Blockchain-based data flow automation compliance checking method and system

Abstract

This invention discloses an automated compliance inspection method and system for data circulation based on blockchain, relating to the field of blockchain compliance inspection technology. The method includes: obtaining a scenario identifier for the current scenario and the proportion of anonymized fields in the data to be inspected, calculating the anonymization degree; obtaining an anonymization degree threshold based on the scenario identifier, and obtaining a scenario matching coefficient based on the scenario identifier and the basic attributes of the data to be inspected; calculating the ratio of the anonymization degree to the anonymization threshold to obtain the anonymization coefficient of the data to be inspected, and calculating a verification coefficient based on the scenario matching coefficient; obtaining the circulation scenario of the data to be inspected and the corresponding verification coefficient, and obtaining the circulation importance of multiple circulation scenarios on the chain, comprehensively evaluating to obtain a comprehensive circulation coefficient, and performing compliance inspection based on the verification coefficient. This invention solves the technical problem in existing technologies where static and fixed rules for blockchain data circulation compliance inspection lead to inaccurate compliance inspection results.

Description

Technical Field

[0001] This invention relates to the field of blockchain compliance inspection technology, specifically to a method and system for automated compliance inspection of data circulation based on blockchain. Background Technology

[0002] In the current environment, compliance requirements at all stages of blockchain data circulation are becoming increasingly stringent. Existing technologies primarily rely on pre-screening based on fixed rules, meaning that manual checks or simple rule engines make judgments according to fixed anonymization standards and data classification directories. While this approach is direct, the rules are rigid and difficult to adapt to the varying compliance granularities required in different business scenarios, resulting in low verification efficiency. Summary of the Invention

[0003] This application provides a blockchain-based automated compliance inspection method and system for data circulation, which addresses the technical problem of inaccurate compliance inspection results caused by the static and fixed rules for compliance inspection of blockchain data circulation in existing technologies.

[0004] In view of the above issues, this application provides a method and system for automated compliance inspection of data circulation based on blockchain.

[0005] Firstly, this application provides a blockchain-based method for automated compliance checks on data circulation, the method comprising:

[0006] Obtain the scene identifier of the current scene, obtain the percentage of de-identified fields in the data to be inspected, and calculate the degree of de-identification;

[0007] Based on the scene identifier, a desensitization threshold is obtained, and based on the scene identifier and the basic attributes of the data to be inspected, a scene matching coefficient is obtained;

[0008] Calculate the ratio of the desensitization degree to the desensitization degree threshold to obtain the desensitization coefficient of the data to be inspected, and combine it with the scene matching coefficient to calculate and obtain the verification coefficient;

[0009] Obtain the flow scenarios and corresponding verification coefficients of the data to be inspected, and obtain the flow importance of multiple flow scenarios on the chain. Comprehensively evaluate and obtain the comprehensive flow coefficient, and combine the verification coefficient to conduct compliance checks.

[0010] Secondly, this application provides a blockchain-based automated compliance inspection system for data circulation, including:

[0011] The desensitization degree acquisition module is used to obtain the scene identifier of the current scene, obtain the proportion of desensitized fields in the data to be inspected, and calculate the desensitization degree;

[0012] The scene matching module is used to obtain a desensitization threshold based on the scene identifier, and to obtain a scene matching coefficient based on the scene identifier and the basic attributes of the data to be inspected;

[0013] The verification coefficient acquisition module is used to calculate the ratio of the desensitization degree to the desensitization degree threshold, obtain the desensitization coefficient of the data to be inspected, and calculate and obtain the verification coefficient in combination with the scene matching coefficient.

[0014] The compliance check module is used to obtain the flow scenarios and corresponding verification coefficients of the data to be checked, obtain the flow importance of multiple flow scenarios on the chain, comprehensively evaluate and obtain the comprehensive flow coefficient, and perform compliance checks in combination with the verification coefficient.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0016] This application proposes a blockchain-based automated compliance inspection method and system for data circulation. By introducing a scenario-identified dynamic compliance benchmark construction mechanism and a trustworthy assessment enhancement mechanism based on on-chain circulation history, it significantly improves the accuracy, adaptability, and decision reliability of data circulation compliance inspection. Compared with traditional methods, this application achieves intelligent and scenario-based data circulation compliance inspection while ensuring strict adherence to rule constraints, thereby effectively promoting the secure and efficient flow of data while precisely controlling risks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the automated compliance check method for data circulation based on blockchain provided in this application embodiment.

[0019] Figure 2 This is a schematic diagram of the structure of a blockchain-based automated compliance inspection system for data circulation provided in an embodiment of this application.

[0020] The components represented by each number in the attached diagram are explained below:

[0021] The module for obtaining desensitization level is 100, the module for scene matching is 200, the module for obtaining verification coefficient is 300, and the module for compliance inspection is 400. Detailed Implementation

[0022] This application provides a blockchain-based automated compliance inspection method and system for data circulation, which addresses the technical problem of inaccurate compliance inspection results caused by static and fixed compliance inspection rules in existing blockchain technologies.

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0024] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0025] Example 1, as Figure 1 As shown, this application provides a blockchain-based automated compliance inspection method for data circulation, wherein the method includes:

[0026] S10: Obtain the scene identifier of the current scene, obtain the percentage of de-identified fields in the data to be inspected, and calculate the de-identification degree.

[0027] Currently, when initiating compliance checks during data circulation, traditional methods often rely on manual spot checks or simple binary judgments, such as simple labels that have been de-identified or not, resulting in a relatively coarse assessment granularity.

[0028] Step S10 in the method provided in this application embodiment includes:

[0029] Obtain the scene identifier of the currently authorized scene, wherein the scene identifier includes at least the processing scene, the usage scene, and the destruction scene;

[0030] Obtain the data to be inspected, and obtain the length of the de-identified fields and the total field length of the data to be inspected;

[0031] Calculate the ratio of the length of the de-identified field to the total field length to obtain the de-identification degree.

[0032] In this embodiment of the application, the scene identifier of the current scene is obtained, and the proportion of de-identified fields in the data to be inspected is obtained, and the de-identification degree is calculated.

[0033] Specifically, firstly, the scenario identifier of the currently authorized scenario is obtained. This scenario identifier includes at least a processing scenario, a usage scenario, and a destruction scenario. For example, when a data query and analysis operation is detected, a query is made to the rule base, which returns the corresponding scenario identifier as "usage scenario." The rule base is a pre-deployed compliance rule library that stores the mapping relationship between different business operations and standardized scenario identifiers.

[0034] Furthermore, the data to be inspected is obtained, along with the length of the de-identified fields and the total field length. For example, if a report has 10 fields, and the inspection reveals that the metadata tags indicate that the fields "Internal Equipment Serial Number," "Production Line Unique Code," and "Responsible Engineer's Employee Number" have been de-identified and replaced using a specific algorithm, then the length of the de-identified fields is recorded as 3, and the total field length of the report is 10.

[0035] Further, the ratio of the length of the de-identified field to the total field length is calculated to obtain the de-identification degree. De-identification degree = De-identified field length / Total field length. For example, if the length of the de-identified field is 3, and the total field length of the report is 10, then the de-identification degree = 3 / 10 = 0.3.

[0036] The method provided in this application enables a refined measurement of the data anonymization status, significantly improving the efficiency and consistency of anonymization assessment.

[0037] S20: Based on the scene identifier, obtain the desensitization threshold, and based on the scene identifier and the basic attributes of the data to be inspected, obtain the scene matching coefficient.

[0038] After obtaining the basic anonymized state of the data, the drawback of traditional static rule bases is that they usually set a uniform threshold for all scenarios, but different scenarios have very different requirements for data sensitivity and availability. A uniform high standard will hinder the mining of data value, while a uniform low standard will amplify the risks.

[0039] Step S20 in the method provided in this application embodiment includes:

[0040] Based on the scene identifier, obtain the basic de-identification requirements;

[0041] Obtain the flow probability of the current scenario, and modify the basic desensitization requirements based on the flow probability to obtain the desensitization threshold;

[0042] Obtain the basic attributes of the data to be inspected, wherein the basic attributes include data content and data source;

[0043] Based on the scene identifier and the basic attributes of the data to be inspected, the scene matching coefficient is obtained.

[0044] In this embodiment of the application, a desensitization threshold is obtained based on the scene identifier, and a scene matching coefficient is obtained based on the scene identifier and the basic attributes of the data to be inspected.

[0045] Specifically, firstly, based on the scenario identifier, basic data anonymization requirements are obtained. For example, the basic data anonymization requirement value for a processing scenario is 0.5, meaning that in principle, at least 50% of the data fields should be anonymized in this scenario.

[0046] Further, the circulation probability of the current scenario is obtained, and the basic desensitization requirements are adjusted based on the circulation probability to obtain a desensitization threshold. For example, by querying historical circulation records stored on the blockchain, the number of times the scenario identifier, such as "processing scenario," appears in all historical data circulation events is counted, and then divided by the total number of circulation events to calculate the frequency of the scenario occurrence, i.e., the circulation probability. For example, if the total number of historical events is 1000, and "processing scenario" appears 300 times, then the circulation probability is 0.3. Further, the basic desensitization requirements are adjusted based on the circulation probability to obtain a dynamic desensitization threshold. When the circulation probability is high, it means that data circulates frequently in this scenario, with potential risks accumulating, therefore the requirements need to be increased; conversely, they are slightly relaxed. A correction factor is set, for example, 0.1. The calculation formula is: Desensitization threshold = Basic desensitization requirement + (Circulation probability × Correction factor). Substituting the above example value, the calculation is: 0.5 + (0.3 × 0.1) = 0.53. The final dynamic desensitization threshold, which takes into account historical circulation frequency, for this processing scenario was 0.53.

[0047] Furthermore, the basic attributes of the data to be inspected are obtained, including data content and data source. For example, the data content attribute describes the business category of the data itself, such as reading the content type from metadata: equipment operating parameter time-series data. The data source attribute describes the generator or provider of the data, such as reading the source from metadata: precision CNC machine tool production line A.

[0048] Further, based on the scene identifier and the basic attributes of the data to be inspected, a scene matching coefficient is obtained. For example, scene matching is performed based on a scene-attribute matching degree mapping table. This table is predefined by domain experts and returns a coefficient value representing the degree of matching, typically ranging from 0 to 1, using a joint query of scene identifier, data content type, and data source type. For instance, when the query scene identifier is "processing scene," the data content is "equipment vibration time series data," and the data source is "precision CNC machine tool production line," the mapping table returns a high matching coefficient value, such as 0.9, indicating that the production data of this type of high-precision equipment is highly relevant and reasonable for model processing analysis. Conversely, if the data content is "office building energy consumption data" used for a "processing scene," the mapping table may return a lower coefficient, such as 0.3. The system can obtain the scene matching coefficient required for this inspection by looking up the table.

[0049] The method provided in this application enables dynamic and precise compliance benchmarks. For high-frequency, high-risk scenarios, stricter thresholds are automatically applied; for low-frequency or internal scenarios, relatively lenient standards can be applied, thereby enabling compliance requirements to intelligently align with the actual risk profile of the business and achieve the best balance between security and utility.

[0050] S30: Calculate the ratio of the desensitization degree to the desensitization degree threshold, obtain the desensitization coefficient of the data to be inspected, and calculate the verification coefficient by combining the scene matching coefficient.

[0051] Existing methods often make isolated judgments, such as first checking whether the desensitization meets the standards, and then manually assessing whether the scenario is appropriate. This fragmented process makes it difficult for automated systems to make accurate and detailed checks.

[0052] Step S30 in the method provided in this application embodiment includes:

[0053] Calculate the ratio of the desensitization degree to the desensitization degree threshold to obtain the desensitization coefficient of the data to be inspected;

[0054] The verification coefficient is obtained by weighting the desensitization coefficient and the scene matching coefficient.

[0055] In this embodiment of the application, the ratio of the desensitization degree to the desensitization degree threshold is calculated to obtain the desensitization coefficient of the data to be inspected, and the verification coefficient is calculated by combining the scene matching coefficient.

[0056] Specifically, the ratio of the desensitization degree to the desensitization threshold is calculated to obtain the desensitization coefficient of the data to be inspected. For example, the desensitization degree of the data to be inspected is 0.3, and the desensitization threshold for the current processing scenario is 0.53. The desensitization coefficient = desensitization degree / desensitization threshold = 0.3 / 0.53 = 0.57, meaning that the actual desensitization degree of the data to be inspected reaches approximately 57% of the threshold required by the current scenario.

[0057] Further, the anonymization coefficient and the scenario matching coefficient are weighted and calculated to obtain the verification coefficient. Verification coefficient = (anonymization coefficient × first weight value) + (scenario matching coefficient × second weight value). Wherein, the first weight value + the second weight value = 1. Specific weight values ​​can be adaptively set based on the scenario. For example, if the current scenario is a processing scenario with high anonymization requirements, the first weight value can be set to 0.6 and the second weight value to 0.4. For instance, if the anonymization coefficient is 0.57 and the scenario matching coefficient is 0.9, then the verification coefficient = (0.57 × 0.6) + (0.9 × 0.4) = 0.702, comprehensively reflecting the overall compliance performance of the data under inspection in the current processing scenario, considering both its anonymization compliance and the data scenario applicability.

[0058] By integrating multi-dimensional compliance indicators, the obtained verification coefficients can be used to quantify the compliance performance of the data to be inspected in the current scenario.

[0059] S40: Obtain the flow scenario and corresponding verification coefficient of the data to be inspected, and obtain the flow importance of multiple flow scenarios on the chain. Comprehensively evaluate and obtain the comprehensive flow coefficient, and combine the verification coefficient to conduct compliance checks.

[0060] Traditional compliance checks and existing blockchain-based evidence storage solutions only make isolated judgments on a single circulation event, ignoring the compliance credit or risk trajectory accumulated by data over time and with changing scenarios throughout the circulation cycle. Data that passes the inspection in the current scenario may have had flaws at some critical stage in its circulation history; conversely, data with a consistently good historical record may warrant a more lenient assessment if its current state experiences slight fluctuations.

[0061] Step S40 in the method provided in this application embodiment includes:

[0062] Obtain all flow scenarios of the data to be inspected, and obtain the scenario verification coefficients of the data to be inspected in multiple flow scenarios;

[0063] Obtain the order of multiple flow scenarios, and evaluate the importance of each flow scenario to obtain the flow importance of the multiple flow scenarios;

[0064] This includes obtaining the sequential order of multiple flow scenarios and assessing their importance based on their respective importance, thereby obtaining the flow importance of multiple flow scenarios, including:

[0065] Obtain the timestamps of the corresponding records of multiple circulation scenarios on the blockchain to determine the order of the circulation scenarios;

[0066] Based on a predefined scene type importance ranking table, the basic importance of multiple circulation scenes is determined;

[0067] Obtain the amount of data transferred in the aforementioned transfer scenario and the success rate of compliance checks, calculate the importance coefficient, correct the basic importance, and obtain the transfer importance.

[0068] Based on the importance of the circulation in multiple circulation scenarios, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient;

[0069] Among them, based on the importance of multiple circulation scenarios, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient, including:

[0070] Based on the importance of the multiple circulation scenarios, the scenario weights are obtained;

[0071] Based on the scenario weights, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient.

[0072] Based on the comprehensive circulation coefficient and the verification coefficient, a compliance check is performed to obtain the compliance check result of the data to be checked.

[0073] The compliance check is performed based on the comprehensive circulation coefficient and the verification coefficient to obtain the compliance check result of the data to be checked, including:

[0074] Based on the importance of the multiple circulation scenarios, high-importance scenarios are identified;

[0075] Determine whether the scenario verification coefficients of the data to be inspected in multiple high-importance scenarios meet the compliance inspection results. If the scenario verification coefficients of two or more high-importance scenarios do not meet the compliance inspection results, then the compliance inspection result of the data to be inspected is non-compliant.

[0076] When there are more than the importance number threshold number of high-importance scenarios whose scenario verification coefficients meet the compliance check results, and at least one of the high-importance scenarios has a flow importance greater than the current scenario, then the compliance check result of the data to be checked is compliant. The importance number threshold number is obtained based on the anonymization degree of the data to be checked.

[0077] When there are fewer than the number of high-importance scenarios whose scenario verification coefficients meet the compliance check results, and there are no more than two high-importance scenarios whose scenario verification coefficients do not meet the compliance check results, the comprehensive flow coefficient is used to correct the verification coefficient, the current verification coefficient is obtained, and a compliance check is performed.

[0078] This includes obtaining the current verification coefficient and performing compliance checks, including:

[0079] Based on the scenario identifier, obtain the compliance threshold;

[0080] If the current verification coefficient is greater than or equal to the compliance threshold, the compliance check result of the data to be checked is compliant; otherwise, it is non-compliant.

[0081] In this embodiment of the application, the flow scenario of the data to be inspected and the corresponding verification coefficient are obtained, and the flow importance of multiple flow scenarios on the chain is obtained. A comprehensive flow coefficient is obtained through comprehensive evaluation, and compliance checks are performed in combination with the verification coefficient.

[0082] Specifically, firstly, all flow scenarios of the data to be inspected are obtained, and the scenario verification coefficients of the data to be inspected in multiple flow scenarios are obtained. For example, based on the unique identifier of the data to be inspected, the historical records on the blockchain are queried, and from these historical records, the scenario identifier recorded on the chain at the time of each flow and the verification coefficient calculated and stored at that time are parsed out. For example, for a piece of equipment data, the query finds that it has three flow records in history, corresponding to the scenarios of internal archiving (verification coefficient 0.95), quality control analysis (verification coefficient 0.88), and processing (verification coefficient 0.60).

[0083] Furthermore, the order of the multiple circulation scenarios is obtained, and the importance of the circulation scenarios is evaluated based on their importance to obtain the circulation importance of the multiple circulation scenarios.

[0084] First, the timestamps of the corresponding records for multiple circulation scenarios on the blockchain are obtained to determine the chronological order of the circulation scenarios. For example, the three scenarios are ordered chronologically as follows: internal archiving scenario (earliest), quality control analysis scenario (middle), and processing scenario (most recent).

[0085] Furthermore, based on a predefined scenario type importance level table, the basic importance of multiple circulation scenarios is determined. For example, a query is performed according to the predefined scenario type importance level table. The scenario type importance level table is defined based on security requirements; for example, it specifies that: "Processing scenario" involves external data exchange, so its basic importance is "high," quantified as 0.9; "Quality control analysis scenario" is a core internal business, so its basic importance is "medium-high," quantified as 0.7; and "Internal archiving scenario" is a backend operation, so its basic importance is "low," quantified as 0.3.

[0086] Further, the volume of data transferred in the aforementioned scenarios and the success rate of compliance checks are obtained. An importance coefficient is calculated, and the basic importance is adjusted to obtain the overall transfer importance. For example, from the blockchain history records, the total number of times a specific scenario, such as a "processing scenario," occurs in all data flow events is counted as the volume of transferred data. The number of times the verification coefficient is greater than or equal to the compliance threshold in this scenario is also counted, and the success rate is calculated. For instance, if there are 1000 data flows in the "processing scenario," and 900 checks pass, then the data flow volume is 1000, and the compliance check success rate = 900 / 1000 = 0.9. Further, if the largest volume of transferred data in all scenarios is 2000, then the data volume impact factor for the "processing scenario" = 1000 / 2000 = 0.5. The importance coefficient is calculated as (data volume impact factor × 0.5) + (compliance check success rate × 0.5) = 0.7. Further, the importance coefficient is used to adjust the basic importance to obtain the overall transfer importance. For example, the importance of circulation = basic importance × (1 + 0.5 × importance coefficient). For instance, if the basic importance is 0.9 and the importance coefficient is 0.7, then the importance of circulation = 0.9 × (1 + 0.5 × 0.7) = 1.22.

[0087] Furthermore, based on the importance of multiple circulation scenarios, the verification coefficients of these scenarios are fused and calculated to obtain a comprehensive circulation coefficient. For example, the importance of multiple circulation scenarios of the data to be inspected is normalized to obtain scenario weights. The verification coefficients of these scenarios are then fused and calculated to obtain the comprehensive circulation coefficient. For instance, if the circulation importance of three scenarios is 0.7, 1.35, and 0.3 respectively, with a total of 2.35, then the weight of the "processing scenario" is 1.35 / 2.35 = 0.57. The comprehensive circulation coefficient = Σ(verification coefficient of each historical scenario × weight corresponding to that scenario), for example, the comprehensive circulation coefficient = (0.6 × 0.3) + (0.88 × 0.57) + (0.95 × 0.13) = 0.81. The comprehensive circulation coefficient represents the overall compliance and credit level assessed based on the historical circulation performance of the data.

[0088] Furthermore, a compliance check is performed based on the comprehensive circulation coefficient and the verification coefficient to obtain the compliance check result of the data to be checked.

[0089] Specifically, based on the importance of the multiple circulation scenarios, high-importance scenarios are identified. For example, an importance threshold, such as 0.8, is obtained based on the distribution of the importance of the multiple circulation scenarios. Scenarios with a circulation importance greater than the importance threshold are then marked as high-importance scenarios.

[0090] Furthermore, it is determined whether the scenario verification coefficients of the data to be inspected in multiple high-importance scenarios meet the compliance check results. If the scenario verification coefficients of two or more high-importance scenarios do not meet the compliance check results, then the compliance check result of the data to be inspected is non-compliant. This condition is not triggered if none of the multiple flow scenarios of the data to be inspected are high-importance scenarios.

[0091] When the scenario verification coefficients of more than the importance threshold number of high-importance scenarios meet the compliance check results, and at least one of the high-importance scenarios has a greater flow importance than the current scenario, then the compliance check result of the data to be checked is compliant. The importance threshold number is obtained based on the anonymization level of the data to be checked. For example, the importance threshold number can be set as (1 - anonymization level) × the number of basic important scenarios. For example, the number of basic important scenarios can be set to 3, meaning that the verification rules are relaxed for scenarios with high anonymization levels. When the scenario verification coefficients of more than the importance threshold number of high-importance scenarios meet the compliance check results, and at least one of the high-importance scenarios has a greater flow importance than the current scenario, it can be determined that the data to be checked has undergone relatively strict compliance verification and can be directly passed to save computing resources and improve efficiency.

[0092] When fewer than the number of high-importance scenarios meet the compliance check results in terms of scenario verification coefficients, and no more than two high-importance scenarios fail to meet the compliance check results in terms of scenario verification coefficients, the comprehensive flow coefficient is used to correct the verification coefficient, and the current verification coefficient is obtained for compliance check. For example, the current verification coefficient = verification coefficient × 0.7 + comprehensive flow coefficient × 0.3, where the weights can be dynamically adjusted based on the actual application scenario. When the verification coefficient is 0.702 and the comprehensive flow coefficient is 0.75, then the current verification coefficient = 0.702 × 0.7 + 0.75 × 0.3 = 0.716.

[0093] Specifically, based on the scene identifier, a compliance threshold is obtained, for example, the compliance threshold corresponding to the current scene identifier "processing scene" is obtained as 0.7.

[0094] If the current verification coefficient is greater than or equal to the compliance threshold, the compliance check result of the data to be checked is compliant; otherwise, it is non-compliant. For example, if the current verification coefficient is 0.716, which is greater than the compliance threshold, then the compliance check result of the data to be checked is compliant.

[0095] By querying the immutable circulation history of the blockchain, the system proactively acquires all past circulation scenarios and corresponding historical verification coefficients of the data to be inspected. This historical information is then combined with the importance of the circulation in representing the business value of each scenario, and a comprehensive circulation coefficient is generated through integrated calculation. This coefficient quantifies the past compliance credit or risk imprint of the data to be inspected. Finally, by collaboratively analyzing the verification coefficient representing the current state and the comprehensive circulation coefficient representing the historical trajectory, the final compliance result is no longer based on an isolated judgment of the current scenario, but rather on a comprehensive judgment of the compliance journey of the data across multiple circulation scenarios. This improves both the credibility of the obtained compliance inspection results and the efficiency of the compliance inspection.

[0096] Example 2, as Figure 2 As shown, based on the same inventive concept as the blockchain-based automated compliance inspection method for data circulation provided in Embodiment 1, this embodiment of the invention also provides a blockchain-based automated compliance inspection system for data circulation, including:

[0097] The desensitization degree acquisition module 100 is used to acquire the scene identifier of the current scene, acquire the proportion of desensitized fields in the data to be inspected, and calculate the desensitization degree.

[0098] The scene matching module 200 is used to obtain a desensitization threshold based on the scene identifier, and to obtain a scene matching coefficient based on the scene identifier and the basic attributes of the data to be inspected;

[0099] The verification coefficient acquisition module 300 is used to calculate the ratio of the desensitization degree to the desensitization degree threshold, obtain the desensitization coefficient of the data to be inspected, and calculate and obtain the verification coefficient in combination with the scene matching coefficient.

[0100] The compliance inspection module 400 is used to obtain the flow scenarios and corresponding verification coefficients of the data to be inspected, obtain the flow importance of multiple flow scenarios on the chain, comprehensively evaluate and obtain the comprehensive flow coefficient, and perform compliance inspection in combination with the verification coefficient.

[0101] In one embodiment, the desensitization degree acquisition module 100 is further configured to:

[0102] Obtain the scene identifier of the currently authorized scene, wherein the scene identifier includes at least the processing scene, the usage scene, and the destruction scene;

[0103] Obtain the data to be inspected, and obtain the length of the de-identified fields and the total field length of the data to be inspected;

[0104] Calculate the ratio of the length of the de-identified field to the total field length to obtain the de-identification degree.

[0105] In one embodiment, the scene matching module 200 is further configured to:

[0106] Based on the scene identifier, obtain the basic de-identification requirements;

[0107] Obtain the flow probability of the current scenario, and modify the basic desensitization requirements based on the flow probability to obtain the desensitization threshold;

[0108] Obtain the basic attributes of the data to be inspected, wherein the basic attributes include data content and data source;

[0109] Based on the scene identifier and the basic attributes of the data to be inspected, the scene matching coefficient is obtained.

[0110] In one embodiment, the verification coefficient acquisition module 300 is further configured to:

[0111] Calculate the ratio of the desensitization degree to the desensitization degree threshold to obtain the desensitization coefficient of the data to be inspected;

[0112] The verification coefficient is obtained by weighting the desensitization coefficient and the scene matching coefficient.

[0113] In one embodiment, the compliance inspection module 400 is also used for:

[0114] Obtain all flow scenarios of the data to be inspected, and obtain the scenario verification coefficients of the data to be inspected in multiple flow scenarios;

[0115] Obtain the order of multiple flow scenarios, and evaluate the importance of each flow scenario to obtain the flow importance of the multiple flow scenarios;

[0116] This includes obtaining the sequential order of multiple flow scenarios and assessing their importance based on their respective importance, thereby obtaining the flow importance of multiple flow scenarios, including:

[0117] Obtain the timestamps of the corresponding records of multiple circulation scenarios on the blockchain to determine the order of the circulation scenarios;

[0118] Based on a predefined scene type importance ranking table, the basic importance of multiple circulation scenes is determined;

[0119] Obtain the amount of data transferred in the aforementioned transfer scenario and the success rate of compliance checks, calculate the importance coefficient, correct the basic importance, and obtain the transfer importance.

[0120] Based on the importance of the circulation in multiple circulation scenarios, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient;

[0121] Among them, based on the importance of multiple circulation scenarios, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient, including:

[0122] Based on the importance of the multiple circulation scenarios, the scenario weights are obtained;

[0123] Based on the scenario weights, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient.

[0124] Based on the comprehensive circulation coefficient and the verification coefficient, a compliance check is performed to obtain the compliance check result of the data to be checked.

[0125] The compliance check is performed based on the comprehensive circulation coefficient and the verification coefficient to obtain the compliance check result of the data to be checked, including:

[0126] Based on the importance of the multiple circulation scenarios, high-importance scenarios are identified;

[0127] Determine whether the scenario verification coefficients of the data to be inspected in multiple high-importance scenarios meet the compliance inspection results. If the scenario verification coefficients of two or more high-importance scenarios do not meet the compliance inspection results, then the compliance inspection result of the data to be inspected is non-compliant.

[0128] When there are more than the importance number threshold number of high-importance scenarios whose scenario verification coefficients meet the compliance check results, and at least one of the high-importance scenarios has a flow importance greater than the current scenario, then the compliance check result of the data to be checked is compliant. The importance number threshold number is obtained based on the anonymization degree of the data to be checked.

[0129] When there are fewer than the number of high-importance scenarios whose scenario verification coefficients meet the compliance check results, and there are no more than two high-importance scenarios whose scenario verification coefficients do not meet the compliance check results, the comprehensive flow coefficient is used to correct the verification coefficient, the current verification coefficient is obtained, and a compliance check is performed.

[0130] This includes obtaining the current verification coefficient and performing compliance checks, including:

[0131] Based on the scenario identifier, obtain the compliance threshold;

[0132] If the current verification coefficient is greater than or equal to the compliance threshold, the compliance check result of the data to be checked is compliant; otherwise, it is non-compliant.

[0133] In summary, the embodiments of this application have at least the following technical effects:

[0134] This application proposes a blockchain-based automated compliance inspection method and system for data circulation. By introducing a scenario-identified dynamic compliance benchmark construction mechanism and a credibility enhancement mechanism based on on-chain circulation history, it significantly improves the accuracy, adaptability, and decision reliability of data circulation compliance inspection. Specifically, firstly, based on the basic requirements for scenario acquisition, and combined with the scenario circulation probability, the anonymization threshold is dynamically adjusted, enabling the compliance benchmark to flexibly adapt to the actual risks and regulatory granularity of different business environments. This overcomes the unnecessary obstacles to data value mining caused by overly strict standards in traditional methods, and also prevents the compliance risks hidden by overly lenient standards. Secondly, this application deeply mines and utilizes the unique, tamper-proof historical information value of blockchain technology, transforming the past circulation scenarios of the data to be inspected and their corresponding historical verification coefficients from simple evidence records into key references for dynamic evaluation. By constructing a comprehensive analysis model covering circulation order, scenario importance, and historical verification performance, a comprehensive circulation coefficient reflecting the compliance credit of the data to be inspected throughout its entire lifecycle is obtained. This ensures that each current compliance inspection is no longer an isolated event, but rather fully considers the compliance footprint accumulated by the data in its historical circulation path, enhancing the contextual awareness of the judgment. Ultimately, by collaboratively judging the verification coefficient representing the current immediate compliance status and the comprehensive flow coefficient representing the historical compliance status, the robustness and reliability of automated compliance check results in complex and continuous scenarios are significantly improved. Compared with traditional methods, this application achieves the technical effect of intelligent and scenario-based data flow compliance checks while ensuring strict adherence to rule constraints, thereby effectively promoting the secure and efficient flow of data while accurately controlling risks.

[0135] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0136] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0137] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A blockchain-based automated compliance inspection method for data circulation, including: Obtain the scene identifier of the current scene, obtain the percentage of de-identified fields in the data to be inspected, and calculate the degree of de-identification; Based on the scene identifier, a desensitization threshold is obtained, and based on the scene identifier and the basic attributes of the data to be inspected, a scene matching coefficient is obtained; Calculate the ratio of the desensitization degree to the desensitization degree threshold to obtain the desensitization coefficient of the data to be inspected, and combine it with the scene matching coefficient to calculate and obtain the verification coefficient; Obtain the flow scenarios and corresponding verification coefficients of the data to be inspected, and obtain the flow importance of multiple flow scenarios on the chain. Comprehensively evaluate and obtain the comprehensive flow coefficient, and combine the verification coefficient to conduct compliance checks. Based on the scene identifier, a desensitization threshold is obtained, and based on the scene identifier and the basic attributes of the data to be inspected, a scene matching coefficient is obtained, including: Based on the scene identifier, obtain the basic de-identification requirements; Obtain the flow probability of the current scenario, and modify the basic desensitization requirements based on the flow probability to obtain the desensitization threshold; Obtain the basic attributes of the data to be inspected, wherein the basic attributes include data content and data source; Based on the scene identifier and the basic attributes of the data to be inspected, the scene matching coefficient is obtained; Obtain the flow scenarios and corresponding scenario verification coefficients of the data to be inspected, and obtain the flow importance of multiple flow scenarios on the chain. A comprehensive flow coefficient is obtained through a comprehensive evaluation, and compliance checks are performed in conjunction with the verification coefficients, including: Obtain all flow scenarios of the data to be inspected, and obtain the scenario verification coefficients of the data to be inspected in multiple flow scenarios; Obtain the order of multiple flow scenarios, and evaluate the importance of each flow scenario to obtain the flow importance of the multiple flow scenarios; Based on the importance of the circulation in multiple circulation scenarios, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient; Compliance checks are performed based on the comprehensive circulation coefficient and the verification coefficient to obtain the compliance check results of the data to be checked.

2. The blockchain-based data flow automation compliance checking method of claim 1, wherein, Obtain the scene identifier for the current scene, and obtain the percentage of anonymized fields in the data to be inspected. Calculate the anonymization degree, including: Obtain the scene identifier of the current scene, wherein the scene identifier includes at least the processing scene, the usage scene, and the destruction scene; Obtain the data to be inspected, and obtain the length of the de-identified fields and the total field length of the data to be inspected; Calculate the ratio of the length of the de-identified field to the total field length to obtain the de-identification degree.

3. The blockchain-based data flow automation compliance checking method of claim 1, wherein, Calculate the ratio of the desensitization degree to the desensitization degree threshold to obtain the desensitization coefficient of the data to be inspected. Combine this with the scene matching coefficient to calculate and obtain the verification coefficient, including: Calculate the ratio of the desensitization degree to the desensitization degree threshold to obtain the desensitization coefficient of the data to be inspected; The verification coefficient is obtained by weighting the desensitization coefficient and the scene matching coefficient.

4. The blockchain-based data flow automation compliance checking method of claim 1, wherein, Obtain the sequential order of multiple flow scenarios, and evaluate their importance based on their importance, thereby obtaining the flow importance of multiple flow scenarios, including: Obtain the timestamps of the corresponding records of multiple circulation scenarios on the blockchain to determine the order of the circulation scenarios; Based on a predefined scene type importance ranking table, the basic importance of multiple circulation scenes is determined; The system obtains the amount of data transferred in the specified transfer scenario and the success rate of compliance checks, calculates the importance coefficient, corrects the basic importance, and obtains the transfer importance.

5. The blockchain-based data flow automation compliance checking method of claim 1, wherein, Based on the importance of multiple circulation scenarios, the verification coefficients of multiple scenarios are fused and calculated to obtain a comprehensive circulation coefficient, including: Based on the importance of the multiple circulation scenarios, the scenario weights are obtained; The comprehensive circulation coefficient is obtained by fusing and calculating multiple scenario verification coefficients based on the scenario weights.

6. The automated compliance inspection method for data circulation based on blockchain according to claim 1, characterized in that, Based on the comprehensive circulation coefficient and the verification coefficient, a compliance check is performed to obtain the compliance check result of the data to be checked, including: Based on the importance of the multiple circulation scenarios, high-importance scenarios are identified; Determine whether the scenario verification coefficients of the data to be inspected in multiple high-importance scenarios meet the compliance inspection results. If the scenario verification coefficients of two or more high-importance scenarios do not meet the compliance inspection results, then the compliance inspection result of the data to be inspected is non-compliant. When there are more than the importance number threshold number of high-importance scenarios whose scenario verification coefficients meet the compliance check results, and at least one of the high-importance scenarios has a flow importance greater than the current scenario, then the compliance check result of the data to be checked is compliant. The importance number threshold number is obtained based on the anonymization degree of the data to be checked. When there are fewer than the number of high-importance scenarios whose scenario verification coefficients meet the compliance check results, and there are no more than two high-importance scenarios whose scenario verification coefficients do not meet the compliance check results, the comprehensive flow coefficient is used to correct the verification coefficient, the current verification coefficient is obtained, and a compliance check is performed.

7. The blockchain-based data flow automation compliance checking method of claim 6, wherein, Obtain the current verification coefficient and perform compliance checks, including: Based on the scenario identifier, obtain the compliance threshold; If the current verification coefficient is greater than or equal to the compliance threshold, the compliance check result of the data to be checked is compliant; otherwise, it is non-compliant.

8. A blockchain-based data flow circulation automated compliance checking system, characterized in that, The system for implementing the blockchain-based automated compliance inspection method for data circulation as described in any one of claims 1-7, the system comprising: The desensitization degree acquisition module is used to obtain the scene identifier of the current scene, obtain the proportion of desensitized fields in the data to be inspected, and calculate the desensitization degree; The scene matching module is used to obtain a desensitization threshold based on the scene identifier, and to obtain a scene matching coefficient based on the scene identifier and the basic attributes of the data to be inspected; The verification coefficient acquisition module is used to calculate the ratio of the desensitization degree to the desensitization degree threshold, obtain the desensitization coefficient of the data to be inspected, and calculate and obtain the verification coefficient in combination with the scene matching coefficient. The compliance check module is used to obtain the flow scenarios and corresponding verification coefficients of the data to be checked, obtain the flow importance of multiple flow scenarios on the chain, comprehensively evaluate and obtain the comprehensive flow coefficient, and perform compliance checks in combination with the verification coefficient.

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