A dynamic allocation method and system for trusted data space value chain

Abstract

The present invention relates to the field of trusted data processing technology. It provides a method and system for dynamically allocating a trusted data space value chain, including steps such as acquiring raw data, initial value assessment, data preprocessing, cluster analysis, time series analysis, future trend prediction, data correlation analysis, learning and optimizing a value allocation algorithm, and data value and equity allocation. This method enables dynamic adjustment of the data value chain, improves the fair and reasonable distribution of data-generated equity to various participants, enhances the accuracy of data value assessment, and provides forward-looking support for the dynamic allocation of the data value chain.

Description

Technical Field

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

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

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

[0004] The following technical pain points exist in the dynamic allocation process of the value chain in the trusted data space. 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 the fair and reasonable distribution of the rights and interests generated by the data. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for dynamic allocation of the value chain in 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:

[0007] In a first aspect, the present invention provides a method for dynamically allocating a trusted data space value chain, comprising:

[0008] 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 including 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, analyzing the initial data value assessment result using the statistical algorithm, calculating and obtaining basic data statistics, which include mean, median and standard deviation, performing data feature extraction on the data in the basic data statistics, obtaining basic data statistics data features, which include data information features and data pattern features, obtaining value chain dynamic business data and system data, which include original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, and system data including system configuration data, system operation data and system privacy and security data;

[0009] Step S102: Preprocess the dynamic business data of the value chain and the data characteristics of basic statistics to obtain preprocessed data to be mined, perform cluster analysis on the preprocessed data to be mined, and use time series analysis methods to predict the future trend of the data to be mined to obtain a prediction result of the future trend of the data to be mined, evaluate the prediction result of the future trend of the data to be mined to obtain a prediction value of the future trend of the data to be mined, perform cluster analysis on the data corresponding to the prediction value of the future trend of the data to be mined, and perform association rule mining on the cluster analysis results to obtain a result of data association analysis;

[0010] Step S103: Determine a quality assessment index based on the result of the data association analysis, and evaluate the result of the data association analysis based on the quality assessment index using a preset data value assessment rule to obtain a data value assessment result;

[0011] Step S104: Match the corresponding value allocation algorithm in the knowledge base according to the market demand data to 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 using the formula to be used, and obtain the data value allocation ratio;

[0012] Step S105: Receive smart contract configuration information, construct a smart contract based on the data value distribution ratio and the smart contract configuration information, and deploy it on the blockchain; match the obtained data value distribution ratio with the smart contract to obtain a smart contract corresponding to the data value distribution ratio; use the smart contract corresponding to the data value distribution ratio to perform value distribution according to the preset value distribution ratio, and obtain a smart contract value distribution result;

[0013] 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 most influencing factors of value distribution, adjust the data value distribution ratio according to the most influencing factors of 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.

[0014] Furthermore, the method for dynamic allocation of the trusted data space value chain described in the present invention also includes encrypting the dynamic business data and system data of the value chain 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.

[0015] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S102, further includes:

[0016] The results of cluster analysis of the pre-processed value chain dynamic business data and system data are used as input data sets, which include the pre-processed value chain dynamic business data, group information after clustering the system data, and attributes of each data point in the value chain dynamic business data;

[0017] Determine the parameters of the association rule mining algorithm, which include the support threshold and the confidence threshold;

[0018] Run the preset association rule mining algorithm 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;

[0019] Identify the association rule list and obtain the association relationship between data providers, data processors and market demands.

[0020] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S104, includes:

[0021] 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.

[0022] 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;

[0023] The correspondence between value allocation algorithm scenarios and 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 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;

[0024] 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.

[0025] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S104, includes:

[0026] Based on 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;

[0027] Extracting historical value allocation data from a database, where the historical value allocation data includes result information processed using various value allocation algorithms;

[0028] Find and identify, in the acquired historical value allocation data, a data set processed using the value allocation algorithm to be used, and determine a formula to be used when processing the data using the value allocation algorithm to be used;

[0029] Extracting formulas related to the value allocation algorithm to be used from historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas;

[0030] Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas;

[0031] The data normalization formula is:

[0032] 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;

[0033] The missing value filling formula is:

[0034] Where x is a dataset containing missing values, and xfilled is a dataset after filling missing values;

[0035] Outlier processing formula:

[0036]

[0037]

[0038] 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;

[0039] Also includes:

[0040] The value calculation formula includes the simple weighted average formula:

[0041]

[0042] 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;

[0043] Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0044]

[0045] 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;

[0046] Market demand sensitive allocation ratio formula:

[0047] Among them, MDi is the market demand index corresponding to the i-th data item;

[0048] Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0049] 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.

[0050] 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:

[0051] An acquisition unit is used to acquire original data in a 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 an initial data value assessment result, match the initial data value assessment result in a preset algorithm knowledge base, obtain a statistical algorithm corresponding to the initial data value assessment result, analyze the initial data value assessment result using the statistical algorithm, calculate 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 basic data statistics data features, the basic data statistics data features include data information features and data pattern features, obtain value chain dynamic business data and system data, the dynamic business data of the value chain includes original business data, data provider information, data processor information, data value indicators, market demand data, and industry dynamic data, and the system data includes system configuration data, system operation data, and system privacy and security data;

[0052] a data analysis and processing unit, configured to preprocess the dynamic business data of the value chain and the data characteristics of basic statistics of the data to obtain preprocessed data to be mined, perform cluster analysis on the preprocessed data to be mined, and use a time series analysis method to predict the future trend of the data to be mined to obtain a prediction result of the future trend of the data to be mined, evaluate the prediction result of the future trend of the data to be mined to obtain a prediction value of the future trend of the data to be mined, perform cluster analysis on the data corresponding to the prediction value of the future trend of the data to be mined, and perform association rule mining on the cluster analysis results to obtain a result of data association analysis;

[0053] The data value evaluation unit is used to determine the quality evaluation index based on the results of the data association analysis, and evaluate the results of the data association analysis according to the quality evaluation index using the preset data value evaluation rules to obtain the data value evaluation result;

[0054] 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 using the formula to be used, and obtain the data value allocation ratio;

[0055] The smart contract unit is used to receive smart contract configuration information, build a smart contract based on the data value distribution ratio and the smart contract configuration information, and deploy it on the blockchain; match the obtained data value distribution ratio with the smart contract to obtain a smart contract corresponding to the data value distribution ratio; use the smart contract corresponding to the data value distribution ratio to perform value distribution according to the preset value distribution ratio, and obtain the smart contract value distribution result;

[0056] 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 most influencing factors of value distribution, adjust the data value distribution ratio according to the most influencing factors of 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.

[0057] 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 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.

[0058] Furthermore, the trusted data space value chain dynamic allocation system and the data analysis and processing unit of the present invention are also used to:

[0059] The results of cluster analysis of the pre-processed value chain dynamic business data and system data are used as input data sets, which include the pre-processed value chain dynamic business data, group information after clustering the system data, and attributes of each data point in the value chain dynamic business data;

[0060] Determine the parameters of the association rule mining algorithm, which include the support threshold and the confidence threshold;

[0061] Run the preset association rule mining algorithm 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;

[0062] Identify the association rule list and obtain the association relationship between data providers, data processors and market demands.

[0063] Furthermore, the trusted data space value chain dynamic allocation system and the value allocation calculation unit of the present invention are also used to:

[0064] 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.

[0065] 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;

[0066] The correspondence between value allocation algorithm scenarios and 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 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;

[0067] 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.

[0068] Furthermore, the trusted data space value chain dynamic allocation system and the value allocation calculation unit of the present invention are also used to:

[0069] Based on 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;

[0070] Extracting historical value allocation data from a database, where the historical value allocation data includes result information processed using various value allocation algorithms;

[0071] Find and identify, in the acquired historical value allocation data, a data set processed using the value allocation algorithm to be used, and determine a formula to be used when processing the data using the value allocation algorithm to be used;

[0072] Extracting formulas related to the value allocation algorithm to be used from historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas;

[0073] Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas;

[0074] The data normalization formula is:

[0075] 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;

[0076] The missing value filling formula is:

[0077] Where x is a dataset containing missing values, and xfilled is a dataset after filling missing values;

[0078] Outlier processing formula:

[0079]

[0080]

[0081] 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;

[0082] Also includes:

[0083] The value calculation formula includes the simple weighted average formula:

[0084]

[0085] 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;

[0086] Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0087]

[0088] 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;

[0089] Market demand sensitive allocation ratio formula:

[0090] Among them, MDi is the market demand index corresponding to the i-th data item;

[0091] Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0092] 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.

[0093] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0094] This solves the problem of the inability to dynamically adjust data within a trusted data space during the dynamic allocation process of the existing value chain, enabling dynamic allocation of the data value chain and improving the flexibility and efficiency of data circulation. Through a dynamic value allocation algorithm, it ensures that the rights and interests generated by data are fairly and reasonably distributed to all participants, enhancing the transparency and fairness of the data value chain. The initial data value assessment combines the scarcity, timeliness, and relevance of the data, and uses statistical algorithms for descriptive analysis to improve the accuracy of the data value assessment and provide a reliable basis for subsequent value allocation. Using time series analysis methods to predict future trends in the data to be mined provides forward-looking support for the dynamic adjustment of the data value chain, helping decision makers better grasp market opportunities.

[0095] Automating value distribution through smart contracts reduces manual intervention, improves efficiency and accuracy, and ensures transparency and traceability. The value distribution algorithm leverages historical data and smart contract execution results to continuously optimize algorithm performance and improve the fairness and rationality of value distribution.

[0096] The dynamic business data and system data of the value chain are encrypted and decrypted through the preset privacy computing model when needed, ensuring the security of data during transmission and storage.

[0097] By matching market demand data with corresponding value allocation algorithms in the knowledge base, value allocation is more closely aligned with market demand, helping to improve the market responsiveness of the data value chain. By resolving security and trust issues between data providers, intermediary service providers, and data users, data sharing and circulation are promoted, providing strong support for data-driven digital transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to 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 any creative work.

[0099] Figure 1 A schematic diagram of a method flow chart provided in an embodiment of the present invention.

[0100] Figure 2 A schematic diagram of a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0101] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings.

[0102] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0103] First, see Figure 1 The present invention provides a method for dynamically allocating a trusted data space value chain, comprising:

[0104] 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 including 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, analyzing the initial data value assessment result using the statistical algorithm, calculating and obtaining basic data statistics, which include mean, median and standard deviation, performing data feature extraction on the data in the basic data statistics, obtaining basic data statistics data features, which include data information features and data pattern features, obtaining value chain dynamic business data and system data, which include original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, and system data including system configuration data, system operation data and system privacy and security data;

[0105] Obtaining raw data from the trusted data space is the starting point of data analysis.

[0106] Conduct an initial value assessment of the acquired data, with the assessment dimensions including scarcity, timeliness, and relevance of the data, in order to gain a preliminary understanding of the value and potential uses of the data.

[0107] 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.

[0108] Use the matched statistical algorithm to perform 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.

[0109] 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.

[0110] 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.

[0111] Through the above steps, step S101 achieves comprehensive acquisition and preliminary analysis of the original data, providing a solid foundation for subsequent data processing and decision-making.

[0112] Step S102: Preprocess the dynamic business data of the value chain and the data characteristics of basic statistics to obtain preprocessed data to be mined, perform cluster analysis on the preprocessed data to be mined, and use time series analysis methods to predict the future trend of the data to be mined to obtain a prediction result of the future trend of the data to be mined, evaluate the prediction result of the future trend of the data to be mined to obtain a prediction value of the future trend of the data to be mined, perform cluster analysis on the data corresponding to the prediction value of the future trend of the data to be mined, and perform association rule mining on the cluster analysis results to obtain a result of data association analysis;

[0113] 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.

[0114] Perform cluster analysis on the pre-processed data to be mined. Through cluster analysis, similar data can be grouped together to better understand the inherent structure and characteristics of the data.

[0115] 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.

[0116] 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.

[0117] Cluster analysis is performed on the data corresponding to the future trend prediction value of the mined data, 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.

[0118] Through the above steps, step S102 realizes the preprocessing and mining of dynamic business data of the value chain and basic statistical data, providing important data support and analysis basis for subsequent data value evaluation and value distribution.

[0119] In step S103, a quality assessment index is determined based on the result of the data association analysis, and the result of the data association analysis is evaluated based on the quality assessment index using a preset data value assessment rule. The specific process of obtaining the data value assessment result is as follows:

[0120] Determine quality assessment indicators: These indicators are based on the results of data association analysis, which reveals inherent connections between data, outliers, duplicate data, and other information. Quality assessment indicators include data completeness, accuracy, consistency, timeliness, and redundancy.

[0121] Applying pre-set data value assessment rules: Pre-set data value assessment rules are developed based on an understanding of and expectations about the characteristics of high-quality data and are used to quantify data quality. These rules involve specific calculation formulas, threshold settings, or logical judgments to convert assessment indicators into quantifiable scores or ratings.

[0122] Perform evaluation: Use preset rules to calculate or judge each quality assessment indicator and generate corresponding scores or ratings.

[0123] Comprehensive evaluation results: The results of all evaluation indicators are combined to form a data value evaluation report or score, which reflects the quality performance of the data in multiple dimensions and provides an important basis for subsequent value allocation.

[0124] Generate data value assessment results: Based on the results of the comprehensive assessment, generate a final data value assessment report or data. This report or data will serve as input for subsequent steps (such as value allocation).

[0125] Feedback and Iteration: Evaluation results may be fed back to data providers or processors to understand the data quality and make improvements. At the same time, evaluation rules and methods may be iterated and optimized based on actual needs.

[0126] Through step S103, the data value is objectively and fairly evaluated, providing a reliable basis for subsequent value distribution.

[0127] The formula for data value assessment includes:

[0128] Data accuracy evaluation formula:

[0129] Accuracy = (number of correct records / total number of records) * 100%

[0130] 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.

[0131] The integrity assessment formula includes:

[0132] Completeness = (Number of filled fields / Total number of fields) * 100%

[0133] The completeness assessment formula is used to evaluate the completeness of the data, that is, the percentage of filled fields, which is used to understand the extent of missing values in the dataset.

[0134] The formula for consistency assessment includes:

[0135] Consistency = (number of records that meet the rules / total number of records) * 100%

[0136] The consistency assessment formula is used to evaluate data consistency, that is, the percentage of records that meet predetermined rules. Data consistency can be determined by checking whether the data conforms to business rules or standards.

[0137] The threshold setting and logical judgment of data value assessment rules include:

[0138] Thresholds include accuracy threshold, completeness threshold, timeliness threshold and consistency threshold;

[0139] Accuracy threshold: Set the accuracy threshold to 95%. If the accuracy of the data is lower than 95%, further data cleaning or verification is required.

[0140] Completeness threshold: The completeness threshold is 90%. If the completeness of key fields is lower than 90%, the data is considered incomplete and needs to be supplemented.

[0141] Consistency Threshold: Set the consistency check pass rate threshold to 98%. If the data consistency falls below this threshold, it indicates that a large amount of data does not comply with business rules and requires further review and adjustment.

[0142] Timeliness threshold: You can set the maximum time interval for data updates to 24 hours. If data is not updated within this time interval, it may be considered outdated.

[0143] Logical judgment includes accuracy logical judgment, completeness logical judgment, consistency logical judgment and timeliness logical judgment;

[0144] 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 permitted by the business), the data is considered inaccurate.

[0145] Integrity logic judgment: If key fields (such as user ID, order number, etc.) are empty or missing, the data is considered incomplete.

[0146] 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.

[0147] Timeliness logic judgment: If the creation or modification time of data is earlier than a specific time point (such as the current time minus a preset time window), the data may be considered outdated.

[0148] In actual operations, based on the threshold settings and logical judgments of the data value assessment rules, you can write automated scripts or use data value management tools to perform data value inspections and assessments.

[0149] Step S104: Match the corresponding value allocation algorithm in the knowledge base according to the market demand data to 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 using the formula to be used, and obtain the data value allocation ratio;

[0150] In step S104, the corresponding value allocation algorithm is matched in the knowledge base according to the market demand data, and the data value evaluation result is calculated using the algorithm to obtain the data value allocation ratio. The specific process is as follows:

[0151] The knowledge base contains value distribution algorithm scenarios and corresponding value distribution algorithms. The value distribution algorithm scenarios include value distribution of data value, value distribution of market demand, value distribution of 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.

[0152] 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 appropriate 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.

[0153] Based on 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.

[0154] 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.

[0155] After selecting the value allocation algorithm, the specific process of the system using the algorithm to calculate the data value assessment results is expanded and expressed by the following formula:

[0156] Value distribution ratio = (data value index weight * data value score) + (market demand weight * market demand index) + (data scarcity weight * data scarcity coefficient)

[0157] The weights for data value indicators, market demand, and data scarcity are set based on actual conditions to adjust the influence of different factors in value distribution. The sum of these weights should equal 1 to ensure a reasonable value distribution ratio.

[0158] 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.

[0159] The market demand index is an indicator that reflects the market demand for data. It can be determined based on market research, user feedback, transaction data, etc. The higher the market demand, the larger the index value.

[0160] The data scarcity coefficient is an indicator of data scarcity. The more scarce the data, the higher its value should be allocated. This coefficient can be determined based on factors such as the data's source, difficulty in obtaining it, and update frequency.

[0161] Through the above formula, the system can comprehensively consider multiple factors such as data quality, market demand, and data scarcity to arrive at a reasonable value distribution ratio. This ratio can be dynamically adjusted based on changes in these factors to ensure fair and reasonable value distribution.

[0162] After the calculation is complete, 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 subsequent smart contract execution.

[0163] Step S104 improves the fairness and rationality of value distribution, while fully considering multiple factors such as market demand and data value. Through automated matching and calculation, the system can efficiently process large amounts of data, providing strong support for value distribution in the trusted data space.

[0164] Step S105: Receive smart contract configuration information, construct a smart contract based on the data value distribution ratio and the smart contract configuration information, and deploy it on the blockchain; match the obtained data value distribution ratio with the smart contract to obtain a smart contract corresponding to the data value distribution ratio; use the smart contract corresponding to the data value distribution ratio to perform value distribution according to the preset value distribution ratio, and obtain a smart contract value distribution result;

[0165] 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 elaborated as follows:

[0166] 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.

[0167] Construct a smart contract based on the data value distribution ratio: Utilize the data value distribution ratio calculated in step S104 and the received smart contract configuration information to construct a smart contract. The smart contract defines in detail the value distribution ratio, distribution conditions, and distribution logic for each participant.

[0168] 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.

[0169] Smart contracts automatically execute value distribution: When the distribution conditions defined in the smart contract are met (such as the completion of a data transaction or reaching a certain time point), the smart contract will automatically trigger and execute value distribution. Based on the preset value distribution ratio, the smart contract automatically distributes the value due to each participant to their respective blockchain accounts.

[0170] After execution, the smart contract records the allocation results and updates the status on the blockchain, ensuring that all allocation operations are traceable.

[0171] Obtaining smart contract value distribution results: After the smart contract is executed, a value distribution result will be generated. This result includes information such as the value each participant received and the distribution timestamp. The generated value distribution result will be recorded on the blockchain for all participants to query and verify.

[0172] 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.

[0173] 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 most influencing factors of value distribution, adjust the data value distribution ratio according to the most influencing factors of 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.

[0174] 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:

[0175] The value distribution algorithm uses historical value distribution data and smart contract value distribution results for learning. The purpose is to enable the algorithm to automatically learn and optimize the value distribution strategy through machine learning technology, thereby improving the accuracy and fairness of value distribution.

[0176] 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.

[0177] 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 identifying the key factors affecting data value allocation, such as data scarcity, timeliness, relevance, and future trends.

[0178] 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.

[0179] 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.

[0180] The adjusted data value distribution ratio will be obtained, which will serve as the basis for subsequent data value distribution.

[0181] 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.

[0182] Obtain data contribution, i.e., the contribution of each data point or dataset to the overall value. Retrieve the participants corresponding to the data contribution, which may be data providers, data processors, or other relevant parties.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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:

[0187] First, select an appropriate privacy-preserving computing model based on the data's security and privacy requirements. Use the selected privacy-preserving computing model to encrypt dynamic business data and system data within the value chain. This step may involve complex encryption algorithms and key management mechanisms. In subsequent data processing steps (S102, S103, S104, S105, and S106), when accessing or using this encrypted data, perform the following operations:

[0188] The data processing unit sends a decryption request to the privacy protection unit when necessary. After receiving the decryption request, the privacy protection unit decrypts the encrypted data using the same decryption algorithm and corresponding key of the privacy computing model used for encryption.

[0189] The decrypted data can be used for subsequent data processing and analysis steps.

[0190] Encryption algorithm selection: Depending on actual needs and scenarios, privacy protection technologies such as homomorphic encryption and secure multi-party computation can be selected. A secure key generation, storage, and distribution mechanism must be established to ensure key security and availability.

[0191] The decryption process should not become a bottleneck in the entire data processing process, 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.

[0192] Specifically, the method for dynamic allocation of a trusted data space value chain provided by the present invention, step S101, includes:

[0193] 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;

[0194] The validity of the data in the value chain dynamic business data table is verified, invalid or abnormal data in the value chain dynamic business data is eliminated, and the pre-processed value chain dynamic business data and system data are obtained.

[0195] Real-time data acquisition: The system captures dynamic business data and system data from various data sources in real time, including databases, log files, and sensor inputs.

[0196] 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. This facilitates subsequent data management and query.

[0197] Data validation: 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 the data for subsequent processing.

[0198] Data preprocessing: After validation, invalid or abnormal data is removed 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.

[0199] Result output: The preprocessed data will serve as input for subsequent steps (such as cluster analysis, association rule mining, etc.), improving the continuity and accuracy of the entire data processing process.

[0200] Specifically, the trusted data space value chain dynamic allocation method provided by the present invention, step S102, further includes:

[0201] The results of cluster analysis of the pre-processed value chain dynamic business data and system data are used as input data sets, which include the pre-processed value chain dynamic business data, group information after clustering the system data, and attributes of each data point in the value chain dynamic business data;

[0202] Determine the parameters of the association rule mining algorithm, which include the support threshold and the confidence threshold;

[0203] Run the preset association rule mining algorithm 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;

[0204] Identify the association rule list and obtain the association relationship between data providers, data processors and market demands.

[0205] 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:

[0206] Construction of the input dataset: The pre-processed value chain dynamic business data and the cluster analysis results of the system data are used as the input dataset. The input dataset contains the pre-processed value chain dynamic business data and the group information after clustering the system data, as well as the attributes of each data point in the value chain dynamic business data.

[0207] 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.

[0208] Association rule mining execution: A pre-set association rule mining algorithm runs on the server. The algorithm applies association rule mining to the cluster analysis results, searching for relationships between item sets in the dynamic business data set of the value chain. A list of association rules is generated, revealing the relationships between data items.

[0209] Identification and interpretation of association rules: Identify and analyze the list of association rules to derive the relationship between data providers, data processors, and market demand.

[0210] 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.

[0211] Specifically, the trusted data space value chain dynamic allocation method provided by the present invention, step S104, includes:

[0212] 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.

[0213] 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;

[0214] The correspondence between value allocation algorithm scenarios and 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 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;

[0215] 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.

[0216] 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.

[0217] 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.

[0218] The correspondence between value allocation algorithm scenarios and value allocation algorithms;

[0219] Data value distribution: This corresponds to the data value weighted distribution algorithm. Based on the data value assessment results, different weights are assigned to data items or data sets of different qualities, and their value ratios are then calculated.

[0220] Market demand-based value allocation: This corresponds to a market demand-sensitive allocation algorithm. It considers market demand factors and assigns a higher value ratio to data items or datasets with high demand.

[0221] Data scarcity value allocation: Allocation algorithms are adjusted based on scarcity. For data items or datasets with high scarcity, their value ratios are adjusted to reflect their scarcity.

[0222] Comprehensively consider multiple factors in value allocation: This corresponds to a multi-factor comprehensive evaluation and allocation algorithm. This algorithm comprehensively considers multiple factors (such as data value, market demand, scarcity, etc.), assigns different weights to each factor, and allocates value proportions after comprehensive evaluation.

[0223] Value distribution based on data usage: This corresponds to the effect feedback distribution algorithm. The value ratio of data items or data sets is dynamically adjusted based on the actual effect feedback after data use.

[0224] 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, and circulation.

[0225] Value allocation algorithm application process;

[0226] Data value assessment: First, perform a quality assessment on the data item or data set to obtain the data value assessment results.

[0227] Scenario matching: Match the evaluation results with the value allocation algorithm scenarios in the knowledge base to determine the applicable value allocation algorithm scenarios.

[0228] Algorithm Selection and Calculation: Based on the matched value allocation algorithm scenario, we select the appropriate value allocation algorithm and, combined with the data value assessment results, calculate the relative value proportion of each data item or dataset. By comprehensively considering multiple factors, we dynamically adjust the value proportion to adapt to the ever-changing market demand and data environment.

[0229] The principle and formula of the value distribution algorithm are as follows:

[0230] Data Value Weighted Allocation Algorithm: This algorithm allocates 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.

[0231] Market Demand Sensitive Allocation Algorithm: This algorithm dynamically adjusts value allocation based on market demand. The formula is Vi = Mi × Pi, where Mi is the market demand index and Pi is the initial estimated value based on the data value.

[0232] Scarcity-adjusted allocation algorithm: This algorithm adjusts value allocation by evaluating the scarcity of data. The formula is Vi = Si × Bi, where Si is the scarcity coefficient and Bi is the base value.

[0233] Multi-factor comprehensive evaluation and allocation algorithm: This algorithm evaluates the value of data by considering multiple factors. The formula is Vi = αQi + βMi + γSi, where α, β, and γ are the weight coefficients of each factor.

[0234] Effect Feedback Allocation Algorithm: This algorithm adjusts value allocation based on data usage performance feedback. An example formula is Vi = Ri × Ci, where Ri is the usage performance evaluation indicator and Ci is the preliminary value based on other factors.

[0235] 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.

[0236] Furthermore, the trusted data space value chain dynamic allocation method of the present invention, step S104, includes:

[0237] Based on 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;

[0238] Extracting historical value allocation data from a database, where the historical value allocation data includes result information processed using various value allocation algorithms;

[0239] Find and identify, in the acquired historical value allocation data, a data set processed using the value allocation algorithm to be used, and determine a formula to be used when processing the data using the value allocation algorithm to be used;

[0240] Extracting formulas related to the value allocation algorithm to be used from historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas;

[0241] Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas;

[0242] The data normalization formula is:

[0243] 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;

[0244] The missing value filling formula is:

[0245] Where x is a dataset containing missing values, and xfilled is a dataset after filling missing values;

[0246] Outlier processing formula:

[0247]

[0248]

[0249] 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;

[0250] Also includes:

[0251] The value calculation formula includes the simple weighted average formula:

[0252]

[0253] 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;

[0254] Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0255]

[0256] 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;

[0257] Market demand sensitive allocation ratio formula:

[0258] Among them, MDi is the market demand index corresponding to the i-th data item;

[0259] Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0260] 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.

[0261] 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:

[0262] An acquisition unit is used to acquire original data in a 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 an initial data value assessment result, match the initial data value assessment result in a preset algorithm knowledge base, obtain a statistical algorithm corresponding to the initial data value assessment result, analyze the initial data value assessment result using the statistical algorithm, calculate 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 basic data statistics data features, the basic data statistics data features include data information features and data pattern features, obtain value chain dynamic business data and system data, the dynamic business data of the value chain includes original business data, data provider information, data processor information, data value indicators, market demand data, and industry dynamic data, and the system data includes system configuration data, system operation data, and system privacy and security data;

[0263] a data analysis and processing unit, configured to preprocess the dynamic business data of the value chain and the data characteristics of basic statistics of the data to obtain preprocessed data to be mined, perform cluster analysis on the preprocessed data to be mined, and use a time series analysis method to predict the future trend of the data to be mined to obtain a prediction result of the future trend of the data to be mined, evaluate the prediction result of the future trend of the data to be mined to obtain a prediction value of the future trend of the data to be mined, perform cluster analysis on the data corresponding to the prediction value of the future trend of the data to be mined, and perform association rule mining on the cluster analysis results to obtain a result of data association analysis;

[0264] The data value evaluation unit is used to determine the quality evaluation index based on the results of the data association analysis, and evaluate the results of the data association analysis according to the quality evaluation index using the preset data value evaluation rules to obtain the data value evaluation result;

[0265] The value allocation calculation unit is used to match the corresponding value allocation algorithm in the knowledge base according to the data value assessment result, and use the value allocation algorithm to calculate the data value assessment result to obtain the data value allocation ratio;

[0266] The smart contract unit is used to receive smart contract configuration information, build a smart contract based on 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 value according to the preset value distribution ratio to obtain the smart contract value distribution result;

[0267] 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 most influencing factors of value distribution, adjust the data value distribution ratio according to the most influencing factors of 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.

[0268] 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 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.

[0269] Specifically, the trusted data space value chain dynamic allocation system and the data analysis and processing unit of the present invention are also used to:

[0270] The results of cluster analysis of the pre-processed value chain dynamic business data and system data are used as input data sets, which include the pre-processed value chain dynamic business data, group information after clustering the system data, and attributes of each data point in the value chain dynamic business data;

[0271] Determine the parameters of the association rule mining algorithm, which include the support threshold and the confidence threshold;

[0272] Run the preset association rule mining algorithm 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;

[0273] Identify the association rule list and obtain the association relationship between data providers, data processors and market demands.

[0274] Specifically, the trusted data space value chain dynamic allocation system and the value allocation calculation unit of the present invention are also used to:

[0275] 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.

[0276] 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;

[0277] The correspondence between value allocation algorithm scenarios and 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 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;

[0278] 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.

[0279] Specifically, the trusted data space value chain dynamic allocation method and the value allocation calculation unit of the present invention are also used to:

[0280] Based on 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;

[0281] Extracting historical value allocation data from a database, where the historical value allocation data includes result information processed using various value allocation algorithms;

[0282] Find and identify, in the acquired historical value allocation data, a data set processed using the value allocation algorithm to be used, and determine a formula to be used when processing the data using the value allocation algorithm to be used;

[0283] Extracting formulas related to the value allocation algorithm to be used from historical value allocation data, including data preprocessing formulas, value calculation formulas, and allocation ratio determination formulas;

[0284] Data preprocessing formulas include data standardization formulas, missing value filling formulas, and outlier processing formulas;

[0285] The data normalization formula is:

[0286] 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;

[0287] The missing value filling formula is:

[0288] Where x is a dataset containing missing values, and xfilled is a dataset after filling missing values;

[0289] Outlier processing formula:

[0290]

[0291]

[0292] 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;

[0293] Also includes:

[0294] The value calculation formula includes the simple weighted average formula:

[0295]

[0296] 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;

[0297] Allocation ratio determination formula: Allocation ratio formula based on value weighting:

[0298]

[0299] 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;

[0300] Market demand sensitive allocation ratio formula:

[0301] Among them, MDi is the market demand index corresponding to the i-th data item;

[0302] Formula for allocation ratio of comprehensive evaluation of multiple factors:

[0303] 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.

[0304] The present invention solves the problem that the existing dynamic value chain allocation process cannot dynamically adjust the data value chain within the trusted data space, and thus cannot fairly and reasonably distribute the rights and interests generated by data, through the following technical solutions:

[0305] Obtain raw data from the trusted data space and conduct an initial value assessment based on data scarcity, timeliness, and relevance. Use statistical algorithms from a pre-defined algorithm knowledge base to perform descriptive analysis on the initial assessment results and extract data features. Preprocess dynamic business data and system data from the value chain to generate the data to be mined. Conduct cluster analysis and use time series analysis to predict future trends in the data to be mined.

[0306] Evaluate the future trend predictions of the data to be mined, and conduct cluster analysis and association rule mining. Based on the data association analysis results, determine quality assessment indicators, and then conduct a comprehensive assessment of the data's value. Based on market demand data, match the corresponding value allocation algorithm in the knowledge base. Use the matched value allocation algorithm to calculate the data value assessment results to determine the data value allocation ratio.

[0307] Receive smart contract configuration information, build the smart contract, and deploy it to the blockchain. When the smart contract's trigger conditions are met, value distribution is automatically executed, ensuring fairness and transparency throughout the entire process. The value distribution algorithm leverages historical data and smart contract execution results for machine learning to continuously optimize algorithm performance. Based on the optimized algorithm, factors influencing value distribution are identified and the data value distribution ratio is dynamically adjusted accordingly. Dynamic business data and system data within the value chain are encrypted to ensure data security during transmission and storage. During data processing, when encrypted data is needed, decryption is performed using a pre-defined privacy computing model.

[0308] 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.

[0309] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such 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 dynamic allocation of 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 including 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, analyzing the initial data value assessment result using the statistical algorithm, calculating and obtaining basic data statistics, which include mean, median and standard deviation, performing data feature extraction on the data in the basic data statistics, obtaining basic data statistics data features, which include data information features and data pattern features, obtaining value chain dynamic business data and system data, which include original business data, data provider information, data processor information, data value indicators, market demand data and industry dynamic data, and system data including system configuration data, system operation data and system privacy and security data; Step S102: Preprocess the dynamic business data of the value chain and the data characteristics of basic statistics to obtain preprocessed data to be mined, perform cluster analysis on the preprocessed data to be mined, and use time series analysis methods to predict the future trend of the data to be mined to obtain a prediction result of the future trend of the data to be mined, evaluate the prediction result of the future trend of the data to be mined to obtain a prediction value of the future trend of the data to be mined, perform cluster analysis on the data corresponding to the prediction value of the future trend of the data to be mined, and perform association rule mining on the cluster analysis results to obtain a result of data association analysis; Step S103: Determine a quality assessment index based on the result of the data association analysis, and evaluate the result of the data association analysis based on the quality assessment index using a preset data value assessment rule to obtain a data value assessment result; Step S104: Match the corresponding value allocation algorithm in the knowledge base according to the market demand data to 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 using the formula to be used, and obtain the data value allocation ratio; Step S105: Receive smart contract configuration information, construct a smart contract based on the data value distribution ratio and the smart contract configuration information, and deploy it on the blockchain; match the obtained data value distribution ratio with the smart contract to obtain a smart contract corresponding to the data value distribution ratio; use the smart contract corresponding to the data value distribution ratio to perform value distribution according to the preset value distribution ratio, and obtain 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 most influencing factors of value distribution, adjust the data value distribution ratio according to the most influencing factors of 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.

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 dynamic business data and system data of the value chain 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.

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 results of cluster analysis of the pre-processed value chain dynamic business data and system data are used as input data sets, which include the pre-processed value chain dynamic business data, group information after clustering the system data, and attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, which include the support threshold and the confidence threshold; Run the preset association rule mining algorithm 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; Identify the association rule list and obtain the association relationship between data providers, data processors and market demands.

4. The method for dynamic allocation of 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 value allocation algorithm scenarios and 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 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 dynamic allocation of a trusted data space value chain according to claim 1, characterized in that: Step S104 includes: Based on 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, where the historical value allocation data includes result information processed using various value allocation algorithms; Find and identify, in the acquired historical value allocation data, a data set processed using the value allocation algorithm to be used, and determine a formula to be used when processing the data using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from 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: Where x is a dataset containing missing values, and xfilled is a dataset after filling missing values; 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, applying the trusted data space value chain dynamic allocation method according to any one of claims 1 to 5, characterized in that: include: An acquisition unit is used to acquire original data in a 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 an initial data value assessment result, match the initial data value assessment result in a preset algorithm knowledge base, obtain a statistical algorithm corresponding to the initial data value assessment result, analyze the initial data value assessment result using the statistical algorithm, calculate 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 basic data statistics data features, the basic data statistics data features include data information features and data pattern features, obtain value chain dynamic business data and system data, the dynamic business data of the value chain includes original business data, data provider information, data processor information, data value indicators, market demand data, and industry dynamic data, and the system data includes system configuration data, system operation data, and system privacy and security data; a data analysis and processing unit, configured to preprocess the dynamic business data of the value chain and the data characteristics of basic statistics of the data to obtain preprocessed data to be mined, perform cluster analysis on the preprocessed data to be mined, and use a time series analysis method to predict the future trend of the data to be mined to obtain a prediction result of the future trend of the data to be mined, evaluate the prediction result of the future trend of the data to be mined to obtain a prediction value of the future trend of the data to be mined, perform cluster analysis on the data corresponding to the prediction value of the future trend of the data to be mined, and perform association rule mining on the cluster analysis results to obtain a result of data association analysis; The data value evaluation unit is used to determine the quality evaluation index based on the results of the data association analysis, and evaluate the results of the data association analysis according to the quality evaluation index using 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 using the formula to be used, and obtain the data value allocation ratio; The smart contract unit is used to receive smart contract configuration information, build a smart contract based on the data value distribution ratio and the smart contract configuration information, and deploy it on the blockchain; match the obtained data value distribution ratio with the smart contract to obtain a smart contract corresponding to the data value distribution ratio; use the smart contract corresponding to the data value distribution ratio to perform value distribution according to the preset value distribution ratio, and 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 most influencing factors of value distribution, adjust the data value distribution ratio according to the most influencing factors of 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 to: The results of cluster analysis of the pre-processed value chain dynamic business data and system data are used as input data sets, which include the pre-processed value chain dynamic business data, group information after clustering the system data, and attributes of each data point in the value chain dynamic business data; Determine the parameters of the association rule mining algorithm, which include the support threshold and the confidence threshold; Run the preset association rule mining algorithm 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; Identify the association rule list and 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 value allocation algorithm scenarios and 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 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 trusted data space value chain dynamic allocation system according to claim 6, characterized in that: The value allocation calculation unit is also used to: Based on 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, where the historical value allocation data includes result information processed using various value allocation algorithms; Find and identify, in the acquired historical value allocation data, a data set processed using the value allocation algorithm to be used, and determine a formula to be used when processing the data using the value allocation algorithm to be used; Extracting formulas related to the value allocation algorithm to be used from 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: Where x is a dataset containing missing values, and xfilled is a dataset after filling missing values; 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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