Data security storage processing method and system for database server

Abstract

The present invention discloses a data security storage processing method and system for a database server, the method comprising: obtaining target storage data to be stored and a plurality of candidate storage spaces currently available in the database server; determining a plurality of indicator parameter data corresponding to the target storage data based on preset data indicator parameter rules; inputting the plurality of indicator parameter data into a security requirement parameter prediction algorithm model to obtain a plurality of security requirement parameters corresponding to the target storage data; based on the plurality of security requirement parameters, screening out a target storage space from the plurality of candidate storage spaces, and sending the target storage data to the target storage space for storage. It can be seen that the present invention can effectively improve the security of data storage, improve the execution efficiency of storage services, and reduce the occurrence of storage security accidents.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data security storage processing method and system for a database server. Background Art

[0002] With the growth of business in data processing fields such as cloud computing, big data, and virtualization, the demand for data storage is also increasing, and the security performance of storage services is also increasing. However, in the existing server data storage technology, when selecting storage space, generally only the matching degree of space size and transmission performance is mechanically considered, and the security requirements for data are not fully considered. Therefore, the security of storage services cannot be guaranteed. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a data security storage processing method and system for a database server, which can effectively improve the security of data storage, improve the execution efficiency of storage services, and reduce the occurrence of storage security accidents.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a data security storage processing method of a database server, the method comprising:

[0005] Acquire target storage data to be stored and a plurality of candidate storage spaces currently available in the database server;

[0006] Based on preset data indicator parameter rules, determine multiple indicator parameter data corresponding to the target storage data;

[0007] Inputting the plurality of indicator parameter data into a safety requirement parameter prediction algorithm model to obtain a plurality of safety requirement parameters corresponding to the target storage data;

[0008] According to the multiple security requirement parameters, a target storage space is screened out from the multiple candidate storage spaces, and the target storage data is sent to the target storage space for storage.

[0009] As an optional implementation, in the first aspect of the present invention, the determining, based on a preset data indicator parameter rule, a plurality of indicator parameter data corresponding to the target storage data includes:

[0010] Based on the information gain algorithm and the historical data storage records of the database server, a plurality of candidate data indicators are screened out from a plurality of preset data feature indicators;

[0011] Obtaining a historical data prediction record corresponding to a data source device corresponding to the target storage data;

[0012] According to the historical data prediction records, based on the prediction accuracy ratio algorithm, a target safety requirement parameter prediction algorithm model is screened out from a plurality of candidate safety requirement parameter prediction algorithm models, and a plurality of target data indicators are screened out from the plurality of candidate data indicators;

[0013] Determine a plurality of indicator parameter data of the target storage data under the plurality of target data indicators.

[0014] As an optional implementation, in the first aspect of the present invention, the information gain algorithm and the historical data storage records of the database server are used to select multiple candidate data indicators from multiple preset data feature indicators, including:

[0015] Obtain historical indicator parameter data of a data record corresponding to any preset data characteristic indicator in the historical data storage record of the database server; the data characteristic indicator includes a combination of one or more of a data growth rate indicator, a data standard deviation value indicator, a data variance value indicator, a data distinction index and a data similarity index;

[0016] The historical indicator parameter data corresponding to each of the data characteristic indicators is processed by a binning algorithm in a variety of ways to obtain a plurality of binned parameter data corresponding to each of the data characteristic indicators; the binning algorithm processing is a binary tree type binning, ID3 binning, cluster binning, equal frequency binning, equal distance binning or chi-square binning;

[0017] Calculate the average value of the IG values ​​corresponding to all the binned parameter data corresponding to each of the data feature indicators to obtain the indicator gain parameter corresponding to each of the data feature indicators;

[0018] Sorting all the data feature indicators from high to low according to the indicator gain parameter to obtain a first indicator sequence;

[0019] The first first number of data feature indicators in the first indicator sequence and the indicator gain parameters of which are greater than a preset gain threshold are screened out to obtain a plurality of candidate data indicators.

[0020] As an optional implementation, in the first aspect of the present invention, the method of selecting a target safety requirement parameter prediction algorithm model from a plurality of candidate safety requirement parameter prediction algorithm models based on the prediction accuracy ratio algorithm according to the historical data prediction record, and selecting a plurality of target data indicators from the plurality of candidate data indicators includes:

[0021] According to the historical data prediction record, based on the prediction accuracy ratio algorithm, multiple candidate safety requirement parameter prediction algorithm models are screened out from multiple preset prediction algorithm models; the historical data prediction record includes the prediction of safety parameters of the data to be stored by the data source device using different prediction algorithm models during multiple storages, and the record of whether a safety leakage accident occurs subsequently;

[0022] Obtain historical index parameters and corresponding storage security parameters corresponding to each candidate data index in the historical data storage security record of the data source device to obtain historical data security record data;

[0023] The historical data security record data is input into each of the candidate safety requirement parameter prediction algorithm models, a plurality of target data indicators are screened out from the plurality of candidate data indicators, and a target safety requirement parameter prediction algorithm model is screened out from the plurality of candidate safety requirement parameter prediction algorithm models.

[0024] As an optional implementation, in the first aspect of the present invention, the method of selecting multiple candidate safety requirement parameter prediction algorithm models from multiple preset prediction algorithm models based on the prediction accuracy ratio algorithm according to the historical data prediction record includes:

[0025] According to the historical data prediction records, the prediction accuracy rate corresponding to each preset prediction algorithm model is calculated; wherein, if the data stored in the storage space corresponding to the safety parameters predicted by any prediction algorithm model in the historical prediction records has a record of a safety accident within a preset time period after storage, it is determined that the prediction algorithm model has a prediction error; otherwise, it is determined that the prediction algorithm model has a prediction error;

[0026] According to the prediction accuracy, all the prediction algorithm models are sorted from high to low to obtain an algorithm sequence;

[0027] The prediction algorithm models of the first second number in the algorithm sequence and whose prediction accuracy is greater than a preset accuracy threshold are screened out to obtain a plurality of candidate safety requirement parameter prediction algorithm models.

[0028] As an optional implementation, in the first aspect of the present invention, the step of inputting the historical data security record data into each of the candidate safety requirement parameter prediction algorithm models, selecting multiple target data indicators from the multiple candidate data indicators, and selecting a target safety requirement parameter prediction algorithm model from the multiple candidate safety requirement parameter prediction algorithm models comprises:

[0029] Inputting the historical indicator parameters and the corresponding storage security parameters corresponding to all the candidate data indicators into each candidate safety requirement parameter prediction algorithm model, and performing training optimization based on the gradient descent algorithm;

[0030] Calculate a first average value of the verification prediction accuracy of each of the candidate safety requirement parameter prediction algorithm models during the verification phase of the training optimization process; determine the one with the highest first average value among all the candidate safety requirement parameter prediction algorithm models as the target safety requirement parameter prediction algorithm model;

[0031] Calculate a second average value of the verification prediction accuracy of the historical indicator parameter and the corresponding safety record corresponding to each of the candidate data indicators when all the candidate safety requirement parameter prediction algorithm models are trained;

[0032] According to the second average value, all the candidate data indicators are sorted from high to low to obtain a second indicator sequence, and the first third number of the candidate data indicators in the second indicator sequence and whose second average value is greater than a preset average value threshold are screened out to obtain multiple target data indicators.

[0033] As an optional embodiment, in the first aspect of the present invention, the security requirement parameters include at least one of the storage space memory address type, storage space size, storage space historical time period data transmission efficiency, storage space historical time period data transmission frequency, storage space historical time period space change frequency, storage space encryption storage algorithm type, and storage space encryption storage key length.

[0034] As an optional implementation, in the first aspect of the present invention, the step of selecting a target storage space from the multiple candidate storage spaces according to the multiple security requirement parameters and sending the target storage data to the target storage space for storage includes:

[0035] For each of the candidate storage spaces, calculating an average value of similarities between a space parameter of the candidate storage space and each of the security requirement parameters to obtain a space similarity parameter of the candidate storage space;

[0036] Calculate a first weight that is proportional to an average value of the amount of historical storage data of the candidate storage space;

[0037] Calculating a second weight inversely proportional to the frequency of modification of historical storage data of the candidate storage space;

[0038] Calculating the spatial similarity parameter and the product of the first weight and the second weight to obtain the spatial parameter of the candidate storage space;

[0039] The candidate storage space with the largest space parameter is determined as the target storage space, and the target storage data is sent to the target storage space for storage.

[0040] A second aspect of an embodiment of the present invention discloses a data security storage and processing system for a database server, the system comprising:

[0041] An acquisition module, used for acquiring target storage data to be stored and a plurality of candidate storage spaces currently available in the database server;

[0042] A determination module, used to determine a plurality of indicator parameter data corresponding to the target storage data based on a preset data indicator parameter rule;

[0043] A prediction module, used for inputting the plurality of indicator parameter data into a safety requirement parameter prediction algorithm model to obtain a plurality of safety requirement parameters corresponding to the target storage data;

[0044] The screening module is used to screen out a target storage space from the multiple candidate storage spaces according to the multiple security requirement parameters, and send the target storage data to the target storage space for storage.

[0045] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the multiple indicator parameter data corresponding to the target storage data based on a preset data indicator parameter rule includes:

[0046] Based on the information gain algorithm and the historical data storage records of the database server, a plurality of candidate data indicators are screened out from a plurality of preset data feature indicators;

[0047] Obtaining a historical data prediction record corresponding to a data source device corresponding to the target storage data;

[0048] According to the historical data prediction records, based on the prediction accuracy ratio algorithm, a target safety requirement parameter prediction algorithm model is screened out from a plurality of candidate safety requirement parameter prediction algorithm models, and a plurality of target data indicators are screened out from the plurality of candidate data indicators;

[0049] Determine a plurality of indicator parameter data of the target storage data under the plurality of target data indicators.

[0050] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module selects multiple candidate data indicators from multiple preset data feature indicators based on the information gain algorithm and the historical data storage records of the database server includes:

[0051] Obtain historical indicator parameter data of a data record corresponding to any preset data characteristic indicator in the historical data storage record of the database server; the data characteristic indicator includes a combination of one or more of a data growth rate indicator, a data standard deviation value indicator, a data variance value indicator, a data distinction index and a data similarity index;

[0052] The historical indicator parameter data corresponding to each of the data characteristic indicators is processed by a binning algorithm in a variety of ways to obtain a plurality of binned parameter data corresponding to each of the data characteristic indicators; the binning algorithm processing is a binary tree type binning, ID3 binning, cluster binning, equal frequency binning, equal distance binning or chi-square binning;

[0053] Calculate the average value of the IG values ​​corresponding to all the binned parameter data corresponding to each of the data feature indicators to obtain the indicator gain parameter corresponding to each of the data feature indicators;

[0054] Sorting all the data feature indicators from high to low according to the indicator gain parameter to obtain a first indicator sequence;

[0055] The first first number of data feature indicators in the first indicator sequence and the indicator gain parameters of which are greater than a preset gain threshold are screened out to obtain a plurality of candidate data indicators.

[0056] As an optional implementation, in the second aspect of the present invention, the determination module selects the target safety requirement parameter prediction algorithm model from multiple candidate safety requirement parameter prediction algorithm models based on the prediction accuracy ratio algorithm according to the historical data prediction record, and the specific manner of selecting multiple target data indicators from the multiple candidate data indicators includes:

[0057] According to the historical data prediction record, based on the prediction accuracy ratio algorithm, multiple candidate safety requirement parameter prediction algorithm models are screened out from multiple preset prediction algorithm models; the historical data prediction record includes the prediction of safety parameters of the data to be stored by the data source device using different prediction algorithm models during multiple storages, and the record of whether a safety leakage accident occurs subsequently;

[0058] Obtain historical index parameters and corresponding storage security parameters corresponding to each candidate data index in the historical data storage security record of the data source device to obtain historical data security record data;

[0059] The historical data security record data is input into each of the candidate safety requirement parameter prediction algorithm models, a plurality of target data indicators are screened out from the plurality of candidate data indicators, and a target safety requirement parameter prediction algorithm model is screened out from the plurality of candidate safety requirement parameter prediction algorithm models.

[0060] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module selects multiple candidate safety requirement parameter prediction algorithm models from multiple preset prediction algorithm models based on the prediction accuracy ratio algorithm according to the historical data prediction record includes:

[0061] According to the historical data prediction records, the prediction accuracy rate corresponding to each preset prediction algorithm model is calculated; wherein, if the data stored in the storage space corresponding to the safety parameters predicted by any prediction algorithm model in the historical prediction records has a record of a safety accident within a preset time period after storage, it is determined that the prediction algorithm model has a prediction error; otherwise, it is determined that the prediction algorithm model has a prediction error;

[0062] According to the prediction accuracy, all the prediction algorithm models are sorted from high to low to obtain an algorithm sequence;

[0063] The prediction algorithm models of the first second number in the algorithm sequence and whose prediction accuracy is greater than a preset accuracy threshold are screened out to obtain a plurality of candidate safety requirement parameter prediction algorithm models.

[0064] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module inputs the historical data security record data into each of the candidate safety requirement parameter prediction algorithm models, selects a plurality of target data indicators from the plurality of candidate data indicators, and selects a target safety requirement parameter prediction algorithm model from the plurality of candidate safety requirement parameter prediction algorithm models includes:

[0065] Inputting the historical indicator parameters and the corresponding storage security parameters corresponding to all the candidate data indicators into each candidate safety requirement parameter prediction algorithm model, and performing training optimization based on the gradient descent algorithm;

[0066] Calculate a first average value of the verification prediction accuracy of each of the candidate safety requirement parameter prediction algorithm models during the verification phase of the training optimization process; determine the one with the highest first average value among all the candidate safety requirement parameter prediction algorithm models as the target safety requirement parameter prediction algorithm model;

[0067] Calculate a second average value of the verification prediction accuracy of the historical indicator parameter and the corresponding safety record corresponding to each of the candidate data indicators when all the candidate safety requirement parameter prediction algorithm models are trained;

[0068] According to the second average value, all the candidate data indicators are sorted from high to low to obtain a second indicator sequence, and the first third number of the candidate data indicators in the second indicator sequence and whose second average value is greater than a preset average value threshold are screened out to obtain multiple target data indicators.

[0069] As an optional embodiment, in the second aspect of the present invention, the security requirement parameters include at least one of the storage space memory address type, storage space size, storage space historical time period data transmission efficiency, storage space historical time period data transmission frequency, storage space historical time period space change frequency, storage space encryption storage algorithm type, and storage space encryption storage key length.

[0070] As an optional implementation, in the second aspect of the present invention, the screening module screens out a target storage space from the multiple candidate storage spaces according to the multiple security requirement parameters, and sends the target storage data to the target storage space for storage, including:

[0071] For each of the candidate storage spaces, calculating an average value of similarities between a space parameter of the candidate storage space and each of the security requirement parameters to obtain a space similarity parameter of the candidate storage space;

[0072] Calculate a first weight that is proportional to an average value of the amount of historical storage data of the candidate storage space;

[0073] Calculating a second weight inversely proportional to the frequency of modification of historical storage data of the candidate storage space;

[0074] Calculating the spatial similarity parameter and the product of the first weight and the second weight to obtain the spatial parameter of the candidate storage space;

[0075] The candidate storage space with the largest space parameter is determined as the target storage space, and the target storage data is sent to the target storage space for storage.

[0076] The third aspect of the present invention discloses another data security storage and processing system for a database server, the system comprising:

[0077] A memory storing executable program code;

[0078] a processor coupled to the memory;

[0079] The processor calls the executable program code stored in the memory to execute part or all of the steps in the data security storage processing method of the database server disclosed in the first aspect of the present invention.

[0080] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the data security storage processing method of the database server disclosed in the first aspect of the present invention.

[0081] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0082] The present invention can calculate multiple indicator parameter data of the target storage data and predict the security requirement parameters of the data based on this, and then screen out a suitable target storage space for data storage according to the security requirement parameters, thereby effectively improving the security of data storage, improving the execution efficiency of the storage service, and reducing the occurrence of storage safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0084] Figure 1 The invention discloses a flow chart of a method for securely storing and processing data in a database server.

[0085] Figure 2 The invention is a schematic diagram of the structure of a data security storage and processing system of a database server disclosed in an embodiment of the invention.

[0086] Figure 3 It is a structural schematic diagram of another data security storage and processing system of a database server disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0089] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0090] The present invention discloses a data security storage processing method and system for a database server, which can calculate multiple index parameter data of target storage data and predict the security requirement parameters of the data based on the data, and then select a suitable target storage space for data storage according to the security requirement parameters, thereby effectively improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage security accidents. The following are detailed descriptions.

[0091] Embodiment 1

[0092] See also Figure 1 , Figure 1 The following is a flow chart of a method for securely storing and processing data in a database server disclosed in an embodiment of the present invention. Figure 1 The data security storage processing method of the database server described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the data security storage processing method of the database server may include the following operations:

[0093] 101. Obtain target storage data to be stored and multiple candidate storage spaces currently available in a database server.

[0094] 102. Based on preset data indicator parameter rules, determine multiple indicator parameter data corresponding to the target storage data.

[0095] 103. Input multiple indicator parameter data into the security requirement parameter prediction algorithm model to obtain multiple security requirement parameters corresponding to the target storage data.

[0096] 104. According to multiple security requirement parameters, a target storage space is selected from multiple candidate storage spaces, and the target storage data is sent to the target storage space for storage.

[0097] It can be seen that the above-mentioned embodiments of the invention can calculate multiple indicator parameter data of the target storage data and predict the security requirement parameters of the data based on this, and then screen out suitable target storage space for data storage according to the security requirement parameters, thereby effectively improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage security accidents.

[0098] As an optional embodiment, in the above steps, based on a preset data indicator parameter rule, determining a plurality of indicator parameter data corresponding to the target storage data includes:

[0099] Based on the information gain algorithm and the historical data storage records of the database server, multiple candidate data indicators are selected from multiple preset data feature indicators;

[0100] Obtain the historical data prediction record corresponding to the data source device corresponding to the target storage data;

[0101] According to historical data prediction records and based on the prediction accuracy ratio algorithm, a target safety requirement parameter prediction algorithm model is selected from multiple candidate safety requirement parameter prediction algorithm models, and multiple target data indicators are selected from multiple candidate data indicators;

[0102] Determine multiple indicator parameter data of target storage data under multiple target data indicators.

[0103] It can be seen that through the above optional embodiments, suitable data indicators can be screened out based on the information gain algorithm and the prediction accuracy ratio algorithm for use in subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0104] As an optional embodiment, in the above steps, based on the information gain algorithm and the historical data storage records of the database server, multiple candidate data indicators are screened out from multiple preset data feature indicators, including:

[0105] Obtain historical indicator parameter data of a data record corresponding to any preset data characteristic indicator in the historical data storage record of the database server; optionally, the data characteristic indicator includes a combination of one or more of a data growth rate indicator, a data standard deviation value indicator, a data variance value indicator, a data distinction index and a data similarity index;

[0106] The historical indicator parameter data corresponding to each data characteristic indicator is processed by a binning algorithm in a variety of ways to obtain a plurality of binned parameter data corresponding to each data characteristic indicator; optionally, the binning algorithm is a binary tree type binning, ID3 binning, cluster binning, equal frequency binning, equal distance binning or chi-square binning;

[0107] Calculate the average IG value corresponding to all binned parameter data corresponding to each data feature index to obtain the index gain parameter corresponding to each data feature index;

[0108] All data feature indicators are sorted from high to low according to the indicator gain parameter to obtain a first indicator sequence;

[0109] The first first number of data feature indicators in the first indicator sequence whose indicator gain parameters are greater than a preset gain threshold are screened out to obtain a plurality of candidate data indicators.

[0110] It can be seen that through the above optional embodiments, suitable data indicators can be screened out based on the information gain algorithm for subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0111] As an optional embodiment, in the above steps, according to the historical data prediction records, based on the prediction accuracy ratio algorithm, the target safety requirement parameter prediction algorithm model is screened out from multiple candidate safety requirement parameter prediction algorithm models, and multiple target data indicators are screened out from multiple candidate data indicators, including:

[0112] According to the historical data prediction records, based on the prediction accuracy ratio algorithm, multiple candidate safety requirement parameter prediction algorithm models are screened out from multiple preset prediction algorithm models; optionally, the historical data prediction records include the prediction of safety parameters of the data to be stored of the data source device using different prediction algorithm models during multiple storages, and the record of whether a safety leakage accident occurs subsequently;

[0113] Obtain historical index parameters and corresponding storage security parameters corresponding to each candidate data index in the historical data storage security record of the data source device to obtain historical data security record data;

[0114] The historical data security record data is input into each candidate safety requirement parameter prediction algorithm model, a plurality of target data indicators are screened out from a plurality of candidate data indicators, and a target safety requirement parameter prediction algorithm model is screened out from a plurality of candidate safety requirement parameter prediction algorithm models.

[0115] It can be seen that through the above-mentioned optional embodiments, target data indicators and target prediction algorithm models can be screened out based on the prediction accuracy ratio algorithm and data security record data for use in subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage security accidents.

[0116] As an optional embodiment, in the above steps, based on the historical data prediction records and the prediction accuracy ratio algorithm, multiple candidate safety requirement parameter prediction algorithm models are screened out from multiple preset prediction algorithm models, including:

[0117] According to the historical data prediction records, the prediction accuracy rate corresponding to each preset prediction algorithm model is calculated; optionally, if the data stored in the storage space corresponding to the safety parameters predicted by any prediction algorithm model in the historical prediction records has a record of a safety accident within a preset time period after storage, it is determined that the prediction algorithm model has a prediction error, otherwise it is determined that the prediction algorithm model has a prediction error;

[0118] According to the prediction accuracy, all prediction algorithm models are sorted from high to low to obtain the algorithm sequence;

[0119] The prediction algorithm models of the first second number in the algorithm sequence and whose prediction accuracy is greater than a preset accuracy threshold are screened out to obtain a plurality of candidate safety requirement parameter prediction algorithm models.

[0120] It can be seen that through the above-mentioned optional embodiments, candidate security requirement parameter prediction algorithm models can be screened out based on prediction accuracy and sorting and screening rules for use in subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0121] As an optional embodiment, in the above steps, inputting the historical data security record data into each candidate safety requirement parameter prediction algorithm model, screening out multiple target data indicators from multiple candidate data indicators, and screening out the target safety requirement parameter prediction algorithm model from multiple candidate safety requirement parameter prediction algorithm models include:

[0122] Input the historical indicator parameters and corresponding storage security parameters corresponding to all candidate data indicators into each candidate safety requirement parameter prediction algorithm model, and perform training optimization based on the gradient descent algorithm;

[0123] Calculate the first average value of the verification prediction accuracy of each candidate safety requirement parameter prediction algorithm model in the verification phase of the training optimization process; optionally, determine the one with the highest first average value among all candidate safety requirement parameter prediction algorithm models as the target safety requirement parameter prediction algorithm model;

[0124] Calculate the second average value of the verification prediction accuracy of the historical indicator parameter and the corresponding safety record corresponding to each candidate data indicator during the training of all candidate safety requirement parameter prediction algorithm models;

[0125] According to the second average value, all candidate data indicators are sorted from high to low to obtain a second indicator sequence, and the first third number of candidate data indicators in the second indicator sequence and whose second average values ​​are greater than a preset average value threshold are screened out to obtain multiple target data indicators.

[0126] It can be seen that through the above-mentioned optional embodiments, the candidate model can be trained and optimized based on the historical indicator parameters corresponding to the candidate data indicators and the corresponding storage security parameters, and the target data indicators and the target prediction algorithm model can be screened out according to the verification prediction accuracy in the training, so as to be used for subsequent spatial screening calculations, and assist in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0127] As an optional embodiment, in the above steps, the security requirement parameters include at least one of the storage space memory address type, storage space size, storage space historical time period data transmission efficiency, storage space historical time period data transmission frequency, storage space historical time period space change frequency, storage space encryption storage algorithm type, and storage space encryption storage key length.

[0128] It can be seen that through the above optional embodiments, the content of the security requirement parameters is clarified to better characterize the security requirements of data storage, facilitate subsequent spatial screening calculations, and assist in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage security accidents.

[0129] As an optional embodiment, in the above steps, selecting a target storage space from multiple candidate storage spaces according to multiple security requirement parameters, and sending the target storage data to the target storage space for storage includes:

[0130] For each candidate storage space, calculating an average value of similarities between a space parameter of the candidate storage space and each security requirement parameter to obtain a space similarity parameter of the candidate storage space;

[0131] Calculate a first weight that is proportional to an average value of the amount of historical storage data of the candidate storage space;

[0132] Calculating a second weight inversely proportional to the frequency of modification of historical storage data of the candidate storage space;

[0133] Calculate the spatial similarity parameter and the product of the first weight and the second weight to obtain the spatial parameter of the candidate storage space;

[0134] The candidate storage space with the largest space parameter is determined as the target storage space, and the target storage data is sent to the target storage space for storage.

[0135] It can be seen that through the above-mentioned optional embodiments, the target storage space can be screened out based on the similarity calculation of the candidate storage space and the weight calculation corresponding to the amount of historical storage data and the frequency of change of historical storage data, thereby improving the security of data storage, improving the execution efficiency of the storage service, and reducing the occurrence of storage safety accidents.

[0136] Embodiment 2

[0137] See also Figure 2 , Figure 2 1 is a schematic diagram of a data security storage and processing system for a database server disclosed in an embodiment of the present invention. Figure 2 The data security storage processing system of the database server described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the data security storage processing system of the database server may include:

[0138] The acquisition module 201 is used to acquire target storage data to be stored and multiple candidate storage spaces currently available in the database server.

[0139] The determination module 202 is used to determine a plurality of indicator parameter data corresponding to the target storage data based on preset data indicator parameter rules.

[0140] The prediction module 203 is used to input a plurality of indicator parameter data into a safety requirement parameter prediction algorithm model to obtain a plurality of safety requirement parameters corresponding to the target storage data.

[0141] The screening module 204 is used to screen out a target storage space from a plurality of candidate storage spaces according to a plurality of security requirement parameters, and send the target storage data to the target storage space for storage.

[0142] It can be seen that the above-mentioned embodiments of the invention can calculate multiple indicator parameter data of the target storage data and predict the security requirement parameters of the data based on this, and then screen out suitable target storage space for data storage according to the security requirement parameters, thereby effectively improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage security accidents.

[0143] As an optional embodiment, the specific manner in which the determination module determines the multiple indicator parameter data corresponding to the target storage data based on the preset data indicator parameter rule includes:

[0144] Based on the information gain algorithm and the historical data storage records of the database server, multiple candidate data indicators are selected from multiple preset data feature indicators;

[0145] Obtain the historical data prediction record corresponding to the data source device corresponding to the target storage data;

[0146] According to historical data prediction records and based on the prediction accuracy ratio algorithm, a target safety requirement parameter prediction algorithm model is selected from multiple candidate safety requirement parameter prediction algorithm models, and multiple target data indicators are selected from multiple candidate data indicators;

[0147] Determine multiple indicator parameter data of target storage data under multiple target data indicators.

[0148] It can be seen that through the above optional embodiments, suitable data indicators can be screened out based on the information gain algorithm and the prediction accuracy ratio algorithm for use in subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0149] As an optional embodiment, the specific manner in which the determination module selects multiple candidate data indicators from multiple preset data feature indicators based on the information gain algorithm and the historical data storage records of the database server includes:

[0150] Obtain historical indicator parameter data of a data record corresponding to any preset data characteristic indicator in the historical data storage record of the database server; optionally, the data characteristic indicator includes a combination of one or more of a data growth rate indicator, a data standard deviation value indicator, a data variance value indicator, a data distinction index and a data similarity index;

[0151] The historical indicator parameter data corresponding to each data characteristic indicator is processed by a binning algorithm in a variety of ways to obtain a plurality of binned parameter data corresponding to each data characteristic indicator; optionally, the binning algorithm is a binary tree type binning, ID3 binning, cluster binning, equal frequency binning, equal distance binning or chi-square binning;

[0152] Calculate the average IG value corresponding to all binned parameter data corresponding to each data feature index to obtain the index gain parameter corresponding to each data feature index;

[0153] All data feature indicators are sorted from high to low according to the indicator gain parameter to obtain a first indicator sequence;

[0154] The first first number of data feature indicators in the first indicator sequence whose indicator gain parameters are greater than a preset gain threshold are screened out to obtain a plurality of candidate data indicators.

[0155] It can be seen that through the above optional embodiments, suitable data indicators can be screened out based on the information gain algorithm for subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0156] As an optional embodiment, the determination module selects the target safety requirement parameter prediction algorithm model from multiple candidate safety requirement parameter prediction algorithm models based on the prediction accuracy ratio algorithm according to the historical data prediction records, and selects multiple target data indicators from multiple candidate data indicators in a specific manner, including:

[0157] According to the historical data prediction records, based on the prediction accuracy ratio algorithm, multiple candidate safety requirement parameter prediction algorithm models are screened out from multiple preset prediction algorithm models; optionally, the historical data prediction records include the prediction of safety parameters of the data to be stored of the data source device using different prediction algorithm models during multiple storages, and the record of whether a safety leakage accident occurs subsequently;

[0158] Obtain historical index parameters and corresponding storage security parameters corresponding to each candidate data index in the historical data storage security record of the data source device to obtain historical data security record data;

[0159] The historical data security record data is input into each candidate safety requirement parameter prediction algorithm model, a plurality of target data indicators are screened out from a plurality of candidate data indicators, and a target safety requirement parameter prediction algorithm model is screened out from a plurality of candidate safety requirement parameter prediction algorithm models.

[0160] It can be seen that through the above-mentioned optional embodiments, target data indicators and target prediction algorithm models can be screened out based on the prediction accuracy ratio algorithm and data security record data for use in subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage security accidents.

[0161] As an optional embodiment, the specific manner in which the determination module selects multiple candidate safety requirement parameter prediction algorithm models from multiple preset prediction algorithm models based on the prediction accuracy ratio algorithm according to the historical data prediction records includes:

[0162] According to the historical data prediction records, the prediction accuracy rate corresponding to each preset prediction algorithm model is calculated; optionally, if the data stored in the storage space corresponding to the safety parameters predicted by any prediction algorithm model in the historical prediction records has a record of a safety accident within a preset time period after storage, it is determined that the prediction algorithm model has a prediction error, otherwise it is determined that the prediction algorithm model has a prediction error;

[0163] According to the prediction accuracy, all prediction algorithm models are sorted from high to low to obtain the algorithm sequence;

[0164] The prediction algorithm models of the first second number in the algorithm sequence and whose prediction accuracy is greater than a preset accuracy threshold are screened out to obtain a plurality of candidate safety requirement parameter prediction algorithm models.

[0165] It can be seen that through the above-mentioned optional embodiments, candidate security requirement parameter prediction algorithm models can be screened out based on prediction accuracy and sorting and screening rules for use in subsequent spatial screening calculations, thereby assisting in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0166] As an optional embodiment, the specific method of the determination module inputting the historical data security record data into each candidate safety requirement parameter prediction algorithm model, selecting multiple target data indicators from multiple candidate data indicators, and selecting the target safety requirement parameter prediction algorithm model from multiple candidate safety requirement parameter prediction algorithm models includes:

[0167] Input the historical indicator parameters and corresponding storage security parameters corresponding to all candidate data indicators into each candidate safety requirement parameter prediction algorithm model, and perform training optimization based on the gradient descent algorithm;

[0168] Calculate the first average value of the verification prediction accuracy of each candidate safety requirement parameter prediction algorithm model in the verification phase of the training optimization process; optionally, determine the one with the highest first average value among all candidate safety requirement parameter prediction algorithm models as the target safety requirement parameter prediction algorithm model;

[0169] Calculate the second average value of the verification prediction accuracy of the historical indicator parameter and the corresponding safety record corresponding to each candidate data indicator during the training of all candidate safety requirement parameter prediction algorithm models;

[0170] According to the second average value, all candidate data indicators are sorted from high to low to obtain a second indicator sequence, and the first third number of candidate data indicators in the second indicator sequence and whose second average values ​​are greater than a preset average value threshold are screened out to obtain multiple target data indicators.

[0171] It can be seen that through the above-mentioned optional embodiments, the candidate model can be trained and optimized based on the historical indicator parameters corresponding to the candidate data indicators and the corresponding storage security parameters, and the target data indicators and the target prediction algorithm model can be screened out according to the verification prediction accuracy in the training, so as to be used for subsequent spatial screening calculations, and assist in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage safety accidents.

[0172] As an optional embodiment, the security requirement parameters include at least one of the storage space memory address type, storage space size, storage space historical time period data transmission efficiency, storage space historical time period data transmission frequency, storage space historical time period space change frequency, storage space encryption storage algorithm type, and storage space encryption storage key length.

[0173] It can be seen that through the above optional embodiments, the content of the security requirement parameters is clarified to better characterize the security requirements of data storage, facilitate subsequent spatial screening calculations, and assist in improving the security of data storage, improving the execution efficiency of storage services, and reducing the occurrence of storage security accidents.

[0174] As an optional embodiment, the screening module screens out the target storage space from the multiple candidate storage spaces according to the multiple security requirement parameters, and sends the target storage data to the target storage space for storage, including:

[0175] For each candidate storage space, calculating an average value of similarities between a space parameter of the candidate storage space and each security requirement parameter to obtain a space similarity parameter of the candidate storage space;

[0176] Calculate a first weight that is proportional to an average value of the amount of historical storage data of the candidate storage space;

[0177] Calculating a second weight inversely proportional to the frequency of modification of historical storage data of the candidate storage space;

[0178] Calculate the spatial similarity parameter and the product of the first weight and the second weight to obtain the spatial parameter of the candidate storage space;

[0179] The candidate storage space with the largest space parameter is determined as the target storage space, and the target storage data is sent to the target storage space for storage.

[0180] It can be seen that through the above-mentioned optional embodiments, the target storage space can be screened out based on the similarity calculation of the candidate storage space and the weight calculation corresponding to the amount of historical storage data and the frequency of change of historical storage data, thereby improving the security of data storage, improving the execution efficiency of the storage service, and reducing the occurrence of storage safety accidents.

[0181] Embodiment 3

[0182] See also Figure 3 , Figure 3 The invention discloses another data security storage and processing system for a database server. Figure 3 The data security storage processing system of the database server described is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the data security storage processing system of the database server may include:

[0183] A memory 301 storing executable program codes;

[0184] a processor 302 coupled to the memory 301;

[0185] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the data security storage processing method of the database server described in the first embodiment.

[0186] Embodiment 4

[0187] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the data security storage processing method of a database server described in the first embodiment.

[0188] Embodiment 5

[0189] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the data security storage processing method of the database server described in the first embodiment.

[0190] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0191] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0192] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0193] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0194] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0198] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0199] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory media such as modulated data signals and carrier waves.

[0200] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0201] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0202] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0203] Finally, it should be noted that the data security storage processing method and system of a database server disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A data security storage processing method for a database server, characterized in that: The method comprises: Acquire target storage data to be stored and a plurality of candidate storage spaces currently available in the database server; Based on the preset data indicator parameter rules, a plurality of indicator parameter data corresponding to the target storage data is determined, including: Based on the information gain algorithm and the historical data storage records of the database server, a plurality of candidate data indicators are screened out from a plurality of preset data feature indicators; Obtaining a historical data prediction record corresponding to a data source device corresponding to the target storage data; According to the historical data prediction record, based on the prediction accuracy ratio algorithm, multiple candidate safety requirement parameter prediction algorithm models are screened out from multiple preset prediction algorithm models; the historical data prediction record includes the prediction of safety parameters of the data to be stored by the data source device using different prediction algorithm models during multiple storages, and the record of whether a safety leakage accident occurs subsequently; Obtain historical index parameters and corresponding storage security parameters corresponding to each candidate data index in the historical data storage security record of the data source device to obtain historical data security record data; Inputting the historical indicator parameters and the corresponding storage security parameters corresponding to all the candidate data indicators into each candidate safety requirement parameter prediction algorithm model, and performing training optimization based on the gradient descent algorithm; Calculate a first average value of the verification prediction accuracy of each of the candidate safety requirement parameter prediction algorithm models during the verification phase of the training optimization process; determine the one with the highest first average value among all the candidate safety requirement parameter prediction algorithm models as the target safety requirement parameter prediction algorithm model; Calculate a second average value of the verification prediction accuracy of the historical indicator parameter and the corresponding safety record corresponding to each of the candidate data indicators when all the candidate safety requirement parameter prediction algorithm models are trained; According to the second average value, all the candidate data indicators are sorted from high to low to obtain a second indicator sequence, and the first third number of the candidate data indicators in the second indicator sequence and whose second average values ​​are greater than a preset average value threshold are screened out to obtain a plurality of target data indicators; Determine a plurality of indicator parameter data of the target storage data under the plurality of target data indicators; Inputting the plurality of indicator parameter data into the target safety requirement parameter prediction algorithm model to obtain a plurality of safety requirement parameters corresponding to the target storage data; According to the multiple security requirement parameters, a target storage space is screened out from the multiple candidate storage spaces, and the target storage data is sent to the target storage space for storage.

2. The data security storage processing method of the database server according to claim 1 is characterized in that: The information gain algorithm and the historical data storage records of the database server are used to select multiple candidate data indicators from multiple preset data feature indicators, including: Obtain historical indicator parameter data of a data record corresponding to any preset data characteristic indicator in the historical data storage record of the database server; the data characteristic indicator includes a combination of one or more of a data growth rate indicator, a data standard deviation value indicator, a data variance value indicator, a data distinction index and a data similarity index; The historical indicator parameter data corresponding to each of the data characteristic indicators is processed by a binning algorithm in a variety of ways to obtain a plurality of binned parameter data corresponding to each of the data characteristic indicators; the binning algorithm processing is a binary tree type binning, ID3 binning, cluster binning, equal frequency binning, equal distance binning or chi-square binning; Calculate the average value of the IG values ​​corresponding to all the binned parameter data corresponding to each of the data feature indicators to obtain the indicator gain parameter corresponding to each of the data feature indicators; Sorting all the data feature indicators from high to low according to the indicator gain parameter to obtain a first indicator sequence; The first first number of data feature indicators in the first indicator sequence and the indicator gain parameters of which are greater than a preset gain threshold are screened out to obtain a plurality of candidate data indicators.

3. The data security storage processing method of the database server according to claim 1 is characterized in that: The method of selecting a plurality of candidate safety requirement parameter prediction algorithm models from a plurality of preset prediction algorithm models based on the prediction accuracy ratio algorithm according to the historical data prediction records includes: According to the historical data prediction records, the prediction accuracy rate corresponding to each preset prediction algorithm model is calculated; wherein, if the data stored in the storage space corresponding to the safety parameters predicted by any prediction algorithm model in the historical data prediction records has a record of a safety accident within a preset time period after storage, it is determined that the prediction algorithm model has a prediction error; otherwise, it is determined that the prediction algorithm model has a prediction error; According to the prediction accuracy, all the prediction algorithm models are sorted from high to low to obtain an algorithm sequence; The prediction algorithm models of the first second number in the algorithm sequence and whose prediction accuracy is greater than a preset accuracy threshold are screened out to obtain a plurality of candidate safety requirement parameter prediction algorithm models.

4. The data security storage processing method of the database server according to claim 1 is characterized in that: The security requirement parameters include at least one of the storage space memory address type, storage space size, storage space historical time period data transmission efficiency, storage space historical time period data transmission frequency, storage space historical time period space change frequency, storage space encryption storage algorithm type, and storage space encryption storage key length.

5. The data security storage processing method of the database server according to claim 1 is characterized in that: The step of selecting a target storage space from the multiple candidate storage spaces according to the multiple security requirement parameters, and sending the target storage data to the target storage space for storage includes: For each of the candidate storage spaces, calculating an average value of similarities between a space parameter of the candidate storage space and each of the security requirement parameters to obtain a space similarity parameter of the candidate storage space; Calculate a first weight that is proportional to an average value of the amount of historical storage data of the candidate storage space; Calculating a second weight inversely proportional to the frequency of modification of historical storage data of the candidate storage space; Calculating the spatial similarity parameter and the product of the first weight and the second weight to obtain the spatial parameter of the candidate storage space; The candidate storage space with the largest space parameter is determined as the target storage space, and the target storage data is sent to the target storage space for storage.

6. A data security storage and processing system for a database server, characterized in that: The system comprises: An acquisition module, used for acquiring target storage data to be stored and a plurality of candidate storage spaces currently available in the database server; A determination module, used to determine a plurality of indicator parameter data corresponding to the target storage data based on a preset data indicator parameter rule, including: Based on the information gain algorithm and the historical data storage records of the database server, a plurality of candidate data indicators are screened out from a plurality of preset data feature indicators; Obtaining a historical data prediction record corresponding to a data source device corresponding to the target storage data; According to the historical data prediction record, based on the prediction accuracy ratio algorithm, multiple candidate safety requirement parameter prediction algorithm models are screened out from multiple preset prediction algorithm models; the historical data prediction record includes the prediction of safety parameters of the data to be stored by the data source device using different prediction algorithm models during multiple storages, and the record of whether a safety leakage accident occurs subsequently; Obtain historical index parameters and corresponding storage security parameters corresponding to each candidate data index in the historical data storage security record of the data source device to obtain historical data security record data; Inputting the historical indicator parameters and the corresponding storage security parameters corresponding to all the candidate data indicators into each candidate safety requirement parameter prediction algorithm model, and performing training optimization based on the gradient descent algorithm; Calculate a first average value of the verification prediction accuracy of each of the candidate safety requirement parameter prediction algorithm models during the verification phase of the training optimization process; determine the one with the highest first average value among all the candidate safety requirement parameter prediction algorithm models as the target safety requirement parameter prediction algorithm model; Calculate a second average value of the verification prediction accuracy of the historical indicator parameter and the corresponding safety record corresponding to each of the candidate data indicators when all the candidate safety requirement parameter prediction algorithm models are trained; According to the second average value, all the candidate data indicators are sorted from high to low to obtain a second indicator sequence, and the first third number of the candidate data indicators in the second indicator sequence and whose second average values ​​are greater than a preset average value threshold are screened out to obtain a plurality of target data indicators; Determine a plurality of indicator parameter data of the target storage data under the plurality of target data indicators; A prediction module, used for inputting the plurality of indicator parameter data into the safety requirement parameter prediction algorithm model of the target to obtain a plurality of safety requirement parameters corresponding to the target storage data; The screening module is used to screen out a target storage space from the multiple candidate storage spaces according to the multiple security requirement parameters, and send the target storage data to the target storage space for storage.

7. A data security storage and processing system for a database server, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the data security storage processing method of the database server according to any one of claims 1 to 5.

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