Data processing intelligent storage system of trusted data space

Abstract

The invention discloses a data processing intelligent storage system of a credible data space, which belongs to the technical field of data processing, and comprises the following steps: monitoring the access process and storage process of data entering the credible data space, and when the credible attribute of the data is changed, carrying out association marking on the changed data and corresponding credible attribute change information, generating a change log; analyzing the marked change data, and calculating a credibility index of each change data; evaluating the severity of the corresponding credible attribute change according to the credibility index of the change data, and dividing risk levels; evaluating a data time sequence consistency deviation value in a storage process by judging a data version time difference degree in a process of synchronizing the marked change data to different storage nodes; and based on the time sequence consistency deviation value, carrying out comprehensive analysis on the change data of different risk levels, and starting different scheduling mechanisms according to an analysis result to realize dynamic storage updating of the change data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a trusted data space intelligent storage system for data processing. Background Technology

[0002] Trusted Data Space is a future-oriented data management paradigm that addresses the pain points of traditional data sharing models by integrating security and trusted sharing technologies. With the acceleration of digitalization and the explosive growth of data volume, traditional storage systems face the dual challenges of ensuring data trustworthiness and dynamic management.

[0003] Existing technologies mostly employ static access control or post-audit models, making it difficult to perceive dynamic changes in data trustworthiness attributes in real time, leading to delayed risk identification. Furthermore, in distributed storage scenarios, cross-node data version synchronization is prone to timing discrepancies, affecting data consistency. Especially in highly sensitive areas, sudden changes in data trustworthiness can trigger a chain of risks, and conventional storage architectures lack the ability to intelligently assess and categorize changes to data. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a data processing intelligent storage system for a trusted data space to solve the problems mentioned in the background art.

[0005] The objective of this invention can be achieved through the following technical solution: a trusted data space intelligent storage system for data processing, comprising:

[0006] The Trusted Monitoring Module is used to monitor the data access and storage process entering the Trusted Data Space. When the trust attribute of the data changes, it associates and marks the changed data and the corresponding trust attribute change information, and generates a change log.

[0007] The risk assessment module analyzes the marked changed data and corresponding trust attribute change information based on the change log, calculates the trustworthiness index of each changed data, and then assesses the severity of the corresponding trust attribute change based on the trustworthiness index of the changed data and classifies the risk level.

[0008] The timing management module is used to determine the degree of data version time difference during the process of synchronizing marked changed data to different storage nodes, and to evaluate the data timing consistency deviation value during the storage process.

[0009] The intelligent scheduling module performs comprehensive analysis on changed data of different risk levels based on the time sequence consistency deviation value, and evaluates the storage reliability of the data storage process in the changed data space; according to the analysis results, it activates different scheduling mechanisms to realize dynamic storage updates of changed data.

[0010] Preferably, the trusted attributes of the data include source authentication information, integrity verification value, and permission identifier; the change log includes the marked changed data, the corresponding trusted attribute change information, and the storage node where it is located.

[0011] Preferably, the method for determining whether the trustworthiness attribute of data has changed is as follows:

[0012] The process of data access into the trusted data space is monitored, and the initial information corresponding to the trusted attributes of the data is recorded. The corresponding initial information refers to the initial recorded values ​​of the data's source authentication information, integrity verification value, and permission identifier.

[0013] The data storage process is monitored, the trustworthiness attributes of the stored data are verified, and the verification information corresponding to the trustworthiness attributes of the data is obtained; where the corresponding verification information refers to the verification value of the trustworthiness attributes of the stored data being re-recorded.

[0014] The verification information is compared with the initial information corresponding to the trusted attributes of the data. When the verification information of any one or more of the trusted attributes is found to be inconsistent with the corresponding initial information, it indicates that the trusted attributes of the data have changed. The changed data and the corresponding trusted attribute change information are immediately associated and marked, and a change log is generated.

[0015] Preferably, the method for calculating the credibility index of each changed data is as follows:

[0016] Traverse the change log to obtain the marked change data and the corresponding trust attribute change information, and calculate the trust index of each change data;

[0017] The formula for calculating the credibility index is as follows:

[0018] ;

[0019] In the formula, Ki represents the credibility index of the i-th changed data, i=1,2,3,...,n, where n is the total amount of changed data; LYi represents the verification index of the source authentication information of the i-th changed data; WZi represents the verification index of the integrity verification value of the i-th changed data; and QXi represents the verification index of the permission identifier of the i-th changed data. These are the weighting coefficients corresponding to the verification indices of source authentication information, integrity check value, and permission identifier, respectively. ;

[0020] If the source authentication information of the i-th modified data has not changed, then LYi=1; if the source authentication information of the i-th modified data has changed, then LYi=0.

[0021] If the verification index of the i-th data integrity check value has not changed, then WZi=1; otherwise, WZi=0.

[0022] If the verification index of the i-th data permission identifier has not changed, then QXi=1; otherwise, QXi=0.

[0023] Preferably, the process of assessing the severity of the corresponding credibility attribute change based on the credibility index of the changed data and classifying the risk level includes:

[0024] Obtain the credibility index for each changed data, and assess the severity of the change in the credibility attribute corresponding to each changed data by constructing a risk level model and classify the risk level;

[0025] The expression for the risk level model is as follows:

[0026] ;

[0027] In the formula, FX(Ki) represents the risk level of the i-th changed data; f1 and f2 are the risk level classification thresholds, and 0 < f1 < f2 < 1;

[0028] If FX(Ki)=1, it means that the risk level of the i-th changed data is high risk; if FX(Ki)=2, it means that the risk level of the i-th changed data is medium risk; if FX(Ki)=3, it means that the risk level of the i-th changed data is low risk.

[0029] Preferably, the specific steps of implementing the timing management module include:

[0030] Based on the association and marking of changed data and corresponding trusted attribute change information, the change timestamp of the changed data is obtained;

[0031] The marked changed data is synchronized to different storage nodes, and the key timestamp of the time when each changed data is received and written to the corresponding storage node is obtained;

[0032] For each storage node, calculate the difference between the key timestamp of each changed data received and the corresponding change timestamp to obtain several absolute differences;

[0033] Based on several absolute differences, the average time deviation of a single storage node is determined using the average difference formula, which is used to assess the timing lag of the storage node.

[0034] Preferably, it further includes: based on synchronizing the marked changed data to different storage nodes, obtaining several single-node time deviations corresponding to multiple storage nodes, and evaluating the consistency of synchronization between nodes by calculating the dispersion of the receiving time of all storage nodes;

[0035] Based on several single-node time deviations, the mean of the time deviations between nodes is obtained using the average deviation formula; then, the standard deviation of the time deviations between nodes is determined using the standard deviation formula.

[0036] Based on the mean time deviation of a single node and the standard deviation of time deviation between nodes, the time consistency deviation value is calculated to comprehensively measure the degree of time difference in data version during storage for a single node and between nodes.

[0037] Preferably, the specific steps of implementing the intelligent scheduling module include:

[0038] Obtain the risk level corresponding to different data changes, and the corresponding calculated time series consistency deviation value;

[0039] The corresponding storage evaluation value is calculated according to the formula PG = FX(Ki) × SXC;

[0040] In the formula: PG is the storage assessment value; FX(Ki) is the risk level of the i-th changed data; SXC is the time sequence consistency deviation value;

[0041] Based on the calculated storage evaluation value, set the preset scheduling thresholds A1 and A2, and obtain the scheduling mechanism of the storage policy corresponding to the changed data;

[0042] If PG < A1, it is a level 1 scheduling, which initiates traffic diversion and directs new write requests to healthy nodes; if A1 < PG ≤ A2, it is a level 2 scheduling, which suspends background synchronization tasks for non-critical businesses; if PG > A2, it is a level 3 scheduling, which automatically isolates suspicious nodes and activates the backup cluster.

[0043] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0044] This invention achieves end-to-end trust management by real-time monitoring of the data access and storage process, associating and marking changes in trust attributes and generating logs to ensure complete data traceability and effectively avoid the risk of data tampering. It also achieves automated risk level classification by calculating a trustworthiness index, providing a basis for differentiated handling decisions. Furthermore, it effectively identifies data synchronization deviations by analyzing the time differences in multi-node storage, ensuring the spatiotemporal consistency of multi-replica data.

[0045] This invention automatically triggers differentiated storage updates based on a comprehensive analysis of risk level and timing deviation, ensuring high-priority processing of high-risk data while optimizing storage resource allocation, thus achieving efficient and reliable dynamic storage updates. This system significantly improves the security, traceability, and storage performance of the data space. Attached Figure Description

[0046] The invention will now be further described with reference to the accompanying drawings.

[0047] Figure 1 This is a module structure diagram of a trusted data space intelligent storage system proposed in this invention. Detailed Implementation

[0048] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] like Figure 1 As shown, the present invention is a data processing intelligent storage system for a trusted data space, including a trusted monitoring module, a risk assessment module, a time-series management module, and an intelligent scheduling module;

[0050] The Trusted Monitoring Module is used to monitor the data access and storage process entering the Trusted Data Space. When the trust attribute of the data changes, it associates and marks the changed data and the corresponding trust attribute change information, and generates a change log.

[0051] The trusted attributes of the data include source authentication information, integrity verification value, and permission identifier; the change log includes the marked changed data, the corresponding trusted attribute change information, and the storage node where it is located.

[0052] It should be further explained that the method for determining whether the trustworthiness attribute of data has changed is as follows:

[0053] The process of data access into the trusted data space is monitored, and the initial information corresponding to the trusted attributes of the data is recorded. The corresponding initial information refers to the initial recorded values ​​of the data's source authentication information, integrity verification value, and permission identifier.

[0054] The data storage process is monitored, the trustworthiness attributes of the stored data are verified, and the verification information corresponding to the trustworthiness attributes of the data is obtained; where the corresponding verification information refers to the verification value of the trustworthiness attributes of the stored data being re-recorded.

[0055] The verification information is compared with the initial information corresponding to the trusted attributes of the data. When the verification information of any one or more of the trusted attributes is found to be inconsistent with the corresponding initial information, it indicates that the trusted attributes of the data have changed. The changed data and the corresponding trusted attribute change information are immediately associated and marked, and a change log is generated. The marking usually includes information such as a unique identifier for recording the changed data, the trusted attribute values ​​before and after the change, and the change timestamp.

[0056] For example, the source authentication information of the data is initially recorded, such as obtaining the initial value of the digital signature of the data; then the source authentication information of the stored data is verified, the digital signature verification value of the data is obtained, and the digital signature verification value of the data is compared with the initial value of the digital signature. If they are inconsistent, it means that the source authentication information of the data has been changed.

[0057] Similarly, the integrity check value and permission identifier of the data are monitored. The data integrity check value is the hash value obtained by hashing the data using the SHA-256 hash function; the data permission identifier includes read, write, and modify permission information.

[0058] It should be noted that using asymmetric encryption technology to digitally sign data is an existing encryption technology, and the specific encryption process will not be described in detail here.

[0059] In this embodiment of the invention, the above steps can effectively monitor and determine changes in the trustworthiness attributes of data, and track these changes through associated tags, thereby ensuring the integrity and security of the data.

[0060] The risk assessment module analyzes the marked changed data and corresponding trust attribute change information based on the change log, calculates the trustworthiness index of each changed data, and then assesses the severity of the corresponding trust attribute change based on the trustworthiness index of the changed data and classifies the risk level.

[0061] It should be further explained that the method for calculating the credibility index of each changed data is as follows:

[0062] Traverse the change log to obtain the marked change data and the corresponding trust attribute change information, and calculate the trust index of each change data;

[0063] The formula for calculating the credibility index is as follows:

[0064] ;

[0065] In the formula, Ki represents the credibility index of the i-th changed data, i=1,2,3,...,n, where n is the total amount of changed data; LYi represents the verification index of the source authentication information of the i-th changed data; WZi represents the verification index of the integrity verification value of the i-th changed data; and QXi represents the verification index of the permission identifier of the i-th changed data. These are the weighting coefficients corresponding to the verification indices of source authentication information, integrity check value, and permission identifier, respectively. The specific values ​​are set by those skilled in the art based on the precise requirements of the data in its specific application;

[0066] Furthermore, if the source authentication information of the i-th modified data has not changed, then LYi=1; if the source authentication information of the i-th modified data has changed, then LYi=0.

[0067] If the verification index of the i-th data integrity check value has not changed, then WZi=1; otherwise, WZi=0.

[0068] If the verification index of the i-th data permission identifier has not changed, then QXi=1; otherwise, QXi=0.

[0069] It should be further explained that the process of assessing the severity of changes in corresponding credibility attributes based on the credibility index of the changed data and classifying the risk level includes:

[0070] Obtain the credibility index for each changed data, and assess the severity of the change in the credibility attribute corresponding to each changed data by constructing a risk level model and classify the risk level;

[0071] The expression for the risk level model is as follows:

[0072] ;

[0073] In the formula, FX(Ki) represents the risk level of the i-th changed data; f1 and f2 are the risk level classification thresholds, and 0 < f1 < f2 < 1; furthermore, the specific values ​​of f1 and f2 are determined by experts in this field through multiple big data experiments;

[0074] Furthermore, if FX(Ki)=1, it means that the risk level of the i-th changed data is high risk; if FX(Ki)=2, it means that the risk level of the i-th changed data is medium risk; if FX(Ki)=3, it means that the risk level of the i-th changed data is low risk.

[0075] The timing management module is used to determine the degree of data version time difference during the process of synchronizing marked changed data to different storage nodes, and to evaluate the data timing consistency deviation value during the storage process.

[0076] Based on the association and marking of changed data and corresponding trusted attribute change information, the change timestamp of the changed data is obtained;

[0077] The marked changed data is synchronized to different storage nodes, and the key timestamp of the time when each changed data is received and written to the corresponding storage node is obtained;

[0078] For each storage node, calculate the difference between the key timestamp of each changed data received and the corresponding change timestamp to obtain several absolute differences;

[0079] Based on several absolute differences, the average time deviation of a single storage node is determined by the average difference formula, which is used to evaluate the timing lag of the storage node.

[0080] Based on synchronizing the marked changed data to different storage nodes, the time deviation of several single nodes corresponding to multiple storage nodes is obtained. By calculating the dispersion of the receiving time of all storage nodes, the consistency of synchronization between nodes is evaluated.

[0081] Based on several single-node time deviations, the mean of the time deviations between nodes is obtained using the average deviation formula; then, the standard deviation of the time deviations between nodes is determined using the standard deviation formula.

[0082] Based on the mean time deviation of a single node and the standard deviation of time deviation between nodes, the time consistency deviation value is calculated to comprehensively measure the degree of time difference in data version during storage for a single node and between nodes.

[0083] The intelligent scheduling module performs comprehensive analysis on changed data of different risk levels based on the time sequence consistency deviation value, and evaluates the storage reliability of the data storage process in the changed data space; according to the analysis results, it activates different scheduling mechanisms to realize dynamic storage updates of changed data.

[0084] Specifically, obtain the risk level corresponding to different data changes, and the corresponding calculated time-series consistency deviation value;

[0085] The corresponding storage evaluation value is calculated according to the formula PG = FX(Ki) × SXC;

[0086] In the formula: PG is the storage assessment value; FX(Ki) is the risk level of the i-th changed data; SXC is the time sequence consistency deviation value;

[0087] Based on the calculated storage evaluation value, set the preset scheduling thresholds A1 and A2, and obtain the scheduling mechanism for dynamic storage updates corresponding to the changed data;

[0088] If PG < A1, it is a level 1 scheduling, which initiates traffic diversion and directs new write requests to healthy nodes; if A1 < PG ≤ A2, it is a level 2 scheduling, which suspends background synchronization tasks for non-critical businesses; if PG > A2, it is a level 3 scheduling, which automatically isolates suspicious nodes and activates the backup cluster.

[0089] It should be further noted that the preset scheduling threshold A1 < A2, and the specific value is determined by experts in this field by combining business scenarios, data characteristics and historical statistical patterns.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing intelligent storage system of a trusted data space, characterized in that, The application comprises: a trusted monitoring module for monitoring data access and storage processes entering a trusted data space, marking changed data and corresponding trusted attribute change information when a change occurs in the trusted attribute of the data, and generating a change log; a risk assessment module for analyzing the marked changed data and corresponding trusted attribute change information based on the change log, and calculating a trust index of each changed data; then evaluating the severity of the corresponding trusted attribute change according to the trust index of the changed data, and dividing the risk level; a time sequence management module for judging the time difference of the data version during the synchronization of the marked changed data to different storage nodes, and evaluating the time sequence consistency deviation of the data storage process; an intelligent scheduling module for comprehensively analyzing the changed data of different risk levels based on the time sequence consistency deviation, evaluating the storage reliability of the data storage process in the changed data space, and starting different scheduling mechanisms according to the analysis results to realize dynamic storage update of the changed data.

2. The data processing intelligent storage system of a trusted data space according to claim 1, characterized in that, The trusted attribute of the data includes source authentication information, integrity check value, and permission identifier; the change log includes the marked changed data, corresponding trusted attribute change information, and the storage node.

3. The data processing intelligent storage system of a trusted data space according to claim 2, characterized in that, The method for judging the change of the trusted attribute of the data is as follows: monitoring the data access process entering the trusted data space, and recording the initial information corresponding to the trusted attribute of the data; wherein the initial information corresponding to the trusted attribute of the data refers to the initial recorded value of the source authentication information, integrity check value, and permission identifier of the data; monitoring the storage process of the data, verifying the trusted attribute of the stored data, and obtaining the verification information corresponding to the trusted attribute of the data; wherein the verification information corresponding to the trusted attribute of the data refers to the verification value of the re-recorded trusted attribute of the stored data; comparing the verification information with the initial information corresponding to the trusted attribute of the data, and when the verification information of any one or more of the trusted attributes is inconsistent with the corresponding initial information, it indicates that the trusted attribute of the data has changed, and the changed data and the corresponding trusted attribute change information are immediately marked and a change log is generated.

4. The data processing intelligent storage system of a trusted data space according to claim 1, characterized in that, The method for calculating the trust index of each changed data is as follows: traversing the change log to obtain the marked changed data and corresponding trusted attribute change information, and calculating the trust index of each changed data; wherein the calculation formula of the trust index is as follows: ； In the formula, Ki represents the credibility index of the i th change data, i = 1, 2, 3,..., n, and n is the total amount of change data; LYi represents the verification index of the i th change data source authentication information, WZi represents the verification index of the i th change data integrity check value; QXi represents the verification index of the i th change data authority identifier; The verification index of the source authentication information, the integrity check value, and the authority identifier respectively corresponds to a weight coefficient, and ; if the source authentication information of the i-th changed data has not changed, LYi=1, and if the source authentication information of the i-th changed data has changed, LYi=0; if the verification index of the integrity check value of the i-th changed data has not changed, WZi=1, and vice versa, WZi=0; if the verification index of the permission identifier of the i-th changed data has not changed, QXi=1, and vice versa, QXi=0.

5. The data processing intelligent storage system of a trusted data space according to claim 4, characterized in that, The process of evaluating the severity of the corresponding trusted attribute change according to the trust index of the changed data, and dividing the risk level comprises: obtaining the trust index of each changed data, evaluating the severity of the corresponding trusted attribute change of each changed data by constructing a risk level model, and dividing the risk level. Wherein, the expression of the risk level model is: ； In the formula, FX(Ki) is the risk level of the i th change data; f1 and f2 are risk level division thresholds respectively, and 0 If FX(Ki)=1, it indicates that the risk level of the i th change data is high risk; if FX(Ki)=2, it indicates that the risk level of the i th change data is medium risk; if FX(Ki)=3, it indicates that the risk level of the i th change data is low risk.

6. The data processing intelligent storage system of a trusted data space according to claim 1, wherein, The specific content of the time sequence management module includes: Based on the association and marking of the change data and the corresponding trusted attribute change information, the change time stamp of the change data is obtained; The marked change data is synchronized to different storage nodes, and the key time stamp when each change data is synchronized to the corresponding storage node and received and written is obtained; For each storage node, the difference between the key time stamp of receiving each change data and the corresponding change time stamp is calculated to obtain a plurality of absolute differences; Based on the plurality of absolute differences, the single node time deviation mean of the storage node is determined by the average difference formula, which is used to evaluate the time sequence lag degree of the storage node.

7. The data processing intelligent storage system of a trusted data space according to claim 6, characterized in that, It also includes: Based on the synchronization of the marked change data to different storage nodes, a plurality of single node time deviations corresponding to a plurality of storage nodes are obtained, and the consistency of inter-node synchronization is evaluated by calculating the dispersion degree of the receiving time of all storage nodes; Based on the plurality of single node time deviations, the inter-node time deviation mean is obtained by the average difference formula; then the standard deviation of the inter-node time deviation is determined by the standard deviation formula; Based on the single node time deviation mean and the standard deviation of the inter-node time deviation, the time sequence consistency deviation value is calculated, which is used to comprehensively evaluate the time difference degree of data version in the storage process of single node and inter-node.

8. The data processing intelligent storage system of a trusted data space according to claim 7, characterized in that, The specific content of the intelligent scheduling module includes: The risk level corresponding to different change data and the corresponding calculated time sequence consistency deviation value are obtained; According to the formula PG=FX(Ki)×SXC, the corresponding storage evaluation value is calculated; In the formula: PG is the storage evaluation value; FX(Ki) is the risk level of the i th change data; SXC is the time sequence consistency deviation value; According to the calculated storage evaluation value, the preset scheduling threshold A1 and A2 are set to obtain the scheduling mechanism of the change data corresponding to the storage strategy; If PG

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