A data quality assessment management method and system based on blockchain

Abstract

The present invention discloses a data quality assessment management method and system based on blockchain, which relates to the field of blockchain technology. The data quality assessment management method based on blockchain includes the following steps: obtaining an assessment index value; obtaining a consistency assessment index; and access encryption. The present invention uploads the data to be assessed to the data quality chain in the blockchain, and simultaneously obtains the assessment index value of the data to be assessed in the data quality chain within a preset time period. Then, based on the obtained assessment index value, a data quality assessment report is generated. At the same time, a consistency assessment is performed on the data quality assessment report to obtain a consistency assessment index. Finally, the data quality assessment report after the consistency assessment is uploaded to the data management chain and access is encrypted. At the same time, the interactive process of the data quality assessment report is monitored in real time, thereby achieving the effect of improving the real-time performance of data quality management in the blockchain and solving the problem of low real-time performance of data quality management in the blockchain in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a blockchain-based data quality assessment management method and system. Background Art

[0002] With the rapid development of information technology, data has become a core element of competitiveness for businesses, organizations, and even nations. Traditional data quality assessment methods often rely on centralized management systems, which are subject to risks of data tampering, lack of transparency, and high trust costs. Ensuring data authenticity and integrity, and preventing malicious tampering or leakage, is a significant challenge, particularly in scenarios involving multi-party data sharing and exchange. Blockchain technology, as a decentralized, immutable, and traceable distributed ledger, offers a new approach to addressing these issues. In the field of data quality assessment, blockchain technology can be applied to every stage of the data lifecycle, including data collection, storage, processing, analysis, and sharing, enabling comprehensive monitoring and assessment of data quality.

[0003] The existing blockchain data quality assessment and management method obtains distributed ledger information and records the distributed ledger information on the blockchain according to preset data quality assessment standards, then evaluates and analyzes the distributed ledger information using the preset data quality assessment standards, and finally feeds back the results of the evaluation and analysis through a data verification mechanism, thereby achieving efficient storage and management of data quality.

[0004] For example, the invention patent announcement with announcement number: CN113077257B discloses a blockchain cross-chain management method and system based on data analysis, including: a first blockchain encrypts a first value coefficient and real funds to be traded to obtain a first latent variable distribution; the first latent variable distribution is sent to multiple second blockchains, so that each second blockchain decrypts the first latent variable distribution to obtain a first estimated transaction amount; the second latent variable distribution is received and decrypted to obtain a second estimated transaction amount corresponding to each second blockchain; the degree of distribution deviation is obtained based on the standard deviation of the first latent variable distribution and the standard deviation of the second latent variable distribution; the transaction matching degree is obtained based on the transaction amount deviation and the distribution deviation degree, and at the same time, the optimal blockchain with the largest transaction matching degree is selected from the second blockchain for transaction based on a preset reception threshold.

[0005] For example, the invention patent announcement with announcement number: CN111242613B discloses a wallet information management method, device, and electronic device based on an online banking system, including: receiving a user's online banking system login request, and determining whether the user is a legitimate user based on identity authentication information; if the user is a legitimate user, sending transaction information to a server, and receiving signature information fed back by the server; storing the signature information and transaction information in a blockchain node; and the blockchain node storing the signature information and transaction information in other blockchain nodes based on a corresponding consensus mechanism.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, the consensus mechanism in the blockchain usually causes high latency when processing large amounts of data, resulting in a decrease in the query and download speed of the distributed database, thereby affecting the real-time performance of data evaluation and the low real-time performance of data quality management in the blockchain. Summary of the Invention

[0008] The embodiments of the present application provide a blockchain-based data quality assessment management method and system, which solves the problem of low real-time performance of data quality management in blockchain in the prior art and improves the real-time performance of data quality management in blockchain.

[0009] An embodiment of the present application provides a data quality assessment management method based on blockchain, comprising the following steps: S1, uploading the data to be evaluated to the data quality chain in the blockchain, and obtaining the evaluation index value of the data to be evaluated in the data quality chain within a preset time period, wherein the data quality chain is used to store and manage the data to be evaluated, and the evaluation index value is used to reflect the stability of the data to be evaluated in the data quality chain; S2, generating a data quality assessment report based on the obtained evaluation index value, and performing a consistency assessment on the data quality assessment report to obtain a consistency assessment index, wherein the consistency assessment index is used to quantify the credibility of the data quality assessment report; S3, uploading the data quality assessment report after the consistency assessment to the data management chain in the blockchain and performing access encryption, and monitoring the interaction process of the data quality assessment report in real time, wherein the data management chain is used to store and manage the data quality assessment report.

[0010] Furthermore, the evaluation index value is calculated using the following formula:

[0011]

[0012] Where i is the number of the data to be evaluated, i=1,2,...,U, U is the total number of data to be evaluated, t is the number of the preset time period, t=1,2,...,T, T is the total number of preset time periods, GUi Indicates the evaluation index value of the i-th data to be evaluated in the data quality chain within the preset time period, A i.t represents the first timestamp of the i-th data to be evaluated in the credit chain within the t-th preset time period, ΔA0 represents the reference first timestamp, B i.t It represents the second timestamp of the i-th data to be evaluated in the data quality chain within the t-th preset time period, and ΔB0 represents the reference second timestamp; the first timestamp represents the difference between the time when the data quality chain receives the data to be evaluated and the time when the credit chain uploads the data to be evaluated; the second timestamp represents the difference between the time when the data quality chain starts to respond to the data to be evaluated and the time when the data to be evaluated is received.

[0013] Furthermore, the specific process of generating a data quality assessment report based on the obtained evaluation index value is as follows: Step 1, determine whether the obtained evaluation index value is equal to the preset evaluation index value. If the obtained evaluation index value is equal to the preset evaluation index value, generate a data quality assessment report based on the obtained evaluation index value, otherwise execute step 2; Step 2, obtain the data to be corrected corresponding to the evaluation index value, the data to be corrected includes a first data to be corrected and a second data to be corrected, the first data to be corrected is the deviation data between the preset evaluation index value and the evaluation index value when the evaluation index value is less than the preset evaluation index value, and the second data to be corrected is the deviation data between the evaluation index value and the preset evaluation index value when the evaluation index value is greater than the preset evaluation index value; Step 3, perform deviation correction on the evaluation index value according to the data to be corrected until the evaluation index value is equal to the preset evaluation index value.

[0014] Furthermore, the deviation correction of the evaluation index value according to the data to be corrected is further included, and then the data correction index is obtained. The data correction index is used to measure the improvement effect of the stability of the data to be evaluated during the deviation correction process. The data correction index is calculated by the following formula:

[0015]

[0016] Where i is the number of the data to be evaluated, i = 1, 2, ..., U, U is the total number of data to be evaluated, t is the number of the preset time period, t = 1, 2, ..., T, T is the total number of preset time periods, e is a natural constant, JIAO i.t Y1 represents the data correction index of the i-th data to be evaluated in the t-th preset time period, i.t represents the deviation reduction of the i-th data to be evaluated in the first data to be corrected within the t-th preset time period, Y10 represents the initial deviation of the first data to be corrected, Y2 i.trepresents the deviation reduction of the i-th data to be evaluated in the second data to be corrected within the t-th preset time period, Y20 represents the initial deviation of the second data to be corrected, K i.t represents the deviation correction time of the i-th data to be evaluated in the data to be corrected within the t-th preset time period, ΔK represents the maximum allowable deviation correction time, and γ represents the stability factor.

[0017] Furthermore, the consistency evaluation index is obtained by the following method: step one, using the public key corresponding to the data to be evaluated to determine whether the digital signature and timestamp mark corresponding to the data to be evaluated are successfully verified. If the verification fails, execute step two, otherwise the consistency evaluation index is not calculated; step two, counting the number of data to be evaluated that has been tampered with after the verification fails, and at the same time obtaining the digital signature consistency in combination with the total number of data to be evaluated in the data quality assessment report, the digital signature consistency represents the ratio of the difference between the total number of data to be evaluated and the number of tampered data to be evaluated to the total number of data to be evaluated; step three, recording the response time corresponding to the data to be evaluated in the data quality assessment report on the data quality chain in real time and combining it with the reference response time to obtain the timestamp mark consistency, the timestamp mark consistency represents the ratio of the difference between the reference response time and the response time to the reference response time; step four, combining the preset evaluation index value, the evaluation index value, the correction index, the digital signature consistency and the timestamp mark consistency to obtain the consistency evaluation index.

[0018] Furthermore, the specific process of uploading the data quality assessment report after consistency assessment to the data management chain in the blockchain and performing access encryption is: judging whether the data management chain executes the verification mechanism and distributes the verification nodes. If not, it indicates that the accessing user has not attempted to access the data quality assessment report. The verification mechanism includes identity authentication and permission verification; generating a key for access encryption in the verification node and generating a ciphertext form of the data quality assessment report according to the access rights set by the smart contract. The key pair includes a public key and a private key.

[0019] Furthermore, the process of determining whether the data management chain implements the verification mechanism and distributes the verification nodes further includes obtaining an access rights compliance index. The access rights compliance index is used to quantify the degree of access rights approval in the data quality assessment report. The specific calculation expression of the access rights compliance index is:

[0020]

[0021] Where y is the number of the access user, y = 1, 2, ..., Y, Y is the total number of access users, t is the number of the preset time period, t = 1, 2, ..., T, T is the total number of preset time periods, e is a natural constant, QUAN yIndicates that the access rights of the yth access user within the preset time period meet the indicators, P y.t Q represents the data management chain response rate when the yth access user accesses the data management chain within the tth preset time period, y.t Indicates the access user level of the data management chain when the yth access user accesses the data management chain within the tth preset time period.

[0022] An embodiment of the present application provides a data quality assessment and management system based on blockchain, including: an assessment index value acquisition module, a consistency assessment index acquisition module and an access encryption module; wherein the assessment index value acquisition module is used to upload the data to be assessed to the data quality chain in the blockchain, and at the same time obtain the assessment index value of the data to be assessed in the data quality chain within a preset time period, the data quality chain is used to store and manage the data to be assessed, and the assessment index value is used to reflect the stability of the data to be assessed in the data quality chain; the consistency assessment index acquisition module is used to generate a data quality assessment report based on the obtained assessment index value, and at the same time perform a consistency assessment on the data quality assessment report to obtain a consistency assessment index, and the consistency assessment index is used to quantify the credibility of the data quality assessment report; the access encryption module is used to upload the data quality assessment report after consistency assessment to the data management chain in the blockchain and perform access encryption, and at the same time monitor the interaction process of the data quality assessment report in real time, and the data management chain is used to store and manage the data quality assessment report.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By obtaining the evaluation index values of the data to be evaluated in the data quality chain within a preset time period, and performing consistency evaluation on the data quality evaluation report generated based on the evaluation index values to obtain consistency evaluation indicators, the data quality evaluation report after consistency evaluation is uploaded to the data management chain in the blockchain and access is encrypted, thereby improving the accuracy of access encryption of the data quality evaluation report, and further improving the real-time performance of data quality management in the blockchain, effectively solving the problem of low real-time performance of data quality management in the blockchain in the existing technology.

[0025] 2. By judging whether the obtained evaluation index value is equal to the preset evaluation index value, if the obtained evaluation index value is equal to the preset evaluation index value, a data quality assessment report is generated based on the obtained evaluation index value, otherwise the data to be corrected corresponding to the evaluation index value is obtained, and at the same time, the deviation of the evaluation index value is corrected according to the data to be corrected until the evaluation index value is equal to the preset evaluation index value, thereby improving the accuracy of obtaining the data to be corrected, and further improving the accuracy and reliability of obtaining the data quality assessment report.

[0026] 3. By specially marking the substandard data quality assessment reports in the blockchain and feeding them back to the data evaluators, and re-digitally signing and timestamping the data to be evaluated in the substandard data quality assessment reports based on the feedback from the data evaluators, and re-obtaining the consistency assessment indicators until the consistency assessment indicators are within the preset consistency assessment indicator range, more accurate identification of substandard data quality assessment reports is achieved, thereby improving the accuracy and reliability of obtaining consistency assessment indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a blockchain-based data quality assessment and management method provided in an embodiment of the present application;

[0028] Figure 2 A three-dimensional coordinate analysis diagram of the evaluation index values provided in the embodiments of the present application;

[0029] Figure 3 A schematic diagram of the structure of a blockchain-based data quality assessment and management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application solve the problem of low real-time performance of data quality management in blockchain in the prior art by providing a blockchain-based data quality assessment management method and system. The method uploads the approved data to be assessed to the data quality chain in the blockchain, obtains the assessment index values of the data to be assessed in the data quality chain within a preset time period, generates a data quality assessment report based on the obtained assessment index values, digitally signs and timestamps the data to be assessed in the data quality assessment report, and performs consistency assessment on the data quality assessment report to obtain consistency assessment indicators. Finally, the data quality assessment report after consistency assessment is uploaded to the data management chain in the blockchain and encrypted for access. Meanwhile, the interaction process of the data quality assessment report is monitored in real time, thereby improving the real-time performance of data quality management in the blockchain.

[0031] The technical solution in the embodiments of this application is to solve the problem of low real-time performance of data quality management in the above-mentioned blockchain. The overall idea is as follows:

[0032] By obtaining the evaluation index values of the data to be evaluated in the data quality chain within a preset time period, and performing consistency evaluation on the data quality assessment report generated based on the evaluation index values to obtain consistency evaluation indicators, the data quality assessment report after consistency evaluation is uploaded to the data management chain in the blockchain and access is encrypted. At the same time, the interaction process of the data quality assessment report is monitored in real time, thereby achieving the effect of improving the real-time performance of data quality management in the blockchain.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0034] like Figure 1 As shown, it is a flowchart of a data quality assessment management method based on blockchain provided by an embodiment of the present application, the method comprising the following steps: S1, uploading the approved data to be assessed to the data quality chain in the blockchain, and obtaining the assessment index value of the data to be assessed in the data quality chain within a preset time period. The blockchain includes a credit chain, a data quality chain and a data management chain. The data quality chain is used to store and manage the data to be assessed, and the assessment index value is used to reflect the stability of the data to be assessed in the data quality chain; S2, generating a data quality assessment report based on the obtained assessment index value, and performing a consistency assessment on the data quality assessment report to obtain a consistency assessment index. The data quality assessment report indicates that the data to be assessed is digitally signed and timestamped by the data quality chain, and the consistency assessment index is used to quantify the credibility of the data quality assessment report; S3, uploading the data quality assessment report after consistency assessment to the data management chain in the blockchain and performing access encryption, and monitoring the interaction process of the data quality assessment report in real time. The data management chain is used to store and manage the data quality assessment report, and access encryption is used to ensure that only authorized users view and download the data quality assessment report.

[0035] In this embodiment, the data to be evaluated that has passed the review is data without duplication and non-numerical data (such as text and tables). Statistical analysis is used to evaluate whether there are any abnormalities in the data to be evaluated in the data quality assessment report. The deviation value (difference) between the data to be evaluated and the average value of the data to be evaluated is usually obtained through the Z-score. When the deviation value is greater than the preset deviation value, the corresponding data to be evaluated is abnormal data (that is, the consistency of the data is 0), wherein the preset deviation value is represented by the sum and average of the historical deviation values in the preset database, and the historical deviation value is the difference between the historical data to be evaluated and the average value of the historical data to be evaluated in the preset database; the event monitoring mechanism already existing in the blockchain is used to monitor transactions related to the data quality assessment report in real time, and the interaction process with the data quality assessment report (such as uploading, accessing and modifying) is recorded in real time, thereby improving the management efficiency of the data to be evaluated in the data quality chain.

[0036] Specifically, in the application scenarios of regulatory agencies, regulatory agencies can use blockchain to regularly check the data compliance of regulated entities, and quickly identify potential compliance risks and conduct supervision by comparing the data in the data quality chain (such as capital flow data, sales transaction record data) with the requirements of relevant laws and regulations; based on the data quality assessment reports and consistency indicators on the blockchain, regulatory agencies can establish a risk early warning mechanism. When it is found that the data quality has declined or the consistency of the assessment report has decreased, the regulatory agency can promptly issue an early warning signal to remind the regulated entity, thereby improving the real-time nature of data quality management in the blockchain.

[0037] Furthermore, the data to be evaluated that has passed the review is uploaded to the data quality chain in the blockchain. Previously, it also included: obtaining the credit data of the accessing user and uploading it to the credit chain of the blockchain. The credit data includes identity authentication information and qualification certification information. The credit chain is used to store and manage the credit data of the accessing user; according to the authorization status of the credit data, it is determined whether to upload the authorized credit data to the data quality chain in the blockchain. If not, the credit data upload is refused and returned to the corresponding accessing user. At the same time, the accessing user who fails the authorization is recorded as an unauthorized user.

[0038] In this embodiment, the credit data of the accessing user is obtained by accessing the credit information uploaded by the accessing user. The identity verification information usually includes the name, ID number and facial information, and the qualification certification information usually includes proof of income, proof of property, loan record and overdue record. After uploading the credit data to the credit chain, the system will initiate a data authorization request to the accessing user, asking the accessing user whether to agree to use their credit data for a specific purpose (such as loan approval). If the accessing user agrees and passes the identity information verification, the accessing user will be recorded as an authorized user and the accessing user's credit data will be uploaded to the data quality chain. Otherwise, it will be recorded as an unauthorized user. The verification of the accessing user's credit data through the authorization mechanism improves the compliance and efficiency of data use, and improves the accuracy and security of the data to be evaluated on the data quality chain.

[0039] Furthermore, the evaluation index value is calculated using the following formula:

[0040]

[0041] Where i is the number of the data to be evaluated, i=1,2,...,U, U is the total number of data to be evaluated, t is the number of the preset time period, t=1,2,...,T, T is the total number of preset time periods, GU i Indicates the evaluation index value of the i-th data to be evaluated in the data quality chain within the preset time period, A i.t represents the first timestamp of the i-th data to be evaluated in the credit chain within the t-th preset time period, ΔA0 represents the reference first timestamp, Bi.t It represents the second timestamp of the i-th data to be evaluated in the data quality chain within the t-th preset time period, and ΔB0 represents the reference second timestamp; the first timestamp represents the difference between the time when the data quality chain receives the data to be evaluated and the time when the credit investigation chain uploads the data to be evaluated; the second timestamp represents the difference between the time when the data quality chain starts to respond to the data to be evaluated and the time when the data to be evaluated is received.

[0042] In this embodiment, in the calculation formula of the evaluation index value in this embodiment, A is defined as i.t ≥ΔA0,B i.t ≥ΔB0, the reference first timestamp and the reference second timestamp are usually the minimum timestamps in the preset database. Therefore, the evaluation index value decreases as the first timestamp and the second timestamp increase. When A i.t =ΔA0 and B i.t =ΔB0, the corresponding evaluation index value is the best, that is, the stability of the data to be evaluated in the data quality chain is the highest.

[0043] To simplify the analysis, define A1 i.t Indicates the first timestamp coefficient of the i-th data to be evaluated in the credit chain within the t-th preset time period, B1 i.t It represents the second timestamp coefficient of the i-th data to be evaluated in the data quality chain within the t-th preset time period. The simplified calculation formula of the evaluation index value is: like Figure 2 As shown, it is a three-dimensional coordinate analysis diagram of the evaluation index value provided in an embodiment of the present application. It can be seen from the diagram that the evaluation index value decreases as the first timestamp coefficient and the second timestamp coefficient increase.

[0044] It should be noted that in the calculation formula of the evaluation index value, the change of the first timestamp and the second timestamp will not affect the total time for the data to be evaluated to be transferred from the credit chain to the data quality chain. The first timestamp will not affect the total time, but it will affect the data quality chain to start receiving the data to be evaluated on the credit chain, because the first timestamp determines the receiving time of the data quality chain. When the first timestamp decreases, the corresponding task start time is advanced (that is, the data quality chain starts receiving the data to be evaluated on the credit chain in advance), that is, the response efficiency of the data quality chain is improved.

[0045] By considering the impact of the first timestamp coefficient and the second timestamp coefficient on the evaluation index value, a quantitative index is provided for the data to be evaluated in the credit chain and the data quality chain, which not only helps to improve the efficiency of transmission of the data to be evaluated in the data quality chain, but also ensures the accuracy of the data to be evaluated. Among them, the first timestamp coefficient reflects the time efficiency of uploading the data to be evaluated from the credit chain to the data quality chain, and the second timestamp coefficient reflects the time efficiency of the data quality chain starting to respond and process the data after receiving the data to be evaluated. Among them, there is no mutual influence relationship between the first timestamp coefficient and the second timestamp coefficient. In practical applications, if only the response rate of the data quality chain needs to be measured, only the value of the second timestamp coefficient needs to be considered, thereby improving the accuracy and reliability of obtaining the evaluation index value, thereby improving the accuracy of access encryption of the data quality evaluation report, and further improving the efficiency of data quality management in the blockchain, effectively solving the problem of low real-time performance of data quality management in the blockchain in the existing technology.

[0046] Furthermore, the specific process of generating a data quality assessment report based on the obtained evaluation index value is as follows: Step 1, determine whether the obtained evaluation index value is equal to the preset evaluation index value. If the obtained evaluation index value is equal to the preset evaluation index value, generate a data quality assessment report based on the obtained evaluation index value, otherwise execute step 2; Step 2, obtain the data to be corrected corresponding to the evaluation index value, the data to be corrected includes a first data to be corrected and a second data to be corrected, the first data to be corrected is the deviation between the preset evaluation index value and the evaluation index value when the evaluation index value is less than the preset evaluation index value, and the second data to be corrected is the deviation between the evaluation index value and the preset evaluation index value when the evaluation index value is greater than the preset evaluation index value; Step 3, perform deviation correction on the evaluation index value according to the data to be corrected until the evaluation index value is equal to the preset evaluation index value. The deviation correction is used to adjust the parameter settings of the data quality chain according to the numerical value of the data to be corrected.

[0047] In this embodiment, when there is first data to be corrected, the data processing speed is improved by reducing the cache of the data quality chain and reducing the number of data to be evaluated within a preset time period; for the second data to be corrected, this indicates that the data chain still has the ability to process more data, and the difference between the evaluation index value and the preset evaluation index value is usually reduced by increasing the number of data to be evaluated within a preset time period (at this time the data processing speed is reduced). It should be noted that the cache of the data quality chain and the number of data to be evaluated cannot always increase or decrease, and are usually increased or decreased according to preset adjustment rules (such as one deviation correction can only increase or decrease 10 data to be evaluated); after the deviation correction, the first timestamp and the second timestamp are re-evaluated to obtain the evaluation index value until it is equal to the preset evaluation index value, and then the deviation correction is stopped, thereby improving the accuracy and reliability of the evaluation index value.

[0048] Furthermore, the evaluation index value is subjected to deviation correction based on the data to be corrected, and then a data correction index is obtained. The data correction index is used to measure the improvement effect of the stability of the data to be evaluated during the deviation correction process. The data correction index is calculated using the following formula:

[0049]

[0050] Where i is the number of the data to be evaluated, i = 1, 2, ..., U, U is the total number of data to be evaluated, t is the number of the preset time period, t = 1, 2, ..., T, T is the total number of preset time periods, e is a natural constant, JIAO i.t Y1 represents the data correction index of the i-th data to be evaluated in the t-th preset time period, i.t represents the deviation reduction of the i-th data to be evaluated in the first data to be corrected within the t-th preset time period, Y10 represents the initial deviation of the first data to be corrected, Y2 i.t represents the deviation reduction of the i-th data to be evaluated in the second data to be corrected within the t-th preset time period, Y20 represents the initial deviation of the second data to be corrected, K i.t represents the deviation correction time of the i-th data to be evaluated in the data to be corrected within the t-th preset time period, ΔK represents the maximum allowable deviation correction time, and γ represents the stability factor.

[0051] In this embodiment, it should be noted that the evaluation index value is either less than the preset evaluation index value or greater than the preset evaluation index value. It is impossible for the value to be both less than and greater than the preset evaluation index value at the same time. i.t or Y2 i.t There is always one value equal to 0. The initial deviation of the first data to be corrected is the deviation of the first data to be corrected before deviation correction (i.e., the difference between the preset evaluation index value and the evaluation index value). The initial deviation of the second data to be corrected is the deviation of the second data to be corrected before deviation correction (i.e., the difference between the evaluation index value and the preset evaluation index value). The deviation correction time is recorded in real time by a timer for the duration of the deviation correction within a preset time period. The maximum allowable deviation correction time is represented by the sum and average of the maximum values of historical deviation corrections in a preset database.

[0052] It should be understood that the data correction index increases with the increase of the deviation reduction of the first data to be corrected and the deviation reduction of the second data to be corrected, and decreases with the increase of the deviation correction time. It should be noted that the deviation reduction of the first data to be corrected and the deviation reduction of the second data to be corrected also affect the value of the deviation correction time. For example, when errors occur in the data to be evaluated during the deviation correction process, more computing resources are required to correct them, which will naturally prolong the correction time.

[0053] Suppose a blockchain system has two datasets that need correction: transaction records and user information. If there are deviations (e.g., incorrect transaction amounts or user identification) in the sensitive data of these two datasets (e.g., incorrect transaction amounts or incorrect user identification), then to reduce the deviation of sensitive data, the system needs to run verification and comparison algorithms, which may increase the time required for deviation correction. By considering the impact of the deviation reduction of the first data to be corrected and the deviation reduction of the second data to be corrected on the deviation correction time, the processing rate of the data to be evaluated can be increased while maintaining a continuous reduction in the deviation reduction. This shortens the data processing time and helps improve the real-time and efficiency of data quality chain management.

[0054] Specifically, the stability factor is the stability factor corresponding to the deviation reduction during the deviation correction process in a preset database. It represents the degree of influence of the deviation reduction on the data correction indicator. The stability factor corresponding to the deviation reduction can be directly obtained from the preset database. The corresponding relationship can be a pre-set mapping relationship. For example, the deviation reduction corresponding to the deviation correction process of the data to be corrected and the stability factor corresponding to the deviation reduction in the preset database form a mapping set. The real-time deviation reduction is input into the mapping set to obtain the corresponding stability factor. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the stability factor has a value range of [0, 1].

[0055] The stability of the data to be corrected during the deviation correction process is evaluated through data correction indicators. When errors occur in the data to be corrected during the deviation correction process, the data quality chain will immediately feed back to the operation and maintenance client for processing, which helps to improve the data processing and management efficiency and real-time performance of the data quality chain, and achieve more accurate acquisition of data correction indicators, thereby achieving improved real-time performance of data quality management in the blockchain, and effectively solving the problem of low real-time performance of data quality management in the blockchain in the existing technology.

[0056] Furthermore, the consistency evaluation index is obtained by the following method: Step 1, use the public key corresponding to the data to be evaluated to determine whether the digital signature and timestamp mark corresponding to the data to be evaluated are successfully verified. If the verification fails, execute step 2, otherwise the consistency evaluation index is not calculated, which is similar to directly setting the verification success situation; Step 2, count the number of unmatched data to be evaluated after the verification fails, and obtain the digital signature consistency in combination with the total number of data to be evaluated in the data quality assessment report. The digital signature consistency represents the ratio of the difference between the total number of data to be evaluated and the number of tampered data to be evaluated to the total number of data to be evaluated; Step 3, record the response time corresponding to the data to be evaluated in the data quality assessment report on the data quality chain in real time and combine it with the reference response time to obtain the timestamp mark consistency. The timestamp mark consistency represents the ratio of the difference between the reference response time and the response time to the reference response time; Step 4, combine the preset evaluation index value, evaluation index value, correction index, digital signature consistency and timestamp mark consistency to obtain the consistency evaluation index.

[0057] In this embodiment, the consistency evaluation index is calculated using the following formula:

[0058]

[0059] Where ZHI i It represents the consistency evaluation index of the i-th data to be evaluated within the preset time period in the data quality evaluation report, M i.t Indicates the digital signature consistency of the i-th data to be evaluated in the data quality assessment report within the t-th preset time period, N i.t Indicates the timestamp consistency of the i-th data to be evaluated in the data quality assessment report within the t-th preset time period, GU i It represents the evaluation index value of the i-th data to be evaluated in the data quality chain within the preset time period, and GU0 represents the preset evaluation index value.

[0060] It should be understood that when the digital signature and timestamp corresponding to the data to be evaluated are successfully verified, it indicates that the data to be evaluated on the data quality assessment report is completely credible (that is, the digital signature and timestamp corresponding to each data to be evaluated are completely accurate). At this time, no deviation correction is required (similar to the case of directly setting the verification success). Therefore, the data correction index is not used as an independent variable of the consistency assessment index (at this time, only the impact of digital signature consistency and timestamp consistency on the consistency assessment index is considered). That is, when GU i=GU0, the consistency evaluation index increases with the increase of digital signature consistency and timestamp consistency. When the digital signature consistency and timestamp consistency are both 1, the corresponding consistency evaluation index takes the maximum value, that is, the digital signature and timestamp on the data to be evaluated are completely accurate, which helps to ensure the uniqueness and non-tamperability of the signature.

[0061] It should be noted that one piece of data to be evaluated corresponds to a digital signature and a timestamp. If they do not correspond, it means that the digital signature and timestamp are wrong. For example, the digital label corresponding to the data to be evaluated should be 1, but the actual label is 2, which means that the digital label is wrong. i When ≠GU0, the consistency evaluation index increases with the increase of the data correction index, digital signature consistency and timestamp consistency. It should be noted that when the data correction index is used as an independent variable, it also indirectly affects the values of digital signature consistency and timestamp consistency. Since the digital signature is verified based on the integrity of the data, when the data is accurate, the consistency of the digital signature also increases accordingly; when the data correction index increases, the data to be evaluated marked by the timestamp is more likely to be consistent and accurate, which enhances the relevance of the timestamp and thus improves its consistency.

[0062] By considering the indirect impact of data correction indicators on the consistency of digital signatures and timestamp marking, errors in the digital signature and timestamp marking process can be corrected quickly, reducing the possibility of erroneous data propagation on the blockchain, improving the real-time performance of the blockchain correction mechanism and the consistency of the data to be evaluated on the blockchain, and achieving improved accuracy and reliability in obtaining consistency evaluation indicators, thereby improving the real-time performance of data quality management in the blockchain, and effectively solving the problem of low real-time performance of data quality management in the blockchain in the existing technology.

[0063] Furthermore, a consistency assessment is performed on the data quality assessment report to obtain consistency assessment indicators, which then includes: step one, determining whether the consistency assessment indicators are within the preset consistency assessment indicator range. If the consistency assessment indicators are within the preset consistency assessment indicator range, the data quality assessment report after the consistency assessment is stored, otherwise step two is executed; step two, special marking is performed on the substandard data quality assessment reports in the blockchain and feedback is given to the data evaluator. The special marking is used by the data evaluator to quickly locate the substandard data quality assessment reports; step three, the data to be evaluated in the substandard data quality assessment report is re-digitally signed and timestamped according to the feedback results of the data evaluator, and the consistency assessment indicators are re-obtained until the consistency assessment indicators are within the preset consistency assessment indicator range.

[0064] In this embodiment, the substandard data quality assessment report is fed back to the data evaluator in the form of email and information notification through the feedback mechanism in the blockchain. The digital signature is based on the corrected content of the data to be evaluated and is signed with a private key, which helps to ensure the uniqueness and non-tamperability of the signature. After re-digital signing, the timestamp mark is updated to reflect the current time point, and the results after re-digital signing and timestamp marking are recorded in real time, including the corrected data items to be evaluated, the new digital signature and the new timestamp mark, thereby improving the integrity and consistency of the data to be evaluated and achieving more accurate feedback on the data quality assessment report.

[0065] In the application scenario of supply chain circulation, each node will verify the information of the goods when receiving them, and may update relevant information (such as location, status changes). At this time, the blockchain system will regularly or trigger the quality assessment of the goods data (such as goods grade, batch number, production date and distributor) to check the accuracy, completeness and consistency of the data. When the data quality assessment report shows that a data item does not meet the standards (such as missing information, timestamp error, digital signature mismatch), the blockchain system will automatically trigger the feedback mechanism and promptly feedback the substandard data quality assessment report to the relevant node person in charge in the form of email and information notification, verify the substandard data items, and confirm the data correction plan. The blockchain system then records the results of the re-digital signature and timestamp in real time. These records are visible to all participants, increasing the transparency and consistency of the supply chain. At the same time, investors, lenders and regulators of supply chain finance can quickly understand the true status and circulation of the goods by querying the records on the blockchain.

[0066] Furthermore, the specific process of uploading the data quality assessment report after consistency assessment to the data management chain in the blockchain and performing access encryption is as follows: determine whether the data management chain executes the verification mechanism and distributes the verification nodes. If not, it indicates that the access user has not attempted to access the data quality assessment report. The verification mechanism includes identity authentication and permission verification. The verification node indicates that multiple access users are allowed to perform access verification on the data quality assessment report at the same time; generate a key for access encryption in the verification node and generate a ciphertext form of the data quality assessment report according to the access rights set by the smart contract. The key pair includes a public key and a private key. The public key is used to encrypt the data quality assessment report, and the private key is used to decrypt the data quality assessment report. The smart contract indicates that access encryption is automatically executed according to preset rules.

[0067] In this embodiment, the smart contract can set access control rules based on the information of the verification mechanism (including the authentication information and permission verification information of the access user) and automatically execute these rules to control access to the encrypted data quality assessment report. For example, the smart contract can check the user's authentication information and permissions. If the user authorizes (i.e., passes the authentication), the user is allowed to obtain the private key for decrypting the data (or the access right to the decryption service); in the data management chain, these nodes can be responsible for verifying whether the user's access request complies with the preset access control rules, and multiple verification nodes can process access requests at the same time, which helps to improve the scalability and fault tolerance of the blockchain. If a verification node fails, other nodes can continue to process the request to ensure the stable operation of the system, thereby improving the security and consistency of the blockchain.

[0068] Furthermore, it is necessary to determine whether the data management chain implements the verification mechanism and distributes the verification nodes. It also includes obtaining the access permission compliance indicator. The access permission compliance indicator is used to quantify the degree of access permission approval in the data quality assessment report. The specific calculation expression of the access permission compliance indicator is:

[0069]

[0070] Where y is the number of the access user, y = 1, 2, ..., Y, Y is the total number of access users, t is the number of the preset time period, t = 1, 2, ..., T, T is the total number of preset time periods, e is a natural constant, QUAN y Indicates that the access rights of the yth access user within the preset time period meet the indicators, P y.t Q represents the data management chain response rate when the yth access user accesses the data management chain within the tth preset time period, y.t Indicates the access user level of the data management chain when the yth access user accesses the chain within the tth preset time period; when the access user is recorded as an unauthorized user, the access user level is equal to 0.

[0071] In this embodiment, the statistical table of changes in access rights compliance indicators is shown in Table 1:

[0072] Table 1 Statistics of changes in access rights compliance indicators

[0073]

[0074] It should be understood that the blockchain processes and manages the data to be evaluated in a stable network environment (such as no electromagnetic interference and network interruption), so the value of the data management chain response rate is not affected by the network environment; the access user level ranges from 0 to 1, and the access level of the access user who is not authorized is usually 0 (that is, the data quality assessment report cannot be accessed). When the authorized user makes an access query, the access user level is automatically generated by the data management chain according to the authorization status of the access user. For example, managers in the regulatory agency will be assigned a higher level (such as 0.8), and authorized access users will be assigned corresponding access levels according to their corresponding credit status (such as 0.4 for overdue loans and 0.6 for non-overdue loans). Blockchain maintenance personnel can directly perform maintenance (that is, the access level is 1); the data management chain response rate is the ratio of the reference response time to the difference between the time the data management chain sends the data to be evaluated and the time it receives the data to be evaluated, where the difference between the time the data management chain sends the data to be evaluated and the time it receives the data to be evaluated is not 0. The reference response time is represented by the result of summing and averaging the historical response times in the preset database.

[0075] Through the hierarchical division of access users and the dynamic authorization mechanism, it is ensured that only authorized access users can access the content of the corresponding data quality assessment report, thereby improving the accuracy and security of access permission management. Secondly, by real-time monitoring of the response rate of the data management chain, problems in the access process (such as network delays) can be discovered in a timely manner, thereby improving the real-time performance of the quality management chain in the blockchain, achieving improved accuracy and reliability in obtaining access permission compliance indicators, and further improving the real-time performance of data quality management in the blockchain, effectively solving the problem of low real-time performance of data quality management in the blockchain in the existing technology.

[0076] like Figure 3 , which is a structural diagram of a data quality assessment and management system based on blockchain provided by an embodiment of the present application, and includes: an assessment index value acquisition module, a consistency assessment index acquisition module, and an access encryption module;

[0077] Among them, the evaluation index value acquisition module is used to upload the approved data to be evaluated to the data quality chain in the blockchain, and at the same time obtain the evaluation index value of the data to be evaluated in the data quality chain within a preset time period. The blockchain includes a credit chain, a data quality chain and a data management chain. The data quality chain is used to store and manage the data to be evaluated, and the evaluation index value is used to reflect the stability of the data to be evaluated in the data quality chain; the consistency evaluation index acquisition module is used to generate a data quality evaluation report based on the obtained evaluation index value, and at the same time perform a consistency evaluation on the data quality evaluation report to obtain a consistency evaluation index. The data quality evaluation report indicates that the data to be evaluated is digitally signed and timestamped through the data quality chain, and the consistency evaluation index is used to quantify the credibility of the data quality evaluation report; the access encryption module is used to upload the data quality evaluation report after consistency evaluation to the data management chain in the blockchain and perform access encryption, and at the same time monitor the interaction process of the data quality evaluation report in real time. The data management chain is used to store and manage the data quality evaluation report, and access encryption is used to ensure that only authorized users view and download the data quality evaluation report.

[0078] In this embodiment, in the field of supply chain finance, the quality of goods is an important basis for financial institutions to decide whether to provide financing. The access encryption module uploads the goods quality assessment report that has undergone consistency assessment to the data management chain in the blockchain. During the upload process, the module uses the encryption technology of the blockchain (such as public key encryption) to encrypt the report to ensure that only authorized users can view and download the report. At the same time, the access encryption module monitors the interactive process of the data quality assessment report in real time, records the visitor's identity, access time and other key information for subsequent auditing and tracking, and sends an access request to the data management chain through the blockchain network, and submits the access user's identity credentials for identity authentication. After the identity authentication is passed, the user's access rights are confirmed, and they are allowed to use the private key to decrypt and view the goods quality assessment report. The user can make financing decisions based on the content of the report. The access encryption module ensures the fairness and consistency of the goods quality during the access and assessment process, and improves the reliability of the goods quality assessment.

[0079] To sum up, the embodiment of the present application obtains the evaluation index value of the data to be evaluated in the data quality chain within a preset time period, and at the same time performs a consistency evaluation on the data quality assessment report generated based on the evaluation index value to obtain the consistency evaluation index, and finally uploads the data quality assessment report after the consistency evaluation to the data management chain in the blockchain and performs access encryption, thereby achieving an improvement in the accuracy of the access encryption of the data quality assessment report, and further achieving an improvement in the real-time performance of data quality management in the blockchain, effectively solving the problem of low real-time performance of data quality management in the blockchain in the prior art.

[0080] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0085] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A data quality assessment and management method based on blockchain, characterized in that: The following steps are involved: S1, uploading the data to be evaluated to the data quality chain in the blockchain, and obtaining the evaluation index value of the data to be evaluated in the data quality chain within a preset time period. The data quality chain is used to store and manage the data to be evaluated, and the evaluation index value is used to reflect the stability of the data to be evaluated in the data quality chain; S2, generating a data quality assessment report based on the obtained assessment index values, and simultaneously performing a consistency assessment on the data quality assessment report to obtain a consistency assessment index, wherein the consistency assessment index is used to quantify the credibility of the data quality assessment report; S3, uploading the data quality assessment report after consistency assessment to the data management chain in the blockchain and encrypting access, while monitoring the interactive process of the data quality assessment report in real time. The data management chain is used to store and manage the data quality assessment report; The data quality chain for uploading the data to be evaluated to the blockchain also includes: Obtain the credit data of the visiting user and upload it to the credit chain of the blockchain. The credit data includes identity verification information and qualification certification information. The credit chain is used to store and manage the credit data of the visiting user; Determine whether to upload the authorized credit data to the data quality chain in the blockchain based on the authorization status of the credit data; The evaluation index value is calculated using the following formula: ； In the formula, i is the number of the data to be evaluated, , U is the total number of data to be evaluated, t is the number of the preset time period, , T is the total number of preset time periods, Indicates the evaluation index value of the i-th data to be evaluated in the data quality chain within the preset time period. Indicates the first timestamp of the i-th data to be evaluated in the credit chain within the t-th preset time period, Indicates the reference first timestamp, Indicates the second timestamp of the i-th data to be evaluated in the data quality chain within the t-th preset time period, Indicates the reference second timestamp; The first timestamp represents the difference between the time when the data to be evaluated is received by the data quality link and the time when the data to be evaluated is uploaded to the credit investigation chain; The second timestamp represents the difference between the time when the data quality chain starts responding to the data to be evaluated and the time when the data to be evaluated is received.

2. A data quality assessment and management method based on blockchain as claimed in claim 1, characterized in that: The specific process of generating a data quality assessment report based on the obtained assessment index values is as follows: Step 1: determine whether the obtained evaluation index value is equal to the preset evaluation index value. If the obtained evaluation index value is equal to the preset evaluation index value, generate a data quality assessment report based on the obtained evaluation index value; otherwise, execute step 2; Step 2: Obtain data to be corrected corresponding to the evaluation index value, wherein the data to be corrected includes first data to be corrected and second data to be corrected, wherein the first data to be corrected is the deviation data between the preset evaluation index value and the evaluation index value when the evaluation index value is less than the preset evaluation index value, and the second data to be corrected is the deviation data between the evaluation index value and the preset evaluation index value when the evaluation index value is greater than the preset evaluation index value; Step three: perform deviation correction on the evaluation index value according to the data to be corrected until the evaluation index value is equal to the preset evaluation index value.

3. A data quality assessment and management method based on blockchain as claimed in claim 2, characterized in that: The deviation correction of the evaluation index value according to the data to be corrected is then followed by obtaining a data correction index, which is used to measure the improvement effect of the stability of the data to be evaluated during the deviation correction process. The data correction index is calculated using the following formula: ； In the formula, i is the number of the data to be evaluated, , U is the total number of data to be evaluated, t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, represents the data correction index of the i-th data to be evaluated in the data to be corrected within the t-th preset time period, represents the deviation reduction of the i-th data to be evaluated in the first data to be corrected within the t-th preset time period, represents the initial deviation of the first data to be corrected, represents the deviation reduction of the i-th data to be evaluated in the second data to be corrected within the t-th preset time period, represents the initial deviation of the second data to be corrected, represents the deviation correction time of the i-th data to be evaluated in the data to be corrected within the t-th preset time period, Indicates the maximum allowable deviation correction time, represents the stability factor.

4. A data quality assessment and management method based on blockchain as claimed in claim 3, characterized in that: The consistency evaluation index is obtained by the following method: Step 1: Use the public key corresponding to the data to be evaluated to determine whether the digital signature and timestamp corresponding to the data to be evaluated are successfully verified. If the verification fails, proceed to step 2; otherwise, do not calculate the consistency evaluation index; Step 2: Count the number of data to be evaluated that has been tampered with after verification failure, and obtain digital signature consistency based on the total number of data to be evaluated in the data quality assessment report. The digital signature consistency represents the ratio of the difference between the total number of data to be evaluated and the number of tampered data to be evaluated to the total number of data to be evaluated; Step 3: Record the response time of the data to be evaluated in the data quality assessment report in real time on the data quality chain and combine it with the reference response time to obtain timestamp consistency. The timestamp consistency represents the ratio of the difference between the reference response time and the response time to the reference response time. Step 4: Combining the preset evaluation index value, the evaluation index value, the correction index, the digital signature consistency and the timestamp consistency to obtain the consistency evaluation index.

5. A data quality assessment and management method based on blockchain as claimed in claim 1, characterized in that: The consistency assessment of the data quality assessment report is performed to obtain consistency assessment indicators, and then further includes: Step 1: Determine whether the consistency assessment indicator is within the preset consistency assessment indicator range. If the consistency assessment indicator is within the preset consistency assessment indicator range, store the data quality assessment report after the consistency assessment; otherwise, execute step 2; Step 2: Specially mark the data quality assessment reports that do not meet the standards in the blockchain and feedback them to the data evaluators; Step three: Based on the feedback from the data evaluators, the data to be evaluated in the data quality assessment report that does not meet the standards is digitally signed and timestamped again, and the consistency assessment indicators are re-obtained until the consistency assessment indicators are within the preset consistency assessment indicator range.

6. A data quality assessment and management method based on blockchain as claimed in claim 1, characterized in that: The specific process of uploading the data quality assessment report after consistency assessment to the data management chain in the blockchain and performing access encryption is as follows: Determine whether the data management chain implements the verification mechanism and distributes verification nodes. If not, it indicates that the access user has not attempted to access the data quality assessment report. The verification mechanism includes identity verification and permission verification. A key for access encryption is generated in the verification node, and a ciphertext form of the data quality assessment report is generated according to the access rights set by the smart contract, wherein the key pair includes a public key and a private key.

7. A data quality assessment and management method based on blockchain as claimed in claim 6, characterized in that: The step of determining whether the data management chain executes the verification mechanism and distributes the verification nodes further includes obtaining an access rights compliance index. The access rights compliance index is used to quantify the degree of access rights approval in the data quality assessment report. The specific calculation expression of the access rights compliance index is: ； Where y is the number of the accessing user, , Y is the total number of visiting users, t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, Indicates that the access rights of the yth access user within the preset time period meet the indicators. It represents the data management chain response rate when the data management chain is accessed by the yth access user within the tth preset time period. Indicates the access user level of the data management chain when the yth access user accesses the data management chain within the tth preset time period.

8. A system using a blockchain-based data quality assessment and management method as described in any one of claims 1 to 7, characterized in that: include: Evaluation index value acquisition module, consistency evaluation index acquisition module and access encryption module; The evaluation index value acquisition module is used to upload the data to be evaluated to the data quality chain in the blockchain, and at the same time obtain the evaluation index value of the data to be evaluated in the data quality chain within a preset time period. The data quality chain is used to store and manage the data to be evaluated, and the evaluation index value is used to reflect the stability of the data to be evaluated in the data quality chain; The consistency evaluation index acquisition module is used to generate a data quality assessment report based on the acquired evaluation index value, and at the same time perform a consistency evaluation on the data quality assessment report to obtain a consistency evaluation index, wherein the consistency evaluation index is used to quantify the credibility of the data quality assessment report; The access encryption module is used to upload the data quality assessment report after consistency assessment to the data management chain in the blockchain and perform access encryption, while monitoring the interaction process of the data quality assessment report in real time. The data management chain is used to store and manage the data quality assessment report.

Images