Trusted data space value chain dynamic allocation method and system

Abstract

The invention relates to the technical field of credible data processing, and provides a credible data space value chain dynamic allocation method and system. Comprising the steps of original data acquisition, initial value evaluation, data preprocessing, clustering analysis, time sequence analysis, future trend prediction, data association analysis, value distribution algorithm learning and optimization, data value distribution, right and interest distribution and the like. According to the invention, the dynamic adjustment of the data value chain is realized, the rights and interests generated by the data can be fairly and reasonably distributed to each participant, the accuracy of data value evaluation is improved, and proactive support is provided for the dynamic distribution of the data value chain.

Description

Technical Field

[0001] The present invention relates to the technical field of trusted data processing, and in particular to a trusted data space value chain dynamic allocation method and system. Background Art

[0002] Trusted Data Matrix (TDM) aims to solve the security and trust issues between data element providers, intermediary service providers and data users. Trusted Data Matrix can be understood as a distributed data infrastructure for data aggregation, sharing, circulation and application built on the existing information network. Through systematic technical arrangements, Trusted Data Matrix ensures the confirmation, implementation and maintenance of data circulation agreements, thereby realizing data-driven digital transformation.

[0003] The data value chain divides data value creation activities into basic value activities and value-added activities. Through basic value activities and value-added activities, data value creation and value-added in the transmission process are realized. The data value chain emphasizes maximizing the value of data through the collection, transmission, storage, analysis and application of data at each node of the value chain.

[0004] In the dynamic allocation process of the value chain, the trusted data space has the following technical pain points. The technical pain points mainly come from data processing, analysis, and collaborative processing between various links in the value chain. The dynamic allocation of the value chain needs to be adjusted in real time according to market changes, customer needs and other factors. The existing dynamic allocation process of the value chain cannot dynamically adjust the data value chain within the trusted data space, resulting in fair and reasonable distribution of the rights and interests generated by the data. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a method and system for dynamic allocation of the value chain of a trusted data space, which solves the problem that the existing dynamic allocation process of the value chain cannot dynamically adjust the data value chain within the trusted data space, resulting in the problem of fair and reasonable distribution of the rights and interests generated by the data.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: In a first aspect, the present invention provides a method for dynamically allocating a trusted data space value chain, comprising: Step S101, obtaining the original data in the trusted data space, performing an initial value assessment on the original data in the trusted data space, the initial value assessment includes data scarcity assessment, timeliness assessment and relevance assessment, obtaining the initial data value assessment result, matching the initial data value assessment result in a preset algorithm knowledge base, obtaining the statistical algorithm corresponding to the initial data value assessment result, using the statistical algorithm to analyze the initial data value assessment result, calculating the basic data statistics, the basic data statistics include the mean, the median and the standard deviation, performing data feature extraction on the data in the basic data statistics, obtaining the basic data statistics data features, the basic data statistics data features include data information features and data pattern features, obtaining the value chain dynamic business data and system data, the value chain dynamic business data includes the original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, the system data includes system configuration data, system operation data and system privacy and security data; Step S102, preprocessing the dynamic business data of the value chain and the data characteristics of the basic statistics of the data to obtain the preprocessed data to be mined, performing cluster analysis on the preprocessed data to be mined, and using the time series analysis method to predict the future trend of the data to be mined to obtain the future trend prediction result of the data to be mined, evaluating the future trend prediction result of the data to be mined to obtain the future trend prediction value of the data to be mined, performing cluster analysis on the data corresponding to the future trend prediction value of the data to be mined, and performing association rule mining on the cluster analysis result to obtain the result of data association analysis; Step S103, determining a quality assessment index based on the result of the data association analysis, and evaluating the result of the data association analysis according to the quality assessment index using a preset data value assessment rule to obtain a data value assessment result; Step S104, matching the corresponding value allocation algorithm in the knowledge base according to the market demand data to obtain the value allocation algorithm to be used, obtaining historical value allocation data, matching the value allocation algorithm to be used with the historical value allocation data, obtaining the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data, using the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data as the formula to be used, calculating the data value assessment result by the formula to be used, and obtaining the data value allocation ratio; Step S105, receiving smart contract configuration information, constructing a smart contract according to the data value distribution ratio and the smart contract configuration information and deploying it on the blockchain, matching the obtained data value distribution ratio with the smart contract, obtaining a smart contract corresponding to the data value distribution ratio, using the smart contract corresponding to the data value distribution ratio to perform value distribution according to a preset value distribution ratio, and obtaining a smart contract value distribution result; Step S106, obtain historical value distribution data, optimize the value distribution algorithm, use the optimized value distribution algorithm to identify the initial data value assessment results and the future trend prediction value of the data to be mined, obtain the factors most influencing value distribution, adjust the data value distribution ratio according to the factors most influencing value distribution, obtain the adjusted data value distribution ratio, allocate corresponding data value distribution ratios to the data corresponding to the initial data value assessment and the future trend prediction value of the data to be mined, obtain the data contribution, retrieve the participants corresponding to the data contribution, and distribute the rights and interests generated by the data to the corresponding participants based on the data contribution.

[0007] Furthermore, the trusted data space value chain dynamic allocation method described in the present invention also includes encrypting the value chain dynamic business data and system data through a preset privacy computing model. During the data processing process in steps S102, S103, S104, S105 and S106, when the encrypted data needs to be used, the data is decrypted using the decryption algorithm and key of the privacy computing model.

[0008] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S102, further includes: The result of cluster analysis of the pre-processed value chain dynamic business data and system data is used as an input data set, wherein the input data set includes the pre-processed value chain dynamic business data and group information after clustering of the system data and the attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, the parameters of the association rule mining algorithm include the support threshold and the confidence threshold; The preset association rule mining algorithm is run on the server side to perform association rule mining on the cluster analysis results. The association rule mining algorithm searches for the relationship between item sets in the value chain dynamic business data set to obtain an association rule list; The association rule list is identified to obtain the association relationship between data providers, data processors and market demands.

[0009] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S104, includes: The knowledge base includes value allocation algorithm scenarios and value allocation algorithms. The value allocation algorithm scenarios include value allocation of data value, value allocation of market demand, value allocation of data scarcity, value allocation based on comprehensive consideration of multiple factors, value allocation based on data usage effects, and value allocation based on cooperation and contribution. Match the data value assessment results with the value allocation algorithm scenarios in the knowledge base to obtain the value allocation algorithm scenarios corresponding to the data value assessment results; The correspondence between the value allocation algorithm scenarios and the value allocation algorithms includes: the value allocation of data value corresponds to the data value weighted allocation algorithm, the value allocation of market demand corresponds to the market demand sensitive allocation algorithm, the value allocation of data scarcity corresponds to the scarcity adjustment allocation algorithm, the value allocation that comprehensively considers multiple factors corresponds to the multi-factor comprehensive evaluation allocation algorithm, the value allocation based on the data usage effect corresponds to the effect feedback allocation algorithm, and the value allocation based on cooperation and contribution corresponds to the cooperation contribution allocation algorithm; Use the matched value allocation algorithm and data value assessment results to calculate and obtain the relative value ratio of each data item or data set.

[0010] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S104, includes: According to the market demand data, search and match the most suitable value allocation algorithm in the knowledge base and determine it as the value allocation algorithm to be used; Extracting historical value allocation data from a database, the historical value allocation data including result information processed using various value allocation algorithms; In the acquired historical value allocation data, searching and identifying the data set processed by the value allocation algorithm to be used, and determining the formula used when processing the data by using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from the historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas; Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas; The data normalization formula is:

[0011] Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and xnorm is the standardized data; The missing value filling formula is:

[0012] Among them, x is a data set containing missing values, and xfilled is the data set after the missing values ​​are filled; Outlier processing formula:

[0013]

[0014]

[0015] Among them, Q1 is the first quartile, Q3 is the third quartile, and data points below the lower limit or above the upper limit are considered outliers; Also includes: The value calculation formula includes the simple weighted average formula:

[0016] Where V is the total value, wi is the weight of the i-th data item, vi is the value of the i-th data item, and n is the number of data items; Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0017] Among them, ratio i is the allocation ratio of the i-th data item, vi is the value of the i-th data item, and the denominator is the sum of the values ​​of all data items; Market demand sensitive allocation ratio formula:

[0018] Among them, MDi is the market demand index corresponding to the i-th data item; Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0019] Among them, α, β, and γ are the weight coefficients of each factor, and si is the scarcity index of the i-th data item.

[0020] In a second aspect, the present invention provides a trusted data space value chain dynamic allocation system, which applies the trusted data space value chain dynamic allocation method as described above, including: An acquisition unit is used to acquire the original data in the trusted data space, perform an initial value assessment on the original data in the trusted data space, the initial value assessment includes data scarcity assessment, timeliness assessment and relevance assessment, obtain the initial data value assessment result, match the initial data value assessment result in a preset algorithm knowledge base, obtain the statistical algorithm corresponding to the initial data value assessment result, use the statistical algorithm to analyze the initial data value assessment result, calculate and obtain basic data statistics, the basic data statistics include mean, median and standard deviation, perform data feature extraction on the data in the basic data statistics, obtain data features of the basic data statistics, the basic data statistics data features include data information features and data mode features, obtain value chain dynamic business data and system data, the value chain dynamic business data includes original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, the system data includes system configuration data, system operation data and system privacy and security data; The data analysis and processing unit is used to pre-process the dynamic business data of the value chain and the data characteristics of the basic statistics of the data to obtain the pre-processed data to be mined, perform cluster analysis on the pre-processed data to be mined, and use the time series analysis method to predict the future trend of the data to be mined to obtain the future trend prediction results of the data to be mined, evaluate the future trend prediction results of the data to be mined to obtain the future trend prediction value of the data to be mined, perform cluster analysis on the data corresponding to the future trend prediction value of the data to be mined, and perform association rule mining on the cluster analysis results to obtain the results of data association analysis; A data value evaluation unit is used to determine the quality evaluation index according to the result of the data association analysis, and evaluate the result of the data association analysis according to the quality evaluation index through the preset data value evaluation rules to obtain the data value evaluation result; A value allocation calculation unit is used to match the corresponding value allocation algorithm in the knowledge base according to the market demand data, obtain the value allocation algorithm to be used, obtain historical value allocation data, match the value allocation algorithm to be used with the historical value allocation data, obtain the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data, use the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data as the formula to be used, calculate the data value assessment result by the formula to be used, and obtain the data value allocation ratio; A smart contract unit is used to receive smart contract configuration information, build a smart contract according to the data value distribution ratio and the smart contract configuration information and deploy it to the blockchain, match the obtained data value distribution ratio with the smart contract, obtain a smart contract corresponding to the data value distribution ratio, use the smart contract corresponding to the data value distribution ratio to distribute value according to a preset value distribution ratio, and obtain a smart contract value distribution result; A data processing optimization unit is used to obtain historical value distribution data, optimize the value distribution algorithm, use the optimized value distribution algorithm to identify the initial data value assessment results and the future trend prediction value of the data to be mined, obtain the factors most influencing value distribution, adjust the data value distribution ratio according to the factors most influencing value distribution, obtain the adjusted data value distribution ratio, allocate corresponding data value distribution ratios to the data corresponding to the initial data value assessment and the future trend prediction value of the data to be mined, obtain the data contribution degree, retrieve the participants corresponding to the data contribution degree, and distribute the rights and interests generated by the data to the corresponding participants based on the data contribution degree.

[0021] Furthermore, the trusted data space value chain dynamic allocation system described in the present invention also includes a privacy protection unit, which is used to encrypt the value chain dynamic business data and system data through a preset privacy computing model. During the data processing process of the data analysis and processing unit, the data value assessment unit, the value allocation calculation unit, the smart contract unit and the data processing optimization unit, when the encrypted data needs to be used, the data is decrypted using the decryption algorithm and key of the privacy computing model.

[0022] Furthermore, the trusted data space value chain dynamic allocation system and the data analysis and processing unit of the present invention are also used for: The result of cluster analysis of the pre-processed value chain dynamic business data and system data is used as an input data set, wherein the input data set includes the pre-processed value chain dynamic business data and group information after clustering of the system data and the attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, the parameters of the association rule mining algorithm include the support threshold and the confidence threshold; The preset association rule mining algorithm is run on the server side to perform association rule mining on the cluster analysis results. The association rule mining algorithm searches for the relationship between item sets in the value chain dynamic business data set to obtain an association rule list; The association rule list is identified to obtain the association relationship between data providers, data processors and market demands.

[0023] Furthermore, the trusted data space value chain dynamic allocation system and the value allocation calculation unit of the present invention are also used for: The knowledge base includes value allocation algorithm scenarios and value allocation algorithms. The value allocation algorithm scenarios include value allocation of data value, value allocation of market demand, value allocation of data scarcity, value allocation based on comprehensive consideration of multiple factors, value allocation based on data usage effects, and value allocation based on cooperation and contribution. Match the data value assessment results with the value allocation algorithm scenarios in the knowledge base to obtain the value allocation algorithm scenarios corresponding to the data value assessment results; The correspondence between the value allocation algorithm scenarios and the value allocation algorithms includes: the value allocation of data value corresponds to the data value weighted allocation algorithm, the value allocation of market demand corresponds to the market demand sensitive allocation algorithm, the value allocation of data scarcity corresponds to the scarcity adjustment allocation algorithm, the value allocation that comprehensively considers multiple factors corresponds to the multi-factor comprehensive evaluation allocation algorithm, the value allocation based on the data usage effect corresponds to the effect feedback allocation algorithm, and the value allocation based on cooperation and contribution corresponds to the cooperation contribution allocation algorithm; Use the matched value allocation algorithm and data value assessment results to calculate and obtain the relative value ratio of each data item or data set.

[0024] Furthermore, the trusted data space value chain dynamic allocation system and the value allocation calculation unit of the present invention are also used for: According to the market demand data, search and match the most suitable value allocation algorithm in the knowledge base and determine it as the value allocation algorithm to be used; Extracting historical value allocation data from a database, the historical value allocation data including result information processed using various value allocation algorithms; In the acquired historical value allocation data, searching and identifying the data set processed by the value allocation algorithm to be used, and determining the formula used when processing the data by using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from the historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas; Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas; The data normalization formula is:

[0025] Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and xnorm is the standardized data; The missing value filling formula is:

[0026] Among them, x is a data set containing missing values, and xfilled is the data set after the missing values ​​are filled; Outlier processing formula:

[0027]

[0028]

[0029] Among them, Q1 is the first quartile, Q3 is the third quartile, and data points below the lower limit or above the upper limit are considered outliers; Also includes: The value calculation formula includes the simple weighted average formula:

[0030] Where V is the total value, wi is the weight of the i-th data item, vi is the value of the i-th data item, and n is the number of data items; Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0031] Among them, ratio i is the allocation ratio of the i-th data item, vi is the value of the i-th data item, and the denominator is the sum of the values ​​of all data items; Market demand sensitive allocation ratio formula:

[0032] Among them, MDi is the market demand index corresponding to the i-th data item; Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0033] Among them, α, β, and γ are the weight coefficients of each factor, and si is the scarcity index of the i-th data item.

[0034] The beneficial effects of the present invention are mainly reflected in the following aspects: It solves the problem that the data cannot be dynamically adjusted in the trusted data space during the dynamic allocation of the existing value chain, realizes the dynamic allocation of the data value chain, and improves the flexibility and efficiency of data circulation. Through the dynamic value allocation algorithm, it ensures that the rights and interests generated by the data can be fairly and reasonably distributed to each participant, which improves the transparency and fairness of the data value chain. The initial data value assessment combines the scarcity, timeliness and relevance of the data, and performs descriptive analysis through statistical algorithms, which improves the accuracy of the data value assessment and provides a reliable basis for subsequent value allocation. The use of time series analysis methods to predict the future trends of the data to be mined provides forward-looking support for the dynamic adjustment of the data value chain, which helps decision makers better grasp market opportunities.

[0035] The automatic execution of value distribution through smart contracts reduces manual intervention, improves distribution efficiency and accuracy, and ensures transparency and traceability of distribution. The value distribution algorithm uses historical data and smart contract execution results for learning, continuously optimizes algorithm performance, and improves the fairness and rationality of value distribution.

[0036] The dynamic business data and system data in the value chain are encrypted and decrypted through the preset privacy computing model when necessary, ensuring the security of data during transmission and storage.

[0037] According to the market demand data, the corresponding value distribution algorithm is matched in the knowledge base, making the value distribution closer to the market demand, which helps to improve the market responsiveness of the data value chain. By solving the security and trust issues between data providers, intermediary service providers and data users, it promotes the sharing and circulation of data and provides strong support for data-driven digital transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A schematic diagram of a method flow chart provided by an embodiment of the present invention.

[0040] Figure 2 A schematic diagram of a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0042] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0043] First, see Figure 1 The present invention provides a method for dynamically allocating a trusted data space value chain, comprising: Step S101, obtaining the original data in the trusted data space, performing an initial value assessment on the original data in the trusted data space, the initial value assessment includes data scarcity assessment, timeliness assessment and relevance assessment, obtaining the initial data value assessment result, matching the initial data value assessment result in a preset algorithm knowledge base, obtaining the statistical algorithm corresponding to the initial data value assessment result, using the statistical algorithm to analyze the initial data value assessment result, calculating the basic data statistics, the basic data statistics include the mean, the median and the standard deviation, performing data feature extraction on the data in the basic data statistics, obtaining the basic data statistics data features, the basic data statistics data features include data information features and data pattern features, obtaining the value chain dynamic business data and system data, the value chain dynamic business data includes the original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, the system data includes system configuration data, system operation data and system privacy and security data; Obtaining raw data from the trusted data space is the starting point for data analysis.

[0044] Conduct an initial value assessment on the acquired data, with the assessment dimensions including the scarcity, timeliness and relevance of the data, in order to gain a preliminary understanding of the value and potential uses of the data.

[0045] The initial data value assessment results are matched in the preset algorithm knowledge base to find the statistical algorithm corresponding to the initial data value feature information, in order to select the analysis algorithm that best suits the current data.

[0046] Use the matched statistical algorithm to conduct descriptive analysis on the initial data value assessment results, and calculate the basic statistics of the data, such as the mean, median, and standard deviation, in order to gain a deeper understanding of the distribution and characteristics of the data.

[0047] Data features are extracted from the data within the basic statistics of the data to obtain the data features of the basic statistics of the data, including data information features and data pattern features. The purpose is to extract the most valuable information and patterns in the data to provide support for subsequent data analysis and decision-making.

[0048] Step S101 also involves obtaining dynamic business data and system data of the value chain, including original business data, data provider information, data processor information, data value indicators, market demand data, industry dynamic data, system configuration data, system operation data, and system privacy and security data.

[0049] Through the above steps, step S101 realizes the comprehensive acquisition and preliminary analysis of the original data, providing a solid foundation for subsequent data processing and decision-making.

[0050] Step S102, preprocessing the dynamic business data of the value chain and the data characteristics of the basic statistics of the data to obtain the preprocessed data to be mined, performing cluster analysis on the preprocessed data to be mined, and using the time series analysis method to predict the future trend of the data to be mined to obtain the future trend prediction result of the data to be mined, evaluating the future trend prediction result of the data to be mined to obtain the future trend prediction value of the data to be mined, performing cluster analysis on the data corresponding to the future trend prediction value of the data to be mined, and performing association rule mining on the cluster analysis result to obtain the result of data association analysis; Data preprocessing: Preprocess the dynamic business data of the value chain and the basic statistical data characteristics to obtain the preprocessed data to be mined. The purpose of this step is to clean the data, fill in missing values, remove outliers, etc., to make the data neater and more accurate, and facilitate subsequent analysis and mining.

[0051] Perform cluster analysis on the preprocessed data to be mined. Through cluster analysis, similar data can be grouped into one category, so as to better understand the internal structure and characteristics of the data.

[0052] The time series analysis method is used to predict the future trend of the data to be mined. The purpose is to capture the time change pattern of the data, predict future data trends, and provide support for decision-making.

[0053] Evaluation of future trend prediction results: Evaluate the future trend prediction results of the data to be mined to obtain the future trend prediction value of the data to be mined. The purpose is to evaluate the accuracy of the prediction results and improve the credibility of the prediction results.

[0054] Cluster analysis is performed on the data corresponding to the future trend prediction value of the data to be mined, and association rule mining is performed on the cluster analysis results. The purpose is to discover the potential associations and rules between the data and provide an important basis for the dynamic allocation of the data value chain.

[0055] Through the above steps, step S102 realizes the preprocessing and mining of the dynamic business data of the value chain and the basic statistical data of the data, providing important data support and analysis basis for the subsequent data value evaluation and value allocation.

[0056] In step S103, the quality assessment index is determined according to the result of the data association analysis, and the result of the data association analysis is evaluated according to the quality assessment index by using the preset data value assessment rules. The specific process of obtaining the data value assessment result is as follows: Determine the quality assessment indicators: These are set based on the results of data association analysis, which will reveal the intrinsic connections between data, outliers, duplicate data, etc. Quality assessment indicators include data integrity, accuracy, consistency, timeliness, redundancy, etc.

[0057] Apply preset data value assessment rules: Preset data value assessment rules are formulated based on the understanding and expectation of high-quality data characteristics and are used to quantify the quality of data. The rules involve specific calculation formulas, threshold settings or logical judgments to convert evaluation indicators into quantifiable scores or ratings.

[0058] Perform evaluation: Use preset rules to calculate or judge each quality assessment indicator and generate corresponding scores or ratings.

[0059] Comprehensive evaluation results: The results of all evaluation indicators are combined to form a data value evaluation report or score, which is used to reflect the quality performance of the data in multiple dimensions and provide an important basis for subsequent value allocation.

[0060] Generate data value assessment results: Based on the results of the comprehensive assessment, generate the final data value assessment report or data. This report or data will serve as input for subsequent steps (such as value allocation).

[0061] Feedback and iteration: The evaluation results may be fed back to the data provider or processor to understand the quality of the data and make improvements. At the same time, the evaluation rules and methods may also be iterated and optimized according to actual needs.

[0062] Through step S103, the data value is objectively and fairly evaluated, providing a reliable basis for subsequent value distribution.

[0063] The formula for data value assessment includes: Data accuracy evaluation formula: Accuracy = (number of correct records / total number of records) * 100% The data accuracy assessment formula is used to calculate the accuracy of the data, that is, the percentage of correct records. By comparing the actual value with the expected value, the correctness of the record can be determined.

[0064] The integrity assessment formula includes: Completeness = (Number of filled fields / Total number of fields) * 100% The formula for completeness assessment is used to evaluate the completeness of the data, that is, the percentage of fields that are filled, which is used to understand the extent of missing values ​​in the dataset.

[0065] The formula for consistency assessment includes: Consistency = (number of records that meet the rules / total number of records) * 100% The consistency evaluation formula is used to evaluate the consistency of data, that is, the percentage of records that meet the predetermined rules. The consistency of data can be determined by checking whether the data meets the business rules or standards.

[0066] The threshold setting and logical judgment of data value assessment rules include: The thresholds include accuracy threshold, completeness threshold, timeliness threshold and consistency threshold; Accuracy threshold: Set the accuracy threshold to 95%. If the accuracy of the data is less than 95%, further data cleaning or verification is required.

[0067] Completeness threshold: The completeness threshold is 90%. If the completeness of key fields is less than 90%, the data is considered incomplete and needs to be supplemented.

[0068] Consistency threshold: Set the consistency check pass rate threshold to 98%. If the consistency of the data is lower than this threshold, it means that a lot of data does not meet the business rules and needs further review and adjustment.

[0069] Timeliness threshold: You can set the maximum time interval for data updates to 24 hours. If the data is not updated for more than this time interval, it may be considered outdated data.

[0070] Logical judgment includes accuracy logical judgment, completeness logical judgment, consistency logical judgment and timeliness logical judgment; Accuracy logic: If the value of a field does not conform to the expected format or range (for example, a date field contains non-date characters, or a numeric field contains negative numbers when negative numbers are not allowed for the business), the data is considered inaccurate.

[0071] Integrity logic judgment: If key fields (such as user ID, order number, etc.) are empty or missing, the data is considered incomplete.

[0072] Consistency logic judgment: If there is a logical contradiction between two or more fields in the data (for example, the end date is earlier than the start date), the data is considered inconsistent.

[0073] Timeliness logic judgment: If the creation or modification time of the data is earlier than a specific time point (such as the current time minus a preset time window), the data may be considered outdated.

[0074] In actual operations, you can write automated scripts or use data value management tools to perform data value inspection and evaluation based on the threshold settings and logical judgments of the data value assessment rules.

[0075] Step S104, step S104, matching the corresponding value allocation algorithm in the knowledge base according to the market demand data, obtaining the value allocation algorithm to be used, obtaining historical value allocation data, matching the value allocation algorithm to be used with the historical value allocation data, obtaining the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data, using the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data as the formula to be used, calculating the data value assessment result by the formula to be used, and obtaining the data value allocation ratio; In step S104, the specific process of matching the corresponding value allocation algorithm in the knowledge base according to the market demand data and using the algorithm to calculate the data value evaluation result to obtain the data value allocation ratio is as follows: The knowledge base contains value allocation algorithm scenarios and corresponding value allocation algorithms. The value allocation algorithm scenarios include value allocation of data value, value allocation of market demand, value allocation of data scarcity, value allocation that comprehensively considers multiple factors, value allocation based on data usage effects, and value allocation based on cooperation and contribution.

[0076] The system first matches the data value assessment results with the value allocation algorithm scenarios in the knowledge base to find the value allocation algorithm scenario that is suitable for the current situation. The matching is based on the specific characteristics of the data value assessment results so that the selected algorithm scenario can appropriately reflect the value of the data and market demand.

[0077] According to the matched value allocation algorithm scenario, the system selects the corresponding value allocation algorithm. For example, if the matched value allocation scenario is data value, the data value weighted allocation algorithm will be selected.

[0078] After selecting the value allocation algorithm, the system uses the algorithm to calculate the data value assessment results. This calculation process takes into account multiple factors, such as data quality indicators, market demand, data scarcity, etc., to obtain a reasonable value allocation ratio.

[0079] After the value allocation algorithm is selected, the specific process of the system using the algorithm to calculate the data value assessment results is expanded and expressed by the following formula: Value distribution ratio = (data value index weight * data value score) + (market demand weight * market demand index) + (data scarcity weight * data scarcity coefficient) Among them: the weight of data value index, market demand weight and data scarcity weight are set according to the actual situation and are used to adjust the influence of different factors in value distribution. The sum of these weights should be equal to 1 to ensure the rationality of the value distribution ratio.

[0080] The data value score is derived based on the results of the data value assessment. It can be a specific numerical value or a rating level, reflecting quality indicators such as data accuracy, completeness, and consistency.

[0081] The market demand index is an indicator that reflects the market demand for data and can be determined based on information such as market research, user feedback, or transaction data. The higher the market demand, the greater the value of the index.

[0082] The data scarcity coefficient is an indicator that reflects the scarcity of data. The more scarce the data is, the higher the value ratio should be when it is allocated. This coefficient can be determined based on factors such as the source of the data, the difficulty of obtaining it, and the frequency of updating.

[0083] Through the above formula, the system can comprehensively consider multiple factors such as data quality, market demand and data scarcity to come up with a reasonable value distribution ratio. This ratio can be dynamically adjusted according to changes in these factors to ensure the fairness and rationality of value distribution.

[0084] After the calculation is completed, the system outputs the relative value ratio of each data item or data set. The relative value ratio of each data item or data set reflects the value of the data in the value chain and provides a basis for the subsequent execution of smart contracts.

[0085] Step S104 improves the fairness and rationality of value distribution, while taking into full account multiple factors such as market demand and data value. Through automated matching and calculation, the system can efficiently process large amounts of data and provide strong support for value distribution in the trusted data space.

[0086] Step S105, receiving smart contract configuration information, constructing a smart contract according to the data value distribution ratio and the smart contract configuration information and deploying it on the blockchain, matching the obtained data value distribution ratio with the smart contract, obtaining a smart contract corresponding to the data value distribution ratio, using the smart contract corresponding to the data value distribution ratio to perform value distribution according to a preset value distribution ratio, and obtaining a smart contract value distribution result; Step S105 receives smart contract configuration information, builds a smart contract, and deploys it on the blockchain to achieve an automatic value distribution process. The specific process can be described in detail as follows: Receiving smart contract configuration information: The system receives smart contract configuration information from the backend control terminal or the user terminal. This configuration information may include contract participants, allocation conditions, trigger events, etc.

[0087] Constructing a smart contract based on the data value distribution ratio: Using the data value distribution ratio calculated in step S104 and the received smart contract configuration information, construct a smart contract. The smart contract defines in detail the value distribution ratio, distribution conditions, and distribution logic of each participant.

[0088] Deploy smart contracts to the blockchain: Deploy the constructed smart contracts to the blockchain network, and use the decentralized and tamper-proof characteristics of the blockchain to ensure the fairness and transparency of the contract execution.

[0089] Smart contracts automatically execute value distribution: When the distribution conditions defined in the smart contract are met (such as data transaction completion, reaching a certain time point, etc.), the smart contract will automatically trigger and execute value distribution. According to the preset value distribution ratio, the smart contract automatically distributes the value that each participant deserves to their respective blockchain accounts.

[0090] After execution, the smart contract records the allocation results and updates the status on the blockchain to ensure that all allocation operations are traceable.

[0091] Get the value distribution result of the smart contract: After the smart contract is executed, the value distribution result will be generated, which includes the value obtained by each participant and the timestamp of the distribution. The generated value distribution result will be recorded on the blockchain for all participants to query and verify.

[0092] Step S105 improves the fairness, transparency and automatic execution of the value distribution process, thereby improving the efficiency and reliability of the entire trusted data space value chain dynamic distribution system.

[0093] Step S106, obtain historical value distribution data, optimize the value distribution algorithm, use the optimized value distribution algorithm to identify the initial data value assessment results and the future trend prediction value of the data to be mined, obtain the factors most influencing value distribution, adjust the data value distribution ratio according to the factors most influencing value distribution, obtain the adjusted data value distribution ratio, allocate corresponding data value distribution ratios to the data corresponding to the initial data value assessment and the future trend prediction value of the data to be mined, obtain the data contribution, retrieve the participants corresponding to the data contribution, and distribute the rights and interests generated by the data to the corresponding participants based on the data contribution.

[0094] Step S106 describes in detail the optimization process of the value allocation algorithm and how to use the optimized algorithm to allocate data value. The following is a detailed explanation of this step: The value allocation algorithm uses historical value allocation data and smart contract value allocation results for learning. The purpose is to enable the algorithm to automatically learn and optimize the value allocation strategy through machine learning technology, thereby improving the accuracy and fairness of value allocation.

[0095] Optimize the value allocation algorithm so that it can more accurately identify the key factors of data value and adjust the data value allocation ratio accordingly.

[0096] The optimized value allocation algorithm identifies the initial data value assessment results and the predicted value of future trends of the data to be mined, with the aim of finding out the key factors affecting data value allocation, such as data scarcity, timeliness, relevance and future trends.

[0097] The factors that have the greatest impact on value distribution are obtained, and these factors will serve as an important basis for adjusting the data value distribution ratio.

[0098] The data value distribution ratio is adjusted according to the factors that have the greatest impact on value distribution. The purpose is to ensure that data value distribution can reflect the true value and contribution of the data, while taking into account the comprehensive effect of various influencing factors.

[0099] The adjusted data value distribution ratio is obtained, which will serve as the basis for subsequent data value distribution.

[0100] The corresponding data value distribution ratio is allocated to the data corresponding to the initial data value assessment and the future trend prediction value of the data to be mined. The purpose is to quantify the data value into a specific distribution ratio to facilitate the subsequent data rights and interests distribution.

[0101] Get the data contribution, that is, the contribution of each data point or data set to the overall value. Retrieve the participants corresponding to the data contribution, who may be data providers, data processors or other related parties.

[0102] The rights and interests generated by the data are distributed to the corresponding participants based on the data contribution. The purpose is to ensure that the rights and interests generated by the data can be distributed fairly and reasonably to each participant, and to encourage them to contribute to the added value of the data value chain.

[0103] Step S106 realizes the optimization of the value distribution algorithm and the fairness and accuracy of data value distribution, providing strong support for the dynamic distribution of the data value chain.

[0104] Specifically, the trusted data space value chain dynamic allocation method provided by the present invention also includes encrypting the value chain dynamic business data and system data through a preset privacy computing model. During the data processing process in steps S102, S103, S104, S105 and S106, when the encrypted data needs to be used, the data is decrypted using the decryption algorithm and key of the privacy computing model.

[0105] This paper describes in detail how to use the preset privacy computing model in the data processing process to encrypt and decrypt the dynamic business data and system data of the value chain. The specific process is as follows: First, select a suitable privacy computing model based on the data security requirements and privacy protection requirements. Use the selected privacy computing model to encrypt the dynamic business data and system data of the value chain. This step may involve complex encryption algorithms and key management mechanisms. In the subsequent data processing steps (S102, S103, S104, S105, S106), when you need to access or use these encrypted data, perform the following operations: The data processing unit sends a decryption request to the privacy protection unit when necessary. After receiving the decryption request, the privacy protection unit uses the decryption algorithm and corresponding key of the same privacy computing model as that used for encryption to decrypt the encrypted data.

[0106] The decrypted data can be used for subsequent data processing and analysis steps.

[0107] Selection of encryption algorithm: Based on actual needs and scenarios, you can choose privacy protection technologies such as homomorphic encryption and secure multi-party computing. It is necessary to establish a secure key generation, storage, and distribution mechanism to ensure the security and availability of keys.

[0108] The decryption process should not become the bottleneck of the entire data processing flow, so the performance and efficiency of the decryption algorithm need to be considered. During the data encryption and decryption process, security verification should be performed regularly to ensure the confidentiality and integrity of the data.

[0109] Specifically, the trusted data space value chain dynamic allocation method provided by the present invention, step S101, includes: Acquire dynamic business data and system data of the value chain in real time, store the dynamic business data of the value chain in a database table, and obtain a dynamic business data table of the value chain; The validity of the data in the value chain dynamic business data table is verified, and invalid or abnormal data in the value chain dynamic business data is eliminated to obtain the pre-processed value chain dynamic business data and system data.

[0110] Real-time data acquisition: The system captures dynamic business data of the value chain and system data from various data sources in real time. These data sources may include databases, log files, sensor inputs, etc.

[0111] Data storage: The captured dynamic business data of the value chain is stored in a database table to form a dynamic business data table of the value chain, which is conducive to subsequent data management and query.

[0112] Data verification: Verify the validity of dynamic business data in the value chain stored in the database table to eliminate invalid or abnormal data and ensure the value and accuracy of data for subsequent processing.

[0113] Data preprocessing: After validity verification, invalid or abnormal data is eliminated to obtain preprocessed value chain dynamic business data and system data. Preprocessing may also include steps such as data cleaning and formatting to prepare for subsequent data analysis and mining.

[0114] Result output: The preprocessed data will be used as input for subsequent steps (such as cluster analysis, association rule mining, etc.), improving the continuity and accuracy of the entire data processing process.

[0115] Specifically, the trusted data space value chain dynamic allocation method provided by the present invention, step S102, further includes: The result of cluster analysis of the pre-processed value chain dynamic business data and system data is used as an input data set, wherein the input data set includes the pre-processed value chain dynamic business data and group information after clustering of the system data and the attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, the parameters of the association rule mining algorithm include the support threshold and the confidence threshold; The preset association rule mining algorithm is run on the server side to perform association rule mining on the cluster analysis results. The association rule mining algorithm searches for the relationship between item sets in the value chain dynamic business data set to obtain an association rule list; The association rule list is identified to obtain the association relationship between data providers, data processors and market demands.

[0116] In step S102, the process of in-depth analysis of the pre-processed value chain dynamic business data and system data specifically includes the following steps: Construction of input data set: The pre-processed value chain dynamic business data and the cluster analysis results of system data are used as input data set. The input data set contains the pre-processed value chain dynamic business data and the group information after clustering of the system data, as well as the attributes of each data point in the value chain dynamic business data.

[0117] Determination of association rule mining algorithm parameters: The parameters of the association rule mining algorithm mainly include the support threshold and the confidence threshold. The association rule mining algorithm parameters are used to control the screening criteria of item set frequency and rule strength in the association rule mining process.

[0118] Execution of association rule mining: Run the preset association rule mining algorithm on the server side. The algorithm performs association rule mining on the cluster analysis results and searches for the relationship between item sets in the value chain dynamic business data set. Generate a list of association rules that reveal the correlation between data items.

[0119] Identification and interpretation of association rules: Identify and analyze the list of association rules to derive the association between data providers, data processors, and market demand.

[0120] Through these steps, the system can deeply understand and analyze the complex relationships between dynamic business data in the value chain and system data, providing an important basis for subsequent data value assessment and value distribution.

[0121] Specifically, the trusted data space value chain dynamic allocation method provided by the present invention, step S104, includes: The knowledge base includes value allocation algorithm scenarios and value allocation algorithms. The value allocation algorithm scenarios include value allocation of data value, value allocation of market demand, value allocation of data scarcity, value allocation based on comprehensive consideration of multiple factors, value allocation based on data usage effects, and value allocation based on cooperation and contribution. Match the data value assessment results with the value allocation algorithm scenarios in the knowledge base to obtain the value allocation algorithm scenarios corresponding to the data value assessment results; The correspondence between the value allocation algorithm scenarios and the value allocation algorithms includes: the value allocation of data value corresponds to the data value weighted allocation algorithm, the value allocation of market demand corresponds to the market demand sensitive allocation algorithm, the value allocation of data scarcity corresponds to the scarcity adjustment allocation algorithm, the value allocation that comprehensively considers multiple factors corresponds to the multi-factor comprehensive evaluation allocation algorithm, the value allocation based on the data usage effect corresponds to the effect feedback allocation algorithm, and the value allocation based on cooperation and contribution corresponds to the cooperation contribution allocation algorithm; Use the matched value allocation algorithm and data value assessment results to calculate and obtain the relative value ratio of each data item or data set.

[0122] Value distribution algorithm scenarios: Different value distribution scenarios are defined, including value distribution based on data value, value distribution based on market demand, value distribution based on data scarcity, value distribution based on comprehensive consideration of multiple factors, value distribution based on data usage effects, and value distribution based on cooperation and contribution.

[0123] Value allocation algorithm: For each value allocation scenario, the knowledge base stores the corresponding value allocation algorithm, which is used to actually calculate the value ratio of data items or data sets.

[0124] The correspondence between value allocation algorithm scenarios and value allocation algorithms; Data value distribution: corresponds to the data value weighted distribution algorithm. According to the data value assessment results, different weights are assigned to data items or data sets of different qualities, and then their value ratios are calculated.

[0125] Market demand value allocation: Corresponding to the market demand sensitive allocation algorithm. Considering market demand factors, a higher value ratio is allocated to data items or data sets with high demand.

[0126] Value allocation based on data scarcity: The allocation algorithm is adjusted according to the scarcity. For data items or data sets with higher scarcity, the value ratio is adjusted through the algorithm to reflect its scarcity.

[0127] Comprehensive consideration of multiple factors in value allocation: Corresponding to the multi-factor comprehensive evaluation allocation algorithm. Comprehensively consider multiple factors (such as data value, market demand, scarcity, etc.), assign different weights to each factor, and allocate the value ratio after comprehensive evaluation.

[0128] Value distribution based on data usage effect: Corresponding effect feedback distribution algorithm. Dynamically adjust the value ratio of data items or data sets based on the actual effect feedback after data use.

[0129] Value distribution based on cooperation and contribution: Corresponding to the cooperation and contribution distribution algorithm. The corresponding value ratio is allocated according to the degree of cooperation and contribution of each participant in the process of data collection, processing, circulation, etc.

[0130] Value allocation algorithm application process; Data value assessment: First, perform a quality assessment on the data item or data set to obtain the data value assessment result.

[0131] Scenario matching: Match the evaluation results with the value allocation algorithm scenarios in the knowledge base to determine the applicable value allocation algorithm scenarios.

[0132] Algorithm selection and calculation: According to the matched value allocation algorithm scenario, select the corresponding value allocation algorithm, and calculate the relative value ratio of each data item or data set in combination with the data value assessment results. By comprehensively considering multiple factors, dynamically adjust the value ratio to adapt to the ever-changing market demand and data environment.

[0133] The principle and formula of the value distribution algorithm are as follows: Data value weighted allocation algorithm: This algorithm weights the allocation of value based on the value of the data. The formula is Vi=Qi×Wi, where Vi is the value of the data item, Qi is the quality score of the data item, and Wi is the normalized weight.

[0134] Market demand sensitive allocation algorithm: This algorithm dynamically adjusts the value allocation according to market demand. The formula is Vi=Mi×Pi, where Mi is the market demand index and Pi is the initial evaluation value based on the data value.

[0135] Scarcity Adjusted Allocation Algorithm: This algorithm adjusts the value allocation by evaluating the scarcity of data. The formula is Vi=Si×Bi, where Si is the scarcity coefficient and Bi is the basic value.

[0136] Multi-factor comprehensive evaluation allocation algorithm: This algorithm comprehensively considers multiple factors to evaluate the value of data. The formula is Vi=αQi+βMi+γSi, where α, β, γ are the weight coefficients of each factor.

[0137] Effect feedback allocation algorithm: This algorithm adjusts the value allocation based on the data usage effect feedback. The example formula is Vi=Ri×Ci, where Ri is the usage effect evaluation indicator and Ci is the preliminary value based on other factors.

[0138] The value allocation algorithm combined with the data value assessment results can systematically and dynamically allocate the value of data based on multiple factors such as data quality, market demand, and scarcity. This is both fair and reasonable and can incentivize the increase of data value.

[0139] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S104, includes: According to the market demand data, search and match the most suitable value allocation algorithm in the knowledge base and determine it as the value allocation algorithm to be used; Extracting historical value allocation data from a database, the historical value allocation data including result information processed using various value allocation algorithms; In the acquired historical value allocation data, searching and identifying the data set processed by the value allocation algorithm to be used, and determining the formula used when processing the data by using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from the historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas; Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas; The data normalization formula is:

[0140] Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and xnorm is the standardized data; The missing value filling formula is:

[0141] Among them, x is a data set containing missing values, and xfilled is the data set after the missing values ​​are filled; Outlier processing formula:

[0142]

[0143]

[0144] Among them, Q1 is the first quartile, Q3 is the third quartile, and data points below the lower limit or above the upper limit are considered outliers; Also includes: The value calculation formula includes the simple weighted average formula:

[0145] Where V is the total value, wi is the weight of the i-th data item, vi is the value of the i-th data item, and n is the number of data items; Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0146] Among them, ratio i is the allocation ratio of the i-th data item, vi is the value of the i-th data item, and the denominator is the sum of the values ​​of all data items; Market demand sensitive allocation ratio formula:

[0147] Among them, MDi is the market demand index corresponding to the i-th data item; Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0148] Among them, α, β, and γ are the weight coefficients of each factor, and si is the scarcity index of the i-th data item.

[0149] In a second aspect, the present invention provides a trusted data space value chain dynamic allocation system, which applies the trusted data space value chain dynamic allocation method as described above, including: An acquisition unit is used to acquire the original data in the trusted data space, perform an initial value assessment on the original data in the trusted data space, the initial value assessment includes data scarcity assessment, timeliness assessment and relevance assessment, obtain the initial data value assessment result, match the initial data value assessment result in a preset algorithm knowledge base, obtain the statistical algorithm corresponding to the initial data value assessment result, use the statistical algorithm to analyze the initial data value assessment result, calculate and obtain basic data statistics, the basic data statistics include mean, median and standard deviation, perform data feature extraction on the data in the basic data statistics, obtain data features of the basic data statistics, the basic data statistics data features include data information features and data mode features, obtain value chain dynamic business data and system data, the value chain dynamic business data includes original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, the system data includes system configuration data, system operation data and system privacy and security data; The data analysis and processing unit is used to pre-process the dynamic business data of the value chain and the data characteristics of the basic statistics of the data to obtain the pre-processed data to be mined, perform cluster analysis on the pre-processed data to be mined, and use the time series analysis method to predict the future trend of the data to be mined to obtain the future trend prediction results of the data to be mined, evaluate the future trend prediction results of the data to be mined to obtain the future trend prediction value of the data to be mined, perform cluster analysis on the data corresponding to the future trend prediction value of the data to be mined, and perform association rule mining on the cluster analysis results to obtain the results of data association analysis; A data value evaluation unit is used to determine the quality evaluation index according to the result of the data association analysis, and evaluate the result of the data association analysis according to the quality evaluation index through the preset data value evaluation rules to obtain the data value evaluation result; A value allocation calculation unit is used to match the corresponding value allocation algorithm in the knowledge base according to the data value evaluation result, and use the value allocation algorithm to calculate the data value evaluation result to obtain the data value allocation ratio; The smart contract unit is used to receive the smart contract configuration information, build the smart contract according to the data value distribution ratio and the smart contract configuration information, and deploy it to the blockchain. When the conditions corresponding to the smart contract are met, the smart contract will automatically distribute the value according to the preset value distribution ratio to obtain the smart contract value distribution result; A data processing optimization unit is used to obtain historical value distribution data, optimize the value distribution algorithm, use the optimized value distribution algorithm to identify the initial data value assessment results and the future trend prediction value of the data to be mined, obtain the factors most influencing value distribution, adjust the data value distribution ratio according to the factors most influencing value distribution, obtain the adjusted data value distribution ratio, allocate corresponding data value distribution ratios to the data corresponding to the initial data value assessment and the future trend prediction value of the data to be mined, obtain the data contribution degree, retrieve the participants corresponding to the data contribution degree, and distribute the rights and interests generated by the data to the corresponding participants based on the data contribution degree.

[0150] Specifically, the trusted data space value chain dynamic allocation system described in the present invention also includes a privacy protection unit, which is used to encrypt the value chain dynamic business data and system data through a preset privacy computing model. During the data processing process of the data analysis and processing unit, the data value assessment unit, the value allocation calculation unit, the smart contract unit and the data processing optimization unit, when the encrypted data needs to be used, the data is decrypted using the decryption algorithm and key of the privacy computing model.

[0151] Specifically, the trusted data space value chain dynamic allocation system and the data analysis and processing unit of the present invention are also used for: The result of cluster analysis of the pre-processed value chain dynamic business data and system data is used as an input data set, wherein the input data set includes the pre-processed value chain dynamic business data and group information after clustering of the system data and the attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, the parameters of the association rule mining algorithm include the support threshold and the confidence threshold; The preset association rule mining algorithm is run on the server side to perform association rule mining on the cluster analysis results. The association rule mining algorithm searches for the relationship between item sets in the value chain dynamic business data set to obtain an association rule list; The association rule list is identified to obtain the association relationship between data providers, data processors and market demands.

[0152] Specifically, the trusted data space value chain dynamic allocation system and the value allocation calculation unit of the present invention are also used for: The knowledge base includes value allocation algorithm scenarios and value allocation algorithms. The value allocation algorithm scenarios include value allocation of data value, value allocation of market demand, value allocation of data scarcity, value allocation based on comprehensive consideration of multiple factors, value allocation based on data usage effects, and value allocation based on cooperation and contribution. Match the data value assessment results with the value allocation algorithm scenarios in the knowledge base to obtain the value allocation algorithm scenarios corresponding to the data value assessment results; The correspondence between the value allocation algorithm scenarios and the value allocation algorithms includes: the value allocation of data value corresponds to the data value weighted allocation algorithm, the value allocation of market demand corresponds to the market demand sensitive allocation algorithm, the value allocation of data scarcity corresponds to the scarcity adjustment allocation algorithm, the value allocation that comprehensively considers multiple factors corresponds to the multi-factor comprehensive evaluation allocation algorithm, the value allocation based on the data usage effect corresponds to the effect feedback allocation algorithm, and the value allocation based on cooperation and contribution corresponds to the cooperation contribution allocation algorithm; Use the matched value allocation algorithm and data value assessment results to calculate and obtain the relative value ratio of each data item or data set.

[0153] Specifically, the trusted data space value chain dynamic allocation method and the value allocation calculation unit of the present invention are also used for: According to the market demand data, search and match the most suitable value allocation algorithm in the knowledge base and determine it as the value allocation algorithm to be used; Extracting historical value allocation data from a database, the historical value allocation data including result information processed using various value allocation algorithms; In the acquired historical value allocation data, searching and identifying the data set processed by the value allocation algorithm to be used, and determining the formula used when processing the data by using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from the historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas; Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas; The data normalization formula is:

[0154] Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and xnorm is the standardized data; The missing value filling formula is:

[0155] Among them, x is a data set containing missing values, and xfilled is the data set after the missing values ​​are filled; Outlier processing formula:

[0156]

[0157]

[0158] Among them, Q1 is the first quartile, Q3 is the third quartile, and data points below the lower limit or above the upper limit are considered outliers; Also includes: The value calculation formula includes the simple weighted average formula:

[0159] Where V is the total value, wi is the weight of the i-th data item, vi is the value of the i-th data item, and n is the number of data items; Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0160] Among them, ratio i is the allocation ratio of the i-th data item, vi is the value of the i-th data item, and the denominator is the sum of the values ​​of all data items; Market demand sensitive allocation ratio formula:

[0161] Among them, MDi is the market demand index corresponding to the i-th data item; Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0162] Among them, α, β, and γ are the weight coefficients of each factor, and si is the scarcity index of the i-th data item.

[0163] The present invention solves the problem that the existing value chain dynamic allocation process cannot dynamically adjust the data value chain in the trusted data space, and thus cannot fairly and reasonably allocate the rights and interests generated by the data through the following technical solutions: Obtain raw data from the trusted data space and conduct an initial value assessment, with the assessment dimensions including scarcity, timeliness, and relevance of the data. Use the statistical algorithms in the preset algorithm knowledge base to conduct a descriptive analysis of the initial assessment results and extract data features. Preprocess the dynamic business data and system data of the value chain to obtain the data to be mined. Perform cluster analysis and use time series analysis methods to predict the future trend of the data to be mined.

[0164] Evaluate the future trend prediction results of the data to be mined, and perform cluster analysis and association rule mining. Determine the quality assessment indicators based on the data association analysis results, and then conduct a comprehensive assessment of the data value. Match the corresponding value allocation algorithm in the knowledge base based on market demand data. Use the matched value allocation algorithm to calculate the data value assessment results to determine the data value allocation ratio.

[0165] Receive smart contract configuration information, build smart contracts, and deploy them on the blockchain. When the triggering conditions of the smart contract are met, the value distribution is automatically executed to ensure the fairness and transparency of the entire process. The value distribution algorithm uses historical data and the execution results of the smart contract for machine learning to continuously optimize the algorithm performance. According to the optimized algorithm, the influencing factors of value distribution are identified, and the data value distribution ratio is dynamically adjusted accordingly. The dynamic business data and system data of the value chain are encrypted to ensure the security of the data during transmission and storage. During the data processing process, when encrypted data needs to be used, the decryption operation is performed through the preset privacy computing model.

[0166] Through the above-mentioned comprehensive technical solution, the present invention successfully realizes the dynamic adjustment of the data value chain in the trusted data space, and ensures that the rights and interests generated by the data can be fairly and reasonably distributed to each participant.

[0167] 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, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for dynamically allocating a trusted data space value chain, characterized in that: include: Step S101, obtaining the original data in the trusted data space, performing an initial value assessment on the original data in the trusted data space, the initial value assessment includes data scarcity assessment, timeliness assessment and relevance assessment, obtaining the initial data value assessment result, matching the initial data value assessment result in a preset algorithm knowledge base, obtaining the statistical algorithm corresponding to the initial data value assessment result, using the statistical algorithm to analyze the initial data value assessment result, calculating the basic data statistics, the basic data statistics include the mean, the median and the standard deviation, performing data feature extraction on the data in the basic data statistics, obtaining the basic data statistics data features, the basic data statistics data features include data information features and data pattern features, obtaining the value chain dynamic business data and system data, the value chain dynamic business data includes the original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, the system data includes system configuration data, system operation data and system privacy and security data; Step S102, preprocessing the dynamic business data of the value chain and the data characteristics of the basic statistics of the data to obtain the preprocessed data to be mined, performing cluster analysis on the preprocessed data to be mined, and using the time series analysis method to predict the future trend of the data to be mined to obtain the future trend prediction result of the data to be mined, evaluating the future trend prediction result of the data to be mined to obtain the future trend prediction value of the data to be mined, performing cluster analysis on the data corresponding to the future trend prediction value of the data to be mined, and performing association rule mining on the cluster analysis result to obtain the result of data association analysis; Step S103, determining a quality assessment index based on the result of the data association analysis, and evaluating the result of the data association analysis according to the quality assessment index using a preset data value assessment rule to obtain a data value assessment result; Step S104, matching the corresponding value allocation algorithm in the knowledge base according to the market demand data to obtain the value allocation algorithm to be used, obtaining historical value allocation data, matching the value allocation algorithm to be used with the historical value allocation data, obtaining the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data, using the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data as the formula to be used, calculating the data value assessment result by the formula to be used, and obtaining the data value allocation ratio; Step S105, receiving smart contract configuration information, constructing a smart contract according to the data value distribution ratio and the smart contract configuration information and deploying it on the blockchain, matching the obtained data value distribution ratio with the smart contract, obtaining a smart contract corresponding to the data value distribution ratio, using the smart contract corresponding to the data value distribution ratio to perform value distribution according to a preset value distribution ratio, and obtaining a smart contract value distribution result; Step S106, obtain historical value distribution data, optimize the value distribution algorithm, use the optimized value distribution algorithm to identify the initial data value assessment results and the future trend prediction value of the data to be mined, obtain the factors most influencing value distribution, adjust the data value distribution ratio according to the factors most influencing value distribution, obtain the adjusted data value distribution ratio, allocate corresponding data value distribution ratios to the data corresponding to the initial data value assessment and the future trend prediction value of the data to be mined, obtain the data contribution, retrieve the participants corresponding to the data contribution, and distribute the rights and interests generated by the data to the corresponding participants based on the data contribution.

2. The method for dynamic allocation of a trusted data space value chain according to claim 1, characterized in that: It also includes encrypting the value chain dynamic business data and system data through a preset privacy computing model. During the data processing in steps S102, S103, S104, S105 and S106, when the encrypted data needs to be used, the data is decrypted using the decryption algorithm and key of the privacy computing model.

3. The method for dynamic allocation of a trusted data space value chain according to claim 1, characterized in that: Step S102 further includes: The result of cluster analysis of the pre-processed value chain dynamic business data and system data is used as an input data set, wherein the input data set includes the pre-processed value chain dynamic business data and group information after clustering of the system data and the attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, the parameters of the association rule mining algorithm include the support threshold and the confidence threshold; The preset association rule mining algorithm is run on the server side to perform association rule mining on the cluster analysis results. The association rule mining algorithm searches for the relationship between item sets in the value chain dynamic business data set to obtain an association rule list; The association rule list is identified to obtain the association relationship between data providers, data processors and market demands.

4. The method for dynamically allocating a trusted data space value chain according to claim 1, characterized in that: Step S104 includes: The knowledge base includes value allocation algorithm scenarios and value allocation algorithms. The value allocation algorithm scenarios include value allocation of data value, value allocation of market demand, value allocation of data scarcity, value allocation based on comprehensive consideration of multiple factors, value allocation based on data usage effects, and value allocation based on cooperation and contribution. Match the data value assessment results with the value allocation algorithm scenarios in the knowledge base to obtain the value allocation algorithm scenarios corresponding to the data value assessment results; The correspondence between the value allocation algorithm scenarios and the value allocation algorithms includes: the value allocation of data value corresponds to the data value weighted allocation algorithm, the value allocation of market demand corresponds to the market demand sensitive allocation algorithm, the value allocation of data scarcity corresponds to the scarcity adjustment allocation algorithm, the value allocation that comprehensively considers multiple factors corresponds to the multi-factor comprehensive evaluation allocation algorithm, the value allocation based on the data usage effect corresponds to the effect feedback allocation algorithm, and the value allocation based on cooperation and contribution corresponds to the cooperation contribution allocation algorithm; Use the matched value allocation algorithm and data value assessment results to calculate and obtain the relative value ratio of each data item or data set.

5. The method for dynamically allocating a trusted data space value chain according to claim 1, characterized in that: Step S104 includes: According to the market demand data, search and match the most suitable value allocation algorithm in the knowledge base and determine it as the value allocation algorithm to be used; Extracting historical value allocation data from a database, the historical value allocation data including result information processed using various value allocation algorithms; In the acquired historical value allocation data, searching and identifying the data set processed by the value allocation algorithm to be used, and determining the formula used when processing the data by using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from the historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas; Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas; The data normalization formula is: Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and xnorm is the standardized data; The missing value filling formula is: Among them, x is a data set containing missing values, and xfilled is the data set after the missing values ​​are filled; Outlier processing formula: Among them, Q1 is the first quartile, Q3 is the third quartile, and data points below the lower limit or above the upper limit are considered outliers; Also includes: The value calculation formula includes the simple weighted average formula: Where V is the total value, wi is the weight of the i-th data item, vi is the value of the i-th data item, and n is the number of data items; Allocation ratio determination formula: Allocation ratio formula based on value weighting: Among them, ratio i is the allocation ratio of the i-th data item, vi is the value of the i-th data item, and the denominator is the sum of the values ​​of all data items; Market demand sensitive allocation ratio formula: Among them, MDi is the market demand index corresponding to the i-th data item; Formula for allocation ratio of comprehensive evaluation of multiple factors: Among them, α, β, and γ are the weight coefficients of each factor, and si is the scarcity index of the i-th data item.

6. A trusted data space value chain dynamic allocation system, using the trusted data space value chain dynamic allocation method as described in any one of claims 1 to 5, characterized in that: include: An acquisition unit is used to acquire the original data in the trusted data space, perform an initial value assessment on the original data in the trusted data space, the initial value assessment includes data scarcity assessment, timeliness assessment and relevance assessment, obtain the initial data value assessment result, match the initial data value assessment result in a preset algorithm knowledge base, obtain the statistical algorithm corresponding to the initial data value assessment result, use the statistical algorithm to analyze the initial data value assessment result, calculate and obtain basic data statistics, the basic data statistics include mean, median and standard deviation, perform data feature extraction on the data in the basic data statistics, obtain data features of the basic data statistics, the basic data statistics data features include data information features and data mode features, obtain value chain dynamic business data and system data, the value chain dynamic business data includes original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, the system data includes system configuration data, system operation data and system privacy and security data; The data analysis and processing unit is used to pre-process the dynamic business data of the value chain and the data characteristics of the basic statistics of the data to obtain the pre-processed data to be mined, perform cluster analysis on the pre-processed data to be mined, and use the time series analysis method to predict the future trend of the data to be mined to obtain the future trend prediction results of the data to be mined, evaluate the future trend prediction results of the data to be mined to obtain the future trend prediction value of the data to be mined, perform cluster analysis on the data corresponding to the future trend prediction value of the data to be mined, and perform association rule mining on the cluster analysis results to obtain the results of data association analysis; A data value evaluation unit is used to determine the quality evaluation index according to the result of the data association analysis, and evaluate the result of the data association analysis according to the quality evaluation index through the preset data value evaluation rules to obtain the data value evaluation result; A value allocation calculation unit is used to match the corresponding value allocation algorithm in the knowledge base according to the market demand data, obtain the value allocation algorithm to be used, obtain historical value allocation data, match the value allocation algorithm to be used with the historical value allocation data, obtain the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data, use the formula involved in the data processing process using the value allocation algorithm to be used in the historical value allocation data as the formula to be used, calculate the data value assessment result by the formula to be used, and obtain the data value allocation ratio; A smart contract unit is used to receive smart contract configuration information, build a smart contract according to the data value distribution ratio and the smart contract configuration information and deploy it to the blockchain, match the obtained data value distribution ratio with the smart contract, obtain a smart contract corresponding to the data value distribution ratio, use the smart contract corresponding to the data value distribution ratio to distribute value according to a preset value distribution ratio, and obtain a smart contract value distribution result; A data processing optimization unit is used to obtain historical value distribution data, optimize the value distribution algorithm, use the optimized value distribution algorithm to identify the initial data value assessment results and the future trend prediction value of the data to be mined, obtain the factors most influencing value distribution, adjust the data value distribution ratio according to the factors most influencing value distribution, obtain the adjusted data value distribution ratio, allocate corresponding data value distribution ratios to the data corresponding to the initial data value assessment and the future trend prediction value of the data to be mined, obtain the data contribution degree, retrieve the participants corresponding to the data contribution degree, and distribute the rights and interests generated by the data to the corresponding participants based on the data contribution degree.

7. The trusted data space value chain dynamic allocation system according to claim 6 is characterized in that: It also includes a privacy protection unit, which is used to encrypt the dynamic business data and system data of the value chain through a preset privacy computing model. During the data processing process of the data analysis and processing unit, the data value assessment unit, the value distribution calculation unit, the smart contract unit and the data processing optimization unit, when the encrypted data needs to be used, the data is decrypted using the decryption algorithm and key of the privacy computing model.

8. The trusted data space value chain dynamic allocation system according to claim 6 is characterized in that: The data analysis and processing unit is also used for: The result of cluster analysis of the pre-processed value chain dynamic business data and system data is used as an input data set, wherein the input data set includes the pre-processed value chain dynamic business data and group information after clustering of the system data and the attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, the parameters of the association rule mining algorithm include the support threshold and the confidence threshold; The preset association rule mining algorithm is run on the server side to perform association rule mining on the cluster analysis results. The association rule mining algorithm searches for the relationship between item sets in the value chain dynamic business data set to obtain an association rule list; The association rule list is identified to obtain the association relationship between data providers, data processors and market demands.

9. The trusted data space value chain dynamic allocation system according to claim 6 is characterized in that: The value allocation calculation unit is also used to: The knowledge base includes value allocation algorithm scenarios and value allocation algorithms. The value allocation algorithm scenarios include value allocation of data value, value allocation of market demand, value allocation of data scarcity, value allocation based on comprehensive consideration of multiple factors, value allocation based on data usage effects, and value allocation based on cooperation and contribution. Match the data value assessment results with the value allocation algorithm scenarios in the knowledge base to obtain the value allocation algorithm scenarios corresponding to the data value assessment results; The correspondence between the value allocation algorithm scenarios and the value allocation algorithms includes: the value allocation of data value corresponds to the data value weighted allocation algorithm, the value allocation of market demand corresponds to the market demand sensitive allocation algorithm, the value allocation of data scarcity corresponds to the scarcity adjustment allocation algorithm, the value allocation that comprehensively considers multiple factors corresponds to the multi-factor comprehensive evaluation allocation algorithm, the value allocation based on the data usage effect corresponds to the effect feedback allocation algorithm, and the value allocation based on cooperation and contribution corresponds to the cooperation contribution allocation algorithm; Use the matched value allocation algorithm and data value assessment results to calculate and obtain the relative value ratio of each data item or data set.

10. The method for dynamically allocating a trusted data space value chain according to claim 6, characterized in that: The value allocation calculation unit is also used to: According to the market demand data, search and match the most suitable value allocation algorithm in the knowledge base and determine it as the value allocation algorithm to be used; Extracting historical value allocation data from a database, the historical value allocation data including result information processed using various value allocation algorithms; In the acquired historical value allocation data, searching and identifying the data set processed by the value allocation algorithm to be used, and determining the formula used when processing the data by using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from the historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas; Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas; The data normalization formula is: Among them, x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and xnorm is the standardized data; The missing value filling formula is: Among them, x is a data set containing missing values, and xfilled is the data set after the missing values ​​are filled; Outlier processing formula: Among them, Q1 is the first quartile, Q3 is the third quartile, and data points below the lower limit or above the upper limit are considered outliers; Also includes: The value calculation formula includes the simple weighted average formula: Where V is the total value, wi is the weight of the i-th data item, vi is the value of the i-th data item, and n is the number of data items; Allocation ratio determination formula: Allocation ratio formula based on value weighting: Among them, ratio i is the allocation ratio of the i-th data item, vi is the value of the i-th data item, and the denominator is the sum of the values ​​of all data items; Market demand sensitive allocation ratio formula: Among them, MDi is the market demand index corresponding to the i-th data item; Formula for allocation ratio of comprehensive evaluation of multiple factors: Among them, α, β, and γ are the weight coefficients of each factor, and si is the scarcity index of the i-th data item.

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