute;

[0096] The external threat score L of the assessment data is:

[0097]

[0098] in is the current time, is the most recent time when the i-th threat occurred, and are the impact coefficient and attenuation rate of the i-th threat, is the dataset of the threat category i The impact of the data set is evaluated by its threat level. The number of external threats that occurred;

[0099] The data security detection formula is defined as:

[0100]

[0101] in Data security detection score, the value range is , where the larger the value of S is, the greater the impact of security threats in the data. , as well as They are the weight coefficients of the integrity of the data to be stored, the privacy sensitivity of the data, and the external threat score of the data.

[0102] By introducing the time decay coefficient and decay constant, dynamic monitoring of data integrity is achieved. This dynamism ensures that the security detection mechanism can adapt to data changes over time and promptly discover potential security threats. By evaluating the privacy sensitivity of data, the present invention can identify highly sensitive data and take corresponding protection measures, which helps prevent data leakage, protect user privacy, and comply with the requirements of data protection regulations. By evaluating the external threat score of data, the present invention can monitor and evaluate external threats to data security in real time. This monitoring mechanism enables the database management system to respond to external attacks in a timely manner and effectively reduce the risk of data loss and leakage. The comprehensive security scoring mechanism enables the database management system to perform detailed security assessments on stored data, thereby improving the system's external trust. This is particularly important for enterprises that need to process large amounts of sensitive data, and helps to build user trust and meet compliance requirements. By providing specific security scores, the method provides database administrators with a quantitative security assessment tool to support them in making more informed data security decisions, such as data encryption, access control, and security audits.

[0103] Furthermore, implementing security measures based on the detection results means calculating the external threat score of the stored data and then judging the data security:

[0104] If the data security detection score is 0, it means that the data to be stored has no security threats at all. The database stores the data according to the intelligent index and marks it as safe data. There is no need to perform regular data detection.

[0105] If the data security detection score is not 0, it means that there is a security threat in the data to be stored, and the larger the data security detection score, the more serious the security threat. The database notifies the staff to check the data to be stored. The staff handles the data to be stored to different degrees according to the data security detection score. For low-threat data, the staff directly checks it in the database. For high-threat data, the database first isolates the data from the database and transfers it to the security analysis sandbox. The staff conducts a detailed check of the data content and re-scores the security detection score of the processed data until the data security detection score is 0, then the data is stored and a data check record is generated for synchronous storage.

[0106] By classifying data into security levels and implementing differentiated management, the present invention greatly improves the efficiency of data processing and management. By dynamically monitoring data security threats and implementing corresponding security measures, the present invention significantly enhances the security of the database and effectively prevents the risk of data leakage and damage. The present invention supports adjusting security management strategies according to real-time changes in data security conditions, making database security management more flexible and effective. Through the generation of security detection scores and data investigation records, the present invention enhances the awareness of the importance of data security and promotes the improvement of security management awareness.

[0107] S3. Regularly check stored data and formulate data access control strategies.

[0108] Specifically, regular testing of stored data means that the database regularly performs data verification operations on the stored data according to the intelligent index, confirms the integrity and consistency of the data, detects data corruption or tampering, synchronizes the backup data and verifies the integrity and availability of the data backup.

[0109] By performing data verification through intelligent indexing, the system can quickly locate the data that needs to be verified, greatly improving the efficiency of data management. Intelligent indexing ensures the high pertinence and accuracy of data verification operations, thereby optimizing the data management process. Regular data verification can promptly detect data corruption or tampering problems and effectively protect the integrity and consistency of data. This is especially important for data that requires long-term storage and frequent access, such as financial transaction data, medical records and other sensitive information. Synchronous backup of data and verification of the integrity and availability of data backups provide double protection for data security. Even in the face of hardware failures, software errors or external attacks, data can be quickly restored to ensure business continuity and high data availability. Through regular detection and synchronous backup, data snapshots at multiple time points can be provided for data recovery, supporting flexible data recovery strategies. This allows you to restore to the most suitable data version as needed when encountering data errors or loss, reducing data recovery time and loss.

[0110] Furthermore, formulating a data access control strategy refers to the database setting access security measures for the stored data, including setting access permissions and access passwords. The access passwords and access permissions correspond to and are unique. When data access is performed, the database maintains full monitoring, and detects data integrity and consistency after the data access is completed, and generates data access records and detection records for synchronous storage.

[0111] By setting a unique access right and password for each data access, data security is greatly enhanced. This mechanism ensures that only authenticated and authorized users can access data, effectively preventing the risk of unauthorized access and data leakage. The whole process monitoring and integrity detection after data access provide real-time data usage and subsequent security guarantees for database management. This not only improves the efficiency of data management, but also enhances the monitoring ability of data security, so that any abnormal access can be discovered and handled in time. The generated data access records and detection records not only provide additional protection for data security, but also enhance the transparency and traceability of data use. These records detail who accessed which data and when, as well as the status of the data after access, which are essential for auditing, compliance checks and security analysis.

[0112] Example 2

[0113] Reference Figure 3 , which is the second embodiment of the present invention, and which is different from the previous embodiment, provides an intelligent storage database management system, which includes:

[0114] A data acquisition module, used to acquire data to be stored and pre-process the data;

[0115] Intelligent classification module, used to build a data classification model to classify the data to be stored and generate data intelligent index;

[0116] A security detection module is used to detect the security of the data to be stored and implement security measures based on the security detection results;

[0117] The storage control module is used to perform regular checks on stored data and formulate data access control policies to maintain monitoring of stored data.

[0118] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0119] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0120] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0121] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent storage database management method, characterized in that: include, The database collects the data to be stored, performs preprocessing, and automatically classifies and generates intelligent indexes; Perform data security testing based on the data to be stored and implement security measures based on the test results; Regularly test stored data and formulate data access control strategies; The database collects the data to be stored for preprocessing, which means that the database collects the data to be stored to form a data set D, which is represented by Gaussian distribution: ,in is the distribution function of the data, is the input value, is the total number of data in data set D, is a Gaussian distribution, and are the mean and variance of the j-th Gaussian distribution, is the weight of the jth Gaussian distribution, and ; Use Gaussian smoothing to clean and remove noise from the dataset D: ,in is the preprocessed data set, and are the mean and variance of the data in data set D respectively; The automatic classification and generation of intelligent index refers to building a data classification model to classify the pre-processed data, including: Define the kernel function for: ,in and are the i-th data and j-th data in the preprocessed data set, respectively. is the kernel function parameter, is the Euclidean distance between two data points; The objective function and constraints are established as follows: , ,in and b are the weight vector and bias term respectively, is the regularization parameter, is the total number of data in the preprocessed dataset, is the slack variable of the i-th data, is the label value of the i-th data; Use the dual problem to train the model: , ,in and are the i-th and j-th Lagrange multipliers respectively, is the label value of the jth data; Obtain a data classification model through model training: , Use cross-validation and model accuracy to evaluate model performance through the test set and iteratively train the model until there is no obvious loss in the model or the preset number of iterations is reached, and then stop training the output data classification model; The preprocessed dataset The data is automatically classified in the data classification model, and an intelligent index is generated based on the data classification; The data security detection based on the data to be stored includes: Measuring the integrity of data to be stored for: ,in For the dataset The i-th updated version of For hash encryption, is the total number of updated versions, is the frequency of access to the ith version, is the attenuation constant, is the time attenuation coefficient, is the adjustment factor; Evaluate the data privacy sensitivity G as: ,in Score the sensitivity of the data, is the data access control level, is the number of sensitive information types, For the dataset In the score of the lth privacy attribute, is the total number of privacy attributes, is the weight of the data privacy attribute; The external threat score L of the assessed data is: ,in is the current time, is the most recent time when the i-th threat occurred, and are the impact coefficient and attenuation rate of the i-th threat, The dataset for the i-th threat The degree of influence The number of external threats that occurred; The data security detection formula is defined as: ,in Score the data security test. , as well as They are the weight coefficients of the integrity of the data to be stored, the sensitivity of data privacy, and the score of external threats to the data; Implementing security measures based on detection results refers to calculating the external threat score of the stored data and then judging the data security: If the data security detection score is 0, it means that the data to be stored has no security threats at all. The database stores the data according to the intelligent index and marks it as safe data. There is no need to perform regular data detection. If the data security detection score is not 0, it means that there is a security threat in the data to be stored, and the larger the data security detection score, the more serious the security threat. The database notifies the staff to check the data to be stored. The staff processes the data to be stored to different degrees according to the data security detection score, and re-scores the processed data until the data security detection score is 0, then the data is stored, and a data investigation record is generated for synchronous storage.

2. The intelligent storage database management method according to claim 1, characterized in that: The periodic detection of stored data refers to the database regularly performing data verification operations on the stored data according to the intelligent index, confirming the integrity and consistency of the data, detecting data damage or tampering, synchronizing the backup data and verifying the integrity and availability of the data backup.

3. The intelligent storage database management method according to claim 2, characterized in that: The formulation of data access control strategy refers to the database setting access security measures for stored data, including setting access rights and access passwords. The access passwords and access rights correspond to and are unique. When data access is performed, the database maintains full monitoring, and detects data integrity and consistency after the data access is completed, and generates data access records and detection records for synchronous storage.

4. A management system based on the intelligent storage database management method according to any one of claims 1 to 3, characterized in that: include, A data acquisition module, used to acquire data to be stored and pre-process the data; Intelligent classification module, used to build a data classification model to classify the data to be stored and generate data intelligent index; A security detection module is used to detect the security of the data to be stored and implement security measures based on the security detection results; The storage control module is used to perform regular checks on stored data and formulate data access control policies to maintain monitoring of stored data.

5. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of the intelligent storage database management method described in any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent storage database management method according to any one of claims 1 to 3 are implemented.

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