Cross-domain data trusted sharing management and control method and system based on digital object architecture

Abstract

The application relates to the technical field of cross-domain data sharing and authorization management and control, and discloses a cross-domain data trusted sharing management and control method and system based on a digital object architecture, wherein the cross-domain data trusted sharing management and control method based on the digital object architecture comprises multiple steps of authorized propagation path acquisition, authorized chain risk pre-judgment, risk adaptability strategy generation and delay verification. The application performs limited depth traversal to pre-judge the authorized chain risk, performs differentiated risk management and control under the premise of guaranteeing the timeliness of emergency authorization, and avoids the problem that high-risk nodes and low-risk nodes obtain the same permission in the traditional mechanism. Meanwhile, through the separation processing and closed-loop feedback mechanism of pre-authorization and delay verification, the contradiction between complete risk assessment and the timeliness of emergency business is overcome, and data support is provided for traversal parameter optimization.

Description

Technical Field

[0001] This invention relates to the field of cross-domain data sharing and authorization management technology, and more specifically, to a cross-domain data trusted sharing management method and system based on digital object architecture. Background Technology

[0002] In cross-domain service scenarios for cloud communication SMS sending, when enterprise customers need to urgently send large batches of marketing SMS or verification code SMS, SMS service providers need to quickly call upon other operators or third-party channel resources for SMS delivery. Existing emergency channel authorization mechanisms primarily verify the requester's emergency qualifications, but this approach, relying solely on the requester's qualifications to determine whether to grant channel access, ignores the requester's potential risk position in the authorization chain.

[0003] The main drawbacks of existing technologies are as follows: First, the complete authorization chain risk accumulation assessment process involves multi-level node traversal and complex risk calculations, resulting in excessively long response delays that fail to meet the timeliness requirements for channel acquisition in emergency SMS sending. Second, nodes with medium- to high-risk records in the historical authorization chain (such as those that have sent illegal content or have outstanding fees) can obtain the same channel access permissions as low-risk nodes through the emergency channel, causing emergency authorization to bypass conventional risk control mechanisms. Third, existing mechanisms lack closed-loop verification of the accuracy of risk prediction after emergency authorization execution, failing to provide feedback for the continuous optimization of risk assessment parameters.

[0004] This leads to the following main technical challenges: how to effectively assess the risks of the authorization chain and conduct differentiated risk management while ensuring the timeliness of emergency channel authorization; how to overcome the contradiction between complete risk assessment and emergency delivery timeliness; and how to provide closed-loop feedback for the subsequent optimization and adjustment of risk assessment parameters. Summary of the Invention

[0005] This invention provides a cross-domain data trusted sharing management method and system based on digital object architecture, which solves the technical problems in the traditional emergency authorization mechanism in related technologies, such as high-risk nodes obtaining the same permissions, excessively long risk assessment time, and lack of post-event verification feedback.

[0006] This invention discloses a cross-domain data trusted sharing management method based on a digital object architecture, comprising the following steps: receiving a cross-domain data access request, detecting an emergency access identifier; when an emergency access identifier is detected, extracting the emergency event type code, the requester's emergency qualification certificate, and the requester's domain identifier; verifying the validity of the requester's emergency qualification certificate, and performing a finite-depth traversal in the authorization relationship graph based on the requester's domain identifier to obtain a set of potential authorization propagation paths with the requester as a node; retrieving the historical risk level of each node on each path in the set of potential authorization propagation paths, converting the historical risk level into a corresponding node risk rate value, calculating the estimated cumulative risk value of each path according to a risk accumulation algorithm, and selecting the estimated cumulative risk... The maximum value among the values ​​is used as the authorization chain risk prediction score; the authorization discretion level is determined based on the authorization chain risk prediction score; the authorized resource category range and effective duration of the baseline strategy template corresponding to the emergency event type code are reduced and adjusted according to the authorization discretion level to generate a risk-adaptive pre-authorization strategy; a temporary access token is generated based on the risk-adaptive pre-authorization strategy and issued to the data requester; the temporary access token includes the authorized resource category, authorized operation type, effective duration and the authorization chain risk prediction score; wherein, the risk accumulation algorithm calculates the link risk value in the following way: the link risk value is equal to the value 1 minus the product of the risk rate complements of all nodes on the path, where the risk rate complement is the value 1 minus the risk rate of the corresponding node.

[0007] Further, the step of performing a finite-depth traversal of the authorization relationship graph based on the requester's domain identifier to obtain a set of potential authorization propagation paths with the requester as a node includes:

[0008] Starting from the requesting node, the nodes in the authorization relationship graph are traversed downstream a preset number of layers according to the authorization direction to obtain potential data propagation paths;

[0009] The traversal depth ranges from 2 to 5 layers.

[0010] Furthermore, the step of traversing a preset number of layers downstream in the authorization relationship graph from the requesting node according to the authorization direction also includes:

[0011] A breadth-first traversal strategy is adopted and an upper limit is set on the number of nodes traversed. When the number of nodes traversed reaches the upper limit, the traversal is terminated and the set of potential authorized propagation paths that have been obtained is returned.

[0012] Furthermore, selecting the maximum value among the estimated cumulative risk values ​​as the authorization chain risk prediction score also includes:

[0013] Identify the node with the highest risk rate on the path corresponding to the maximum value as the critical risk node, and write the critical risk node identifier into the attribute of the temporary access token.

[0014] Furthermore, the authorized discretion level defines the scope of authorization reduction ratio and the duration reduction ratio;

[0015] The classification of the authorized discretion levels includes low risk level, low-to-medium risk level, medium risk level, medium-to-high risk level and high risk level;

[0016] The low-risk level corresponds to a 0% reduction in the scope of authorization and a 0% reduction in the duration of authorization, while the high-risk level corresponds to a 60% reduction in the scope of authorization and a 70% reduction in the duration of authorization.

[0017] Furthermore, the reduction and adjustment of the authorized resource category range and effective duration of the baseline strategy template corresponding to the emergency event type code based on the authorized discretion level includes: reducing the authorized resource category range according to the resource sensitivity priority rule, prioritizing the retention of resource categories directly related to the emergency event type code, and prioritizing the exclusion of resource categories with high sensitivity and low correlation with the emergency event type code.

[0018] Furthermore, after generating a temporary access token based on the risk-adaptive pre-authorization policy and issuing it to the data requester, the process further includes:

[0019] Record the access operation log when the data requester uses the temporary access token to access the target digital object. The access operation log includes the access time, accessed data items, and operation type.

[0020] Furthermore, it also includes:

[0021] When the temporary access token expires or the emergency event end flag is submitted, a complete authorization relationship graph traversal is performed to calculate the actual authorization chain path and actual risk accumulation value of the requester.

[0022] The actual cumulative risk value is compared and analyzed with the authorized chain risk prediction score to calculate the prediction deviation rate. The prediction deviation rate is the ratio of the absolute value of the difference between the actual cumulative risk value and the authorized chain risk prediction score to the actual cumulative risk value.

[0023] The delayed verification results, the access operation logs, and the predicted deviation rate are correlated to generate an emergency authorization audit report.

[0024] This invention also discloses a cross-domain data trusted sharing management and control system based on a digital object architecture, comprising: a request parsing module, used to receive cross-domain data access requests, detect emergency access identifiers, and when an emergency access identifier is detected, extract an emergency event type code, the requester's emergency qualification certificate, and the requester's domain identifier; a qualification verification and path acquisition module, used to verify the validity of the requester's emergency qualification certificate and, based on the requester's domain identifier, perform a finite-depth traversal in the authorization relationship graph to obtain a set of potential authorization propagation paths with the requester as a node; and a risk prediction module, used to retrieve the historical risk level of each node on each path in the set of potential authorization propagation paths and convert the historical risk level into a variable value. For the corresponding node risk rate value, the estimated cumulative risk value of each path is calculated according to the risk accumulation algorithm, and the maximum value among the estimated cumulative risk values ​​is selected as the authorization chain risk prediction score; the strategy generation module is used to determine the authorization discretion level according to the authorization chain risk prediction score, and to reduce and adjust the authorized resource category range and effective duration of the baseline strategy template corresponding to the emergency event type code according to the authorization discretion level, thereby generating a risk-adaptive pre-authorization strategy; the token issuance module is used to generate a temporary access token based on the risk-adaptive pre-authorization strategy and issue it to the data requester, wherein the temporary access token includes the authorized resource category, authorized operation type, effective duration and the authorization chain risk prediction score.

[0025] The beneficial effects of this invention are as follows:

[0026] First, by performing a limited-depth traversal of the authorization relationship graph and calculation of the risk accumulation formula during the emergency authorization decision-making stage, the present invention advances the authorization chain risk assessment to the emergency authorization process, thus overcoming the contradiction that the response delay of the complete risk assessment process is too long and cannot meet the timeliness of emergency business.

[0027] Secondly, this invention dynamically adjusts the authorization scope and duration of the benchmark strategy template by mapping the risk prediction score of the authorization chain to the authorization discretion level, overcoming the problem that high-risk nodes and low-risk nodes obtain the same permissions in the traditional emergency authorization mechanism, and realizing risk-based differentiated emergency authorization.

[0028] Third, by separating pre-authorization and delayed verification, this invention can quickly grant risk-adaptive adjusted permissions during the emergency phase and complete the attribute mapping and policy matching verification after the emergency event ends, thus overcoming the contradiction between complete verification and the timeliness of emergency business.

[0029] Fourth, this invention calculates the prediction deviation rate by comparing and analyzing the actual cumulative risk value with the predicted score, providing a closed-loop feedback basis for the subsequent optimization and adjustment of the traversal depth parameter and risk rate mapping table, thereby continuously improving the accuracy of risk prediction. Attached Figure Description

[0030] Figure 1 This is a flowchart of the cross-domain data trusted sharing management method based on digital object architecture of the present invention. Detailed Implementation

[0031] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0032] This implementation provides a cross-domain data trusted sharing management method based on a digital object architecture, such as... Figure 1 As shown, it includes the following steps:

[0033] Step 100: Parse the cross-domain data access request and extract the emergency authorization-related parameters.

[0034] Obtain cross-domain data access requests, detect the emergency authorization identifier from the request header, and when the emergency authorization identifier is detected, extract the emergency event type, the requester's emergency qualification certificate, the requester's domain identifier, and the target digital object identifier, and store the extracted parameters in the request parsing cache.

[0035] At 10:25 AM on January 19, 2026, an e-commerce platform urgently needed to send limited-time discount SMS messages to 500,000 users for a promotional activity. The platform initiated a cross-domain access request to Company B through cloud communication service provider "Company A". The system detected that the emergency authorization identifier field value was "EMERGENCY_AUTH" in the request header, and the extracted parameters are shown in the table below.

[0036]

[0037] Step 200: Perform emergency qualification verification and authorization propagation path acquisition in parallel to obtain qualification verification results and a set of potential authorization propagation paths.

[0038] The emergency authorization qualification registry is queried based on the requester's emergency qualification credentials to verify the validity of the requester's qualifications and obtain the qualification verification result. Simultaneously, a finite-depth traversal is performed on the authorization relationship graph based on the requester's domain identifier to obtain the set of potential authorization propagation paths with the requester as a node.

[0039] It should be noted that finite depth traversal refers to starting from the requesting node and traversing downstream through a preset number of nodes in the authorization relationship graph according to the authorization direction to obtain possible data propagation paths. The traversal depth is determined based on the system configuration, and the value of the traversal depth ranges from 2 to 5 layers.

[0040] In this embodiment of the application, in order to control traversal latency while ensuring risk assessment coverage, a breadth-first traversal strategy is adopted and an upper limit is set on the number of traversed nodes. When the number of traversed nodes reaches the upper limit, the traversal is terminated and the obtained path set is returned.

[0041] Furthermore, the upper limit of the number of nodes traversed is adaptively determined based on the traversal depth. When the traversal depth is 2 levels, the upper limit of the number of nodes is 100 nodes; when the traversal depth is 3 levels, the upper limit of the number of nodes is 200 nodes; when the traversal depth is 4 levels, the upper limit of the number of nodes is 300 nodes; and when the traversal depth is 5 levels, the upper limit of the number of nodes is 500 nodes.

[0042] The system queries the emergency authorization qualification registration form to verify the validity of certificate CERT-HFXD-20260119-B847. It confirms that the certificate was issued by the Anhui Provincial Communications Administration in December 2025 and is valid until December 2026; the qualification verification result is passed. Simultaneously, the system is configured with a traversal depth of 3 levels, performing a breadth-first traversal starting from the requester node domain.sms.hefeixunda, obtaining 5 potential authorization propagation paths.

[0043]

[0044] Qualification verification result: Passed;

[0045] Total number of nodes traversed: 13 nodes;

[0046] Traversal time: 127 milliseconds.

[0047] Step 300: Calculate the risk prediction score of the authorization chain based on the set of potential authorization propagation paths, and identify key risk nodes.

[0048] For each path in the set of potential authorized propagation paths, retrieve the historical risk level of each node on the path from the risk assessment result cache. For the retrieved historical risk level, convert it into the corresponding node risk rate value according to the preset level-risk rate mapping table.

[0049] Furthermore, the node risk rate ranges from [0, 1], representing the probability of a risk event occurring at that node. The level-risk rate mapping table defines the correspondence between historical risk levels and node risk rates: low risk level corresponds to a node risk rate of 0.1, low-medium risk level corresponds to a node risk rate of 0.3, medium risk level corresponds to a node risk rate of 0.5, medium-high risk level corresponds to a node risk rate of 0.7, and high risk level corresponds to a node risk rate of 0.9.

[0050] Calculate the estimated cumulative risk value for each path using the risk accumulation formula:

[0051]

[0052] in, Indicates the link risk value. Indicates the number of nodes on the path. Indicates the node index. Indicates the first Risk rate of each node.

[0053] The maximum value among the estimated cumulative risk values ​​of each path is selected as the risk prediction score of the authorization chain, and the node with the highest risk rate on the path corresponding to the maximum value is identified as the key risk node.

[0054] It should be noted that the risk accumulation formula uses the product of probability complements to calculate the overall probability of a risk event occurring at any node on the link. When there are multiple medium-risk nodes on the path, even if the risk rate of a single node is not high, the accumulated link risk value may still reach a high level.

[0055] The system retrieves the historical risk levels of each node on the five paths from the risk assessment result cache, converts them into node risk rates according to the level-risk rate mapping table, and calculates the estimated cumulative risk value of each path.

[0056]

[0057] Authorized Chain Risk Prediction Score:

[0058]

[0059] Key risk node: domain.reseller.huawei (historically classified as medium to high risk, with a node risk rate of 0.7).

[0060] Step 400: Generate a risk-adaptive pre-authorization strategy based on the risk prediction score of the authorization chain.

[0061] Based on the risk prediction score of the authorization chain, the corresponding authorization discretion level is retrieved from the risk threshold mapping table. The authorization discretion level defines the reduction ratio of the authorization scope and the reduction ratio of the authorization duration. A baseline strategy template corresponding to the emergency event type is retrieved from the pre-authorization strategy template library. The baseline strategy template contains the standard authorized resource category range and standard effective duration for that emergency event type. Based on the authorization discretion level, the authorized resource category range and effective duration in the baseline strategy template are reduced and adjusted to generate a risk-adaptive pre-authorization strategy.

[0062] It should be noted that the authorization discretion levels are divided into low-risk, low-to-medium-risk, medium-risk, medium-to-high-risk, and high-risk levels. A low-risk level corresponds to a 0% reduction in both the scope and duration of authorization, while a high-risk level corresponds to a 60% reduction in both the scope and duration of authorization.

[0063] Furthermore, the risk threshold mapping table defines the correspondence between the authorization chain risk prediction score and the authorization discretion level. When the authorization chain risk prediction score is in the range [0, 0.2), it corresponds to a low risk level; in the range [0.2, 0.4), it corresponds to a low-to-medium risk level; in the range [0.4, 0.6), it corresponds to a medium risk level; in the range [0.6, 0.8), it corresponds to a medium-to-high risk level; and in the range [0.8, 1.0], it corresponds to a high risk level. Specifically, the low-to-medium risk level corresponds to a 15% reduction in authorization scope and a 20% reduction in duration; the medium risk level corresponds to a 30% reduction in authorization scope and a 40% reduction in duration; and the medium-to-high risk level corresponds to a 45% reduction in authorization scope and a 55% reduction in duration.

[0064] Based on the authorization chain risk prediction score of 0.865, which falls within the range of [0.8, 1.0], the system determines that the authorization discretion level is high-risk, corresponding to a 60% reduction in the authorization scope and a 70% reduction in duration. The system retrieves the baseline strategy template corresponding to the emergency event type "mass marketing SMS sending" from the pre-authorization strategy template library. This template includes five standard authorization channel categories, with a standard effective duration of 8 hours. The standard authorization channel categories in the baseline strategy template are "International Channel," "Premium Channel," "Regular Channel," "Verification Code Dedicated Channel," and "Marketing Dedicated Channel," with channel sensitivity from highest to lowest as follows: "International Channel" (sensitivity 0.95), "Premium Channel" (sensitivity 0.85), "Verification Code Dedicated Channel" (sensitivity 0.75), "Regular Channel" (sensitivity 0.65), and "Marketing Dedicated Channel" (sensitivity 0.45).

[0065]

[0066] Risk-adaptive pre-authorization strategy generation process: First, calculate the number of channel categories to be excluded based on the authorization scope contraction ratio: Then, sort the channels by sensitivity from highest to lowest, starting with the least relevant channel categories for elimination. Exclude "International Channel" (low to medium relevance), "Premium Channel" (medium relevance), and "Verification Code Dedicated Channel" (high to medium relevance), retaining only "Marketing Dedicated Channel" and "Regular Channel," which are directly relevant to sending large volumes of marketing SMS messages. Finally, adjust the effective duration: Hour.

[0067] The generated risk-adaptive pre-authorization strategy reduces the scope of authorized channels to "marketing-specific channels" and "normal channels," and adjusts the effective duration to 2.4 hours (144 minutes).

[0068] In this embodiment of the application, the reduction of the scope of authorized resource categories follows the resource sensitivity priority rule, prioritizing the retention of resource categories directly related to the type of emergency event, and prioritizing the exclusion of resource categories with high sensitivity and low relevance to emergency events.

[0069] Furthermore, the reduction of the authorized resource category range is achieved through the following steps: First, calculate the number of resource categories to be excluded based on the authorized range reduction ratio, using the following formula: ,in This indicates the number of resource categories that need to be excluded. This indicates the total number of standard authorized resource categories in the baseline policy template. Indicates the percentage reduction in the scope of authorization. The value range is [0,1]. This indicates rounding up; then, sorting the standard authorized resource categories from the baseline strategy template in descending order of resource sensitivity; finally, selecting the resource category with the lowest relevance to the emergency event type from the sorted resource category list for exclusion, until the number of exclusions reaches the calculated number of resource categories to be excluded.

[0070] Furthermore, the reduction and adjustment of the effective duration is calculated using the duration reduction formula:

[0071]

[0072] in, Indicates the adjusted effective duration. This indicates the standard effective duration in the baseline strategy template. This indicates the percentage reduction in time corresponding to the level of authorized discretion. The value range is [0, 1].

[0073] Step 500: Generate and issue temporary access tokens based on the risk-adaptive pre-authorization policy.

[0074] Temporary access tokens are generated based on a risk-adaptive pre-authorization strategy. Token attributes include the authorized resource category, authorized operation type, validity period, authorization chain risk assessment score, key risk node identifiers, and a list of required verification items. The token information is written to the emergency authorization pending review queue, and the token and risk warning information are returned to the requester and data provider.

[0075] Furthermore, the validity period parameter of the temporary access token is set to the adjusted validity period calculated in step 400 using the duration reduction formula. The authorized resource category parameter takes the range of resource categories after reduction and adjustment.

[0076] Based on the risk-adaptive pre-authorization policy, the system generates a temporary access token TOKEN_20260119_A8F2K7, and the token attribute information is shown in the table below.

[0077]

[0078] The system writes the token information into the emergency authorization pending review queue, and at the same time returns the token and risk warning information to the requesting company A: "The current access has a high risk level (0.865), and the policy has been adjusted, with the authorized channel range and duration reduced accordingly." It also returns the token and audit information to the channel provider company B.

[0079] In addition to the steps described above, the following steps are also included:

[0080] Step 600: Record the emergency authorization access operation log.

[0081] The requester uses a temporary access token to access the target digital object. All operation records during the access process are written to the emergency authorized access operation log, which includes the access time, access channel item, and operation type.

[0082] Company A used a temporary access token (TOKEN_20260119_A8F2K7) to send SMS messages through Company B's channel between 10:31 and 11:45. The emergency authorization access operation log recorded by the system includes: 10:31:05 initiation of marketing-dedicated channel sending; 10:35:20 switching to the normal channel; 11:20:15 completion of the first batch of 250,000 SMS messages; and 11:45:32 completion of the second batch of 250,000 SMS messages and termination of access. All sending operations were SMS sending operations; no channel parameter modifications or data export occurred.

[0083] Step 700: Execute the delayed verification process and calculate the actual cumulative risk value.

[0084] When a temporary access token expires or an emergency event end flag is submitted, a delayed verification process is initiated. Records awaiting verification are retrieved from the emergency authorization review queue. A complete attribute mapping and policy matching process is performed on these records. Simultaneously, a full traversal of the authorization relationship graph is re-executed to calculate the requester's actual authorization chain path and actual risk accumulation value.

[0085] Furthermore, the complete attribute mapping and policy matching process includes: extracting requester attributes and access target attributes from the record to be verified; matching and verifying the extracted attributes with the access control policies in the pre-authorization policy template library; determining whether the requester attributes meet the access subject constraints defined in the policy; determining whether the access target attributes meet the access object constraints defined in the policy; and generating an authorization compliance judgment based on the matching results. A complete traversal of the authorization relationship graph refers to traversing all reachable authorization propagation paths starting from the requester node without setting traversal depth limits or node count limits, obtaining a complete set of authorization chain paths, and calculating the actual risk accumulation value according to the risk accumulation formula in step 300.

[0086] At 14:55 on January 19, 2026, the temporary access token expired. The system retrieved the verification record of token TOKEN_20260119_A8F2K7 from the emergency authorization pending review queue and executed a complete attribute mapping and policy matching process. The complete attribute mapping results showed that the requester's attributes met the constraints of "holding a telecommunications value-added service license" and "having emergency channel usage qualifications," while the access target's attributes met the constraints of "sufficient channel capacity" and "service area matching." The authorization compliance was determined to be compliant. Simultaneously, without setting any traversal depth or node number limits, the system performed a complete traversal of the authorization relationship graph, obtaining a total of 12 reachable authorization propagation paths originating from the requester node. The calculated actual cumulative risk values ​​are compared in the table below.

[0087]

[0088] Actual cumulative risk value:

[0089] Authorized Chain Risk Prediction Score:

[0090] Step 800: Generate an emergency authorization review report.

[0091] The actual cumulative risk value is compared and analyzed with the risk prediction score of the authorized chain to calculate the prediction deviation rate:

[0092]

[0093] in, Indicates the prediction deviation rate. This represents the actual cumulative risk value. This indicates the risk prediction score for the authorization chain.

[0094] Furthermore, when the actual risk accumulation value When the prediction deviation rate is equal to 0, the risk prediction score of the authorization chain is directly taken. The product of 100%.

[0095] By correlating the delayed verification results, emergency authorization access operation logs, and predicted deviation rates, an emergency authorization audit report is generated. The audit report includes authorization compliance determination, access behavior audit, and predicted deviation analysis.

[0096] The system compares and analyzes the actual cumulative risk value of 0.923 with the authorized chain risk prediction score of 0.865, and calculates the prediction deviation rate:

[0097]

[0098] The system correlates the delayed verification results, emergency authorization access operation logs, and predicted deviation rate to generate an emergency authorization audit report.

[0099]

[0100] Audit report conclusion: This emergency access authorization complies with authorization compliance requirements, the access behavior is normal, and the risk-adaptive pre-authorization strategy effectively controlled the scope and duration of authorization. The risk prediction deviation rate is 6.28%, which is within an acceptable range. However, the full traversal revealed a high-risk node, domain.offshore.data09, which was not covered by the limited-depth traversal. It is recommended to adjust the traversal depth parameter from 3 layers to 4 layers to improve the risk prediction coverage.

[0101] It is understood that data preprocessing methods known to those skilled in the art include data cleaning, data transformation, and data reduction. Data transformation includes type conversion and normalization and standardization. Although the dimensions and types of data were omitted in the description of the preceding embodiments, data preprocessing is a technical knowledge known to those skilled in the art and a prerequisite step in data processing. Therefore, the previously described well-known data preprocessing steps were not described independently.

[0102] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for cross-domain trusted data sharing and management based on a digital object architecture, characterized in that, Includes the following steps: Receive cross-domain data access requests, detect emergency access identifiers, and when an emergency access identifier is detected, extract the emergency event type code, the requester's emergency qualification certificate, and the requester's domain identifier. While verifying the validity of the requester's emergency qualification certificate, a finite depth traversal is performed in the authorization relationship graph based on the requester's domain identifier to obtain a set of potential authorization propagation paths with the requester as a node; Retrieve the historical risk level of each node on each path in the potential authorization propagation path set, convert the historical risk level into the corresponding node risk rate value, calculate the estimated cumulative risk value of each path according to the risk accumulation algorithm, and select the maximum value among the estimated cumulative risk values ​​as the authorization chain risk prediction score. Based on the risk prediction score of the authorization chain, the authorization discretion level is determined. Based on the authorization discretion level, the scope of authorized resource categories and the effective duration of the baseline strategy template corresponding to the emergency event type code are reduced and adjusted to generate a risk-adaptive pre-authorization strategy. Based on the risk-adaptive pre-authorization strategy, a temporary access token is generated and issued to the data requester. The temporary access token includes the authorized resource category, authorized operation type, validity period, and the risk prediction score of the authorization chain. The risk accumulation algorithm calculates the link risk value by subtracting the product of the risk rate complements of all nodes on the path from the value 1. The risk rate complement is the value 1 minus the risk rate of the corresponding node.

2. The method according to claim 1, characterized in that, The step of performing a finite-depth traversal of the authorization relationship graph based on the requester's domain identifier to obtain a set of potential authorization propagation paths with the requester as a node includes: Starting from the requesting node, the nodes in the authorization relationship graph are traversed downstream a preset number of layers according to the authorization direction to obtain potential data propagation paths; The depth range for finite depth traversal is 2 to 5 levels.

3. The method according to claim 2, characterized in that, The step of traversing a preset number of layers downstream in the authorization relationship graph from the requesting node also includes: A breadth-first traversal strategy is adopted and an upper limit is set on the number of nodes traversed. When the number of nodes traversed reaches the upper limit, the traversal is terminated and the set of potential authorized propagation paths that have been obtained is returned.

4. The method according to claim 1, characterized in that, The step of selecting the maximum value among the estimated cumulative risk values ​​as the authorization chain risk prediction score also includes: Identify the node with the highest risk rate on the path corresponding to the maximum value as the critical risk node, and write the critical risk node identifier into the attribute of the temporary access token.

5. The method according to claim 1, characterized in that, The authorized discretion level defines the reduction ratio of the scope of authorization and the reduction ratio of the duration; The classification of the authorized discretion levels includes low risk level, low-to-medium risk level, medium risk level, medium-to-high risk level and high risk level; The low-risk level corresponds to a 0% reduction in the scope of authorization and a 0% reduction in the duration of authorization, while the high-risk level corresponds to a 60% reduction in the scope of authorization and a 70% reduction in the duration of authorization.

6. The method according to claim 1, characterized in that, The reduction and adjustment of the authorized resource category range and effective duration of the baseline strategy template corresponding to the emergency event type code based on the authorized discretion level includes: The authorized resource category range is narrowed according to the resource sensitivity priority rule, prioritizing the retention of resource categories directly related to the emergency event type code, and prioritizing the exclusion of resource categories with high sensitivity and low correlation to the emergency event type code.

7. The method according to claim 1, characterized in that, After generating a temporary access token based on the risk-adaptive pre-authorization policy and issuing it to the data requester, the process further includes: Record access operation logs when the data requester uses the temporary access token to access the target digital object. The access operation logs include access time, accessed resource items, and operation type.

8. The method according to claim 7, characterized in that, Also includes: When the temporary access token expires or the emergency event end flag is submitted, a complete authorization relationship graph traversal is performed to calculate the actual authorization chain path and actual risk accumulation value of the requester. The actual cumulative risk value is compared and analyzed with the authorized chain risk prediction score to calculate the prediction deviation rate. The prediction deviation rate is the ratio of the absolute value of the difference between the actual cumulative risk value and the authorized chain risk prediction score to the actual cumulative risk value.

9. The method according to claim 8, characterized in that, Also includes: The delayed verification results, the access operation logs, and the predicted deviation rate are correlated to generate an emergency authorization audit report.

10. A cross-domain data trusted sharing management and control system based on a digital object architecture, used to execute the method according to any one of claims 1 to 9, characterized in that, include: The request parsing module is used to receive cross-domain data access requests, detect emergency access identifiers, and when an emergency access identifier is detected, extract the emergency event type code, the requester's emergency qualification certificate, and the requester's domain identifier. The qualification verification and path acquisition module is used to verify the validity of the requester's emergency qualification certificate, and perform a finite depth traversal in the authorization relationship graph based on the requester's domain identifier to obtain a set of potential authorization propagation paths with the requester as a node. The risk prediction module is used to retrieve the historical risk level of each node on each path in the potential authorization propagation path set, convert the historical risk level into the corresponding node risk rate value, calculate the estimated cumulative risk value of each path according to the risk accumulation algorithm, and select the maximum value among the estimated cumulative risk values ​​as the authorization chain risk prediction score. The strategy generation module is used to determine the authorization discretion level based on the risk prediction score of the authorization chain, and to reduce and adjust the scope of authorized resource categories and effective duration of the baseline strategy template corresponding to the emergency event type code according to the authorization discretion level, so as to generate a risk-adaptive pre-authorization strategy. The token issuance module is used to generate temporary access tokens based on the risk-adaptive pre-authorization strategy and issue them to the data requester. The temporary access token includes the authorized resource category, authorized operation type, validity period, and the risk prediction score of the authorization chain.

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