Data operation and maintenance storage system and method based on data encryption

Abstract

The invention relates to the technical field of data storage, and discloses a data operation and maintenance storage system and method based on data encryption, and the system comprises: a data acquisition module receives all to-be-stored data at a preset time interval, and carries out the data association of a plurality of to-be-stored data based on an Eclat algorithm to obtain to-be-stored associated data; the data classification module inputs the to-be-stored associated data and the to-be-stored data without data association into a classification model to obtain classification result data; the sensitive encryption module extracts and marks data features of the classification result data, calculates data sensitivity based on the number of marks, judges whether to perform data encryption on the classification result data or not based on the data sensitivity, and determines a corresponding encryption strategy according to the data sensitivity when judging to perform data encryption on the classification result data; obtaining encrypted data; and the storage management module determines whether to adjust the data in the sub-database according to the load level of the sub-database. The method has a good encryption storage characteristic.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and in particular to a data operation and maintenance storage system and method based on data encryption. Background Art

[0002] With the rapid development of big data and cloud computing technologies, enterprises and individuals need to store a variety of data, including text data and image data, as well as non-sensitive data, privacy and sensitive data of commercial secrets. However, traditional data storage cannot classify data, but adopts a unified storage strategy, which leads to the mixed storage of sensitive data and non-sensitive data, increasing the risk of data leakage, thus affecting storage efficiency. In addition, since the data is not effectively classified, even if the data is encrypted and stored, it is impossible to select the appropriate encryption algorithm according to the sensitivity of the data, which affects the security of data storage. In addition, when data redundancy occurs in traditional data storage, it is impossible to dynamically adjust the storage strategy, resulting in insufficient storage performance.

[0003] Therefore, how to provide a data operation and maintenance storage system and method based on data encryption is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In view of this, the present invention proposes a data operation and maintenance storage system and method based on data encryption, aiming to solve the problems of being unable to classify data, being unable to select an appropriate encryption algorithm according to the sensitivity of the data, and being unable to dynamically adjust the storage strategy when data redundancy occurs.

[0005] In one aspect, the present invention provides a data operation and maintenance storage system based on data encryption, comprising: The data acquisition module is configured to establish a network communication connection with an Internet server, receive all the data to be stored at a preset time interval, and associate the multiple data to be stored based on the Eclat algorithm to obtain the associated data to be stored; A data classification module is configured to input the associated data to be stored and the unassociated data to be stored into a classification model to obtain classification result data; a sensitive encryption module, configured to extract data features of the classification result data and mark them, calculate data sensitivity based on the number of marks, determine whether to encrypt the classification result data based on the data sensitivity, and when it is determined that the classification result data is to be encrypted, determine a corresponding encryption strategy according to the data sensitivity to obtain encrypted data; The storage management module is configured to create a storage database and set identity authentication, and determine corresponding access rights based on the identity authentication, wherein the storage database includes a sub-database and a sensitive database, and the classification result data that has not been encrypted is stored in the sub-database, and the encrypted data is stored in the sensitive database, and whether to adjust the data in the sub-database is determined according to the load level of the sub-database.

[0006] Further, before receiving all the data to be stored at the preset time interval, the method includes: The data acquisition module captures the initial storage data and performs data processing on the initial storage data, wherein the data processing includes data dimension reduction and wavelet transformation, and performs data adjustment on the initial storage data after the data processing, wherein the data adjustment includes image data adjustment and text data adjustment; The image data adjustment includes color data balancing and geometric data transformation, the text data adjustment includes text data deduplication and text data compression, and data conversion is performed on the initial storage data after data adjustment. The data conversion includes data standardization and data normalization. The integrity and consistency of the initial storage data after data conversion are verified and whether it complies with the preset storage format. When it is judged that it complies with the preset storage format, the initial storage data after data conversion is used as the data to be stored.

[0007] Furthermore, when multiple data to be stored are associated based on the Eclat algorithm to obtain associated data to be stored, the process includes: The data acquisition module determines a transaction item set according to the data to be stored, adopts a vertical data layout, maps each data to be stored to the corresponding transaction item set, calculates the support of the candidate item set through a recursive intersection operation, and screens out frequent item sets that meet a minimum support threshold, and obtains the associated data to be stored based on the frequent item sets.

[0008] Furthermore, when the associated data to be stored and the unassociated data to be stored are input into the classification model to obtain the classification result data, it includes: The data classification module obtains historical data, the historical data including historical associated data to be stored and historical data to be stored without data association, and divides the historical associated data to be stored and the historical data to be stored into a model training set and a model test set, selects a decision tree model and iteratively trains the decision tree model according to the model training set, and verifies the iteratively trained decision tree model according to the model test set; If the classification accuracy of the decision tree model after the current iterative training inputted into the model test set is lower than the classification accuracy of the decision tree model after the previous iterative training, a grid search is used to find the model optimization parameters of the decision tree model and iterative training is continued until the preset number of iterations is reached; If the classification accuracy of the decision tree model after the current iterative training inputted into the model test set is greater than or equal to the classification accuracy of the decision tree model after the previous iterative training, the iterative training is stopped, and the decision tree model after the current iterative training is determined as the classification model; The associated data to be stored and the unassociated data to be stored are input into a classification model to obtain classification result data, wherein the classification result data includes personal type data, enterprise type data and comprehensive type data.

[0009] Furthermore, when extracting data features of the classification result data and marking them, and calculating data sensitivity based on the number of marks, it includes: The sensitive encryption module extracts data features of each data in the classification result data and generates corresponding data tags, wherein the data features include social information, credit information, transaction information and basic enterprise information, and the data tags include sensitive tags, non-sensitive tags and neutral tags; The sensitive encryption module generates the non-sensitive mark for the social information, generates the neutral mark for the basic enterprise information, generates the sensitive mark for the credit information and the transaction information, counts the number of the sensitive marks and records it as the sensitive number, counts the number of the non-sensitive marks and records it as the non-sensitive number, counts the number of the neutral marks and records it as the neutral number, and determines the initial sensitivity according to the sensitive number, the non-sensitive number and the neutral number; The initial sensitivity is given by the following formula: ; in, represents the initial sensitivity, Represents the sum of sensitive quantity, insensitive quantity and neutral quantity, Indicates sensitive quantity, Indicates an insensitive quantity. Represents a neutral quantity, , and represents the weight coefficient, and ; The length of time to obtain the data characteristics , and determining a correction factor of the initial sensitivity according to the time length; Presetting a first preset correction factor, a second preset correction factor, and a third preset correction factor; when >10 days, the first preset correction factor is used as the correction factor of the initial sensitivity; When 5 days ≤ ≤10 days, the second preset correction factor is used as the correction factor of the initial sensitivity; when <5 days, the third preset correction factor is used as the correction factor of the initial sensitivity; Determine the data sensitivity according to the product value of the correction factor of the initial sensitivity and the initial sensitivity .

[0010] Further, when determining whether to encrypt the classification result data based on the data sensitivity, it includes: The sensitive encryption module converts the data sensitivity With pre-set data sensitivity threshold Performing a comparison, and determining whether to encrypt the data in the classification result data according to the comparison result; when ≥ When the classification result data is encrypted, it is determined that the data in the classification result data is encrypted; when < When the classification result data is not encrypted, it is determined that the data in the classification result data is not encrypted.

[0011] Furthermore, when it is determined to encrypt the classification result data, a corresponding encryption strategy is determined according to the sensitivity of the data, including: The sensitive encryption module pre-sets a first encryption algorithm, a second encryption algorithm and a third encryption algorithm, and when it is determined to encrypt the data in the classification result data; when >0.8, the first encryption algorithm is used as the encryption strategy for the data in the classification result data; When 0.6< ≤0.8, the second encryption algorithm is used as the encryption strategy for the data in the classification result data; when ≤0.6, the third encryption algorithm is used as the encryption strategy for the data in the classification result data.

[0012] Furthermore, when determining corresponding access rights based on the identity authentication, it includes: The storage management module verifies the login access method. When the login access method is an access password and an access fingerprint, the access permission is determined to be access to the sub-database and access to the sensitive database. When the login access method is one of the access password or the access fingerprint, the access permission is determined to be access to the sub-database.

[0013] Further, when determining whether to adjust the data in the sub-database according to the load level of the sub-database, it includes: The storage management module divides the sub-database into a first storage repository and a second storage repository, stores the classification result data that is not encrypted in the first storage repository, and when the load level of the first storage repository is equal to a preset load level threshold, obtains the historical storage data in the first storage repository, and when there is data in the first storage repository that is the same as the historical storage data, removes the data and continues to store; When the first repository continues to store data after eliminating data, and the load level of the first repository after eliminating data is again equal to the preset load level threshold, or when there is no data in the first repository that is identical to the historical storage data, the classified result data that is not stored in the first repository and is not encrypted is stored in the second repository.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: by adopting the Eclat association algorithm to associate the data to be stored, the potential relationship between the data to be stored can be discovered, thereby improving the accuracy of subsequent data classification, using the classification model to intelligently classify the data to be stored and the data to be stored without data association, improving the accuracy of classification, avoiding the error of human judgment, providing a basis for subsequent encrypted storage, avoiding the storage security risk caused by the mixed storage of traditional data storage, and extracting data features from the classification result data and marking them, accurately obtaining the data sensitivity, ensuring that the encryption strategy of the data can match the security requirements of its data sensitivity, avoiding the waste of computing resources caused by indiscriminate encryption of data storage, and selecting the corresponding encryption strategy according to the data sensitivity, improving the security and storage efficiency of data storage. The sub-database is stored separately from the sensitive database, ensuring the stability and reliability of encrypted data storage, and improving the flexibility of data storage. Whether to adjust the data in the sub-database is determined by the load level, improving the efficiency of sub-database storage, and improving the utilization of storage.

[0015] On the other hand, the present application also provides a data operation and maintenance storage method based on data encryption, which is used to apply the above-mentioned data operation and maintenance storage system based on data encryption, including: Establishing a network communication connection with an Internet server, and receiving all the data to be stored at a preset time interval, and performing data association on multiple data to be stored based on the Eclat algorithm to obtain associated data to be stored; Inputting the associated data to be stored and the unassociated data to be stored into a classification model to obtain classification result data; Extracting data features of the classification result data and marking them, calculating data sensitivity based on the number of marks, judging whether to encrypt the classification result data based on the data sensitivity, and when judging to encrypt the classification result data, determining a corresponding encryption strategy according to the data sensitivity to obtain encrypted data; Create a storage database and set identity authentication, and determine corresponding access rights based on the identity authentication, wherein the storage database includes a sub-database and a sensitive database, store the classification result data that has not been encrypted in the sub-database, and store the encrypted data in the sensitive database, and determine whether to adjust the data in the sub-database based on the load level of the sub-database.

[0016] It is understandable that the above-mentioned data operation and maintenance storage system and method based on data encryption have the same beneficial effects, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A functional block diagram of a data operation and maintenance storage system based on data encryption provided by an embodiment of the present invention; Figure 2 A flowchart of a data operation and maintenance storage method based on data encryption provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] In some embodiments of the present application, see Figure 1 As shown, a data operation and maintenance storage system based on data encryption includes: The data acquisition module is configured to establish a network communication connection with an Internet server, receive all the data to be stored at a preset time interval, and associate the multiple data to be stored based on the Eclat algorithm to obtain the associated data to be stored; A data classification module is configured to input the associated data to be stored and the unassociated data to be stored into a classification model to obtain classification result data; A sensitive encryption module is configured to extract data features of the classification result data and mark them, calculate data sensitivity based on the number of marks, determine whether to encrypt the classification result data based on the data sensitivity, and when it is determined that the classification result data is to be encrypted, determine a corresponding encryption strategy according to the data sensitivity to obtain encrypted data; The storage management module is configured to create a storage database and set identity authentication, and determine corresponding access rights based on the identity authentication, wherein the storage database includes a sub-database and a sensitive database, and the classification result data that has not been encrypted is stored in the sub-database, and the encrypted data is stored in the sensitive database, and whether to adjust the data in the sub-database is determined according to the load level of the sub-database.

[0020] Specifically, the data acquisition module is responsible for network communication with the Internet server and regularly receives the data to be stored. The data acquisition module uses the Eclat frequent item set mining algorithm to perform association analysis on the data to be stored and obtain the associated data to be stored. The Eclat algorithm adopts depth-first search, has good computational efficiency, can perform association mining on a large amount of data to be stored, and the vertical data layout method reduces the number of transaction scans and improves the processing speed of the data to be stored. Through the Eclat algorithm, data association can be found. The implicit relationship between the data to be stored can be improved, and the storage accuracy and adaptability of the data to be stored can be improved. The data classification module adopts the decision tree model in the machine learning model and trains it to obtain an accurate classification model, and inputs the associated data to be stored and the data to be stored without data association into the classification model to obtain the classification result data, which improves the accuracy of system storage and facilitates the encryption of subsequent data. As the amount of data to be stored increases, the classification model can be dynamically adjusted to improve the accuracy of classification. The sensitive encryption module calculates the data sensitivity by analyzing the data characteristics of the classification result data, and then decides whether to encrypt the data and selects the corresponding encryption strategy. The hierarchical encryption improves the security of the encrypted data and effectively protects the classification result data during storage. The storage management module is responsible for storing and managing the classification result data that has not been encrypted and the encrypted data. The storage management module creates a storage database and sets its authentication method, and gives corresponding access rights to different authentication methods. The storage database includes two types of databases: sub-databases and sensitive databases. The sub-database involves all the classification result data that has not been encrypted, and the sensitive database involves all the encrypted data. Separate storage is conducive to improving the utilization of storage space in the system, thereby improving the operating efficiency of the storage database. In addition, encrypted data has higher confidentiality requirements, and its separate storage guarantees the security requirements of encrypted data. Setting different access rights ensures the security of storage and access compliance, while improving the flexibility and controllability of the system. Dynamically adjusting the data in the sub-database according to the load level of the sub-database improves the flexibility and adaptability of storage.

[0021] It can be understood that the use of the Eclat algorithm for data association improves the rationality of storage, prevents data redundancy, selects appropriate encryption strategies based on data sensitivity, ensures independent storage of encrypted data, and combines classified storage, encrypted storage, and dynamic adjustment of data in sub-databases to improve the efficiency and security of data storage.

[0022] In some embodiments of the present application, before receiving all the data to be stored in a preset time interval, the process includes: The data acquisition module captures the initial storage data and performs data processing on the initial storage data, the data processing includes data dimension reduction and wavelet transformation, and performs data adjustment on the initial storage data after data processing, the data adjustment includes image data adjustment and text data adjustment; Image data adjustment includes color data balancing and geometric data transformation, text data adjustment includes text data deduplication and text data compression, data conversion is performed on the initial storage data after data adjustment, data conversion includes data standardization and data normalization, the integrity and consistency of the initial storage data after data conversion are verified and whether it complies with the preset storage format. When it is judged that it complies with the preset storage format, the initial storage data after data conversion is used as the data to be stored.

[0023] Specifically, the data acquisition module is responsible for capturing the initial storage data. The sources of the initial storage data include image data and text data. Data dimension reduction uses principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce data redundancy and improve the efficiency of subsequent storage. Wavelet transform is used to compress the image data of the initial storage data, further improving the storage efficiency. Color data equalization uses gamma correction to improve the image data quality of the initial storage data. Geometric data transformation ensures the consistency of the image data of the initial storage data. Text data deduplication removes redundant text data of the initial storage data based on the hash algorithm, improving storage efficiency. Text data compression uses Huffman coding for lossless compression, reducing excessive storage occupation. Data conversion is performed on the adjusted initial storage data, and the initial storage data is adjusted to a unified numerical range to avoid affecting subsequent calculations due to differences in data magnitude. Data normalization improves storage compatibility. After data conversion, it is necessary to verify the integrity and consistency of the converted initial storage data and determine whether it conforms to the preset storage format to ensure that no damaged or inconsistent data is introduced, including checking field length and range restrictions.

[0024] It is understandable that after data dimension reduction, wavelet transform and text compression and other processes, the redundancy of the data is reduced, thereby improving the storage utilization rate, ensuring that no tampering or damage occurs during the storage process, and improving the availability of the data.

[0025] In some embodiments of the present application, when multiple data to be stored are associated with each other based on the Eclat algorithm to obtain associated data to be stored, the process includes: The data acquisition module determines the transaction item set based on the data to be stored, adopts a vertical data layout, maps each data to be stored to the corresponding transaction item set, calculates the support of the candidate item set through recursive intersection operation, and screens out the frequent item sets that meet the minimum support threshold, and obtains the associated data to be stored based on the frequent item sets.

[0026] Specifically, the Eclat algorithm adopts a vertical data layout, maps each data to be stored to its corresponding transaction item set, calculates the support of the candidate item set through recursive intersection operations, and screens out frequent item sets that meet the minimum support threshold. The minimum support threshold is preferably 2. The frequent item sets are screened to determine the associated data, which can further remove low-redundancy data and improve storage utilization. The transaction item sets are analyzed to automatically discover the implicit associations between the data to be stored, so that the storage system can adapt to the storage needs of the data to be stored and avoid incomplete data caused by storing related data separately.

[0027] In some embodiments of the present application, when the associated data to be stored and the unassociated data to be stored are input into the classification model to obtain the classification result data, the process includes: The data classification module obtains historical data, which includes historical associated data to be stored and historical data to be stored without data association, and divides the historical associated data to be stored and the historical data to be stored into a model training set and a model test set, selects a decision tree model and iteratively trains the decision tree model according to the model training set, and verifies the iteratively trained decision tree model according to the model test set; If the classification accuracy of the decision tree model after the current iterative training of the model test set is less than the classification accuracy of the decision tree model after the previous iterative training, the grid search is used to find the model optimization parameters of the decision tree model and the iterative training is continued until the preset number of iterations is reached; If the classification accuracy of the decision tree model after the current iteration training is greater than or equal to the classification accuracy of the decision tree model after the previous iteration training, the iteration training is stopped, and the decision tree model after the current iteration training is determined as the classification model; The associated data to be stored and the unassociated data to be stored are input into the classification model to obtain classification result data, wherein the classification result data includes personal type data, enterprise type data and comprehensive type data.

[0028] Specifically, the historical associated data to be stored and the historical data to be stored are divided into a model training set and a model test set. The data classification module uses 70%-80% of the data as the model training set and the rest as the model test set to ensure that the model training set and the model test set contain different storage situations to improve the generalization ability of the final classification model. The decision tree model is iteratively trained using the model training set. In each iterative training, the decision tree model learns the patterns and relationships in the model training set to improve its model classification ability. Moreover, after each iterative training, the decision tree model is verified using the data in the model test set. The verification benchmarks include the loss function value and the recall rate, which are used to evaluate the performance of the decision tree model. If the classification accuracy of the decision tree model after the current iterative training inputted into the model test set is less than or equal to the previous one, If the classification accuracy of the decision tree model after the iterative training is low, it means that the performance of the decision tree model is insufficient. In this case, grid search is used to search for the model optimization parameters of the decision tree model in the parameter space and iterative training is continued, which helps the decision tree model to stably approach the optimal classification solution. If the classification accuracy of the decision tree model after the current iterative training input by the model test set is greater than or equal to the classification accuracy of the decision tree model after the previous iterative training, it means that the performance of the decision tree model has improved or remained stable. At this time, the iterative training is stopped and the decision tree model after the current iterative training is determined as the classification model. The obtained classification model can already perform accurate classification. The associated data to be stored and the data to be stored without data association are input into the classification model to obtain the classification result data, which improves the stability and adaptability of the subsequent storage of the classification result data.

[0029] In some embodiments of the present application, when extracting data features of the classification result data and marking them, and calculating the data sensitivity based on the number of marks, it includes: The sensitive encryption module extracts the data features of each data in the classification result data and generates corresponding data tags. The data features include social information, credit information, transaction information and basic enterprise information. The data tags include sensitive tags, non-sensitive tags and neutral tags. The sensitive encryption module generates non-sensitive tags for social information, neutral tags for basic enterprise information, and sensitive tags for credit information and transaction information. It counts the number of sensitive tags and records them as sensitive numbers, counts the number of non-sensitive tags and records them as non-sensitive numbers, counts the number of neutral tags and records them as neutral numbers, and determines the initial sensitivity based on the sensitive numbers, non-sensitive numbers, and neutral numbers. The initial sensitivity is given by the following formula: ; in, represents the initial sensitivity, Represents the sum of sensitive quantity, insensitive quantity and neutral quantity, Indicates sensitive quantity, Indicates an insensitive quantity. Represents a neutral quantity, , and represents the weight coefficient, and ; The length of time to obtain data features , and determine the correction factor of initial sensitivity according to the length of time; Presetting a first preset correction factor, a second preset correction factor, and a third preset correction factor; when >10 days, the first preset correction factor is used as the correction factor for initial sensitivity; When 5 days ≤ When ≤10 days, the second preset correction factor is used as the correction factor for the initial sensitivity; when When the time is less than 5 days, the third preset correction factor is used as the correction factor of the initial sensitivity; The data sensitivity is determined by the product of the correction factor of the initial sensitivity and the initial sensitivity. .

[0030] Specifically, the sensitive encryption module extracts the data features of each data in the classification result data, avoiding the misjudgment of data features due to human experience and improving the accuracy of data sensitivity calculation. Social information includes personal user names and personal social accounts, credit information includes personal credit scores, personal credit history, corporate credit scores and corporate credit, transaction information includes personal consumption records, personal payment information and corporate financial information, and basic corporate information includes corporate headcount information, corporate name and corporate registration address. Moreover, by dividing sensitive labels, non-sensitive labels and neutral labels, the sensitivity of each data in the classification result data can be accurately measured to ensure the accuracy of subsequent encryption and improve the security of the storage system. The first preset correction factor is preferably 1.3, the second preset correction factor is preferably 1.1, and the third preset correction factor is preferably 0.9. The initial sensitivity is corrected in combination with the time length to ensure that it is subsequently encrypted more strictly. For example, if the classification result data is represented as personal type data and the time length of its transaction information is 30 days, its initial sensitivity needs to be improved. The correction factor of the initial sensitivity is dynamically selected according to the time length to ensure the accuracy of data sensitivity and to ensure that the classification result data with a longer time length can obtain a higher data sensitivity, laying a data foundation for subsequent encryption. In addition, data features are automatically extracted and data sensitivity is determined, which effectively reduces human intervention and improves the reliability of secure storage of the storage system.

[0031] In some embodiments of the present application, when determining whether to encrypt the classification result data based on the data sensitivity, the process includes: The sensitive encryption module converts data sensitivity With pre-set data sensitivity threshold Perform a comparison, and determine whether to encrypt the data in the classification result data according to the comparison result; when ≥ When the classification result data is encrypted, it is determined that the data in the classification result data is encrypted; when < When the classification result data is not encrypted, it is determined that the data in the classification result data is not encrypted.

[0032] In some embodiments of the present application, when it is determined to encrypt the classification result data, a corresponding encryption strategy is determined according to the data sensitivity, including: The sensitive encryption module pre-sets a first encryption algorithm, a second encryption algorithm and a third encryption algorithm, and when it is determined that the data in the classification result data is to be encrypted; when >0.8, the first encryption algorithm is used as the encryption strategy for the data in the classification result data; When 0.6< When ≤0.8, the second encryption algorithm is used as the encryption strategy for the data in the classification result data; when When ≤0.6, the third encryption algorithm is used as the encryption strategy for the data in the classification result data.

[0033] Specifically, the data in the classification result data needs to be encrypted based on its data sensitivity. The pre-set data sensitivity threshold is used as the judgment basis to avoid errors in human judgment. The data sensitivity threshold is preferably 0.5. The first encryption algorithm set by the sensitive encryption module adopts a hybrid encryption algorithm (AES and RSA), which provides the highest level of encryption for the data in the classification result data. The second encryption algorithm adopts a symmetric encryption algorithm (AES-256), which takes into account data security and computing efficiency. The third encryption algorithm adopts a lightweight encryption algorithm (DES) to protect data security while reducing the calculation of the encrypted results. The encryption strategy is dynamically adjusted according to the data sensitivity, which improves the encryption compatibility of the storage system for different classification result data and ensures the security of the classification result data storage.

[0034] In some embodiments of the present application, when determining corresponding access rights based on identity authentication, it includes: The storage management module verifies the login access method. When the login access method is an access password and an access fingerprint, the access permission is determined to be access to the sub-database and access to the sensitive database. When the login access method is one of the access password and the access fingerprint, the access permission is determined to be access to the sub-database.

[0035] Specifically, the storage management module is responsible for verifying the login method. When access to a sensitive database is required, an access password and access fingerprint are required to access it, ensuring that the storage system protects the classification result data, effectively reducing the risk of data leakage and improving the security of the system.

[0036] In some embodiments of the present application, when determining whether to adjust the data in the sub-database according to the load level of the sub-database, the process includes: The storage management module divides the sub-database into a first storage repository and a second storage repository, stores the classification result data that is not encrypted in the first storage repository, and when the load level of the first storage repository is equal to a preset load level threshold, obtains the historical storage data in the first storage repository, and when there is data in the first storage repository that is the same as the historical storage data, removes the data and continues to store; When the first repository continues to store data after eliminating the data, and the load level of the first repository after eliminating the data is again equal to the preset load level threshold, or when there is no data in the first repository that is identical to the historical storage data, the classified result data that is not stored in the first repository and is not encrypted is stored in the second repository.

[0037] Specifically, the storage management module uses 80% of the standard load of the first storage library as the preset load level threshold to ensure storage balance. When the first storage library reaches the preset load level threshold, it performs deduplication based on historical storage data to avoid duplicate storage, thereby improving the storage flexibility and adaptability of the storage system. When duplicate data cannot be found, the storage management module will store the classified result data that is not stored in the first storage library and is not encrypted in the second storage library, ensuring the accuracy of the sub-database storage, thereby reducing storage occupancy and improving storage utilization. By dividing the first storage library and the second storage library, the system can dynamically adjust the storage strategy, prevent the occurrence of storage bottlenecks, ensure the continuity of the storage process, and improve the stability and reliability of the system.

[0038] In summary, the beneficial effects of the present invention are: by adopting the Eclat association algorithm to associate the data to be stored, the potential relationship between the data to be stored can be discovered, thereby improving the accuracy of subsequent data classification, using the classification model to intelligently classify the data to be stored and the data to be stored without data association, improving the accuracy of classification, avoiding the error of human judgment, providing a basis for subsequent encrypted storage, avoiding the storage security risk caused by the mixed storage of traditional data storage, and extracting data features from the classification result data and marking them, the data sensitivity can be accurately obtained, ensuring that the encryption strategy of the data can match the security requirements of its data sensitivity, avoiding the waste of computing resources caused by indiscriminate encryption of data storage, and selecting the corresponding encryption strategy according to the data sensitivity, improving the security and storage efficiency of data storage. The sub-database is stored separately from the sensitive database, ensuring the stability and reliability of encrypted data storage, and improving the flexibility of data storage. Determining whether to adjust the data in the sub-database by the load level improves the efficiency of sub-database storage and improves the utilization of storage.

[0039] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a data operation and maintenance storage method based on data encryption, which is used to apply the above-mentioned data operation and maintenance storage system based on data encryption, including: S100: Establishing a network communication connection with an Internet server, receiving all data to be stored at a preset time interval, and performing data association on multiple data to be stored based on an Eclat algorithm to obtain associated data to be stored; S200: Inputting the associated data to be stored and the unassociated data to be stored into a classification model to obtain classification result data; S300: extracting data features of the classification result data and marking them, calculating data sensitivity based on the number of marks, judging whether to encrypt the classification result data based on the data sensitivity, and when judging to encrypt the classification result data, determining a corresponding encryption strategy based on the data sensitivity to obtain encrypted data; S400: Create a storage database and set identity authentication, determine corresponding access rights based on the identity authentication, wherein the storage database includes a sub-database and a sensitive database, store the unencrypted classification result data in the sub-database, and store the encrypted data in the sensitive database, and determine whether to adjust the data in the sub-database based on the load level of the sub-database.

[0040] Specifically, first, a network communication connection is established with the Internet server to ensure that all data to be stored can be received at a preset time interval. The preset time interval is preferably 30 minutes, which can be adjusted according to actual needs. The frequent item set mining algorithm of Eclat depth-first search is adopted to reduce the calculation of data redundancy. The decision tree model in the machine learning model is used as the prediction model and iterative training is performed to obtain an accurate classification model. The associated data to be stored and the unassociated data to be stored are input into the classification model to obtain the classification result data, which lays a reliable data foundation for the subsequent calculation of data sensitivity. The data features of the classification result data are extracted and marked to calculate the data sensitivity. Based on the data sensitivity, it is determined whether to encrypt the classification result data, thereby ensuring the storage security of certain sensitive data. In addition, according to the encryption status of the classification result data, the unencrypted classification result data and the encrypted data are stored in different databases respectively, thereby improving the reliability and flexibility of storage. Dual verification of access passwords and access fingerprints is used to determine different access rights, thereby improving the security of storage access and preventing the leakage of certain sensitive data. By monitoring the load level of the sub-database, the data in the sub-database is adjusted, thereby improving the storage efficiency and being able to adapt to the storage needs in different scenarios.

[0041] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0042] The present application 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 the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of 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.

[0043] 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 including 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.

[0044] 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.

[0045] Finally, 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A data operation and maintenance storage system based on data encryption, characterized in that: include: The data acquisition module is configured to establish a network communication connection with an Internet server, receive all the data to be stored at a preset time interval, and associate the multiple data to be stored based on the Eclat algorithm to obtain the associated data to be stored; A data classification module is configured to input the associated data to be stored and the unassociated data to be stored into a classification model to obtain classification result data; a sensitive encryption module, configured to extract data features of the classification result data and mark them, calculate data sensitivity based on the number of marks, determine whether to encrypt the classification result data based on the data sensitivity, and when it is determined that the classification result data is to be encrypted, determine a corresponding encryption strategy according to the data sensitivity to obtain encrypted data; The storage management module is configured to create a storage database and set identity authentication, and determine corresponding access rights based on the identity authentication, wherein the storage database includes a sub-database and a sensitive database, and the classification result data that has not been encrypted is stored in the sub-database, and the encrypted data is stored in the sensitive database, and whether to adjust the data in the sub-database is determined according to the load level of the sub-database.

2. The data operation and maintenance storage system based on data encryption according to claim 1 is characterized in that: Before receiving all data to be stored at a preset time interval, including: The data acquisition module captures the initial storage data and performs data processing on the initial storage data, wherein the data processing includes data dimension reduction and wavelet transformation, and performs data adjustment on the initial storage data after the data processing, wherein the data adjustment includes image data adjustment and text data adjustment; The image data adjustment includes color data balancing and geometric data transformation, the text data adjustment includes text data deduplication and text data compression, and data conversion is performed on the initial storage data after data adjustment. The data conversion includes data standardization and data normalization. The integrity and consistency of the initial storage data after data conversion are verified and whether it complies with the preset storage format. When it is judged that it complies with the preset storage format, the initial storage data after data conversion is used as the data to be stored.

3. The data operation and maintenance storage system based on data encryption according to claim 2 is characterized in that: When multiple data to be stored are associated based on the Eclat algorithm to obtain associated data to be stored, it includes: The data acquisition module determines a transaction item set according to the data to be stored, adopts a vertical data layout, maps each data to be stored to the corresponding transaction item set, calculates the support of the candidate item set through a recursive intersection operation, and screens out frequent item sets that meet a minimum support threshold, and obtains the associated data to be stored based on the frequent item sets.

4. The data operation and maintenance storage system based on data encryption according to claim 3 is characterized in that: When the associated data to be stored and the unassociated data to be stored are input into the classification model to obtain the classification result data, it includes: The data classification module obtains historical data, the historical data including historical associated data to be stored and historical data to be stored without data association, and divides the historical associated data to be stored and the historical data to be stored into a model training set and a model test set, selects a decision tree model and iteratively trains the decision tree model according to the model training set, and verifies the iteratively trained decision tree model according to the model test set; If the classification accuracy of the decision tree model after the current iterative training inputted into the model test set is lower than the classification accuracy of the decision tree model after the previous iterative training, a grid search is used to find the model optimization parameters of the decision tree model and iterative training is continued until the preset number of iterations is reached; If the classification accuracy of the decision tree model after the current iterative training inputted into the model test set is greater than or equal to the classification accuracy of the decision tree model after the previous iterative training, the iterative training is stopped, and the decision tree model after the current iterative training is determined as the classification model; The associated data to be stored and the unassociated data to be stored are input into a classification model to obtain classification result data, wherein the classification result data includes personal type data, enterprise type data and comprehensive type data.

5. The data operation and maintenance storage system based on data encryption according to claim 4 is characterized in that: When extracting data features of the classification result data and marking them, and calculating data sensitivity based on the number of marks, it includes: The sensitive encryption module extracts data features of each data in the classification result data and generates corresponding data tags, wherein the data features include social information, credit information, transaction information and basic enterprise information, and the data tags include sensitive tags, non-sensitive tags and neutral tags; The sensitive encryption module generates the non-sensitive mark for the social information, generates the neutral mark for the basic enterprise information, generates the sensitive mark for the credit information and the transaction information, counts the number of the sensitive marks and records it as the sensitive number, counts the number of the non-sensitive marks and records it as the non-sensitive number, counts the number of the neutral marks and records it as the neutral number, and determines the initial sensitivity according to the sensitive number, the non-sensitive number and the neutral number; The initial sensitivity is given by the following formula: ； in, represents the initial sensitivity, Represents the sum of sensitive quantity, insensitive quantity and neutral quantity, Indicates sensitive quantity, Indicates an insensitive quantity. Represents a neutral quantity, , and represents the weight coefficient, and ; The length of time to obtain the data characteristics , and determining a correction factor of the initial sensitivity according to the time length; Presetting a first preset correction factor, a second preset correction factor, and a third preset correction factor; when >10 days, the first preset correction factor is used as the correction factor of the initial sensitivity; When 5 days ≤ ≤10 days, the second preset correction factor is used as the correction factor of the initial sensitivity; when <5 days, the third preset correction factor is used as the correction factor of the initial sensitivity; Determine the data sensitivity according to the product value of the correction factor of the initial sensitivity and the initial sensitivity .

6. The data operation and maintenance storage system based on data encryption according to claim 5 is characterized in that: When determining whether to encrypt the classification result data based on the data sensitivity, it includes: The sensitive encryption module converts the data sensitivity With pre-set data sensitivity threshold Performing a comparison, and determining whether to encrypt the data in the classification result data according to the comparison result; when ≥ When the classification result data is encrypted, it is determined that the data in the classification result data is encrypted; when < When the classification result data is not encrypted, it is determined that the data in the classification result data is not encrypted.

7. The data operation and maintenance storage system based on data encryption according to claim 6 is characterized in that: When determining to encrypt the classification result data, a corresponding encryption strategy is determined according to the sensitivity of the data, including: The sensitive encryption module pre-sets a first encryption algorithm, a second encryption algorithm and a third encryption algorithm, and when it is determined to encrypt the data in the classification result data; when >0.8, the first encryption algorithm is used as the encryption strategy for the data in the classification result data; When 0.6< ≤0.8, the second encryption algorithm is used as the encryption strategy for the data in the classification result data; when ≤0.6, the third encryption algorithm is used as the encryption strategy for the data in the classification result data.

8. The data operation and maintenance storage system based on data encryption according to claim 7 is characterized in that: When determining corresponding access rights based on the identity verification, it includes: The storage management module verifies the login access method. When the login access method is an access password and an access fingerprint, the access permission is determined to be access to the sub-database and access to the sensitive database. When the login access method is one of the access password or the access fingerprint, the access permission is determined to be access to the sub-database.

9. The data operation and maintenance storage system based on data encryption according to claim 8, characterized in that: When determining whether to adjust the data in the sub-database according to the load level of the sub-database, the method includes: The storage management module divides the sub-database into a first storage repository and a second storage repository, stores the classification result data that is not encrypted in the first storage repository, and when the load level of the first storage repository is equal to a preset load level threshold, obtains the historical storage data in the first storage repository, and when there is data in the first storage repository that is the same as the historical storage data, removes the data and continues to store; When the first repository continues to store data after eliminating data, and the load level of the first repository after eliminating data is again equal to the preset load level threshold, or when there is no data in the first repository that is identical to the historical storage data, the classified result data that is not stored in the first repository and is not encrypted is stored in the second repository.

10. A data operation and maintenance storage method based on data encryption, used for applying the data operation and maintenance storage system based on data encryption as claimed in any one of claims 1 to 9, characterized in that: include: Establishing a network communication connection with an Internet server, and receiving all the data to be stored at a preset time interval, and performing data association on multiple data to be stored based on the Eclat algorithm to obtain associated data to be stored; Inputting the associated data to be stored and the unassociated data to be stored into a classification model to obtain classification result data; Extracting data features of the classification result data and marking them, calculating data sensitivity based on the number of marks, judging whether to encrypt the classification result data based on the data sensitivity, and when judging to encrypt the classification result data, determining a corresponding encryption strategy according to the data sensitivity to obtain encrypted data; Create a storage database and set identity authentication, and determine corresponding access rights based on the identity authentication, wherein the storage database includes a sub-database and a sensitive database, store the classification result data that has not been encrypted in the sub-database, and store the encrypted data in the sensitive database, and determine whether to adjust the data in the sub-database based on the load level of the sub-database.

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