Blockchain-based compliance governance evidence chain integrity analysis method

Abstract

The application provides a compliance governance evidence chain integrity analysis method based on blockchain storage, relates to the technical field of blockchains, and specifically comprises the following steps: S1: collecting relevant transaction records and storage data in the blockchain storage records of the evidence chain through a blockchain network; S2: managing the life cycle of the evidence chain data; S3: performing hash value comparison and analysis on the blockchain storage records of the evidence chain to determine the integrity of the evidence chain; and S4: performing traceability analysis on the evidence chain data; the application can improve the accuracy of data consistency verification, enhance the standardization and security of data life cycle management, improve the reliability of data traceability evaluation through the traceability analysis of a cause-effect diagram, and generate an integrity evaluation report through multidimensional data analysis and standardized format output.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain, in particular to a compliance governance evidence chain integrity analysis method based on blockchain storage. BACKGROUND

[0002] The rapid development of digital economy has given rise to brilliant digital legal construction. Blockchain technology is an important part of digital economy, and with its technical advantages such as decentralization, information sharing, trustlessness, efficient collaboration and non-tamperability, it plays an increasingly important role in evidence storage, identification and authenticity.

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

[0004] In the context of the continuous development of digital legal system, the emerging blockchain technology combined with the authenticity, reliability, transparency and non-tamperability of blockchain storage evidence data has been increasingly concerned. Traditional evidence preservation methods often face the risk of tampering, loss or forgery. In recent years, with the rise of blockchain technology, a new type of storage method - blockchain storage evidence has begun to receive more widespread attention.

[0005] In the prior art, the publication number is CN108897760A, and the name is an electronic evidence chain integrity verification method based on Merkel tree; the method comprises: the sender uploads the contract through the electronic contract system or selects the contract template, then uses the digital certificate issued by the authoritative CA institution to sign the contract, and then adds the national authorized time stamp service in the contract; the electronic contract is sent to the signing end, and the contract signing end returns the electronic contract, which includes the signing time stamp and the electronic seal of the electronic contract signing party; according to the setting of the signing party and the order by the electronic contract sender, the electronic contract and the final electronic seal returned by all signing parties are received to complete the signing. The electronic contract platform records all node information in the user contract signing process and stores it in the private block chain storage platform; the present application not only guarantees the integrity and non-tamperability of the contract, but also records and stores the information of each process of the contract signing, and can restore the complete evidence chain of the user contract signing process.

[0006] Although blockchain technology has significant advantages in data storage, it still faces some challenges in practical application, such as data consistency verification, data life cycle management and data traceability analysis. Therefore, a compliance governance evidence chain integrity analysis method based on blockchain storage is proposed, which can effectively solve these problems and improve the credibility and compliance of data, and has important theoretical value and practical application significance.

[0007] The above information disclosed in the BACKGROUND section is only for the purpose of enhancing the understanding of the background of the present disclosure, and therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

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

[0009] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0010] The compliance governance evidence chain integrity analysis method based on blockchain notarization, the specific steps include:

[0011] S1: Collecting relevant transaction records and notarization data in the blockchain notarization record of the evidence chain through the blockchain network, and preprocessing the collected relevant transaction records and notarization data to obtain preprocessed notarization data;

[0012] S2: By managing the life cycle of the evidence chain data, ensuring the process integrity of the evidence chain data, and facilitating traceability;

[0013] S3: By extracting the hash value of the evidence chain data in the node, comparing and analyzing the hash value of the blockchain notarization record of the evidence chain, determining the data consistency of the evidence chain, and according to the continuity of time series analysis and the analysis result of data consistency, determining the integrity of the evidence chain;

[0014] S4: By evidence chain data, the traceability of the evidence chain is analyzed and evaluated;

[0015] S5: Based on multi-dimensional data analysis, the integrity check result and the traceability analysis result are comprehensively analyzed, and an integrity evaluation report is generated;

[0016] S6: Based on the evaluation result of the evidence chain data in the standardized format, the standardized format output is carried out, and the data is ensured to be easy to use and subsequent application.

[0017] Further, the S1 related transaction record and notarization data collection specifically includes the following steps:

[0018] Through the API interface of the blockchain network, connect the main chain and the side chain of the blockchain, and obtain the relevant transaction records and notarization data;

[0019] Encrypt the transmission of relevant transaction records and notarization data to ensure data security;

[0020] Extract the hash value and transmission time in the evidence data, extract the transaction ID, transaction time, transaction amount, transaction participants and related block information of the related transaction record.

[0021] Further, the specific steps of the life cycle management of the evidence chain data in S2 are as follows:

[0022] The transmission time of each node of the evidence chain data is collected, and the transmission time is sequentially arranged to establish a time sequence sorting graph;

[0023] According to the time sequence sorting graph, the information of the nodes is obtained, and the nodes are connected according to the time sequence sorting graph to generate a node flow topology graph;

[0024] The evidence chain data of each node is dynamically marked to obtain the information of the evidence chain data flow, and a safety dynamic graph of the evidence chain data is obtained;

[0025] According to the safety dynamic graph, the characteristic information of the evidence chain data is extracted, and the characteristic information is connected with the upper and lower nodes to form a complete time graph of the evidence chain data;

[0026] According to the complete time graph, the node information is obtained, a causal relationship graph of the evidence chain data is established, and the nodes are traced and analyzed through the causal relationship graph.

[0027] Further, the analysis of the hash value comparison result in S3 is as follows:

[0028] Through the matching of the hash value in the node evidence data, the number of correct matching values is , and the number of incorrect matching values is ;

[0029] Then the probability calculation of the node evidence data is as follows:

[0030] ;

[0031] Wherein, represents the probability of the existence of abnormality in the evidence data in all nodes, represents the number of nodes with correct hash value matching after matching the hash value of the node, and represents correct, represents the number of nodes with incorrect hash value matching after matching the hash value of the node, and represents incorrect;

[0032] Then , which represents that the consistency of the evidence chain data is poor in the node flow topology graph, data tracing is needed, and the related transaction records are compared;

[0033] but This indicates that in the node flow topology graph, the data consistency of the evidence chain is poor, requiring data tracing and comparison of relevant transaction records;

[0034] but This indicates that the data consistency of the evidence chain is high in the node flow topology graph.

[0035] Furthermore, the traceability scoring formula in S4 is as follows:

[0036] ;

[0037] in, This is represented as a traceability score for the transaction, ranging from 0 to 1; Represented as weighting coefficients, they control the weight of each factor, typically ; This represents the hash value of the transaction input / output script, used to uniquely identify the script; This represents the verification result of the transaction signature, with 1 indicating validity and 0 indicating invalidity. This represents the number of times a transaction has been confirmed in the blockchain, indicating the confirmation status of the transaction.

[0038] Furthermore, the specific steps of the comprehensive analysis in S5 are as follows:

[0039] Integrate integrity check reports and traceability analysis reports;

[0040] Perform multi-dimensional data analysis to calculate data integrity and traceability scores;

[0041] A completeness assessment report is generated based on the scoring results;

[0042] The assessment report will be output in a standardized format for easy use later.

[0043] Furthermore, the comprehensive scoring formula in S5 is as follows:

[0044] ;

[0045] in, This is represented as a comprehensive score, ranging from 0 to 1; Represented as weight coefficients, they control the weights of each dimension, typically , .

[0046] Furthermore, the comprehensive analysis of the S5 traceability analysis results specifically includes the following steps:

[0047] Integrate integrity check reports, traceability analysis reports, and lifecycle management records;

[0048] Perform multi-dimensional data analysis, calculate data integrity score and traceability score;

[0049] Generate integrity assessment report according to score results;

[0050] Output the assessment report in a standardized format for subsequent use.

[0051] Further, the standardized format output step in S6 is as follows:

[0052] Integrate the assessment results into the standardized format;

[0053] Output the integrity assessment report, including data integrity score, traceability score and life cycle management score;

[0054] Provide a data visualization interface for users to intuitively view the assessment results.

[0055] Compared with the prior art, the beneficial effects of the present application are:

[0056] The compliance governance evidence chain integrity analysis method based on blockchain storage of the present application 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; not only can improve the accuracy of data consistency verification, but also can enhance the standardization and security of data life cycle management; through the causal relationship diagram for traceability analysis, the reliability of data traceability evaluation is further improved; finally, through multi-dimensional data analysis and standardized format output, the integrity assessment report is generated, which provides strong support for subsequent application and audit;

[0057] The present application can effectively verify 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, ensuring 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;

[0058] The transmission time of each node of the evidence chain data is collected, and the transmission time is sequentially arranged to establish a time sequence sorting graph; the information of the nodes is obtained according to the time sequence sorting graph, and the nodes are connected according to the time sequence sorting graph to generate a node flow topology graph; the evidence chain data of each node is dynamically marked to obtain the information of the evidence chain data flow, and a safety dynamic graph of the evidence chain data is obtained; the characteristic information of the evidence chain data is extracted according to the safety dynamic graph, and the characteristic information is connected with the upper and lower nodes to form a complete time graph of the evidence chain data; the node information is obtained according to the complete time graph, a causal relationship graph of the evidence chain data is established, and the nodes are traced back and analyzed through the causal relationship graph; through the traceability analysis of the causal relationship graph, the traceability of the data can be more accurately evaluated, and the source and flow direction of the data can be tracked and verified; the user can quickly locate the data problem, and the credibility and transparency of the data are improved;

[0059] Through multi-dimensional data analysis, an integrity evaluation report is generated, which not only can intuitively show the integrity score and traceability score of the data, but also can provide detailed life cycle management records for subsequent application and auditing; the user can be provided with comprehensive data evaluation results to support the scientificity and accuracy of decision-making; the evaluation results are output in a standardized format for intuitive viewing and subsequent use; the operability and practicality of the data can be improved, and the technical threshold in subsequent application can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 It is a whole method flowchart of the application;

[0061] Figure 2 It is a specific step diagram of the life cycle management of the evidence chain data of the application;

[0062] Figure 3 It is a specific step diagram of the comprehensive analysis of the application;

[0063] Figure 4 It is a specific step diagram of the comprehensive analysis of the traceability analysis result of the application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below in combination with specific embodiments. EMBODIMENT

[0065] Please refer to Figures 1-4 The application provides a technical scheme: a compliance governance evidence chain integrity analysis method based on block chain storage, and the specific steps include:

[0066] S1: Collecting relevant transaction records and evidence data in the blockchain storage records of the evidence chain through the blockchain network, and preprocessing the collected relevant transaction records and evidence data to obtain preprocessed evidence data;

[0067] The collection of relevant transaction records and evidence data specifically includes the following steps:

[0068] Through the API interface of the blockchain network, connect the main chain and side chain of the blockchain to obtain relevant transaction records and evidence data;

[0069] Encrypt the transmission of relevant transaction records and evidence data to ensure data security;

[0070] Extract the hash value and transmission time in the evidence data, and extract the transaction ID, transaction time, transaction amount, transaction participants and related block information of the relevant transaction records.

[0071] The preprocessing of relevant transaction records and evidence data specifically includes the following steps:

[0072] Data cleaning: denoising the collected relevant transaction records and evidence data, and eliminating duplicate data, outliers or incomplete data;

[0073] Data filling: Then calculate the mean value of the relevant transaction records and evidence data, and fill the calculated mean value to the vacancy of the eliminated duplicate data, outliers or incomplete data;

[0074] Data normalization: normalize the relevant transaction records and evidence data so that the evidence data can be mapped to [0, 1], and through normalization, convert data of different sources and dimensions to a unified scale, thereby improving the consistency and comparability of data;

[0075] Data format conversion: convert the collected relevant transaction records and evidence data into a unified data format, such as JSON or XML format, for subsequent analysis;

[0076] Data storage: store the preprocessed relevant transaction records and evidence data into local database or cloud data warehouse for subsequent analysis;

[0077] S2: Through the management of the life cycle of evidence chain data, ensure the process integrity of evidence chain data, and facilitate tracking and tracing;

[0078] Specifically includes the following steps:

[0079] Collect the transmission time of each node of the evidence chain data, and arrange them in order according to the transmission time to establish a time series ordering diagram;

[0080] First, collect the transmission time information of evidence chain data from each node in the zone cross-chain, ensuring that each evidence chain data has an accurate timestamp;

[0081] Arrange the timestamps in ascending order to generate a time series ordering chart. Use data visualization tools such as Tableau or Python's Matplotlib to display the time series in the form of a line chart or bar chart;

[0082] According to the time series ordering chart, obtain the information of the nodes, and connect the nodes according to the time series ordering chart to generate a node flow topology chart;

[0083] Extract detailed information of each node from the time series ordering chart, including node ID and node location;

[0084] Use a graph database or data visualization tool to connect the nodes in chronological order to form a node flow topology chart;

[0085] Mark each node's evidence chain data dynamically to obtain information about the flow of evidence chain data, and obtain a security dynamic graph of evidence chain data;

[0086] Mark each node's evidence chain data, hash encode the node's information, and obtain a unique data identifier. According to the data identifier, mark the flow of evidence chain data, and obtain query data with data identifier;

[0087] ;

[0088] where, and represent two different hash functions, usually is a linear hash, is a polynomial hash to improve the uniformity of the hash function and reduce the probability of collision; is a large prime number used to limit the range of the final hash value, is an operation that limits the hash value to the range of 0 to m-1 by taking the remainder operation;

[0089] The formula of the linear hash function is:

[0090] ;

[0091] where, is a constant used to adjust the distribution of hash values, and a suitable reduces the collision; is another constant used to offset the hash value; a suitable helps the uniform distribution of hash values;

[0092] The formula of the polynomial hash function is:

[0093] ;

[0094] Where, is a base, usually a large prime number such as 31 or 127, to improve the uniqueness of the hash value; is the character of the input data, and the final hash value is obtained by polynomial calculation; is the total number of characters in the input data;

[0095] Based on the label information, generate a security dynamic graph; use dynamic graph tools such as ECharts or D3.js to show the changes of data at different time points;

[0096] According to the security dynamic graph, extract the feature information of the evidence chain data, and associate the feature information of the upper and lower nodes to form a complete time graph of the evidence chain data;

[0097] Correlation calculation of feature information of upper and lower nodes:

[0098] The upper and lower node evidence chain data and contains the following features:

[0099] Timestamp: and ;

[0100] Device ID: and ;

[0101] Log type: and ;

[0102] Data size: and ;

[0103] Data correlation is calculated by the following formula:

[0104] ;

[0105] Where, , , , are the weight coefficients of timestamp, device ID, log type and data size, respectively, indicating the importance of each feature; similarity score between two features, ranging from 0 to 1, where 1 represents a perfect match and 0 represents a perfect mismatch; total score of data correlation;

[0106] extracting key features from the security dynamic graph, such as data correlation of evidence chain data;

[0107] plot the extracted feature information in chronological order, and use time series analysis tools to form a time graph; such as R or Python's Pandas library, to generate line charts or heat maps;

[0108] obtain node information from the complete time graph, establish a causal relationship diagram of evidence chain data, and realize traceability analysis of nodes through the causal relationship diagram;

[0109] Based on the time graph, use causal analysis tools or write scripts to identify key nodes and paths, and identify the causal relationship in data flow;

[0110] Through the causal relationship diagram, use data mining tools such as Apache Spark or Weka for node traceability analysis to identify the root cause of abnormal events and generate detailed traceability reports.

[0111] S3: By extracting the hash value of the evidence chain data in the node from the node flow topology graph, comparing the hash values of the evidence chain block chain record, determining the data consistency of the evidence chain, and according to the continuity of time series analysis and the analysis result of data consistency, determining the integrity of the evidence chain;

[0112] The integrity check includes the following steps:

[0113] Time series analysis of the transmission time of each node on the preprocessed evidence record data;

[0114] ;

[0115] where, is the time integrity score of the evidence chain; is the weight coefficient, which controls the weight of the current state and the historical state, usually ; is the offset term, which is used to balance the influence of different dimensions, usually 0 < B < 1; is the decay coefficient, which considers the decay effect of time series, usually 0 < D < 1; is the time step;

[0116] weight coefficient :

[0117] Definition: ;

[0118] Value range: 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.

[0119] Determination rule: The value can be determined by experimental data or domain knowledge, usually between 0.6 and 0.9.

[0120] Offset term :

[0121] Definition: 0 < B < 1

[0122] Value range: Used to adjust the initial offset of the formula, to ensure that the score is within a reasonable range;

[0123] Determination rule: The value is usually determined by the initial training data, to ensure that the score is consistent with the actual situation.

[0124] Decay coefficient :

[0125] Definition: 0 < D < 1

[0126] Value range: Control the time series decay effect, the larger The value indicates that the decay effect is more obvious;

[0127] Determination rule: The value can be determined by domain knowledge or experimental data, usually between 0.5 and 0.8.

[0128] According to the node flow topology graph, the hash value comparison analysis of the block chain storage record of the evidence chain of each node is carried out in turn;

[0129] Statistical node storage data in the hash value comparison results, data consistency analysis results.

[0130] The analysis of the hash value comparison results is as follows:

[0131] Through the matching of the hash value in the node storage data, and the correct value of the matching is , the value of the matching error is ;

[0132] Then the probability calculation of the node storage data is as follows:

[0133] ;

[0134] Among them, This represents the probability that there are anomalies in the evidence data across all nodes. This represents the number of nodes whose hash values ​​correctly match after matching the hash values ​​of the nodes. This is considered correct. This represents the number of nodes whose hash values ​​do not match correctly after matching the hash values ​​of the nodes. This is considered an error.

[0135] Set matching threshold The probability of an anomaly Matching threshold Comparison:

[0136] but This indicates that the data consistency of the evidence chain is extremely poor in the node flow topology graph, requiring data tracing and comparison of relevant transaction records;

[0137] but This indicates that in the node flow topology graph, the data consistency of the evidence chain is poor, requiring data tracing and comparison of relevant transaction records;

[0138] but This indicates that the data consistency of the evidence chain is high in the node flow topology graph.

[0139] The steps for comparing relevant transaction records are as follows:

[0140] The transaction ID, transaction time, transaction amount, transaction participants, and relevant block information of the relevant transaction records are compared.

[0141] First, compare the relevant transaction records of the abnormal nodes with those of the normal nodes;

[0142] Then, the abnormal node is compared with its related transaction records;

[0143] The result was determined in... In this case, if the transaction records of the abnormal node are the same as those of the normal node, and the transaction records of the abnormal node are different from those of the normal node, it is determined that there is a data transmission error, or that the analysis of the abnormal node and the normal node are not on the same time series.

[0144] exist In this case, if the transaction records of the abnormal node are different from those of the normal node, and the transaction records of the abnormal node are the same, then it is determined that the analysis of the abnormal node and the normal node are on the same time series, and the transmitted data has been modified.

[0145] S4: Traceability analysis of the evidence chain data of the node through the causal relationship diagram, evaluation of the traceability of the evidence chain;

[0146] Traceability score formula:

[0147] ;

[0148] wherein, represents the traceability score of the transaction, ranging from 0 to 1; represents the weight coefficient, which controls the weight of each factor, and is usually ; represents the hash value of the transaction input and output script, which is used to uniquely identify the script; represents the verification result of the transaction signature, 1 indicating validity and 0 indicating invalidity; represents the number of confirmations of the transaction in the blockchain, indicating the confirmation status of the transaction;

[0149] Weight coefficient : controls the weight of the transaction script authenticity, a larger value indicates that the script authenticity has a greater impact on traceability; The value of is determined through experimental data or domain knowledge, usually between 0.6 and 0.9.

[0150] Weight coefficient : controls the impact of transaction signature validity on traceability, a larger value indicates that the signature validity has a greater impact on traceability; The value of is usually determined through initial training data to ensure that the score is consistent with the actual situation.

[0151] Weight coefficient : controls the impact of transaction confirmation times on traceability, a larger value indicates that the confirmation times have a greater impact on traceability; The value of is determined through domain knowledge or experimental data, usually between 0.5 and 0.8.

[0152] Parameter changes and technical effects:

[0153] When increases, the impact of transaction script authenticity on traceability is greater;

[0154] When increases, the impact of transaction signature validity on traceability is greater;

[0155] When increases, the impact of transaction confirmation times on traceability is greater;

[0156] When An increase indicates a higher degree of authenticity in the transaction script. Increase;

[0157] when When =1, it indicates that the transaction signature is valid. Increase;

[0158] when An increase indicates that the number of transaction confirmations is higher. Increase.

[0159] Output range and its relation to technology:

[0160] The output value range is limited to (0,1) to ensure the stability and readability of the score;

[0161] when When the value approaches 0, it indicates poor traceability of the transaction and may indicate fraudulent activity.

[0162] when When the value approaches 1, it indicates that the transaction has a high degree of traceability and meets compliance requirements;

[0163] Special value analysis:

[0164] when hour, =0 indicates that the transaction signature is invalid and has poor traceability;

[0165] when hour, The lowest value indicates that the transaction has not been confirmed and has the worst traceability.

[0166] when When it is a random value, near The value represents the impact of script authenticity on traceability;

[0167] Stability analysis:

[0168] By adjusting the weighting coefficients Setting a reasonable range of values ​​ensures that the formula output remains stable within the range of (0,1).

[0169] Traceability analysis specifically includes the following steps:

[0170] Analyze the transaction input / output scripts to verify the authenticity of the transaction participants;

[0171] Verify the validity of the transaction signature to ensure the legality of the transaction;

[0172] Check the transaction's record status on the blockchain to confirm whether the transaction has been confirmed;

[0173] A traceability analysis report is generated.

[0174] S5: Comprehensive analysis of the integrity check results and traceability analysis results based on multi-dimensional data analysis, generating an integrity evaluation report;

[0175] Comprehensive score formula:

[0176]

[0177] Wherein, is the comprehensive score, ranging from 0 to 1.

[0178] is the weight coefficient, controlling the weight of each dimension, usually ,

[0179] is the integrity score, which is mapped between 0 and 1 through normalization processing;

[0180] is the traceability score, ranging from 0 to 1.

[0181] Weight coefficient : controls the impact of integrity score on comprehensive score, greater value indicates that the integrity score has a greater impact on the comprehensive score; The value of is determined by experimental data or domain knowledge, usually between 0.3 and 0.7;

[0182] Weight coefficient : controls the impact of traceability score on comprehensive score, greater value indicates that the traceability score has a greater impact on the comprehensive score; The value of is usually determined by initial training data to ensure that the score is consistent with the actual situation;

[0183] Parameter changes and technical effects:

[0184] When increases, the impact of integrity score on comprehensive score is greater;

[0185] When increases, the impact of traceability score on comprehensive score is greater;

[0186] When increases, it indicates that the data has higher integrity, increases;

[0187] When increases, it indicates that the data has higher traceability, ​​Increase.

[0188] Output value range and technical association:

[0189] The output value range is limited within the range of (0, 1), ensuring the stability and readability of the score;

[0190] When tends to 0, it indicates that the comprehensive score is low, and there may be problems in integrity, traceability or life cycle management;

[0191] When tends to 1, it indicates that the comprehensive score is high, meeting the compliance requirements;

[0192] Special value behavior and stability:

[0193] Special value analysis

[0194] When , , it indicates that the integrity score has no effect on the comprehensive score;

[0195] When , , it indicates that the traceability score has no effect on the comprehensive score.

[0196] Stability analysis:

[0197] By setting a reasonable value range for the weight coefficient , , it is ensured that the formula output remains stable within the range of (0, 1).

[0198] Specifically, the following steps are included:

[0199] Integrate the integrity check report and traceability analysis report;

[0200] Perform multi-dimensional data analysis to calculate data integrity score and traceability score;

[0201] Generate an integrity assessment report based on the score results;

[0202] Output the assessment report in a standardized format for easy subsequent use.

[0203] S6: Based on the standardized format of the evidence chain data evaluation results, output the standardized format to ensure the ease of use and subsequent application of the data;

[0204] Specifically, the following steps are included:

[0205] Integrate the evaluation results into the standardized format;

[0206] Output the integrity assessment report, including data integrity score, traceability score and life cycle management score;

[0207] Provide a data visualization interface to facilitate intuitive viewing of the evaluation results by users.

[0208] It should be noted that all the calculation formulas in this application file use regression analysis including but not limited to machine learning algorithms to analyze the collected relevant parameters in depth, identify their natural trends and mutual relationships. Professional software such as Python's Scikit-learn library or R language is used to automatically generate mathematical models that match the data. Then, the performance of the model is objectively evaluated through methods such as cross-validation, and combined with continuous feedback and optimization to ensure that the created formula truly reflects the inherent law of the data, thereby ensuring its effectiveness and accuracy. In all the calculation formulas in this application, the parameters in each formula are processed by consistent range of dimensionless to ensure that different physical quantities are compared on the same scale; the dimensionless technique includes but is not limited to Min-Max Normalization and Z-Score standardization.

[0209] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application 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 application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for analyzing integrity of a compliance governance evidence chain based on blockchain storage, characterized in that, The specific steps include: S1: Collecting relevant transaction records and evidence data in the blockchain storage record of the evidence chain through the blockchain network, and preprocessing the collected relevant transaction records and evidence data to obtain preprocessed evidence data; S2: By managing the life cycle of evidence chain data, ensure the process integrity of evidence chain data, and facilitate traceability; The specific steps of the life cycle management of the evidence chain data are as follows: Collect the transmission time of each node of the evidence chain data, and arrange the transmission time in sequence to establish a time sequence sorting graph; According to the time sequence sorting graph, the information of the node is obtained, and the node is connected according to the time sequence sorting graph to generate a node flow topology graph; The evidence chain data of each node is dynamically marked to obtain the information of the evidence chain data flow, and a safety dynamic graph of the evidence chain data is obtained; According to the safety dynamic graph, the characteristic information of the evidence chain data is extracted, and the characteristic information is connected with the upper and lower nodes to form a complete time graph of the evidence chain data; According to the complete time graph, the node information is obtained, and a causal relationship graph of the evidence chain data is established, and the node is analyzed by the causal relationship graph; S3: By extracting the hash value of the evidence chain data in the node, comparing and analyzing the hash value of the evidence chain data in the node, determining the data consistency of the evidence chain, and according to the continuity of the time sequence analysis and the data consistency analysis result, the integrity of the evidence chain is determined; S4: Through the evidence chain data, the traceability of the evidence chain is evaluated; S5: Based on multi-dimensional data analysis, the integrity check result and the traceability analysis result are comprehensively analyzed, and an integrity evaluation report is generated; S6: The evaluation result of the evidence chain data in the standardized format is output in the standardized format, ensuring the convenience of data use and subsequent application.

2. The blockchain-based compliance governance evidence chain integrity analysis method according to claim 1, characterized in that: The S1 related transaction record and evidence data collection specifically includes the following steps: Through the API interface of the blockchain network, connect the main chain and the side chain of the blockchain to obtain the relevant transaction records and evidence data; The relevant transaction records and evidence data are encrypted and transmitted to ensure data security; Extract the hash value and transmission time of the evidence data, and extract the transaction ID, transaction time, transaction amount, transaction participants and related block information of the relevant transaction records.

3. The blockchain-based compliance governance evidence chain integrity analysis method according to claim 1, characterized in that: The analysis of the hash value comparison result in S3 is as follows: By matching the hash value in the node storage data, and the correct value matched is , and the incorrect value matched is ; Setting a matching threshold The probability of an anomaly is compared to the matching threshold The probability of an anomaly is compared to the matching threshold The probability of an anomaly is compared to the matching threshold ； wherein, represents the probability that there is an abnormality in the stored data in all nodes, represents the number of nodes whose hash values match correctly after matching the hash values of the nodes, and represents correct, represents the number of nodes whose hash values do not match correctly after matching the hash values of the nodes, and represents incorrect; Then , represented as in the node flow topology, the data consistency of the evidence chain is poor, data tracing needs to be carried out, and relevant transaction records are compared; Then , it is represented that the data consistency of the evidence chain is poor in the node flow topology graph, data tracing needs to be carried out, and the related transaction records are compared; Then , is represented as the data consistency of the evidence chain is high in the node flow topology graph.

4. The blockchain-based compliance governance evidence chain integrity analysis method according to claim 1, characterized in that: The specific steps of the comprehensive analysis in S5 are as follows: Integrate the integrity check report, traceability analysis report and life cycle management record; Perform multi-dimensional data analysis to calculate data integrity score and traceability score; According to the score result, an integrity evaluation report is generated; The evaluation report is output in the standardized format for subsequent use.

5. The blockchain-based compliance governance evidence chain integrity analysis method according to claim 1, characterized in that: The specific steps of the comprehensive analysis of the traceability analysis result in S5 are as follows: Integrate the integrity check report, traceability analysis report and life cycle management record; Perform multi-dimensional data analysis to calculate data integrity score and traceability score; According to the score result, an integrity evaluation report is generated; The evaluation report is output in the standardized format for subsequent use.

6. The blockchain-based compliance governance evidence chain integrity analysis method according to claim 5, characterized in that: The standardized format output step in S6 is as follows: Integrate the evaluation results into a standardized format; Output the integrity evaluation report, including the data integrity score, traceability score; Provide a data visualization interface for users to intuitively view the evaluation results.

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