Data quality assessment management method and system based on block chain

Abstract

The invention discloses a block chain-based data quality evaluation management method and system, and relates to the technical field of block chains. The block chain-based data quality evaluation management method comprises the following steps of obtaining an evaluation index value; obtaining a consistency evaluation index; and access encryption. According to the invention, the to-be-evaluated data is uploaded to the data quality chain in the block chain, the evaluation index value of the to-be-evaluated data in the data quality chain in the preset time period is obtained, and then the data quality evaluation report is generated based on the obtained evaluation index value. Meanwhile, consistency evaluation is carried out on the data quality evaluation report to obtain a consistency evaluation index, finally, the data quality evaluation report after consistency evaluation is uploaded to a data management chain and access encryption is carried out, and meanwhile, the interaction process of the data quality evaluation report is monitored in real time. The effect of improving the real-time performance of data quality management in the block chain is achieved, and the problem of low real-time performance of data quality management in the block chain in the prior art is solved.

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 one of the core elements of competitiveness of enterprises, organizations and even countries. Traditional data quality assessment methods often rely on centralized management systems, which have problems such as data tampering risks, lack of transparency, and high trust costs. Especially in data sharing and exchange scenarios involving multiple parties, how to ensure the authenticity and integrity of data and prevent data from being maliciously tampered with or leaked has become a huge challenge. Blockchain technology, as a decentralized, tamper-proof, and traceable distributed ledger technology, provides a new approach to solving the above problems. In the field of data quality assessment, blockchain technology can be applied to all aspects of the data life cycle, including data collection, storage, processing, analysis, and sharing, so as to achieve full monitoring and evaluation of data quality.

[0003] The existing blockchain data quality assessment 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: the first blockchain encrypts the first value coefficient and the real funds to be traded to obtain a first hidden variable distribution; the first hidden variable distribution is sent to multiple second blockchains, so that each second blockchain decrypts the first hidden variable distribution to obtain a first estimated transaction amount; the second hidden variable distribution is received and decrypted to obtain a second estimated transaction amount corresponding to each second blockchain; the distribution deviation degree is obtained according to the standard deviation of the first hidden variable distribution and the standard deviation of the second hidden variable distribution; the transaction matching degree is obtained according to 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 according to a preset receiving threshold for transaction.

[0005] For example, the invention patent with announcement number CN111242613B discloses a wallet information management method, device and electronic device based on an online banking system, including: receiving an online banking system login request from a user, 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 signature information and transaction information in a blockchain node; and the blockchain node stores signature information and transaction information in other blockchain nodes based on a corresponding consensus mechanism.

[0006] However, in the process of implementing the technical solution 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 prior art, the consensus mechanism in the blockchain usually causes high latency when processing large amounts of data, resulting in reduced query and download speeds of distributed databases, thereby affecting the real-time performance of data evaluation and resulting in low real-time performance of data quality management in the blockchain. Summary of the invention

[0008] 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, thereby improving the real-time performance of data quality management in blockchain.

[0009] The 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 consistency assessment on the data quality assessment report to obtain consistency assessment indicators, wherein the consistency assessment indicators are 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, wherein the data management chain is used to store and manage the data quality assessment report.

[0010] Furthermore, the evaluation index value is calculated by 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 A represents the evaluation index value of the i-th data to be evaluated in the data quality chain within the preset time period. 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, 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 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 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 acquired evaluation index value is as follows: Step 1, determine whether the acquired evaluation index value is equal to the preset evaluation index value. If the acquired evaluation index value is equal to the preset evaluation index value, generate a data quality assessment report based on the acquired 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 is performed according to the data to be corrected, and then the data correction index is obtained, and 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 represents the data correction index of the i-th data to be evaluated in the t-th preset time period, Y1 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, and 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 1, 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 2, otherwise the consistency evaluation index is not calculated; Step 2, counting the number of data to be evaluated that have been tampered with after the verification fails, and 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 3, real-time recording the response time corresponding to the data to be evaluated in the data quality assessment report on the data quality chain 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 4, 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 authority 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 determination of whether the data management chain executes the verification mechanism and distributes the verification nodes also includes obtaining an access rights compliance index, which is used to quantify the degree of access rights passing of 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 index, P y.t represents the data management chain response rate when the yth access user accesses the data management chain within the tth preset time period, Q 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] The embodiment of the present application provides a data quality assessment 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 evaluated to the data quality chain in the blockchain, and at the same time obtain the assessment 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 assessment index value is used to reflect the stability of the data to be evaluated in the data quality chain; the consistency assessment index acquisition module is used to generate a data quality assessment report based on the acquired 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 the 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 the present application have at least the following technical effects or advantages:

[0024] 1. By obtaining the evaluation index value 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 value to obtain the consistency evaluation index, 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 then 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 prior art.

[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 evaluation index value is bias-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 data quality assessment reports that do not meet the standards in the blockchain and feeding them back to the data evaluators, the data to be evaluated in the data quality assessment reports that do not meet the standards are re-digitally signed and timestamped according to the feedback from the data evaluators, and the consistency assessment indicators are re-obtained until the consistency assessment indicators are within the preset consistency assessment indicator range, thereby achieving more accurate identification of data quality assessment reports that do not meet the standards, and then achieving improved accuracy and reliability in obtaining consistency assessment indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a blockchain-based data quality assessment 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 embodiment of the present application;

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

[0030] The embodiment of the present application solves 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 value 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 value, digitally signs and timestamps the data to be assessed in the data quality assessment report, performs consistency assessment on the data quality assessment report to obtain consistency assessment index, and finally uploads the data quality assessment report after consistency assessment to the data management chain in the blockchain and performs access encryption. The interactive 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 embodiment of the present 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 in conjunction with 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 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 blockchain comprises 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; 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, the data quality assessment report indicates that the data to be evaluated is digitally signed and timestamped through 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 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, the data management chain is used to store and manage the data quality assessment report, and the 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 the data without duplication and non-numerical data (such as text, table), and the statistical analysis method is used to evaluate whether there are 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 result of summing and averaging the historical deviation values ​​in the preset database, and the historical deviation value is the difference between the historical data to be evaluated in the preset database and the average value of the historical data to be evaluated; 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 quality chain for the data to be evaluated.

[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, which also includes: 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, and the credit chain is used to store and manage the credit data of the accessing user; judging whether to upload the authorized credit data to the data quality chain in the blockchain according to the authorization status of the credit data, if not, refusing to upload the credit data and returning it to the corresponding accessing user, and recording the accessing user who fails the authorization as an unauthorized user.

[0038] In this embodiment, the credit data of the accessing user is obtained through the credit information uploaded by the accessing user. The identity verification information usually includes the name, ID number and face information. The qualification certification information usually includes income certification, property certification, 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 his 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 credit data of the accessing user 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 by 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 A represents the evaluation index value of the i-th data to be evaluated in the data quality chain within the preset time period. 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 data to be evaluated is uploaded in the credit investigation chain; 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 with the increase of the first timestamp and the second timestamp. i.t =ΔA0 and B i.t =ΔB0, the corresponding evaluation index value has the best effect, that is, the stability of the data to be evaluated in the data quality chain is the highest.

[0043] To simplify the analysis, we 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 the embodiment of the present application. It can be seen from the figure 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 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 to receive 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 influence 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 transmission efficiency of the data to be evaluated in the data quality chain, but also ensures the accuracy of the data to be evaluated. 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 to and process the data after receiving the data to be evaluated. 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, which improves the accuracy and reliability of obtaining the evaluation index value, thereby improving the encryption accuracy 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 prior art.

[0046] Furthermore, the specific process of generating a data quality assessment report based on the acquired evaluation index value is as follows: Step 1, determine whether the acquired evaluation index value is equal to the preset evaluation index value. If the acquired evaluation index value is equal to the preset evaluation index value, generate a data quality assessment report based on the acquired 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, and 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 increase or decrease all the time, and are usually increased or decreased according to preset adjustment rules (such as a 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-obtained 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 according to the data to be corrected, 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:

[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 represents the data correction index of the i-th data to be evaluated in the t-th preset time period, Y1 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, and 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 that is equal to 0. The initial deviation of the first data to be corrected is the deviation of the first data to be corrected before the deviation is corrected (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 the deviation is corrected (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, and the maximum allowable deviation correction time is represented by the result of summing and averaging the maximum values ​​of historical deviation corrections in the 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] Assume that in a blockchain system, there are two data sets that need to be corrected: transaction records and user information. If there is a deviation in the sensitive data (such as transaction amount or user identity) of these two data sets (such as an error in transaction amount or user identity recognition), then in order to reduce the deviation of sensitive data, the system needs to run verification and comparison algorithms, which may increase the deviation correction time. 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 the continuous reduction of the deviation reduction, that is, the data processing time is shortened, which helps to improve the real-time and efficiency of data quality chain management.

[0054] Specifically, the stability factor is a stability factor corresponding to the deviation reduction amount in the deviation correction process in the preset database, which represents the numerical value of the influence degree of the deviation reduction amount on the data correction index. When used, the stability factor corresponding to the deviation reduction amount can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the deviation reduction amount corresponding to the deviation correction process of the data to be corrected and the stability factor corresponding to the deviation reduction amount in the preset database form a mapping set, and the real-time deviation reduction amount is input into the mapping set to obtain the corresponding stability factor, wherein the mapping relationship can be one-to-one or many-to-one. The value range of the stability factor in this example is [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 prior art.

[0056] Furthermore, the consistency evaluation index is obtained by the following method: Step 1, 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 2, otherwise the consistency evaluation index is not calculated, which is similar to directly setting the verification success situation; Step 2, counting the number of unmatched data to be evaluated after the verification fails, and 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 3, real-time recording the response time corresponding to the data to be evaluated in the data quality assessment report on the data quality chain 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 4, 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.

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

[0058]

[0059] Where ZHI i It represents the consistency evaluation index of the i-th data to be evaluated in 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 evaluation 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 where the verification is directly set to be successful). Therefore, the data correction index is not used as an independent variable of the consistency assessment index (at this time, only the impact of the digital signature consistency and the 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 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 improves 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, it helps to quickly correct errors in the digital signature and timestamp marking process, reduces the possibility of erroneous data propagation on the blockchain, improves the real-time performance of the blockchain correction mechanism and the consistency of the data to be evaluated on the blockchain, and achieves improved accuracy and reliability in obtaining consistency evaluation 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 prior art.

[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 data quality assessment report that does not meet the standards in the blockchain and the feedback is given to the data evaluator. The special marking is used by the data evaluator to quickly locate the data quality assessment report that does not meet the standards; step three, according to the feedback results of the data evaluator, the data to be evaluated in the data quality assessment report that does not meet the standards is re-digitally signed and timestamped, 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 is updated to reflect the current time point, and the results after re-digital signing and timestamp are recorded in real time, including the corrected data items to be evaluated, the new digital signature and the new timestamp, 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 when receiving the goods, and may update the relevant information (such as location, status change). 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 some data items do not meet the standards (such as missing information, timestamp mark error, digital signature mismatch), the blockchain system automatically triggers the feedback mechanism and promptly feedbacks the substandard data quality assessment report to the relevant node person in charge in the form of email and information notification, verifies the substandard data items, and confirms the data correction plan. Then the blockchain system records the results of the re-digital signature and timestamp in real time. These records are visible to all participants, which increases 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 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 authority 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 identity verification 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 identity verification information and permissions. If the user authorizes (i.e., passes the identity verification), the user is allowed to obtain the private key for decrypting the data (or the access rights 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 determined whether the data management chain implements the verification mechanism and distributes the verification nodes. Then, it also includes obtaining the access permission compliance index. The access permission compliance index is used to quantify the degree of access permission passing of the data quality assessment report. The specific calculation expression of the access permission compliance index 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 index, P y.t represents the data management chain response rate when the yth access user accesses the data management chain within the tth preset time period, Q y.t It 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, the management personnel in the regulatory agency will be assigned a higher level (such as 0.8), and the 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). The maintenance personnel of the blockchain 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 when the data management chain sends the data to be evaluated and the time when the data management chain receives the data to be evaluated, where the difference between the time when the data management chain sends the data to be evaluated and the time when the data management chain receives the data to be evaluated is not 0, and 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 prior art.

[0076] like Figure 3 As shown, it is a structural schematic diagram of a data quality assessment management system based on blockchain provided in an embodiment of the present application. A data quality assessment management system based on blockchain provided in an embodiment of the present application 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 acquired 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 the 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 identity of the visitor, 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 identity credentials of the access user for identity authentication. After the identity authentication is passed, the user's access rights are confirmed, and he is allowed to use the private key to decrypt and view the goods quality assessment report. The user can make a financing decision 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 summarize, 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 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] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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 capable of directing a computer or other programmable data processing device to operate 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 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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 can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data quality assessment 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, 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 assessment index value, and performing 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, upload the data quality assessment report after consistency assessment to the data management chain in the blockchain and encrypt access, 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.

2. A data quality assessment management method based on blockchain as claimed in claim 1, characterized in that: The data quality chain of 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, and 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.

3. A data quality assessment management method based on blockchain as claimed in claim 1, characterized in that: The evaluation index value is calculated by the following formula: 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 A represents the evaluation index value of the i-th data to be evaluated in the data quality chain within the preset time period. 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 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 indicates 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 by the credit investigation chain; The second timestamp indicates 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.

4. A data quality assessment 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 evaluation report based on the obtained evaluation index value, otherwise execute step 2; Step 2, obtaining the 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.

5. A data quality assessment management method based on blockchain as claimed in claim 4, characterized in that: The deviation correction of the evaluation index value according to the data to be corrected also includes 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 by the following formula: 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 represents the data correction index of the i-th data to be evaluated in the t-th preset time period, Y1 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, and 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.

6. A data quality assessment management method based on blockchain as claimed in claim 5, 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, execute step 2, otherwise the consistency evaluation index is not calculated; Step 2: Count the number of data to be evaluated that have been tampered with after verification failure, and obtain 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 of the data to be evaluated in the data quality chain in real time in the data quality evaluation report and combine it with the reference response time to obtain the 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 mark consistency to obtain the consistency evaluation index.

7. A blockchain-based data quality assessment management method 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 feed them back to the data assessors; Step three: based on the feedback from the data evaluators, the data to be evaluated in the data quality evaluation report that does not meet the standards is digitally signed and timestamped again, and the consistency evaluation indicators are re-obtained until the consistency evaluation indicators are within the preset consistency evaluation indicator range.

8. A data quality assessment 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 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 verification and authority verification. A key for accessing 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.

9. A data quality assessment management method based on blockchain as claimed in claim 8, 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, which is used to quantify the degree of access rights passing of the data quality assessment report. The specific calculation expression of the access rights compliance index is: 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 index, P y.t represents the data management chain response rate when the yth access user accesses the data management chain within the tth preset time period, Q 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.

10. A data quality assessment and management system based on blockchain, 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 evaluation report based on the acquired evaluation index value, and at the same time, perform consistency evaluation on the data quality evaluation report to obtain consistency evaluation indicators, wherein the consistency evaluation indicators are used to quantify the credibility of the data quality evaluation 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.

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