Cross-department data security sharing system based on government affair block chain

Abstract

The application relates to the technical field of e-government data management, and particularly discloses a cross-department data security sharing system based on a government affair blockchain, which comprises a data resource perception module, a fusion strategy construction module, a sharing behavior evaluation module and an intelligent collaborative scheduling module. The scheme constructs a unified government affair data and blockchain security sharing fusion model, establishes an accurate and quantitative mapping relationship between data sharing operation instructions and blockchain verifiable audit tracks, and accurately reflects the influence of data sensitivity levels, user roles and sharing contexts on access control through a dynamic attribute-based encryption strategy model and data-driven calibration. Through high-precision strategy simulation and real-time monitoring, the controllability and risks of data sharing are quantified, access strategies are dynamically adjusted according to security situations, and intelligent contracts and optimization algorithms are used to automatically optimize between data value maximization and risk minimization, so that the safety, compliance and efficiency of government affair data sharing are ensured.

Description

Technical Field

[0001] This invention relates to the field of e-government data management technology, specifically to a cross-departmental data security sharing system based on government blockchain. Background Technology

[0002] With the deepening of digital transformation, government data has become an important foundational resource for the modernization of national governance. However, the current sharing of government data across departments still faces many challenges: traditional government data sharing models and blockchain trust models are heterogeneous, one based on hierarchical approval and centralized authorization, and the other based on distributed consensus and cryptographic verification. It is difficult for the two to form a unified view in terms of permission mapping and audit traceability, making it impossible to assess the impact of data sharing behavior on security and compliance from a global perspective; traditional blockchain sharing solutions focus more on data on-chain storage and evidence storage, lacking fine-grained and dynamic access control over the data content itself, and failing to achieve precise sharing of data that is usable but invisible, controllable and traceable. Summary of the Invention

[0003] To address the aforementioned issues and overcome the shortcomings of existing technologies, this invention provides a cross-departmental data security sharing system based on government blockchain. Addressing the heterogeneity between traditional government data sharing models and blockchain trust models—one based on hierarchical approval and centralized authorization, the other on distributed consensus and cryptographic verification—it is difficult to establish a unified view for permission mapping and audit traceability. This results in an inability to assess the impact of data sharing activities on security and compliance from a global perspective. This solution constructs a unified government data and blockchain security sharing fusion model, establishing a precise and quantifiable mapping relationship between data sharing operation instructions and verifiable blockchain audit trails, and employing a dynamic attribute-based encryption strategy. Model- and data-driven calibration accurately reflects the impact of data sensitivity levels, user roles, and sharing contexts on access control, effectively improving the accuracy and credibility of shared security audits. Addressing the issue that traditional blockchain sharing solutions often focus on on-chain data storage and lack fine-grained, dynamic access control over the data content itself, failing to achieve precise sharing that ensures data is usable but not visible, controllable, and traceable, this solution quantifies the controllability and risks of data sharing through high-precision policy simulation and real-time monitoring. It dynamically adjusts access policies based on the security situation and automatically optimizes the balance between maximizing data value and minimizing risk through smart contracts and optimization algorithms, ensuring the security, compliance, and efficiency of government data sharing.

[0004] The cross-departmental data security sharing system based on government blockchain provided by this invention includes a data resource perception module, a fusion strategy construction module, a sharing behavior evaluation module, and an intelligent collaborative scheduling module;

[0005] The data resource perception module collects data resources from various government departments, standardizes the description of heterogeneous data resources, adds sensitivity level tags, collects contextual information related to shared business in real time, records the data source, the processing steps, and the new data derived therefrom, forming shared perception data, and sends the shared perception data to the fusion strategy construction module and the shared behavior evaluation module.

[0006] The fusion strategy construction module constructs a secure sharing fusion model that describes the intrinsic relationship between government data sharing strategies and blockchain distributed trust through ternary fusion modeling, and sends the secure sharing fusion model to the sharing behavior evaluation module.

[0007] The shared behavior assessment module, based on shared perception data and a secure sharing fusion model, simulates and extrapolates data sharing requests initiated by various government departments, predicts the potential security risks, compliance status, and performance impacts, and generates a complete simulation assessment report, which is then sent to the intelligent collaborative scheduling module.

[0008] The intelligent collaborative scheduling module uses a multi-objective optimization algorithm to solve for the optimal resource scheduling scheme based on the simulation evaluation report, and uses smart contracts to jointly automate the data access process and blockchain evidence verification.

[0009] Furthermore, the fusion strategy construction module includes: a subject trust measurement unit, a strategy parameter setting unit, a blockchain trust mechanism modeling unit, a data security strategy mechanism modeling unit, a data-driven strategy learning unit, and a fusion model generation unit;

[0010] The subject trust measurement unit inputs shared perception data, establishes initial trust profiles for each participating government department subject, sets trust evaluation indicators, collects trust credentials, historical sharing behavior statistics, and security qualification information for each subject, and obtains the initial trust level through weighted calculation using the following formula: ;

[0011] In the formula, Indicates the subject's initial level of trust. An index representing a trust credential. Indicates the number of evaluation indicators. Indicates the first The quantitative value of each evaluation indicator, Indicates the first The weight of each evaluation indicator;

[0012] The strategy parameter setting unit initializes the dynamic strategy weight model to dynamically adjust the strictness of the access control policy for shared data. It analyzes historical sharing behavior statistics to set basic strategy parameters, including data sensitivity weight coefficient, subject role weight coefficient, context risk weight coefficient, and basic strategy adjustment constant. The formulas used are as follows: ;

[0013] In the formula, This represents the output value of the dynamic policy weight model. Indicates the data sensitivity level. This represents the data sensitivity weighting coefficient. This indicates the permission level of the entity requesting access. This represents the weighting coefficient of the main role. Indicates the risk coefficient of shared context. This represents the contextual risk weighting coefficient. This represents the basic policy adjustment constant;

[0014] The blockchain trust mechanism modeling unit uses a permissioned consortium blockchain architecture to encapsulate the core trust mechanism of the blockchain, constructs a blockchain trust mechanism model, and performs trusted verification of shared operations. The core form is as follows: ;

[0015] In the formula, Indicates a sharing operation. Indicates a shared operation Output confidence score, This represents the verification function. Represents the consensus algorithm. This indicates that the private key is used to digitally sign the shared operation. Represents the hash value of the associated block;

[0016] The data security policy mechanism modeling unit adopts a hybrid access control policy based on attributes and roles to construct a data security policy mechanism model. It defines user attribute sets, data attribute sets, and environment context attribute sets, as well as policy rules. A policy engine is then used to evaluate the policy rules, determining whether access is authorized. The result is output as a boolean value. The judgment logic is as follows: ;

[0017] In the formula, Indicates the judgment result. Represents the strategy engine, Represents a set of user attributes. Represents a collection of data attributes. Represents a collection of environment context properties;

[0018] The data-driven strategy learning unit constructs high-dimensional feature vectors based on historical shared behavior statistics and builds an anomaly detection model based on the Gradient Boosting Decision Tree (XGBoost) model. The model predicts the potential risk score for each sharing operation, and takes the following form: ;

[0019] In the formula, This indicates the predicted potential risk score for this sharing operation. This represents an anomaly detection model. Represents a high-dimensional feature vector;

[0020] The fusion model generation unit outputs the optimal parameters of the dynamic policy weight model based on the potential risk score of the shared operation, and dynamically calibrates the parameters in the blockchain trust mechanism model and the data security policy mechanism model, outputting the final secure sharing fusion model. .

[0021] Furthermore, the fusion model generation unit includes: a context-aware subunit, a policy parameter calibration subunit, and a shared fusion subunit;

[0022] The context-aware subunit collects shared operation context, network situation, and threat intelligence in real time, and combines this with the potential risk score of the shared operation to generate a dynamic context vector representing the characteristics of the current shared scenario. ;

[0023] The strategy parameter calibration subunit uses a lightweight neural network to construct a dynamic calibration model. The dynamic policy weight model outputs optimized policy parameters for the current shared scenario, using the following formula: ;

[0024] In the formula, This represents the optimization policy parameters of the dynamic policy weight model. This represents a dynamic calibration model. Represents the dynamic context vector. Indicate the sensitivity level of the data to be shared;

[0025] The shared fusion subunit dynamically calibrates the parameters in the blockchain trust mechanism model and the data security strategy mechanism model. After optimizing the strategy parameters, it substitutes them into the dynamic strategy weight model, which is then coupled with the data security strategy mechanism model and the blockchain trust mechanism model to form and output the final secure shared fusion model. .

[0026] Furthermore, the shared behavior assessment module includes a simulation scenario definition unit, a behavior inference unit, a risk quantification unit, and a report generation unit;

[0027] The simulation scenario definition unit receives shared sensing data from various government departments, collects data sharing requests, and sets the simulation scenario and simulation time step.

[0028] The behavior inference unit generates simulated user access behavior based on the data sharing request, simulates the decision process of the strategy engine, the invocation and execution of smart contracts, and introduces a disturbance factor to simulate possible abnormal situations, and records the intermediate state and result of each step.

[0029] The risk quantification unit inputs the intermediate states and results of each simulation into the security sharing fusion model to generate an evaluation index vector, including security risk value, compliance score, and sharing processing delay.

[0030] The report generation unit performs in-depth analysis on the evaluation index vectors output by the simulation, generates multi-dimensional evaluation charts including risk heatmaps, compliance gap analysis, and performance bottleneck location, and generates a complete simulation evaluation report for each data sharing request.

[0031] Furthermore, the intelligent collaborative scheduling module includes a multi-objective optimization function construction unit, a system constraint modeling unit, an intelligent optimization solution unit, and a strategy decomposition and execution unit;

[0032] The multi-objective optimization function construction unit constructs a comprehensive negative utility function based on the simulation evaluation report;

[0033] The system constraint modeling unit constructs the hard and soft constraints for system operation;

[0034] The intelligent optimization solution unit uses the NSGA-II multi-objective genetic algorithm to iteratively optimize the resource scheduling scheme. It sets the encoding method and population size, adopts the elite retention strategy, and stops iterating when the Pareto front has no significant improvement for 100 consecutive generations. It outputs a set of Pareto optimal solutions as the final execution scheme.

[0035] The strategy decomposition and execution unit decomposes the final execution plan into executable smart contract call instruction sets, access control policy update instruction sets, and data service routing instruction sets, and distributes them to the blockchain platform, policy management server, and data gateways of various government departments, respectively, to achieve automated and secure execution of cross-departmental data sharing processes.

[0036] The beneficial effects achieved by the present invention using the above solution are as follows:

[0037] (1) In view of the heterogeneity between the traditional government data sharing model and the blockchain trust model, one is based on hierarchical approval and centralized authorization, and the other is based on distributed consensus and cryptographic verification. It is difficult for the two to form a unified view in terms of permission mapping and audit traceability, which makes it impossible to assess the impact of data sharing behavior on security and compliance from a global perspective. This solution constructs a unified government data and blockchain security sharing fusion model, establishes a precise and quantitative mapping relationship between data sharing operation instructions and blockchain verifiable audit traces, and accurately reflects the impact of data sensitivity level, user role and sharing context on access control through dynamic attribute base encryption strategy model and data-driven calibration, effectively improving the accuracy and credibility of sharing security audit.

[0038] (2) In view of the fact that traditional blockchain sharing solutions focus on data storage on the chain and lack fine-grained and dynamic access control over the data content itself, and cannot achieve precise sharing of data that is available but not visible, controllable and traceable, this solution quantifies the controllability and risk of data sharing through high-precision strategy simulation and real-time monitoring, dynamically adjusts the access strategy according to the security situation, and automatically seeks the optimal balance between maximizing data value and minimizing risk through smart contracts and optimization algorithms, so as to ensure the security, compliance and efficiency of government data sharing. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the cross-departmental data security sharing system based on government blockchain proposed in this invention.

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0042] Example 1, see Figure 1 The cross-departmental data security sharing system based on government blockchain provided by this invention includes a data resource perception module, a fusion strategy construction module, a sharing behavior evaluation module, and an intelligent collaborative scheduling module.

[0043] The data resource perception module collects data resources from various government departments, standardizes the description of heterogeneous data resources, adds sensitivity level tags, collects contextual information related to shared business in real time, records the data source, the processing steps, and the new data derived therefrom, forming shared perception data, and sends the shared perception data to the fusion strategy construction module and the shared behavior evaluation module.

[0044] The fusion strategy construction module constructs a secure sharing fusion model that describes the intrinsic relationship between government data sharing strategies and blockchain distributed trust through ternary fusion modeling, and sends the secure sharing fusion model to the sharing behavior evaluation module.

[0045] The shared behavior assessment module, based on shared perception data and a secure sharing fusion model, simulates and extrapolates data sharing requests initiated by various government departments, predicts the potential security risks, compliance status, and performance impacts, and generates a complete simulation assessment report, which is then sent to the intelligent collaborative scheduling module.

[0046] The intelligent collaborative scheduling module uses a multi-objective optimization algorithm to solve for the optimal resource scheduling scheme based on the simulation evaluation report, and uses smart contracts to jointly automate the data access process and blockchain evidence verification.

[0047] Example 2, see Figure 1 This embodiment is based on the above embodiment. The data resource perception module collects data resources within each government department, including: the IP address and geographical location of the terminal that initiated the sharing request, the request time, the urgency of the associated business process, and the current network security status level.

[0048] Example 3, see Figure 1 This embodiment is based on the above embodiment, and the fusion strategy construction module includes: a subject trust measurement unit, a strategy parameter setting unit, a blockchain trust mechanism modeling unit, a data security strategy mechanism modeling unit, a data-driven strategy learning unit, and a fusion model generation unit;

[0049] The subject trust measurement unit inputs shared perception data, establishes initial trust profiles for each participating government department subject, sets trust evaluation indicators, collects trust credentials, historical sharing behavior statistics, and security qualification information for each subject, and obtains the initial trust level through weighted calculation using the following formula: ;

[0050] In the formula, Indicates the subject's initial level of trust. An index representing a trust credential. Indicates the number of evaluation indicators. Indicates the first The quantitative value of each evaluation indicator, Indicates the first The weight of each evaluation indicator;

[0051] The strategy parameter setting unit initializes the dynamic strategy weight model to dynamically adjust the strictness of access control policies for shared data. It analyzes historical sharing behavior statistics to set basic strategy parameters, including data sensitivity weight coefficient, subject role weight coefficient, context risk weight coefficient, and basic strategy adjustment constant. For shared data with high sensitivity levels, the data sensitivity weight coefficient is set to 0.5. The context risk weight coefficient is set according to time, location, and business urgency; the context risk weight coefficient for regular business hours is set to 0.1, using the following formula: ;

[0052] In the formula, This represents the output value of the dynamic policy weight model. Indicates the sensitivity level of the data to be shared. This represents the data sensitivity weighting coefficient. This indicates the permission level of the entity requesting access. This represents the weighting coefficient of the main role. Indicates the risk coefficient of shared context. This represents the contextual risk weighting coefficient. This represents the basic policy adjustment constant;

[0053] The blockchain trust mechanism modeling unit uses a permissioned consortium blockchain architecture to encapsulate the core trust mechanism of the blockchain, constructs a blockchain trust mechanism model, and performs trusted verification of shared operations. The core form is as follows: ;

[0054] In the formula, Indicates a sharing operation. Indicates a shared operation Output confidence score, This represents the verification function. Represents the consensus algorithm. This indicates that the private key is used to digitally sign the shared operation. Represents the hash value of the associated block;

[0055] The data security policy mechanism modeling unit adopts a hybrid access control policy based on attributes and roles to construct a data security policy mechanism model. It defines user attribute sets, data attribute sets, and environment context attribute sets, defines policy rules based on the XACML standard, and uses the AuthZForce open-source policy engine to evaluate the policy rules, determining whether access is authorized. The result is output as a boolean value, and the judgment logic is as follows: ;

[0056] In the formula, Indicates the judgment result. Represents the strategy engine, Represents a set of user attributes. Represents a collection of data attributes. Represents a collection of environment context properties;

[0057] The data-driven strategy learning unit constructs high-dimensional feature vectors based on historical shared behavior statistics and builds an anomaly detection model based on the Gradient Boosting Decision Tree (XGBoost) model. The model predicts the potential risk score for each sharing operation, and takes the following form: ;

[0058] In the formula, This indicates the predicted potential risk score for this sharing operation. This represents an anomaly detection model. Represents a high-dimensional feature vector;

[0059] The fusion model generation unit outputs the optimal parameters of the dynamic policy weight model based on the potential risk score of the shared operation, and dynamically calibrates the parameters in the blockchain trust mechanism model and the data security policy mechanism model, outputting the final secure sharing fusion model. .

[0060] Example 4, see Figure 1 This embodiment is based on the above embodiment. In the data security policy mechanism modeling unit, the content of the attribute set specifically includes: User attribute set: including department, job title, and security level; Data attribute set: including category, security level, and owner; The set of environment context attributes includes time, network location, and device type.

[0061] Example 5, see Figure 1 This embodiment is based on the above embodiment, and in the data-driven strategy learning unit, the high-dimensional feature vector include: User behavior baseline characteristics: the user's average daily number of visits in the past 30 days, the most frequently visited data types, typical visit time periods, and the geographical region of the visit source; Deviation characteristics of this request: deviation of the time of this request from the user baseline, difference between the data type of the request and historical preferences, and ratio of the data volume of the request to the historical average. Group behavior characteristics: the number of concurrent requests for the same type of data within the same department in the past hour, and the number of abnormal login attempts across the entire network in the past 10 minutes; Resource sensitivity correlation characteristics: historical access frequency of the data requested this time, whether it has been recently marked as high-risk data, and the degree of collaboration between the data owner department and the requester department; External intelligence correlation characteristics: whether the requesting source IP appears in the recent threat intelligence blacklist, and whether the current time corresponds to a known high-risk period.

[0062] Example 6, see Figure 1 This embodiment is based on the above embodiment, and the fusion model generation unit includes: a context-aware subunit, a policy parameter calibration subunit, and a shared fusion subunit;

[0063] The context-aware subunit collects shared operation context, network situation, and threat intelligence in real time, and combines this with the potential risk score of the shared operation to generate a dynamic context vector representing the characteristics of the current shared scenario. ;

[0064] The strategy parameter calibration subunit uses a lightweight neural network to construct a dynamic calibration model. The dynamic policy weight model outputs optimized policy parameters for the current shared scenario, using the following formula: ;

[0065] In the formula, This represents the optimization policy parameters of the dynamic policy weight model. This represents a dynamic calibration model. Represents the dynamic context vector. Indicate the sensitivity level of the data to be shared;

[0066] The shared fusion subunit dynamically calibrates the parameters in the blockchain trust mechanism model and the data security strategy mechanism model. After optimizing the strategy parameters, it substitutes them into the dynamic strategy weight model, which is then coupled with the data security strategy mechanism model and the blockchain trust mechanism model to form and output the final secure shared fusion model. .

[0067] Example 7, see Figure 1This embodiment is based on the above embodiment, and in the strategy parameter calibration subunit, the dynamic calibration model A three-layer fully connected neural network is used, with the following structure: The neurons in the input layer correspond to dynamic context vectors. Potential risk score and data sensitivity level ; The hidden layer consists of one layer containing 16 neurons, and uses the ReLU activation function to learn nonlinear calibration relationships; The output layer contains 4 neurons, each corresponding to... The Sigmoid activation function is used to linearly scale the output value to the range of 0 to 1.

[0068] By performing the aforementioned operations, this solution addresses the heterogeneity between traditional government data sharing models and blockchain trust models. One is based on hierarchical approval and centralized authorization, while the other is based on distributed consensus and cryptographic verification. The two struggle to form a unified view in terms of permission mapping and audit tracing, making it impossible to assess the impact of data sharing activities on security and compliance from a global perspective. This solution constructs a unified government data and blockchain secure sharing fusion model. It establishes a precise and quantifiable mapping relationship between data sharing operation instructions and verifiable blockchain audit trails. Through a dynamic attribute-based encryption strategy model and data-driven calibration, it accurately reflects the impact of data sensitivity levels, user roles, and sharing context on access control, effectively improving the accuracy and credibility of shared security audits.

[0069] Example 8, see Figure 1 This embodiment is based on the above embodiment, and the shared behavior assessment module includes a simulation scenario definition unit, a behavior inference unit, a risk quantification unit, and a report generation unit;

[0070] The simulation scenario definition unit receives shared sensing data from various government departments, collects data sharing requests, and sets the simulation scenario and simulation time step.

[0071] The behavior inference unit generates simulated user access behavior based on the data sharing request, simulates the decision process of the strategy engine, the invocation and execution of smart contracts, and introduces a disturbance factor to simulate possible abnormal situations, and records the intermediate state and result of each step.

[0072] The risk quantification unit inputs the intermediate states and results of each simulation into the security sharing fusion model to generate an evaluation index vector, including security risk value, compliance score, and sharing processing latency. The security risk value is obtained by statistically analyzing the number of policy violations, the number of successful simulated attacks, and the anomaly detection model during the simulation. The predicted potential risk score is quantified by averaging; the compliance score is calculated by automatically comparing all operation records in the simulation with legal and regulatory provisions; the shared processing latency is calculated by the simulation clock and transaction count.

[0073] The report generation unit performs in-depth analysis on the evaluation index vectors output by the simulation, generates multi-dimensional evaluation charts including risk heatmaps, compliance gap analysis, and performance bottleneck location, and generates a complete simulation evaluation report for each data sharing request.

[0074] Example 9, see Figure 1 This embodiment is based on the above embodiment, and the intelligent collaborative scheduling module includes a multi-objective optimization function construction unit, a system constraint modeling unit, an intelligent optimization solution unit, and a strategy decomposition and execution unit;

[0075] The multi-objective optimization function construction unit constructs a comprehensive negative utility function based on the simulation evaluation report, which includes four dimensions: safety, efficiency, compliance, and value.

[0076] The system constraint modeling unit constructs the hard and soft constraints for system operation, including data privacy regulations, departmental authority and responsibility boundaries, system performance constraints, and blockchain resource constraints.

[0077] The intelligent optimization solution unit uses the NSGA-II multi-objective genetic algorithm to iteratively optimize the resource scheduling scheme. The encoding method adopts a hybrid encoding: binary represents whether approval is granted, and real numbers represent scheduling parameters. The population size is set to 200, and an elite retention strategy is adopted. When the Pareto frontier has no significant improvement for 100 consecutive generations, the iteration stops, and a set of Pareto optimal solutions is output as the final execution scheme.

[0078] The strategy decomposition and execution unit decomposes the final execution plan into executable smart contract call instruction sets, access control policy update instruction sets, and data service routing instruction sets, and distributes them to the blockchain platform, policy management server, and data gateways of various government departments, respectively, to achieve automated and secure execution of cross-departmental data sharing processes.

[0079] By performing the aforementioned operations, this solution addresses the problem that traditional blockchain sharing solutions often focus on on-chain data storage and lack fine-grained, dynamic access control over the data content itself, thus failing to achieve precise sharing that ensures data is usable but not visible, controllable, and traceable. This solution quantifies the controllability and risks of data sharing through high-precision policy simulation and real-time monitoring. It dynamically adjusts access policies based on the security situation and automatically optimizes the balance between maximizing data value and minimizing risk through smart contracts and optimization algorithms, ensuring the security, compliance, and efficiency of government data sharing.

[0080] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0081] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0082] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A cross-departmental data security sharing system based on government blockchain, characterized in that: It includes a data resource perception module, a fusion strategy construction module, a shared behavior evaluation module, and an intelligent collaborative scheduling module; The data resource perception module collects data resources from various government departments, standardizes the description of heterogeneous data resources, adds sensitivity level tags, collects contextual information related to shared business in real time, records the data source, the processing steps, and the new data derived therefrom, forming shared perception data, and sends the shared perception data to the fusion strategy construction module and the shared behavior evaluation module. The fusion strategy construction module constructs a secure sharing fusion model that describes the intrinsic relationship between government data sharing strategies and blockchain distributed trust through ternary fusion modeling, and sends the secure sharing fusion model to the sharing behavior evaluation module. The shared behavior assessment module, based on shared perception data and a secure sharing fusion model, simulates and extrapolates data sharing requests initiated by various government departments, predicts the potential security risks, compliance status, and performance impacts, and generates a complete simulation assessment report, which is then sent to the intelligent collaborative scheduling module. The intelligent collaborative scheduling module uses a multi-objective optimization algorithm to solve for the optimal resource scheduling scheme based on the simulation evaluation report, and uses smart contracts to jointly automate the data access process and blockchain evidence verification.

2. The cross-departmental data security sharing system based on government blockchain as described in claim 1, characterized in that: The fusion strategy construction module includes: a subject trust measurement unit, a strategy parameter setting unit, a blockchain trust mechanism modeling unit, a data security strategy mechanism modeling unit, a data-driven strategy learning unit, and a fusion model generation unit; The subject trust measurement unit inputs shared perception data, establishes initial trust profiles for each government department subject participating in the sharing, sets trust evaluation indicators, and obtains the initial trust level through weighted calculation; The strategy parameter setting unit initializes the dynamic strategy weight model and sets basic strategy parameters, including data sensitivity weight coefficient, subject role weight coefficient, context risk weight coefficient, and basic strategy adjustment constant. The blockchain trust mechanism modeling unit uses a permissioned consortium blockchain architecture to encapsulate the core trust mechanism of the blockchain, constructs a blockchain trust mechanism model, and performs trusted verification of shared operations. The core form is as follows: ； In the formula, Indicates a sharing operation. Indicates a shared operation Output confidence score, This represents the verification function. Represents the consensus algorithm. This indicates that the private key is used to digitally sign the shared operation. Represents the hash value of the associated block; The data security policy mechanism modeling unit adopts a hybrid access control policy based on attributes and roles to construct a data security policy mechanism model. It defines user attribute sets, data attribute sets, environment context attribute sets, and policy rules, and uses a policy engine to evaluate the policy rules, determining whether access is authorized. The result is output as a boolean value, and the judgment logic is as follows: ； In the formula, Indicates the judgment result. Represents the strategy engine, Represents a set of user attributes. Represents a collection of data attributes. Represents a collection of environment context properties; The data-driven strategy learning unit constructs high-dimensional feature vectors and builds an anomaly detection model based on the gradient boosting decision tree (XGBoost) model to predict the potential risk score for each sharing operation. The model form is as follows: ； In the formula, This indicates the predicted potential risk score for this sharing operation. This represents an anomaly detection model. Represents a high-dimensional feature vector; The fusion model generation unit outputs the optimal parameters of the dynamic strategy weight model based on the potential risk score of the shared operation, and dynamically calibrates the parameters in the blockchain trust mechanism model and the data security strategy mechanism model, and outputs the final secure sharing fusion model.

3. The cross-departmental data security sharing system based on government blockchain as described in claim 2, characterized in that: The fusion model generation unit includes: a context-aware subunit, a policy parameter calibration subunit, and a shared fusion subunit; The context-aware subunit collects shared operation context, network situation and threat intelligence in real time, and combines them with the potential risk score of shared operation to generate a dynamic context vector that represents the characteristics of the current shared scenario. The policy parameter calibration subunit uses a lightweight neural network to construct a dynamic calibration model, and outputs optimized policy parameters for the current shared scenario using a dynamic policy weight model. The formula used is as follows: ； In the formula, This represents the optimization policy parameters of the dynamic policy weight model. This represents a dynamic calibration model. Represents the dynamic context vector. Indicate the sensitivity level of the data to be shared; The shared fusion subunit dynamically calibrates the parameters in the blockchain trust mechanism model and the data security strategy mechanism model. After substituting the optimized strategy parameters into the dynamic strategy weight model, it couples with the data security strategy mechanism model and the blockchain trust mechanism model to form and output the final secure shared fusion model.

4. The cross-departmental data security sharing system based on government blockchain as described in claim 1, characterized in that: The shared behavior assessment module includes a simulation scenario definition unit, a behavior inference unit, a risk quantification unit, and a report generation unit; The simulation scenario definition unit receives shared sensing data from various government departments, collects data sharing requests, and sets the simulation scenario and simulation time step. The behavior inference unit generates simulated user access behavior based on the data sharing request, simulates the decision process of the strategy engine, the invocation and execution of smart contracts, and introduces a disturbance factor to simulate possible abnormal situations, and records the intermediate state and result of each step. The risk quantification unit inputs the intermediate states and results of each simulation into the security sharing fusion model to generate an evaluation index vector, including security risk value, compliance score, and sharing processing delay. The report generation unit performs in-depth analysis on the evaluation index vectors output by the simulation, generates multi-dimensional evaluation charts including risk heatmaps, compliance gap analysis, and performance bottleneck location, and generates a complete simulation evaluation report for each data sharing request.

5. The cross-departmental data security sharing system based on government blockchain according to claim 1, characterized in that: The intelligent collaborative scheduling module includes a multi-objective optimization function construction unit, a system constraint modeling unit, an intelligent optimization solution unit, and a strategy decomposition and execution unit; The multi-objective optimization function construction unit constructs a comprehensive negative utility function based on the simulation evaluation report; The system constraint modeling unit constructs the hard and soft constraints for system operation; The intelligent optimization solution unit uses the NSGA-II multi-objective genetic algorithm to iteratively optimize the resource scheduling scheme. It sets the encoding method and population size, adopts the elite retention strategy, and stops iterating when the Pareto front has no significant improvement for 100 consecutive generations. It outputs a set of Pareto optimal solutions as the final execution scheme. The strategy decomposition and execution unit decomposes the final execution plan into an executable smart contract call instruction set, an access control policy update instruction set, and a data service routing instruction set, and distributes them to the blockchain platform, the strategy management server, and the data gateways of various government departments, respectively.

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