Compliance governance evidence chain integrity analysis method based on block chain evidence storage

Abstract

The invention provides a compliance governance evidence chain integrity analysis method based on block chain evidence storage, and relates to the technical field of block chains, and the method specifically comprises the steps: S1, collecting related transaction records and evidence storage data in block chain evidence storage records of an evidence chain through a block chain network; s2, managing the life cycle of the evidence chain data; s3, performing hash value comparative analysis on the block chain evidence storage record of the evidence chain to determine the integrity of the evidence chain; s4, carrying out traceability analysis through the evidence chain data; according to the method, the accuracy of data consistency verification can be improved, the normalization and safety of data life cycle management are enhanced, traceability analysis is performed through the causal relationship graph, and the reliability of data traceability evaluation is improved; and through multi-dimensional data analysis and standardized format output, generating an integrity evaluation report.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and specifically to a method for analyzing the integrity of a compliance governance evidence chain based on blockchain evidence storage. Background Art

[0002] The rapid development of the digital economy has given rise to a brilliant digital rule of law. Blockchain technology is an important part of the digital economy. With its technical advantages such as decentralization, information sharing, trustlessness, efficient collaboration and immutability, it plays an increasingly important role in the fields of evidence storage, identification and authentication.

[0003] In recent years, with the vigorous development of Internet technology, the overall economic structure of society has undergone earth-shaking changes. In order to better adapt to this digital economic model, digital rule of law has emerged and entered a period of rapid development.

[0004] As digital rule of law continues to evolve, the authenticity, reliability, transparency, and immutability of blockchain-based evidence are gaining increasing attention. Traditional methods of preserving evidence often face the risk of tampering, loss, or forgery. In recent years, with the rise of blockchain technology, a new type of evidence preservation method—blockchain-based evidence—has begun to attract widespread attention.

[0005] In the prior art, the publication number is CN108897760A, and the name is the electronic evidence chain integrity verification method based on the Merkle tree; the method includes: the sender uploads the contract or selects a contract template through the electronic contract system, and then uses the digital certificate issued by the authoritative CA organization to digitally sign the contract, and then adds the national authorized timestamp service to the contract; sends the electronic contract to the signing end, and receives the electronic contract returned by the contract signing end, which includes the timestamp of the signing time and the electronic seal of the electronic contract signatory; sets the signatory and the order according to the sender of the electronic contract, and receives the electronic contract and the final electronic signature returned by all signatories to complete the signing. The electronic contract platform records all node information in the process of the user signing the contract and stores it in the private blockchain evidence storage platform; the present invention not only ensures the integrity and non-tamperability of the contract, but also records and stores the information of each process of the contract signing, which can restore the complete evidence chain of the entire process of the user's contract signing.

[0006] Although blockchain technology offers significant advantages in data storage, it still faces challenges in practical applications, such as data consistency verification, data lifecycle management, and data traceability analysis. Therefore, this paper proposes a method for analyzing the integrity of the evidence chain for compliance governance based on blockchain storage. This method can effectively address these issues, improve data credibility and compliance, and has important theoretical value and practical application significance.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0008] The purpose of the present invention is to provide a compliance governance evidence chain integrity analysis method based on blockchain evidence storage to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions: The integrity analysis method of the evidence chain of compliance governance based on blockchain evidence storage includes the following specific steps: S1: Collect relevant transaction records and evidence data from the blockchain evidence records of the evidence chain through the blockchain network, and pre-process the collected relevant transaction records and evidence data to obtain pre-processed evidence data; S2: By managing the lifecycle of evidence chain data, the process integrity of the evidence chain data is ensured, making it easier to track and trace the source; S3: Determine the data consistency of the evidence chain by extracting the hash value of the evidence chain data in the node, and performing hash value comparison analysis on the blockchain evidence record of the evidence chain. Determine the integrity of the evidence chain based on the continuity of the time series analysis and the data consistency analysis results. S4: Conduct traceability analysis through evidence chain data and evaluate the traceability of the evidence chain; S5: Conduct a comprehensive analysis of the integrity check results and traceability analysis results based on multi-dimensional data analysis to generate an integrity assessment report; S6: The evaluation results of the evidence chain data in a standardized format are output in a standardized format to ensure the ease of use and subsequent application of the data.

[0010] Furthermore, the collection of relevant transaction records and evidence data in S1 specifically includes the following steps: Through the blockchain network API interface, connect to the blockchain main chain and side chain to obtain relevant transaction records and evidence data; Encrypt and transmit relevant transaction records and evidence data to ensure data security; Extract the hash value and transmission time from the stored evidence data, and extract the transaction ID, transaction time, transaction amount, transaction participants, and related block information of the relevant transaction records.

[0011] Furthermore, the specific steps of lifecycle management of the evidence chain data in S2 are as follows: The transmission time of each node of the evidence chain data is collected, and the data is arranged in sequence according to the transmission time to establish a time series sorting diagram; Obtain node information according to the time series sorting graph, and connect the nodes according to the time series sorting graph to generate a node flow topology graph; Dynamically mark the evidence chain data of each node, obtain the information of the evidence chain data flow, and obtain a secure dynamic graph of the evidence chain data; Extract the characteristic information of the evidence chain data based on the security dynamic graph, and connect the upper and lower nodes of the characteristic information to form a complete temporal graph of the evidence chain data; Obtain node information based on the complete temporal graph, establish a causal relationship graph of the evidence chain data, and perform traceability analysis on the nodes through the causal relationship graph.

[0012] Furthermore, the analysis of the hash value comparison result in S3 is as follows: The hash value in the node evidence data matches, and the correct value is , the value of the matching error is ; The probability of node storage data is calculated as follows: ; in, It is expressed as the probability that there are anomalies in the evidence data of all nodes. It means that after matching the hash values ​​of the nodes, the hash values ​​match the correct number of nodes, and, Indicates correct, It is represented by the number of nodes with incorrect hash value matching after matching the hash values ​​of the nodes, and Indicates an error; but , which means that in the node flow topology graph, the data consistency of the evidence chain is high; but , which means that in the node flow topology diagram, the data consistency of the evidence chain is poor, and data tracing and comparison of related transaction records are required; but , which means that in the node flow topology diagram, the data consistency of the evidence chain is extremely poor, and data tracing and comparison of related transaction records are required.

[0013] Furthermore, the traceability scoring formula in S4 is: ; in, Expressed as a transaction traceability score, ranging from 0 to 1; Expressed as a weight coefficient, controlling the weight of each factor, usually ; Represented as the hash value of the transaction input and output script, used to uniquely identify the script; Indicates the verification result of the transaction signature, 1 indicates valid, 0 indicates invalid; It is expressed as the number of times the transaction is confirmed in the blockchain, indicating the confirmation status of the transaction.

[0014] Furthermore, the specific steps of the comprehensive analysis in S5 are as follows: Integrate integrity check reports and traceability analysis reports; Conduct multi-dimensional data analysis and calculate data integrity and traceability scores; Generate a completeness assessment report based on the scoring results; Export the assessment report into a standardized format for subsequent use.

[0015] Furthermore, the comprehensive scoring formula in S5 is: ; in, It is expressed as a composite score ranging from 0 to 1; Expressed as a weight coefficient, controlling the weight of each dimension, usually , .

[0016] Furthermore, the comprehensive analysis of the S5 traceability analysis results specifically includes the following steps: Integrate integrity check reports, traceability analysis reports, and lifecycle management records; Conduct multi-dimensional data analysis and calculate data integrity and traceability scores; Generate a completeness assessment report based on the scoring results; Export the assessment report into a standardized format for subsequent use.

[0017] Furthermore, the standardized format output step in S6 is as follows: Integrate assessment results into a standardized format; Output integrity assessment report, including data integrity score, traceability score and lifecycle management score; Provides a data visualization interface to facilitate users to intuitively view the evaluation results.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The proposed blockchain-based evidence chain integrity analysis method for compliance governance can comprehensively verify the integrity and traceability of the evidence chain through multi-step data collection, preprocessing, analysis, and evaluation, ensuring the authenticity, accuracy, and compliance of the data. It not only improves the accuracy of data consistency verification, but also enhances the standardization and security of data lifecycle management. It also conducts traceability analysis through causal relationship diagrams, further improving the reliability of data traceability assessments. Finally, through multi-dimensional data analysis and standardized format output, it generates an integrity assessment report, providing strong support for subsequent applications and audits. By extracting the hash value of the evidence chain data in the node and performing hash value comparison analysis, the present invention can effectively verify the data consistency of the evidence chain and ensure the integrity and authenticity of the data; it can quickly identify data anomalies, reduce the risk of human error and system failure, and improve the accuracy and efficiency of data verification; The transmission time of each node of the evidence chain data is collected, and the data is arranged in sequence according to the transmission time to establish a time series sorting diagram; the node information is obtained according to the time series sorting diagram, and the nodes are connected according to the time series sorting diagram to generate a node flow topology diagram; the evidence chain data of each node is dynamically marked to obtain the information of the evidence chain data flow and obtain a safe dynamic diagram of the evidence chain data; the feature information of the evidence chain data is extracted according to the safe dynamic diagram, and the feature information is connected to the upper and lower nodes to form a complete temporal diagram of the evidence chain data; the node information is obtained according to the complete temporal diagram, and a causal relationship diagram of the evidence chain data is established, and the nodes are traced back and analyzed through the causal relationship diagram; the traceability analysis through the causal relationship diagram can more accurately evaluate the traceability of the data, ensure that the source and flow of the data can be tracked and verified; it can help users quickly locate data problems and improve the credibility and transparency of the data; Through multi-dimensional data analysis, an integrity assessment report is generated, which can not only intuitively display the integrity score and traceability score of the data, but also provide detailed lifecycle management records to facilitate subsequent application and auditing; it can provide users with comprehensive data assessment results to support the scientific and accurate decision-making; the assessment results are output in a standardized format for users to intuitively view and subsequent use; it can improve the operability and practicality of the data and reduce the technical threshold in subsequent applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram of the specific steps of lifecycle management of evidence chain data in the present invention; Figure 3 Schematic diagram of the specific steps of the comprehensive analysis of the present invention; Figure 4 Schematic diagram of the specific steps for comprehensive analysis of the traceability analysis results of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments. Example

[0021] See also Figure 1-Figure 4 The present invention provides a technical solution: a method for analyzing the integrity of the evidence chain of compliance governance based on blockchain evidence storage, which specifically includes the following steps: S1: Collect relevant transaction records and evidence data from the blockchain evidence records of the evidence chain through the blockchain network, and pre-process the collected relevant transaction records and evidence data to obtain pre-processed evidence data; The collection of relevant transaction records and evidence data specifically includes the following steps: Through the blockchain network API interface, connect to the blockchain main chain and side chain to obtain relevant transaction records and evidence data; Encrypt and transmit relevant transaction records and evidence data to ensure data security; Extract the hash value and transmission time from the stored evidence data, and extract the transaction ID, transaction time, transaction amount, transaction participants, and related block information of the relevant transaction records.

[0022] The specific steps for preprocessing relevant transaction records and evidence data include: Data cleaning: De-noising the collected transaction records and evidence data to remove duplicate data, outliers, or incomplete data; Data filling: Then, the mean of relevant transaction records and evidence data is calculated, and the calculated mean is filled until the gaps in duplicate data, outliers, or incomplete data are removed; Data normalization: Normalize relevant transaction records and evidence data so that the evidence data can be mapped to the range [0, 1]. Through normalization, data from different sources and dimensions are converted to a unified scale, thereby improving data consistency and comparability. Data format conversion: Convert the collected transaction records and evidence data into a unified data format, such as JSON or XML, to facilitate subsequent analysis; Data storage: Store pre-processed transaction records and evidence data in a local database or cloud data warehouse for subsequent analysis; S2: By managing the lifecycle of evidence chain data, the process integrity of the evidence chain data is ensured, making it easier to track and trace the source; The specific steps include: The transmission time of each node of the evidence chain data is collected, and the data is arranged in sequence according to the transmission time to establish a time series sorting diagram; First, the transmission time information of the evidence chain data is collected from each node in the cross-chain to ensure that each piece of evidence chain data has an accurate timestamp; Arrange the timestamps in ascending order to generate a time series sorting chart; use data visualization tools such as Tableau or Python's Matplotlib to display the time series as a line chart or bar chart; Obtain node information according to the time series sorting graph, and connect the nodes according to the time series sorting graph to generate a node flow topology graph; Extract detailed information of each node from the time series sorting graph, including node ID and node position; Use a graph database or data visualization tool to connect nodes in chronological order to form a node flow topology diagram; Dynamically mark the evidence chain data of each node, obtain the information of the evidence chain data flow, and obtain a secure dynamic graph of the evidence chain data; Mark the evidence chain data of each node, perform hash coding on the node information to obtain a unique data identifier, identify the flow of evidence chain data according to the data identifier, and obtain query data with the data identifier; ; in, and Represented as two different hash functions, usually Expressed as a linear hash, Expressed as a polynomial hash to improve the uniformity of the hash function and reduce the probability of collision; Represented as a large prime number, used to limit the range of the final hash value, It is expressed as an operation that restricts the hash value to the range of 0 to m-1 by taking the remainder operation; The formula for the linear hash function is: ; in, Expressed as a constant, used to adjust the distribution of hash values ​​and choose the appropriate Reduce collisions; Expressed as another constant used to offset the hash value; choose the appropriate Helps to evenly distribute the hash value; The formula for a polynomial hash function is: ; in, Expressed as a base number, usually a large prime number such as 31 or 127 is chosen to improve the uniqueness of the hash value; Represents the input data characters, and the final hash value is obtained through polynomial calculation; Indicates that the input data has a total of characters; Generate secure dynamic graphs based on the tag information; use dynamic charting tools, such as ECharts or D3.js, to display data changes at different time points; Extract the characteristic information of the evidence chain data based on the security dynamic graph, and connect the upper and lower nodes of the characteristic information to form a complete temporal graph of the evidence chain data; Calculate the correlation between the feature information of the upper and lower nodes: Up and down node evidence chain data and , including the following features: Timestamp: and ; Device ID: and ; Log type: and ; Data size: and ; Data relevance is calculated using the following formula: ; in, 、 、 、 The weight coefficients are timestamp, device ID, log type, and data size, which are used to indicate the importance of each feature; It is expressed as the similarity score of two features, with a value range of [0,1], where 1 indicates a perfect match and 0 indicates a complete mismatch; It is expressed as the total score of data relevance; Extract key features from security dynamic graphs, such as data correlation of evidence chain data; Plot the extracted feature information in time series and use time series analysis tools to form temporal graphs; such as the Pandas library in R or Python to generate line graphs or heat maps; Obtain node information based on the complete temporal graph, establish a causal relationship graph for the evidence chain data, and perform retrospective analysis of the nodes through the causal relationship graph; Based on the temporal diagram, use causal analysis tools or write scripts to identify key nodes and paths, and identify causal relationships in data flow; Through the causal relationship diagram, use data mining tools such as Apache Spark or Weka to perform node traceback analysis to identify the root cause of abnormal events and generate detailed traceback reports.

[0023] S3: Extract the hash value of the evidence chain data in the node flow topology diagram, perform hash value comparison analysis on the blockchain evidence record of the evidence chain, determine the data consistency of the evidence chain, and determine the integrity of the evidence chain based on the continuity and data consistency analysis results of the time series analysis; The integrity check specifically includes the following steps: The transmission time of each node is used to perform time series analysis on the pre-processed evidence data; ; in, Expressed as time The completeness score of the chain of evidence; Expressed as a weight coefficient, controlling the weight of the current state and the historical state, usually ; Expressed as an offset term, used to balance the impact of different dimensions, usually 0 <B<1; Expressed as the attenuation coefficient, considering the attenuation effect of the time series, usually 0 <D<1; Expressed as the number of time steps; Weight coefficient : Domain: ; Value range: Control the weight of the current state and the historical state, the larger The value indicates that the historical state has a greater impact on the current state; Determine the rules: The value of can be determined through experimental data or domain knowledge, and is usually between 0.6 and 0.9.

[0024] Offset Item : Domain: 0 <B<1; Value range: Used to adjust the initial offset of the formula to ensure that the score is within a reasonable range; Determine the rules: The value of is usually determined by the initial training data to ensure that the score is consistent with the actual situation.

[0025] Attenuation coefficient : Domain: 0 <D<1; Value range: Control the time series attenuation effect, the larger Values ​​indicate that the attenuation effect is more obvious; Determine the rules: The value of can be determined through domain knowledge or experimental data, and is usually between 0.5 and 0.8.

[0026] According to the node flow topology diagram, the hash value comparison and analysis of the blockchain evidence records of each node's evidence chain are carried out in turn; Compare the hash values ​​in the statistical node evidence data and analyze the data consistency.

[0027] The analysis of the hash value comparison results is as follows: The hash value in the node evidence data matches, and the correct value is , the value of the matching error is ; The probability of node storage data is calculated as follows: ; in, It is expressed as the probability that there are anomalies in the evidence data of all nodes. It means that after matching the hash values ​​of the nodes, the hash values ​​match the correct number of nodes, and, Indicates correct, It is represented by the number of nodes with incorrect hash value matching after matching the hash values ​​of the nodes, and Indicates an error; Set matching threshold , the probability of abnormality Matching threshold For comparison: but , which means that in the node flow topology graph, the data consistency of the evidence chain is high; but , which means that in the node flow topology diagram, the data consistency of the evidence chain is poor, and data tracing and comparison of related transaction records are required; but , which means that in the node flow topology diagram, the data consistency of the evidence chain is extremely poor, and data tracing and comparison of related transaction records are required.

[0028] The steps for comparing related transaction records are as follows: Compare the transaction ID, transaction time, transaction amount, transaction participants and related block information of relevant transaction records; First, compare the relevant transaction records of abnormal nodes with those of normal nodes; The abnormal node is then compared with the related transaction records of the abnormal node; The result is determined in In the case of , the relevant transaction records of the abnormal node and the normal node are the same, and the relevant transaction records of the abnormal node and the abnormal node are different, it is determined that there is a data transmission error, or the analysis of the abnormal node and the normal node is not in the same time series; exist In the case that the relevant transaction records of the abnormal node and the normal node are different at this time, and the relevant transaction records of the abnormal node and the abnormal node are the same, it is determined that the analysis of the abnormal node and the normal node is in the same time series, and the transmission data is modified.

[0029] S4: Conduct traceability analysis on the evidence chain data of the nodes through the causal relationship diagram and evaluate the traceability of the evidence chain; Traceability scoring formula: ; in, Expressed as a transaction traceability score, ranging from 0 to 1; Expressed as a weight coefficient, controlling the weight of each factor, usually ; Represented as the hash value of the transaction input and output script, used to uniquely identify the script; Indicates the verification result of the transaction signature, 1 indicates valid, 0 indicates invalid; It is expressed as the number of times the transaction is confirmed in the blockchain, indicating the confirmation status of the transaction; Weight coefficient : The weight of controlling the authenticity of the transaction script, the larger The value indicates that the authenticity of the script has a greater impact on traceability; The value of is determined by experimental data or domain knowledge and is usually between 0.6 and 0.9.

[0030] Weight coefficient :Control the impact of transaction signature validity on traceability, the larger The value indicates that the signature validity has a greater impact on traceability; The value of is usually determined by the initial training data to ensure that the score is consistent with the actual situation.

[0031] Weight coefficient :Control the impact of transaction confirmation times on traceability, the larger A value of indicates that the number of confirmations has a greater impact on traceability; The value of is determined by domain knowledge or experimental data and is usually between 0.5 and 0.8.

[0032] Parameter changes and technical effects: when When it increases, the authenticity of the transaction script has a greater impact on traceability; when When it increases, the validity of the transaction signature has a greater impact on traceability; when When it increases, the number of transaction confirmations has a greater impact on traceability; when When it increases, it means the authenticity of the transaction script is higher. Increase; when , it means the transaction signature is valid. Increase; when When it increases, it means more transaction confirmations. Increase.

[0033] Output value range and technology association: The output value range is limited to the range of (0,1) to ensure the stability and readability of the score; when When it approaches 0, it means that the transaction traceability is poor and there may be fraud; when When it approaches 1, it means that the transaction traceability is high and meets the compliance requirements; Special value analysis: when hour, , indicating that the transaction signature is invalid and traceability is poor; when hour, The lowest value indicates that the transaction is not confirmed and has the worst traceability; when When is a random value, near The value of ,indicates the impact of script authenticity on traceability; Stability analysis: By weighting the coefficient The reasonable value range of is set to ensure that the formula output remains stable in the range of (0,1).

[0034] The traceability analysis specifically includes the following steps: Analyze transaction input and output scripts to confirm the authenticity of transaction participants; Verify the validity of the transaction signature to ensure the legitimacy of the transaction; Check the transaction record status in the blockchain to confirm whether the transaction has been confirmed; Generate traceability analysis reports.

[0035] S5: Conduct a comprehensive analysis of the integrity check results and traceability analysis results based on multi-dimensional data analysis to generate an integrity assessment report; Comprehensive scoring formula: ; in, Expressed as a composite score ranging from 0 to 1.

[0036] Expressed as a weight coefficient, controlling the weight of each dimension, usually , ; Expressed as a completeness score, the completeness score is mapped between 0 and 1 through normalization; Expressed as a traceability score ranging from 0 to 1.

[0037] Weight coefficient :The impact of control integrity score on comprehensive score is greater A value of indicates that the completeness score has a greater impact on the overall score; The value of is determined by experimental data or domain knowledge, usually between 0.3 and 0.7; Weight coefficient :Control the impact of traceability score on the comprehensive score, the larger A value of indicates that the traceability score has a greater impact on the overall score; The value of is usually determined by the initial training data to ensure that the score is consistent with the actual situation; Parameter changes and technical effects: when When it increases, the completeness score has a greater impact on the comprehensive score; when When it increases, the traceability score has a greater impact on the comprehensive score; when When it increases, it means the data integrity is higher. Increase; when When it increases, it means that the data is more traceable. Increase.

[0038] Output value range and technology association: The output value range is limited to the range of (0,1) to ensure the stability and readability of the score; when When it approaches 0, it means the overall score is low, and there may be problems with integrity, traceability, or lifecycle management; when When it approaches 1, it means the overall score is high and meets the compliance requirements; Special Value Behavior and Stability: Special Value Analysis when hour, , indicating that the completeness score has no effect on the comprehensive score; when hour, , indicating that the traceability score has no impact on the overall score.

[0039] Stability analysis: By weighting the coefficient 、 The reasonable value range of is set to ensure that the formula output remains stable in the range of (0,1).

[0040] The specific steps include: Integrate integrity check reports and traceability analysis reports; Conduct multi-dimensional data analysis and calculate data integrity and traceability scores; Generate a completeness assessment report based on the scoring results; Export the assessment report into a standardized format for subsequent use.

[0041] S6: Output the evaluation results of the evidence chain data in a standardized format to ensure the ease of use and subsequent application of the data; The specific steps include: Integrate assessment results into a standardized format; Output integrity assessment report, including data integrity score, traceability score and lifecycle management score; Provides a data visualization interface to facilitate users to intuitively view the evaluation results.

[0042] It should be noted that: All calculation formulas in this application document use regression analysis including but not limited to machine learning algorithms to deeply analyze the relevant parameters collected and identify their natural trends and interrelationships. Use professional software, such as Python's Scikit-learn library or R language, to automatically generate mathematical models that match the data. Then, objectively evaluate the performance of the model through methods such as cross-validation, and combine continuous feedback and optimization to ensure that the created formula truly reflects the inherent laws of the data, thereby ensuring its effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula are dimensionally non-scaled through a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless technical means include but are not limited to maximum and minimum normalization (Min-MaxNormalization) and Z-Score standardization.

[0043] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. The integrity analysis method of the evidence chain of compliance governance based on blockchain evidence storage is characterized by: The specific steps include: S1: Collect relevant transaction records and evidence data from the blockchain evidence records of the evidence chain through the blockchain network, and pre-process the collected relevant transaction records and evidence data to obtain pre-processed evidence data; S2: By managing the lifecycle of evidence chain data, the process integrity of the evidence chain data is ensured, making it easier to track and trace the source; S3: Determine the data consistency of the evidence chain by extracting the hash value of the evidence chain data in the node, and perform hash value comparison analysis on the blockchain evidence record of the evidence chain. Determine the integrity of the evidence chain based on the continuity of the time series analysis and the data consistency analysis results. S4: Conduct traceability analysis through evidence chain data and evaluate the traceability of the evidence chain; S5: Conduct a comprehensive analysis of the integrity check results and traceability analysis results based on multi-dimensional data analysis to generate an integrity assessment report; S6: The evaluation results of the evidence chain data in a standardized format are output in a standardized format to ensure the ease of use and subsequent application of the data.

2. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 1 is characterized by: The collection of relevant transaction records and evidence data in S1 specifically includes the following steps: Through the blockchain network API interface, connect to the blockchain main chain and side chain to obtain relevant transaction records and evidence data; Encrypt and transmit relevant transaction records and evidence data to ensure data security; Extract the hash value and transmission time from the stored evidence data, and extract the transaction ID, transaction time, transaction amount, transaction participants, and related block information of the relevant transaction records.

3. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 2 is characterized by: The specific steps for lifecycle management of evidence chain data in S2 are as follows: The transmission time of each node of the evidence chain data is collected, and the data is arranged in sequence according to the transmission time to establish a time series sorting diagram; Obtain node information according to the time series sorting graph, and connect the nodes according to the time series sorting graph to generate a node flow topology graph; Dynamically mark the evidence chain data of each node, obtain the information of the evidence chain data flow, and obtain a secure dynamic graph of the evidence chain data; Extract the characteristic information of the evidence chain data based on the security dynamic graph, and connect the upper and lower nodes of the characteristic information to form a complete temporal graph of the evidence chain data; Obtain node information based on the complete temporal graph, establish a causal relationship graph of the evidence chain data, and perform traceability analysis on the nodes through the causal relationship graph.

4. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 1 is characterized by: The analysis of the hash value comparison results in S3 is as follows: The hash value in the node evidence data matches, and the correct value is , the value of the matching error is ; The probability of node storage data is calculated as follows: ； in, It is expressed as the probability that there are anomalies in the evidence data of all nodes. It means that after matching the hash values ​​of the nodes, the hash values ​​match the correct number of nodes, and, Indicates correct, It is represented by the number of nodes with incorrect hash value matching after matching the hash values ​​of the nodes, and Indicates an error; but , which means that in the node flow topology graph, the data consistency of the evidence chain is high; but , which means that in the node flow topology diagram, the data consistency of the evidence chain is poor, and data tracing and comparison of related transaction records are required; but , which means that in the node flow topology diagram, the data consistency of the evidence chain is extremely poor, and data tracing and comparison of related transaction records are required.

5. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 4 is characterized by: The traceability scoring formula in S4 is: ； in, Expressed as a transaction traceability score, ranging from 0 to 1; Expressed as a weight coefficient, controlling the weight of each factor, usually ; Represented as the hash value of the transaction input and output script, used to uniquely identify the script; Indicates the verification result of the transaction signature, 1 indicates valid, 0 indicates invalid; It is expressed as the number of times the transaction is confirmed in the blockchain, indicating the confirmation status of the transaction.

6. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 5 is characterized by: The specific steps of the comprehensive analysis in S5 are as follows: Integrate integrity check reports and traceability analysis reports; Conduct multi-dimensional data analysis and calculate data integrity and traceability scores; Generate a completeness assessment report based on the scoring results; Export the assessment report into a standardized format for subsequent use.

7. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 6 is characterized by: The comprehensive scoring formula in S5 is: ; in, It is expressed as a composite score ranging from 0 to 1; Expressed as a weight coefficient, controlling the weight of each dimension, usually , .

8. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 7 is characterized by: The comprehensive analysis of the S5 traceability analysis results specifically includes the following steps: Integrate integrity check reports, traceability analysis reports, and lifecycle management records; Conduct multi-dimensional data analysis and calculate data integrity and traceability scores; Generate a completeness assessment report based on the scoring results; Export the assessment report into a standardized format for subsequent use.

9. The method for analyzing the integrity of the evidence chain for compliance governance based on blockchain evidence storage according to claim 7 is characterized by: The steps for outputting the standardized format in S6 are as follows: Integrate assessment results into a standardized format; Output integrity assessment report, including data integrity score, traceability score and lifecycle management score; Provides a data visualization interface to facilitate users to intuitively view the evaluation results.

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