Multi-source heterogeneous data value mining and intelligent matching system

Abstract

The invention relates to the technical field of data management systems, and particularly discloses a multi-source heterogeneous data value mining and intelligent matching system, which is characterized by comprising a data access and governance layer, a value quantification and portrait layer, an intelligent matching and recommendation layer and a transaction operation and safety protection layer which are linked in sequence, and each layer realizes bidirectional circulation of data and instructions through a standardized interface. According to the system, a complete closed loop of data integration, value evaluation, accurate matching, dynamic pricing, secure transaction and continuous optimization is constructed, the threshold and cost of data transaction are effectively reduced, the data circulation efficiency is improved, data elements are promoted to be converted from scattered storage to efficient utilization, and powerful support is provided for high-quality development of digital economy.

Description

Technical Field

[0001] This invention relates to the field of data management system technology, specifically to a multi-source heterogeneous data value mining and intelligent matching system. Background Technology

[0002] With the rapid development of the digital economy, data has become a core factor of production, and its value extraction and efficient circulation are crucial for industrial upgrading and economic growth. However, the current field of data trading and management still faces many technical challenges, hindering the full utilization of data resources. Existing technologies struggle to effectively integrate multi-source, heterogeneous data. Significant differences in communication protocols and data formats across different data sources lead to fragmented storage, hindering interoperability and creating "data silos." This prevents the formation of standardized data assets to support subsequent value analysis. Data value assessment relies on a single dimension, often depending on static costs or simple market indicators, failing to consider the dynamic changes in intrinsic data quality and extrinsic market value, resulting in assessments lacking objectivity and adaptability. Supply-demand matching mechanisms are imprecise, often based on keywords or simple semantic similarity calculations, failing to integrate data value characteristics with the personalized needs of demanders. This makes it difficult to accurately identify high-quality data, and demanders cannot quickly obtain suitable data, leading to resource waste. Rigid pricing models, often fixed or simply cost-plus pricing, fail to consider dynamic factors such as supply and demand, value fluctuations, and matching degrees, lacking flexibility. Insufficient privacy and security protection during data circulation, coupled with a lack of effective closed-loop feedback mechanisms, prevents continuous optimization of data processing, evaluation, and matching strategies based on transaction results, hindering long-term system efficiency. These problems result in high barriers to entry, high costs, and low efficiency in data transactions, severely restricting the circulation and value release of data elements and failing to meet the demands of the digital economy for efficient data utilization. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a multi-source heterogeneous data value mining and intelligent matching system, which solves the problems mentioned in the background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solution: a multi-source heterogeneous data value mining and intelligent matching system, comprising a data access and governance layer, a value quantification and profiling layer, an intelligent matching and recommendation layer, and a transaction operation and security protection layer linked in sequence, with each layer realizing bidirectional flow of data and instructions through standardized interfaces; The data access and governance layer is used to adapt to the communication protocols and data formats of multi-source heterogeneous data sources, perform real-time cleaning, entity alignment and knowledge enhancement on the raw data, and output standardized and unified data assets that meet the quality threshold. The value quantification and profiling layer, based on standardized data assets, calculates the dynamic comprehensive value index of data assets through a multi-dimensional coupling algorithm, and constructs a data asset profile on the supply side and a dynamic demand profile on the demand side. The intelligent matching and recommendation layer integrates value matching degree and semantic similarity to calculate the two-way matching degree between supply and demand, and generates a personalized recommendation list with interpretable explanations; The transaction operation and security protection layer achieves dynamic intelligent pricing based on matching results and value index, completes transaction settlement through smart contracts, and relies on privacy computing technology to ensure data flow security. At the same time, it optimizes the model parameters of each preceding layer through a closed-loop feedback mechanism.

[0005] Preferably, the value quantification and profiling layer includes a data value multidimensional quantification unit and a dual-end profiling construction unit; The data value multidimensional quantification unit is used to calculate data assets. exist Comprehensive value index of time This index integrates the intrinsic quality of data with its external market value, and introduces a value decay and gain mechanism, as shown in the following formula: ; in, For data assets At the initial moment The benchmark value index is based on preset data collection costs and processing complexity; The value decay coefficient is the static data range. The dynamic data value range is ; for Moment data assets The normalized value of market access frequency, ranging from 0 to 1; for Moment data assets The application effectiveness score is calculated based on user feedback and ranges from 0 to 1. For the weighting coefficients, satisfying The default value is It can be dynamically adjusted according to the data application scenario; The dual-end portrait construction unit is based on, on the one hand, On the one hand, data metadata, domain tags, and quality scores are used to generate a multi-dimensional data asset profile for the supply side; on the other hand, natural language processing technology is used to analyze the textual intent input by the demand side, extract core demand dimensions, and construct a structured dynamic demand profile. ; The dual-end profile building unit also includes a profile update module, which updates the corresponding profile when the fit of the data asset profile or the dynamic demand profile of the demand side is less than a set fit threshold. The specific steps are as follows: S01. Confirm the update ratio of metadata, domain tags, and quality scores of data assets as components of profile update c1; S02. Confirm the types of changes in the core needs of the demanders, and use them as components of the profile update c2. S03. Perform a judgment on the need to supplement portrait features and output the judgment result; S04. When the judgment result indicates that supplementation is needed, confirm the newly added portrait feature labels as c3, a component of the portrait update. S05. When the result of S03 is that it is needed, integrate the portrait update components c1, c2 and c3 to complete the update of the corresponding portrait; when the result of S03 is that it is not needed, integrate the portrait update components c1 and c2 to complete the update of the corresponding portrait.

[0006] Preferably, the intelligent matching and recommendation layer includes a hybrid intelligent matching engine for calculating supply-side assets. With demand side exist Time-of-fact matching The formula is as follows: ; in, is a cosine similarity function used to calculate the degree of fit between the data asset value vector and the demand profile vector in the feature space, with a value range of 0-1; The confidence factor for historical transactions is based on the relationship between the demand side and the historical transaction confidence factor over the past 90 days. Similar user groups on assets The feedback score is calculated using a weighted average, with a value range of 0-1. For dynamic weighting coefficients, satisfying The range of values ​​for enterprise demand is: The range of values ​​for individual demanders is: It can be dynamically adjusted according to the type of demand.

[0007] Preferably, the intelligent matching and recommendation layer further includes an interpretable recommendation unit; The explainable recommendation unit is based on Sort the candidate data assets in descending order to generate a Top-N recommendation list; For each recommended item, a demand profile is calculated. Matching degree of each core dimension partial derivatives ,in Profiling Needs We identified the top three key demand characteristics in terms of contribution across several dimensions. The explainability recommendation unit also includes a recommendation description optimization module, which optimizes the explainability description when the user approval rating of the recommendation description is less than a set approval rating threshold. The specific steps are as follows: S11. Confirm the deviation in the contribution calculation of key demand features, as an explanation for optimizing component d1; S12. Confirm the score for the accessibility of the natural language expression, as an explanation of the optimization component d2; S13. Perform supplementary judgment on the description of requirements features and output the judgment result; S14. When the judgment result indicates that supplementation is needed, confirm the newly added feature descriptions as part of the explanation of the optimized component d3. S15. When the result of S13 is that it is required, the optimized components d1, d2 and d3 will be integrated to generate an optimized interpretable description; when the result of S13 is that it is not required, the optimized components d1 and d2 will be integrated to generate an optimized interpretable description.

[0008] Preferably, the transaction operation and security protection layer includes a dynamic intelligent pricing unit for generating data assets. For the demand side Personalized transaction reference price The formula is as follows: ; in, For assets The base price is calculated based on a comprehensive assessment of data collection costs, processing costs, and industry benchmark prices. for The average comprehensive value index of similar data assets at any given time is the average of the top 100 assets in the same field within the platform; The value elasticity coefficient, with a range of values ​​of [value range missing]. For scarce data, the upper limit is used; for general data, the lower limit is used. To match the premium factor, the value range is: The higher the matching degree, the higher the premium ratio.

[0009] Preferably, the dynamic intelligent pricing unit further includes a market popularity adjustment factor. This is used to smooth the impact of market supply and demand fluctuations on prices. The final recommended trading price formula is as follows: ; The market heat adjustment factor is defined as follows: ; in, for Time platform and assets The number of relevant active demanders; for The number of similar valid data assets within the platform; To prevent extremely small positive numbers from being divided by zero, the value is taken as... ; The hyperbolic tangent function is used to make... To avoid drastic price fluctuations; The dynamic intelligent pricing unit also includes a price adjustment calibration module, used to calibrate price adjustment parameters when the deviation between the final suggested transaction price and the actual transaction price exceeds a set deviation threshold. The specific steps are as follows: S21. Confirm market heat adjustment factors The calculated deviation is used as a calibration component e1; S22, Confirming the Value Elasticity Coefficient Matching premium coefficient The adaptation deviation is used as a calibration component e2; S23. Perform supplementary calibration parameter judgment and output the judgment result; S24. When the judgment result indicates that supplementation is required, confirm the newly added calibration parameter item as calibration component e3. S25. When the result of S23 indicates that it is necessary, integrate the calibration components e1, e2 and e3 to complete the calibration of the price adjustment parameters; when the result of S23 indicates that it is not necessary to supplement, integrate the calibration components e1 and e2 to complete the calibration of the price adjustment parameters.

[0010] Preferably, the intelligent matching and recommendation layer further includes a matching quality closed-loop optimization unit; After the transaction is completed, the data asset requester will collect the data assets. Actual user feedback rating and normalized to ; Calculate the confidence level of this match. The formula is as follows: ; in, Weighting coefficients, satisfying The default value is ; For every 10 transactions accumulated, the hybrid intelligent matching engine, based on confidence level... For dynamic weighting coefficients Fine-tuning is performed, with the adjustment range not exceeding ±0.05, to achieve online iterative optimization of the recommendation model; The matching quality closed-loop optimization unit also includes a weight fine-tuning calibration module, which is used to calibrate the weight fine-tuning rules when the improvement rate of the matching degree after fine-tuning is less than a set improvement rate threshold. The specific steps are as follows: S31. Confirm Confidence Level The calculated deviation is used as calibration component f1; S32. Confirm dynamic weighting coefficients The fine-tuning amplitude deviation is used as a calibration component f2; S33. Perform fine-tuning rule supplementation judgment and output the judgment result; S34. When the judgment result indicates that supplementation is needed, confirm the newly added fine-tuning constraints as calibration component f3. S35. When the result of S33 indicates that it is necessary, integrate the calibration components f1, f2 and f3 to generate the calibrated weight fine-tuning rules; when the result of S33 indicates that it is not necessary to supplement, integrate the calibration components f1 and f2 to generate the calibrated weight fine-tuning rules.

[0011] Preferably, the value quantification and profiling layer further includes a value collaborative evaluation unit, which is deployed within the federated learning framework of the transaction operation and security protection layer. Each data provider calculates the gradient update information of the comprehensive value index model locally using its own data characteristics, and encrypts the gradient using a homomorphic encryption algorithm. The encrypted gradient information is uploaded to the federated aggregation server, which uses a secret-sharing secure aggregation algorithm to aggregate the gradients and update the parameters of the global value assessment model. Global model parameters are encrypted and distributed to all suppliers, ensuring that data remains within the domain and models are jointly optimized, thereby improving the objectivity and accuracy of value assessment, and the collaborative update cycle does not exceed 7 days; The value collaborative evaluation unit also includes a collaborative update verification module, which is used to verify the collaborative update process when the value evaluation accuracy after the global model parameter update is less than a set accuracy threshold. The specific steps are as follows: S41. Confirm the encryption deviation of the gradient update information of each supplier as a verification component g1; S42. Confirm the gradient aggregation deviation of the federated aggregation server as a verification component g2; S43. Perform supplementary judgments in the update process and output the judgment results; S44. When the judgment result indicates that supplementation is required, confirm the newly added process verification node as a verification component element g3. S45. When the result of S43 indicates that it is required, the verification components g1, g2 and g3 are integrated to complete the verification and correction of the collaborative update process; when the result of S43 indicates that it is not required to supplement, the verification components g1 and g2 are integrated to complete the verification and correction of the collaborative update process.

[0012] Preferably, the data access and governance layer includes a cloud-edge collaborative processing unit and a four-dimensional quality assessment unit; The cloud-edge collaborative processing unit is responsible for receiving multi-source data in real time at the edge, performing protocol parsing and preliminary filtering, and for a three-layer progressive governance approach at the cloud, which includes semantic alignment, dimension alignment, and entity alignment. The four-dimensional quality assessment unit scores the data from four dimensions: completeness, accuracy, consistency, and timeliness, resulting in a quality score. ; Set quality threshold ,when When this happens, the system automatically triggers a data replenishment or secondary cleaning process until the quality requirements are met. The four-dimensional quality assessment unit also includes a quality control optimization module, which is used to optimize the quality control process when the quality compliance rate after data supplementation or secondary cleaning is less than a set compliance rate threshold. The specific steps are as follows: S51. Confirm the scoring deviations of each dimension of the four-dimensional quality assessment and use them as optimization component h1; S52. Confirm the efficiency of the data supplementation or secondary cleaning process as an optimization component h2; S53. Perform supplementary judgments on the control process and output the judgment results; S54. When the judgment result indicates that supplementation is needed, confirm the newly added quality control node as an optimization component h3. S55. When the result of S53 is that it is necessary, the optimized components h1, h2 and h3 will be integrated to generate an optimized quality control process; when the result of S53 is that it is not necessary to supplement, the optimized components h1 and h2 will be integrated to generate an optimized quality control process.

[0013] A method for value mining and intelligent matching of multi-source heterogeneous data includes the following steps: Data standardization and governance: Through the cloud-edge collaborative architecture of data access and governance layer, it adapts to the protocols and formats of multi-source heterogeneous data, and outputs standardized and unified data assets through three-layer alignment and four-dimensional quality assessment; if the quality score does not meet the standard, it triggers data supplementation or secondary cleaning process until the quality requirements are met. Dual-end profiling and value quantification: The value quantification and profiling layer is based on standardized data assets and calculates the comprehensive value index through a multi-dimensional coupling algorithm with attenuation and gain mechanisms. They also constructed supply-side data asset profiles and demand-side dynamic demand profiles, respectively. If the profile does not meet the requirements, update the corresponding profile according to the preset steps; at the same time, through the federated learning framework, coordinate with various suppliers to optimize the value assessment model. Intelligent Matching and Recommendation: The intelligent matching and recommendation layer calculates the supply-demand matching degree through a hybrid intelligent matching engine. Generate a Top-N recommendation list and explanatory descriptions; if the recommendation descriptions do not meet the acceptance criteria, optimize the explanatory descriptions; simultaneously, transaction operations and security protection layers are based on... and The final suggested trading price is generated by combining market sentiment adjustment factors; if the price deviation is too large, the price adjustment parameters are calibrated. Transaction execution and closed-loop optimization: Transaction settlement is completed based on smart contracts, and user feedback ratings are collected after the transaction to calculate the matching confidence level. The results are fed back to the intelligent matching engine to fine-tune the weight coefficients; if the weight fine-tuning effect is not good, the fine-tuning rules are calibrated; at the same time, the value evaluation model is updated collaboratively through the federated learning framework to achieve dynamic optimization throughout the entire process.

[0014] This invention provides a multi-source heterogeneous data value mining and intelligent matching system, which has the following beneficial effects: 1. Achieve efficient integration of multi-source heterogeneous data: Through the adaptation mechanism and hierarchical governance logic of data access and governance layer, the interoperability barriers of different protocols and formats of data are broken down, and the scattered multi-source heterogeneous data is transformed into standardized and unified data assets, effectively solving the problem of data silos and laying a solid foundation for subsequent value mining and transaction circulation.

[0015] 2. Precisely quantify the dynamic value of data: The value quantification and profiling layer adopt a multi-dimensional coupled evaluation mechanism, which comprehensively considers the intrinsic quality of data and its external market value. Combined with dynamic adjustment logic, it reflects the changes in data value in real time, which solves the shortcomings of the traditional evaluation dimension being single and static, and provides an objective and credible value basis for data transactions.

[0016] 3. Improve the accuracy and credibility of supply and demand matching: The intelligent matching and recommendation layer integrates data value relevance and semantic similarity, and dynamically optimizes the matching strategy based on historical feedback, which significantly improves the accuracy of supply and demand matching; at the same time, through an interpretable recommendation mechanism, the matching logic is clearly presented, enhancing the credibility of the recommendation results and reducing the decision-making cost for demanders.

[0017] 4. Achieve personalized dynamic pricing: The transaction operation and security protection layer dynamically adjusts the pricing strategy based on data value, supply and demand matching degree and market popularity, and generates personalized transaction reference prices, which takes into account the core interests of both the supply and demand sides and improves the rationality and feasibility of data transactions.

[0018] 5. Ensure privacy and security and continuous system optimization: Relying on privacy computing technology and federated learning framework, the system completes collaborative model optimization while ensuring that data does not leave the domain and privacy is protected; through a closed-loop feedback mechanism, the system continuously iterates the parameters of each preceding layer of the model using transaction feedback to ensure the long-term efficiency and adaptability of the system and adapt to the data transaction needs in different scenarios.

[0019] In summary, this system constructs a complete closed loop encompassing data integration, value assessment, precise matching, dynamic pricing, secure transactions, and continuous optimization. This effectively lowers the barriers and costs of data transactions, improves data flow efficiency, promotes the transformation of data elements from decentralized storage to efficient utilization, and provides strong support for the high-quality development of the digital economy. Attached Figure Description

[0020] Figure 1 This is a block diagram illustrating the principle of a multi-source heterogeneous data value mining and intelligent matching system as described in this invention. Figure 2 This is a block diagram illustrating the principle of value quantification and profiling layer as described in this invention; Figure 3 This is a block diagram illustrating the principle of the intelligent matching and recommendation layer described in this invention. Figure 4 This is a block diagram illustrating the principle of the data access and governance layer described in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figures 1-4As shown, this invention provides a technical solution: a multi-source heterogeneous data value mining and intelligent matching system, characterized by comprising a data access and governance layer, a value quantification and profiling layer, an intelligent matching and recommendation layer, and a transaction operation and security protection layer linked sequentially. Each layer achieves bidirectional data and instruction flow through standardized interfaces. The data access and governance layer is used to adapt to the communication protocols and data formats of multi-source heterogeneous data sources, perform real-time cleaning, entity alignment, and knowledge enhancement on the raw data, and output standardized unified data assets that meet quality thresholds. The value quantification and profiling layer calculates the dynamic comprehensive value index of the data assets based on the standardized data assets through a multi-dimensional coupling algorithm, and constructs a data asset profile on the supply side and a dynamic demand profile on the demand side. The intelligent matching and recommendation layer integrates value fit and semantic similarity to calculate the bidirectional matching degree of supply and demand, and generates a personalized recommendation list with interpretable explanations. The transaction operation and security protection layer realizes dynamic intelligent pricing based on the matching results and value index, completes transaction settlement through smart contracts, and ensures data flow security by relying on privacy computing technology, while optimizing the model parameters of the preceding layers through a closed-loop feedback mechanism.

[0023] In this embodiment of the invention, the value quantification and profiling layer includes a data value multidimensional quantification unit and a dual-end profiling construction unit; A multi-dimensional quantification unit for data value, used to calculate data assets. exist Comprehensive value index of time This index integrates the intrinsic quality of data (completeness, accuracy) with its extrinsic market value (timeliness, scarcity), and introduces a value decay and gain mechanism, as shown in the following formula: ; in, For data assets At the initial moment The benchmark value index is based on preset data collection costs and processing complexity; The value decay coefficient is the static data range. The dynamic data value range is ; for Moment data assets The normalized value of market access frequency, ranging from 0 to 1; for Moment data assets The application effectiveness score is calculated based on user feedback and ranges from 0 to 1. For the weighting coefficients, satisfying The default value is It can be dynamically adjusted according to the data application scenario; The dual-end profile building unit is based on... On the one hand, data metadata (type, source, update cycle), domain tags, and quality scores are used to generate a multi-dimensional data asset profile for the supply side; on the other hand, natural language processing technology is used to analyze the textual intent input by the demand side, extract core demand dimensions (such as data type, accuracy requirements, and usage scenarios), and construct a structured dynamic demand profile. (in (For the demand side number); The dual-end profile building unit also includes a profile update module, which updates the corresponding profile when the fit of the data asset profile or the dynamic demand profile of the demand side is less than a set fit threshold. The specific steps are as follows: S01. Confirm the update ratio of metadata, domain tags, and quality scores of data assets as components of profile update c1; S02. Confirm the types of changes (addition, deletion, adjustment) to the core needs of the demanders, and use them as components of the profile update c2. S03. Perform a judgment on the need to supplement portrait features and output the judgment result; S04. When the judgment result indicates that supplementation is needed, confirm the newly added portrait feature labels as c3, a component of the portrait update. S05. When the result of S03 is that it is needed, integrate the portrait update components c1, c2 and c3 to complete the update of the corresponding portrait; when the result of S03 is that it is not needed, integrate the portrait update components c1 and c2 to complete the update of the corresponding portrait.

[0024] Furthermore, the multi-dimensional quantification unit of data value integrates the intrinsic quality of data (completeness, accuracy) with its extrinsic market value (timeliness, scarcity), and introduces a value decay and gain mechanism. This breaks through the limitations of traditional data value assessment, which relies solely on static costs or single market indicators. The value decay coefficient... By differentiating values ​​based on data type (static / dynamic), the value of data over time can be accurately reflected, while the normalized value of market access frequency... Application effect rating The introduction of this technology can capture market demand trends and the practical application value of data in real time. Weighting coefficient , Its dynamic adjustability adapts to the different priorities of data value dimensions in different industries and scenarios, ultimately resulting in a comprehensive value index. It objectively reflects the inherent value of data while responding to real-time market dynamics, providing a scientific and reliable value benchmark for data transactions; the dual-end profile construction unit... At its core, the supply-side data asset profile generated by combining data metadata, domain tags, and quality scores achieves a deep binding between data value and data attributes. Furthermore, by using natural language processing technology to analyze the textual intent of the demand side and extract core demand dimensions, it can accurately depict the personalized needs of the demand side, constructing a structured and dynamic demand profile. With clear dimensional orientation and in conjunction with the profile update module, when changes in data asset attributes or adjustments to the core demands of the demand side lead to insufficient profile adaptability, the profile is dynamically updated by integrating elements such as the update ratio of meta information / tags / ratings, the type of demand dimension change, and newly added feature tags. This ensures that the supply-side profile iterates synchronously with data value and that the demand-side profile accurately matches user demands. It provides a dual-end profile foundation with dimensional alignment and complete information for subsequent intelligent matching and recommendation layers, significantly improving the accuracy and efficiency of supply and demand matching.

[0025] In this embodiment, taking a user credit behavior data transaction scenario of a fintech company as an example, the specific implementation is as follows: The user credit behavior data provided by the data supplier is dynamic data (updated in real time, including user repayment records, credit application records, etc.). In the multi-dimensional quantification unit of data value, the cost of the data acquisition equipment (50,000 yuan) and the data cleaning and structuring processing time (100 person-days, with an average cost of 800 yuan per person per day) are preset at the initial time. Benchmark Value Index (as of January 1, 2025) Because the data is dynamically updated, a value decay coefficient is set. According to statistics from the platform's backend, As of March 15, 2025, the market access frequency for this data was 120 times per day, compared to a daily peak access frequency of 200 times for similar data on the platform. After normalization... Based on feedback ratings (out of 5) from 15 stakeholders who used the data over the past 90 days, the average application effectiveness score was calculated to be 4.2. After normalization... Use default weighting coefficients , Substitute into the formula ,in March 15, 2019 (days), calculated This accurately quantifies the comprehensive value of the credit behavior data at the current moment; Dual-end portrait construction unit based on Combining data metadata (type: credit behavior data, source: a bank's credit system, update cycle: real-time), domain tags (financial credit, user behavior, real-time updates), and quality scores (completeness 0.92, accuracy 0.95, consistency 0.91, timeliness 0.98), the overall quality score is... ), generating supply-side data asset profiles, including high-value ( The core characteristics include financial lending, real-time updates, high completeness, and high accuracy. The demand side inputs text requesting real-time updated user credit behavior data from the financial industry for training a credit risk assessment model. Data completeness must be ≥0.9 and accuracy ≥0.9. After parsing using natural language processing (NLP), the core demand dimensions are extracted: data domain (financial industry), data type (user credit behavior data), update frequency (real-time), quality requirements (completeness ≥0.9, accuracy ≥0.9), and application scenario (credit risk assessment model training). This constructs a structured dynamic demand profile. ; The system periodically checks the profile fit. It was found that the profile fit dropped to 0.82 (below the set threshold of 0.85) due to the data provider adding a new data segment on user loan delinquency days (meta-information update) and the demand side adding a request for data including the user loan application amount dimension (core demand dimension adjustment). This triggered the profile update module: S01 confirms that the data meta-information update accounts for 30%, one domain tag is added (delinquency analysis), and the quality score improves to 0.95, which is considered as profile update component c1; S02 confirms that the demand side's core demand dimension change type is new (new loan application amount dimension), which is considered as profile update component c2; S03 determines that profile feature tags need to be added and outputs the results; S04 confirms the addition of two feature tags, "including delinquency days" and "including loan application amount dimension," which are considered as profile update component c3; S05 integrates c1, c2, and c3 to complete the update of the supply-side data asset profile and the demand side's dynamic demand profile. After the update, the profile fit improved to 0.93, ensuring that the dual-end profiles can accurately support the subsequent supply and demand matching process.

[0026] In this embodiment of the invention, the intelligent matching and recommendation layer includes a hybrid intelligent matching engine for calculating supply-side assets. With demand side exist Time-of-fact matching The formula is as follows: ; in, is a cosine similarity function used to calculate the degree of fit between the data asset value vector and the demand profile vector in the feature space, with a value range of 0-1; The confidence factor for historical transactions is based on the relationship between the demand side and the historical transaction confidence factor over the past 90 days. Similar user groups on assets The feedback score is calculated using a weighted average, with a value range of 0-1. For dynamic weighting coefficients, satisfying The range of values ​​for enterprise demand is: The range of values ​​for individual demanders is: It can be dynamically adjusted according to the type of demand.

[0027] Furthermore, the hybrid intelligent matching engine functions as a comprehensive value index based on supply-side data assets. Structured dynamic demand profiles with demanders Accurately calculate supply-side assets through scientific quantitative methods. With demand side exist Time-of-fact matching This effectively addresses the technical pain points of traditional matching methods, such as vague measurement of feature fit, neglect of historical transaction feedback, fixed weights, and poor adaptability, providing a reliable quantitative basis for subsequent accurate recommendations.

[0028] The engine uses formulas To achieve accurate calculation of matching degree, the cosine similarity function is used. It can accurately capture the degree of fit between the data asset value vector and the demand profile vector in the feature space, ensuring that the matching results closely match the core characteristics of both supply and demand sides. The value range is limited to 0-1, which can intuitively reflect the degree of fit; historical transaction confidence factor Based on the past 90 days and the demand side Similar user groups on assets The weighted calculation of feedback scores incorporates user feedback experience from real-world application scenarios, making the matching results more practical and credible. Similarly, the value range of 0-1 is limited to facilitate unified quantitative comparison. Dynamic weighting coefficients , Strictly follow The constraints are set, and the value range is differentiated according to the type of demander. For enterprise demanders, The value is set at 0.7-0.8, focusing on feature fit to meet the professional and stability requirements of enterprises, while catering to individual needs. Setting the weight to 0.5-0.6 balances feature fit and historical feedback, adapts to individual needs with flexibility and personalization, and dynamically adjusts the weight according to the type of need. This design greatly improves the matching engine's ability to adapt to different scenarios, ensuring that the matching results under different types and scenarios have high accuracy. This reduces the decision-making cost for the demand side, improves the efficiency and success rate of data transactions, and provides strong support for the efficient circulation of data elements.

[0029] In this embodiment, based on the aforementioned scenario of user credit behavior data transaction in fintech companies, the specific application of the hybrid intelligent matching engine is implemented as follows: The aforementioned supply-side assets... For the user credit behavior data of this fintech company ( (Demand side) For enterprises that require this type of data to train credit risk assessment models, dynamic weighting coefficients are therefore set. , (satisfy And meets the needs of enterprises (The range of values); by calculating the data asset value vector (including characteristics such as high value, financial credit field, real-time update, high completeness, and high accuracy) and demand side. Dynamic demand profile The cosine similarity of the vectors (including core requirements dimensions such as financial industry data, user credit behavior data, real-time updates, completeness ≥ 0.9, accuracy ≥ 0.9, and credit risk assessment model training scenarios) is obtained. This indicates a high degree of compatibility between the two characteristics; based on the past 90 days and demand side Eight corporate users with similar characteristics (all engaged in credit risk assessment in the financial sector) have similar data assets. The feedback scores (out of 5, average score 4.3, weighted after normalization) are used to obtain the historical transaction confidence factor. Substituting the above parameters into the matching degree calculation formula, we can obtain... This matching score (close to 0.9) indicates that the supply-side assets... With demand side The system exhibits extremely high adaptability. Through differentiated weight settings and multi-dimensional factor fusion, the hybrid intelligent matching engine accurately captures the core points of convergence between supply and demand, providing scientific and reliable quantitative support for the subsequent generation of Top-N recommendation lists and interpretable explanations. This ensures that the recommendation results accurately match the needs of the users. This addresses the training needs of credit risk assessment models and improves the accuracy and efficiency of data transactions.

[0030] In this embodiment of the invention, the intelligent matching and recommendation layer further includes an interpretable recommendation unit; Explainable recommendation units are based on Sort the candidate data assets in descending order to generate a Top-N recommendation list (where N∈[5,10]). For each recommended item, a demand profile is calculated. Matching degree of each core dimension partial derivatives ,in Profiling Needs We identified the top three key demand characteristics in terms of contribution across several dimensions. Generate interpretable descriptions in natural language, such as "The 'real-time update' feature of this data (contribution 62%) and 'financial sector tag' (contribution 25%) are highly compatible with your needs"; The explainable recommendation unit also includes a recommendation description optimization module, which optimizes the explainability of a recommendation description when the user approval rating is less than a set approval rating threshold. The specific steps are as follows: S11. Confirm the deviation in the contribution calculation of key demand features, as an explanation for optimizing component d1; S12. Confirm the score for the accessibility of the natural language expression, as an explanation of the optimization component d2; S13. Perform supplementary judgment on the description of requirements features and output the judgment result; S14. When the judgment result indicates that supplementation is needed, confirm the newly added feature descriptions as part of the explanation of the optimized component d3. S15. When the result of S13 is that it is required, the optimized components d1, d2 and d3 will be integrated to generate an optimized interpretable description; when the result of S13 is that it is not required, the optimized components d1 and d2 will be integrated to generate an optimized interpretable description.

[0031] Furthermore, the interpretable recommendation unit addresses the technical pain point of the black box nature of traditional data recommendation by calculating the matching degree in a hybrid intelligent matching engine. Based on this, a standardized process is used to generate traceable and easy-to-understand recommendation results, which not only improves the credibility of the recommendation results, but also helps demanders quickly grasp the recommendation logic and identify core points of fit, further reducing the decision-making costs of demanders and facilitating the efficient advancement of data transactions.

[0032] Explainable recommendation units are first based on matching degree All candidate data assets are sorted in descending order to generate a Top-N recommendation list (N is limited to the range [5,10]). This avoids both insufficient selection due to too few recommendations and excessive selection burden on the demand side, achieving a reasonable balance in the number of recommendations. For each recommendation in the list, a demand profile is calculated. Matching degree of each core dimension partial derivatives (in Profiling Needs (Multiple dimensions) to accurately identify the top 3 key demand features that contribute to the matching degree, ensuring the relevance and scientific nature of the explainability explanation; then generate intuitive and easy-to-understand explainability explanations in natural language, clearly presenting the key features and their contribution, allowing demanders to quickly understand the basis of the recommendation, breaking the limitation of traditional recommendation that "only knows the result, not the reason".

[0033] Simultaneously, a recommendation description optimization module is set up. When the user acceptance of the recommendation description is lower than a set threshold, the system process accurately locates the optimization elements, sequentially confirms the contribution calculation deviation, the natural language expression's accessibility score, and determines whether additional feature descriptions are needed. Based on the judgment results, the corresponding optimization elements are integrated to generate an optimized description, ensuring the accuracy, accessibility, and completeness of the explainable description, adapting to the understanding capabilities of different users, further improving the acceptability of the recommendation results, promoting the smooth completion of data transactions, and providing user feedback support for the closed-loop optimization of intelligent matching and the recommendation layer.

[0034] In this embodiment, based on the aforementioned scenario of user credit behavior data transaction in fintech companies, the specific application of the explainable recommendation unit is implemented as follows: The aforementioned hybrid intelligent matching engine calculates the supply-side assets... (User credit behavior data) and demand side (Company demander) matching degree At the same time, the system filters out those that match the needs of the client. The 12 candidate data assets related to the demand were sorted in descending order by the interpretability recommendation unit based on the matching degree of each candidate asset. With N=5, a list of supply-side assets was generated. The Top-5 recommended list includes supply-side assets within the recommended list. Calculate the demand profile Matching degree of each core dimension (financial industry, user credit behavior data, real-time updates, completeness ≥0.9, accuracy ≥0.9, credit risk assessment model training scenario) partial derivatives We identified the top three key features contributing to the data's performance: real-time updates (65% contribution), accuracy ≥ 0.9 (22% contribution), and financial industry tags (13% contribution). Based on this, we generated an interpretability statement: the data's real-time update characteristics (65% contribution), accuracy ≥ 0.9 (22% contribution), and financial industry tags (13% contribution) are highly compatible with your credit risk assessment model training needs. Subsequent data collection by the system from the requesting party Rate the approval of this recommendation (out of 10, with an approval threshold of 8). If the requester... If a score of 7 is given (below the threshold), the recommendation description optimization module is triggered: S11 confirms that the contribution calculation deviation of the real-time update of key demand features is 2%, which is used as the description optimization component d1; S12 uses the system's built-in scoring model to obtain a commonality score of 7.5 (out of 10) for the natural language expression, which is used as the description optimization component d2; S13 performs supplementary judgment on the demand feature expression, combined with the demand side... The core requirement (credit risk assessment) determines the need for supplementary feature descriptions and outputs the necessary supplementary judgment results; S14 confirms the newly added feature descriptions as "effectively supports the credit risk assessment model's need for real-time data" and "high accuracy ensures model training precision," as an optimized component d3; S15 integrates d1, d2, and d3 to generate an optimized interpretability description: the real-time update characteristics of this data (contribution 63%, effectively supports the credit risk assessment model's need for real-time data), accuracy ≥ 0.9 (contribution 22%, high accuracy ensures model training precision), and financial industry tags (contribution 13%) are highly consistent with your credit risk assessment model training needs. After optimization, user approval is collected again, increasing to 9 points, meeting the set threshold, ensuring that the interpretability description can accurately and clearly present the recommendation logic, helping the demand side make quick decisions.

[0035] In this embodiment of the invention, the transaction operation and security protection layer includes a dynamic intelligent pricing unit for generating data assets. For the demand side Personalized transaction reference price The formula is as follows: ; in, For assets The base price is calculated based on a comprehensive assessment of data collection costs, processing costs, and industry benchmark prices. for The average comprehensive value index of similar data assets at any given time is the average of the top 100 assets in the same field within the platform; The value elasticity coefficient, with a range of values ​​of [value range missing]. For scarce data, the upper limit is used; for general data, the lower limit is used. To match the premium factor, the value range is: The higher the matching degree, the higher the premium ratio.

[0036] Furthermore, the dynamic intelligent pricing unit included in the transaction operation and security protection layer addresses the technical pain points of traditional data transaction pricing, such as rigidity, lack of personalized adaptation, and the disconnect between value and price. Based on the inherent value of data assets, relative market value, and supply and demand matching, it generates personalized transaction reference prices through scientific quantitative formulas. This approach balances reasonable returns for suppliers with cost-effectiveness for demanders, promotes fair, efficient, and sustainable data transactions, and provides scientific pricing support for the market-oriented circulation of data elements.

[0037] Through formula To achieve personalized pricing, including a basic price Based on a comprehensive calculation of data collection costs, processing costs, and industry benchmark prices, we ensure the rationality and bottom line of pricing, and avoid losses for suppliers or pricing that deviates from the fair industry level. The value elasticity coefficient is the ratio of the current comprehensive value index of data assets to the average comprehensive value index of similar data assets in the same field during the same period. It accurately reflects the relative value level of the data asset in the market, achieving a value orientation of "high quality, high price"; Values ​​are differentiated based on data scarcity (range [0.3, 0.7]), with the upper limit for scarce data and the lower limit for general data, to accommodate market supply and demand differences for different types of data and avoid the problem of underpricing of scarce data and overpricing of general data; a premium coefficient is matched. The price is dynamically adjusted based on the matching degree (range [0.2, 0.5]). The higher the matching degree, the higher the premium, which fully reflects the value of the data asset and the demand of the demander, making the pricing more personalized and targeted.

[0038] The entire pricing logic integrates four core elements: cost, value, market, and matching degree. It avoids the limitations of traditional pricing that relies solely on cost or a single market indicator, while also achieving dynamic and personalized pricing. This ensures reasonable returns for suppliers, reduces unreasonable expenditures for demanders, and provides a quantitative basis for the fairness of data transactions. It promotes the transformation of data transactions from vague pricing to precise pricing, and helps data elements circulate efficiently.

[0039] In this embodiment, based on the aforementioned scenario of user credit behavior data trading in a fintech company, the specific application of the dynamic intelligent pricing unit is implemented as follows: The aforementioned supply-side asset i is the user credit behavior data of the fintech company. First, the basic pricing is calculated. Based on the data acquisition equipment cost of 50,000 yuan and the data cleaning and structuring processing cost of 80,000 yuan (100 person-days × 800 yuan / person-day), and referring to the industry benchmark price of similar data assets in the financial and credit field, the following comprehensive calculations were made. The system calculates the average comprehensive value index by analyzing the comprehensive value index of the top 100 similar data assets in the financial lending sector on the platform at time t (March 15, 2025). The aforementioned comprehensive value index of the asset has been calculated. ,therefore This user's credit behavior data contains core information such as real-time repayment records and overdue days, which is scarce data in the financial lending field. Therefore, a value elasticity coefficient is set. (Upper limit of the value range); The aforementioned hybrid intelligent matching engine has calculated the asset and the demand side. Matching degree The matching degree is relatively high, therefore a matching premium coefficient is set. (Approaching the upper limit of the value range); Substituting the above parameters into the personalized trading reference price calculation formula, we can obtain... Ten thousand yuan.

[0040] The pricing result reflects the high value (superior to similar assets), scarcity, and high matching degree of the data asset, while also taking into account the costs and benefits of the supplier and meeting the reasonable payment expectations of the demander for high-quality data. Compared with traditional fixed pricing (such as a uniform price of 200,000 yuan), this personalized pricing is more scientific and reasonable. It not only guarantees the reasonable returns of the supplier, but also allows the demander to obtain high-quality data that matches the payment price, effectively promoting the smooth completion of this data transaction and fully demonstrating the core value of the dynamic intelligent pricing unit.

[0041] In this embodiment of the invention, the dynamic intelligent pricing unit further includes a market popularity adjustment factor. This is used to smooth the impact of market supply and demand fluctuations on prices. The final recommended trading price formula is as follows: ; The market heat adjustment factor is defined as follows: ; in, for Time platform and assets The number of relevant active demanders; for The number of similar valid data assets within the platform; To prevent extremely small positive numbers from being divided by zero, the value is taken as... ; The hyperbolic tangent function is used to make... To avoid drastic price fluctuations; The dynamic intelligent pricing unit also includes a price adjustment calibration module, which is used to calibrate price adjustment parameters when the deviation between the final suggested transaction price and the actual transaction price exceeds a set deviation threshold. The specific steps are as follows: S21. Confirm market heat adjustment factors The calculated deviation is used as a calibration component e1; S22, Confirming the Value Elasticity Coefficient Matching premium coefficient The adaptation deviation is used as a calibration component e2; S23. Perform supplementary calibration parameter judgment and output the judgment result; S24. When the judgment result indicates that supplementation is required, confirm the newly added calibration parameter item as calibration component e3. S25. When the result of S23 indicates that it is necessary, integrate the calibration components e1, e2 and e3 to complete the calibration of the price adjustment parameters; when the result of S23 indicates that it is not necessary to supplement, integrate the calibration components e1 and e2 to complete the calibration of the price adjustment parameters.

[0042] Furthermore, the dynamic intelligent pricing unit adds a market popularity adjustment factor. The role of the price adjustment and calibration module is to further optimize the personalized pricing logic, solve the technical pain points of drastic price fluctuations caused by market supply and demand fluctuations and the disconnect between pricing and actual transactions, ensure the stability, rationality and adaptability of the final transaction suggestion price, and further improve the closed-loop optimization mechanism of data transaction pricing.

[0043] Among them, market heat adjustment factor Through formula The calculations show the impact of market supply and demand fluctuations on prices: and Reflecting respectively Time platform and assets The ratio of the number of active demanders and the number of similar valid data assets can accurately capture the degree of market supply and demand imbalance; a very small positive number. Effectively avoids calculation anomalies caused by denominators of zero, ensuring the stability of formula calculations; hyperbolic tangent function. By strictly limiting the adjustment factor value to the range of [0.5, 1.5], we can avoid price spikes when demand is strong and price crashes when supply is excessive, while also reasonably reflecting the impact of market sentiment on prices and achieving stable dynamic price adjustments.

[0044] The price adjustment and calibration module addresses the issue of excessive discrepancies between the final suggested transaction price and the actual transaction price. Through a standardized process, it sequentially confirms the calculation deviations of the market heat adjustment factor and the adaptation deviations of the value elasticity coefficient and matching premium coefficient. It then determines whether additional calibration parameters are needed, integrates the corresponding calibration elements to complete parameter calibration, and ensures that pricing parameters can dynamically adapt to market changes and actual transaction conditions. This continuously improves pricing accuracy, ensuring that the final suggested transaction price aligns with the value of data assets, supply and demand matching, and market heat, while also meeting the fair level of the actual transaction scenario. Furthermore, it balances the interests of both suppliers and demanders, promoting fair and efficient data transactions and enhancing the closed-loop optimization capabilities of the dynamic intelligent pricing unit.

[0045] In this embodiment, based on the aforementioned scenario of user credit behavior data trading in fintech companies, the specific application of the market heat adjustment factor and price adjustment calibration module is implemented as follows: The personalized transaction reference price of the data asset i has been calculated above. The system calculates the final suggested transaction price based on a market sentiment adjustment factor, using a figure of 10,000 yuan; it also counts the number of active demanders related to the user's credit behavior data on the platform at time t (March 15, 2025). Home, number of similar valid data assets A rule is set to prevent division by zero for extremely small positive numbers. Substitute the values ​​into the market heat adjustment factor formula to calculate: The adjustment factor is within the range of [0.5, 1.5], which meets the requirements; therefore, the final recommended trading price is... Ten thousand yuan.

[0046] Subsequently, the actual transaction price was 500,000 yuan. A price deviation threshold of 5% was set, and the deviation rate was calculated. If the value exceeds the set threshold, the price adjustment calibration module is triggered: S21 confirms the market heat adjustment factor. The calculation deviation is 0.02 (due to statistical bias in active demand), which is used as calibration component e1; S22 confirms the value elasticity coefficient. Matching premium coefficient The adaptation deviation is 0.03 (adaptation deviation for financial credit scenarios), which is used as calibration component e2; S23 performs a supplementary calibration parameter judgment, and based on the actual transaction situation, determines the calibration parameter items that need to be supplemented, and outputs the judgment results that need to be supplemented; S24 confirms that the newly added calibration parameter item is the calibration coefficient of the average transaction price of similar data assets (with a value of 0.98), which is used as calibration component e3; S25 integrates e1, e2, and e3 to complete the price adjustment parameter calibration: the number of active demanders in the market heat adjustment factor calculation is corrected to 40, and the calculation is recalculated. ; value elasticity coefficient Adjusted to 0.68, matching the premium coefficient. The value was adjusted to 0.43; a new calibration parameter was introduced to correct the final price. After calibration, the final suggested transaction price was approximately 502,000 yuan, and the deviation rate from the actual transaction price was reduced to 0.4%, which is lower than the set threshold. This ensures that the pricing parameters are adapted to the actual transaction scenario and improves the accuracy of pricing.

[0047] In this embodiment of the invention, the intelligent matching and recommendation layer further includes a matching quality closed-loop optimization unit; After the transaction is completed, the data asset requester will collect the data assets. Actual user feedback rating (5 points is the optimal score), and normalized to ; Calculate the confidence level of this match. The formula is as follows: ; in, Weighting coefficients, satisfying The default value is ; For every 10 transactions accumulated, the hybrid intelligent matching engine, based on confidence level... For dynamic weighting coefficients Fine-tuning is performed, with the adjustment range not exceeding ±0.05, to achieve online iterative optimization of the recommendation model; The matching quality closed-loop optimization unit also includes a weight fine-tuning calibration module, which is used to calibrate the weight fine-tuning rules when the improvement rate of the matching degree after fine-tuning is less than a set improvement rate threshold. The specific steps are as follows: S31. Confirm Confidence Level The calculated deviation is used as calibration component f1; S32. Confirm dynamic weighting coefficients The fine-tuning amplitude deviation is used as a calibration component f2; S33. Perform fine-tuning rule supplementation judgment and output the judgment result; S34. When the judgment result indicates that supplementation is needed, confirm the newly added fine-tuning constraints as calibration component f3. S35. When the result of S33 indicates that it is necessary, integrate the calibration components f1, f2 and f3 to generate the calibrated weight fine-tuning rules; when the result of S33 indicates that it is not necessary to supplement, integrate the calibration components f1 and f2 to generate the calibrated weight fine-tuning rules.

[0048] Furthermore, the matching quality closed-loop optimization unit addresses the technical pain points of traditional intelligent matching models, such as the inability to achieve online iteration, the difficulty in continuously improving matching accuracy, and the lack of verification of fine-tuning effects. It constructs a closed-loop optimization mechanism for transaction feedback, confidence quantification, weight fine-tuning, and rule calibration, ensuring that the matching accuracy of the hybrid intelligent matching engine can continuously improve with the accumulation of transaction scenarios. This further enhances the accuracy and adaptability of supply and demand matching, providing long-term support for the efficient achievement of data transactions.

[0049] The matching quality closed-loop optimization unit first collects feedback scores from the demand side regarding the actual use of data asset i after the transaction is completed. and through Normalization is performed to convert the feedback scores into standardized values ​​in the 0-1 range, facilitating unified quantitative integration with matching degree and relative value index; subsequently, a formula is used... Calculate the confidence level of this match. The weighting coefficient satisfy Default value It emphasizes the core role of actual user feedback while also considering matching accuracy and the relative value of data, ensuring that the confidence level comprehensively and objectively reflects the true level of matching effectiveness; for every 10 transactions, the hybrid intelligent matching engine, based on the confidence level... For dynamic weighting coefficients Fine-tuning is performed, with the adjustment range strictly controlled within ±0.05, enabling online iteration of the recommendation model while avoiding fluctuations in matching performance caused by sudden weight changes. A supporting weight fine-tuning calibration module addresses the issue of matching accuracy improvement rates falling below a set threshold after fine-tuning. Through a standardized process, it sequentially confirms the confidence calculation deviation and weight fine-tuning range deviation, determines whether additional fine-tuning constraints are needed, and then integrates the corresponding calibration elements to generate calibrated fine-tuning rules. This ensures the scientific validity and effectiveness of weight fine-tuning, drives continuous optimization of the matching model, gradually improves the accuracy and stability of supply and demand matching, adapts to the dynamic changes of different transaction scenarios, further reduces decision-making costs for demanders, and improves the success rate and satisfaction of data transactions.

[0050] In this embodiment, based on the aforementioned scenario of financial technology company user credit behavior data transaction, the specific application of the matching quality closed-loop optimization unit is implemented as follows: the aforementioned supply-side assets With demand side After the transaction is completed, the demand side is collected. Feedback score on the actual use of the data asset The score (5 points is optimal) is calculated using the normalization formula. The matching degree of this transaction has been calculated above. The ratio of the comprehensive value index of data assets to the average of similar assets Using default weighting coefficients Substituting into the confidence level calculation formula, we can obtain This indicates that the matching effect was excellent.

[0051] The system continuously accumulates transaction records of this type of user's credit behavior data. When the cumulative total reaches 10 transactions, the system extracts the average confidence level of these 10 transactions (calculated to be 0.89). Based on this average, the system adjusts the dynamic weighting coefficients of the hybrid intelligent matching engine. Fine-tuning: original enterprise demand side weighting Combining the matching effect reflected by the mean confidence level, Fine-tuned to 0.78 (fine-tuning range 0.03, not exceeding ±0.05). The corresponding adjustment is 0.22 (which satisfies the requirement). The matching improvement rate threshold was set at 5%. After fine-tuning, the average matching rate of the subsequent 5 similar transactions was 0.91, which is about 3.4% higher than the average matching rate of 0.88 before fine-tuning. This is lower than the set threshold, triggering the weight fine-tuning calibration module: S31 confirms confidence level. The calculation deviation is 0.015 (due to statistical deviations in some transaction feedback scores), which is used as calibration component f1; S32 confirms the dynamic weighting coefficient. The fine-tuning deviation is 0.01 (the fine-tuning amplitude does not fully match the change in confidence level), which is used as calibration component f2; S33 performs supplementary judgment on the fine-tuning rules, and determines that fine-tuning constraints need to be added based on the characteristics of enterprise needs in the financial credit scenario, and outputs the judgment result that needs to be added; S34 confirms that the newly added fine-tuning constraints are for the enterprise demand side. After fine-tuning, the value should not exceed 0.8 (the upper limit of the range), and the fine-tuning amplitude should be positively correlated with the mean deviation of the confidence level, serving as calibration component f3; S35 integrates f1, f2, and f3 to generate the calibrated weight fine-tuning rules: correcting the statistical bias of feedback scoring in the confidence level calculation, adjusting the fine-tuning amplitude to be linked to the mean deviation of the confidence level, and re-aligning... Fine-tune to 0.77. After adjusting to 0.23 and calibrating, the average matching degree of the subsequent 5 similar transactions increased to 0.94, an improvement rate of approximately 6.8%, which is higher than the set threshold. This ensures that the weight fine-tuning can effectively improve the matching accuracy and achieve efficient online iteration of the recommendation model.

[0052] In this embodiment of the invention, the value quantification and profiling layer also includes a value collaborative evaluation unit, which is deployed within the federated learning framework of the transaction operation and security protection layer. Each data provider calculates the gradient update information of the comprehensive value index model locally using its own data characteristics, and encrypts the gradient using a homomorphic encryption algorithm. The encrypted gradient information is uploaded to the federated aggregation server. The server uses a secret-sharing secure aggregation algorithm to summarize the gradients and update the parameters of the global value assessment model (e.g., ...). ); Global model parameters are encrypted and distributed to all suppliers, ensuring that data remains within the domain and models are jointly optimized, thereby improving the objectivity and accuracy of value assessment, and the collaborative update cycle does not exceed 7 days; The value collaborative assessment unit also includes a collaborative update verification module, which verifies the collaborative update process when the value assessment accuracy after the global model parameter update is less than a set accuracy threshold. The specific steps are as follows: S41. Confirm the encryption deviation of the gradient update information of each supplier as a verification component g1; S42. Confirm the gradient aggregation deviation of the federated aggregation server as a verification component g2; S43. Perform supplementary judgments in the update process and output the judgment results; S44. When the judgment result indicates that supplementation is required, confirm the newly added process verification node as a verification component element g3. S45. When the result of S43 indicates that it is required, the verification components g1, g2 and g3 are integrated to complete the verification and correction of the collaborative update process; when the result of S43 indicates that it is not required to supplement, the verification components g1 and g2 are integrated to complete the verification and correction of the collaborative update process.

[0053] Furthermore, the value collaborative assessment unit addresses the technical pain points of traditional data value assessment, such as data privacy leaks from multiple suppliers, lack of collaborative model optimization, and subjective and one-sided assessment results. Relying on the federated learning framework built on the transaction operation and security protection layer, it enables data to remain within the domain and models to be jointly optimized, significantly improving the objectivity, accuracy, and security of the comprehensive data value index assessment, and providing reliable model support for value quantification and profiling.

[0054] The value collaborative assessment unit is deployed within a federated learning framework. Instead of requiring data providers to share raw data, each provider calculates gradient updates for the comprehensive value index model locally using its own data features. These gradient updates are then encrypted using a homomorphic encryption algorithm, effectively mitigating data privacy leaks during gradient transmission and ensuring the security of the data providers' core data. After the encrypted gradient information is uploaded to the federated aggregation server, the server uses a secret-sharing secure aggregation algorithm to aggregate and merge the gradients from all providers, accurately updating the global value assessment model parameters (such as the value decay coefficient). Weighting coefficients (etc.) This ensures that the global model can integrate data features from multiple suppliers, avoiding evaluation distortion caused by data bias from a single supplier. The updated global model parameters are encrypted and then distributed to each supplier, enabling synchronous optimization of each supplier's local model and the global model. This forms a collaborative closed loop of local calculation, encrypted upload, global aggregation, encrypted distribution, and local update, with the collaborative update cycle strictly controlled within 7 days to ensure that model parameters can adapt to dynamic changes in data value in a timely manner. A supporting collaborative update verification module is used to address the issue of the value assessment accuracy falling below a set threshold after the global model parameters are updated. Through a standardized process, the gradient encryption deviation of each supplier and the gradient aggregation deviation of the server are confirmed sequentially to determine whether additional process verification nodes are needed. Then, the corresponding verification elements are integrated to complete the process verification and correction, ensuring the standardization and effectiveness of the collaborative update process. This continuously improves the accuracy of the global value assessment model, providing a solid guarantee for the accurate quantification of the comprehensive data value index and further improving the closed-loop optimization capabilities of value quantification and profiling layers.

[0055] In this embodiment, based on the aforementioned scenario of user credit behavior data transactions by a fintech company, the specific application of the value collaborative assessment unit is implemented as follows: The aforementioned data provider (a fintech company) collaborates with two other similar financial data providers to jointly optimize the global value assessment model. All three deploy the value collaborative assessment unit and conduct collaborative work based on the federated learning framework of transaction operation and security protection layers; the collaborative update cycle is set to 7 days, and the initial parameters of the global value assessment model are... The threshold for accuracy of value assessment is set at 90%.

[0056] Each supplier uses its own user credit behavior data locally (none of which are shared externally) to calculate the gradient update information for the comprehensive value index model: Supplier 1 (the aforementioned fintech company) calculates... The gradient bias is -0.002. The gradient bias is 0.01. The gradient deviation is -0.01; calculated by supplier 2. The gradient bias is -0.001. The gradient bias is 0.008. The gradient deviation is -0.009; calculated by supplier 3. The gradient bias is -0.003. The gradient bias is 0.012. The gradient bias is -0.011. Each supplier encrypts its own gradient update information using a homomorphic encryption algorithm before uploading it to the federated aggregation server. The server uses a secret-sharing secure aggregation algorithm to aggregate the encrypted gradients from the three suppliers, calculates the global gradient update value, and updates the parameters of the global value assessment model accordingly. The updated parameters were encrypted and distributed to the three suppliers, completing the first collaborative update.

[0057] After the update, 10 similar user credit behavior data were selected for value assessment accuracy testing. The calculated accuracy rate was 87%, lower than the set threshold of 90%, triggering the collaborative update verification module: S41 confirmed the encryption deviation of the gradient update information from the three suppliers (supplier 1 deviation 0.001, supplier 2 deviation 0.0015, supplier 3 deviation 0.002), and took the average as the verification component g1=0.0015; S42 confirmed that the gradient aggregation deviation of the federated aggregation server was 0.002, and used it as the verification component g2; S43 performed update process supplementation. S44 performs a judgment, combining the verification results to determine which process verification nodes need to be added, and outputs the judgment results for the additions; S45 confirms that the newly added process verification nodes are "gradient encryption pre-consistency verification" and "gradient aggregation post-integrity verification", as verification component g3; S45 integrates g1, g2, and g3 to complete the verification and correction of the collaborative update process: corrects the gradient encryption deviations of each supplier, adds a gradient encryption pre-consistency verification step (to ensure uniform gradient format) and a gradient aggregation post-integrity verification step (to ensure no gradient loss), and restarts the collaborative update. After the update, the global model parameters are... The accuracy rate of the retest assessment improved to 92%, which is higher than the set threshold, ensuring the effectiveness of the collaborative update process and improving the objectivity and accuracy of value assessment.

[0058] In this embodiment of the invention, the data access and governance layer includes a cloud-edge collaborative processing unit and a four-dimensional quality assessment unit; Cloud-edge collaborative processing unit: The edge side is responsible for receiving multi-source data in real time, performing protocol parsing and preliminary filtering; the cloud side is responsible for a three-layer progressive governance of semantic alignment (unified data semantic description), dimension alignment (standardized data fields), and entity alignment (multi-source data related to the same entity); The four-dimensional quality assessment unit scores data based on four dimensions: completeness (data field missing rate), accuracy (deviation from actual values), consistency (logical consistency across data sources), and timeliness (time difference between data generation and access). ; Set quality threshold ,when When this happens, the system automatically triggers a data replenishment (for missing fields) or secondary cleaning (for erroneous data) process until the quality requirements are met; The four-dimensional quality assessment unit also includes a quality control optimization module, which optimizes the quality control process when the quality compliance rate after data supplementation or secondary cleaning is less than a set compliance rate threshold. The specific steps are as follows: S51. Confirm the scoring deviations of each dimension of the four-dimensional quality assessment and use them as optimization component h1; S52. Confirm the efficiency of the data supplementation or secondary cleaning process as an optimization component h2; S53. Perform supplementary judgments on the control process and output the judgment results; S54. When the judgment result indicates that supplementation is needed, confirm the newly added quality control node as an optimization component h3. S55. When the result of S53 is that it is necessary, the optimized components h1, h2 and h3 will be integrated to generate an optimized quality control process; when the result of S53 is that it is not necessary to supplement, the optimized components h1 and h2 will be integrated to generate an optimized quality control process.

[0059] Furthermore, the data access and governance layer includes a cloud-edge collaborative processing unit and a four-dimensional quality assessment unit. These units address the technical pain points of traditional data access, such as chaotic multi-source data formats, low processing efficiency, incomplete quality control, and insufficient compliance rates. They construct a full-process data governance system that features efficient access, layered governance, comprehensive quality inspection, and closed-loop optimization. This provides a high-quality, standardized, and reusable data foundation for subsequent upper-layer modules such as value quantification and intelligent matching, ensuring the objectivity of data asset value and the reliability of its application from the source.

[0060] The cloud-edge collaborative processing unit adopts a layered architecture of preliminary processing at the edge and deep governance in the cloud. The edge is responsible for receiving multi-source heterogeneous data (such as bank credit systems, third-party data platforms, etc.) in real time, quickly performing protocol parsing and preliminary filtering to remove invalid and redundant data, reduce the data processing pressure on the cloud, and ensure the real-time nature of data access. The cloud, on the other hand, uses a three-layer progressive governance approach of semantic alignment, dimension alignment, and entity alignment to unify data semantic description, standardize data field formats, and associate multi-source data with the same entity, thereby solving the problem of heterogeneity of multi-source data, achieving data normalization processing, and providing a unified data benchmark for subsequent value quantification and supply-demand matching.

[0061] The four-dimensional quality assessment unit comprehensively scores the treated data from four core dimensions: completeness, accuracy, consistency, and timeliness. ∈[0,1], enabling quantifiable and traceable data quality, avoiding the one-sidedness caused by a single-dimensional quality assessment; by setting quality thresholds. =0.85, when the data quality score is... When the data falls below this threshold, the system automatically triggers a data supplementation or secondary cleaning process to ensure that all data entering the upper-level modules meets quality requirements, thus mitigating the negative impact of low-quality data on value assessment and matching recommendations from the source. A supporting quality control optimization module addresses the issue of the quality compliance rate falling below a set threshold after data supplementation or secondary cleaning. Through a standardized process, it sequentially confirms the scoring deviations and process efficiency of each quality dimension, determines whether additional control nodes are needed, and then integrates corresponding optimization elements to generate an optimized control process. This continuously improves the quality and efficiency of data governance, enhances the closed-loop optimization capabilities of the data access and governance layers, and lays a solid foundation for the efficient operation of the entire data trading system.

[0062] In this embodiment, based on the aforementioned scenario of user credit behavior data transactions in a fintech company, the specific application of the data access and governance layer is implemented as follows: The fintech company's data access and governance layer deploys a cloud-edge collaborative processing unit and a four-dimensional quality assessment unit to process multi-source user credit behavior data (from its own credit system, partner banks, and third-party credit reporting platforms); the edge side receives raw data from each data source in real time, performs protocol parsing (adapting to the transmission protocols of different data sources) and preliminary filtering, and removes invalid data (such as records with incorrect formats or too many null values). After preliminary filtering each day, approximately 100,000 valid data entries are retained, reducing the processing pressure on the cloud; the cloud performs a three-layer progressive governance on the filtered valid data: semantic alignment unifies the semantic descriptions of core fields such as credit delinquency and delinquency days to avoid ambiguity; dimensional alignment standardizes fields such as credit application amount and repayment date from different data sources to unify data formats; entity alignment associates the credit records of the same user from different data sources to form a complete user credit behavior profile.

[0063] The four-dimensional quality assessment unit scores the processed data, with each dimension having the same weight. For a certain batch of processed data: completeness (field missing rate 5%) score 0.95, accuracy (deviation from real credit data 3%) score 0.97, consistency (logic deviation across data sources 2%) score 0.98, and timeliness (data generation and access time difference ≤ 1 hour) score 0.96. The overall quality score is then calculated. =(0.95+0.97+0.98+0.96) / 4=0.965, which is higher than the quality threshold. =0.85, directly proceeding to the subsequent value quantification stage. After a later batch of data undergoes governance, the overall quality score will be... =0.82 (below the threshold), the system automatically triggers a secondary cleaning (correcting erroneous data) and data supplementation (supplementing missing credit overdue days) process; the quality compliance rate threshold is set at 90%, after this supplementation and secondary cleaning, the compliance rate is only 85%, which is below the set threshold, triggering the quality control optimization module: S51 confirms the scoring deviation of each dimension of the four-dimensional quality assessment (completeness scoring deviation 0.02, accuracy scoring deviation 0.01), as an optimization component h1; S52 confirms that the data supplementation process is inefficient (supplementation time exceeds the standard time by 20%), as an optimization component h2; S53 performs supplementary judgment on the control process, combined with the insufficient compliance rate. The reasons were identified, and the need for additional control nodes was determined, with the results of the necessary additions output. S54 confirmed that the newly added quality control nodes were pre-data source quality inspection and real-time monitoring of supplementary data collection progress, serving as optimization component h3. S55 integrated h1, h2, and h3 to generate the optimized quality control process: correcting scoring deviations in various dimensions, adding a pre-data source quality inspection step (pre-filtering low-quality data sources), adding a real-time monitoring node for supplementary data collection progress (adjusting supplementary data collection strategies in a timely manner), and reprocessing the same type of data after optimization. The quality compliance rate after supplementary data collection and secondary cleaning increased to 93%, exceeding the set threshold, ensuring stable and reliable data quality and providing high-quality data support for subsequent value quantification and profile construction.

[0064] In this embodiment of the invention, based on a multi-source heterogeneous data value mining and intelligent matching system, a method for multi-source heterogeneous data value mining and intelligent matching is provided, comprising the following steps: Data standardization and governance: Through the cloud-edge collaborative architecture of data access and governance layer, it adapts to the protocols and formats of multi-source heterogeneous data, and outputs standardized and unified data assets (quality score ≥ 0.85) after three-layer alignment and four-dimensional quality assessment; if the quality score does not meet the standard, it triggers data supplementation or secondary cleaning process until the quality requirements are met. Dual-end profiling and value quantification: The value quantification and profiling layer is based on standardized data assets and calculates the comprehensive value index through a multi-dimensional coupling algorithm with attenuation and gain mechanisms. They also constructed supply-side data asset profiles and demand-side dynamic demand profiles, respectively. If the profile does not meet the requirements, update the corresponding profile according to the preset steps; at the same time, through the federated learning framework, coordinate with various suppliers to optimize the value assessment model. Intelligent Matching and Recommendation: The intelligent matching and recommendation layer calculates the supply-demand matching degree through a hybrid intelligent matching engine. Generate a Top-N recommendation list and explanatory descriptions; if the recommendation descriptions do not meet the acceptance criteria, optimize the explanatory descriptions; simultaneously, transaction operations and security protection layers are based on... and The final suggested trading price is generated by combining market sentiment adjustment factors; if the price deviation is too large, the price adjustment parameters are calibrated. Transaction execution and closed-loop optimization: Transaction settlement is completed based on smart contracts, and user feedback ratings are collected after the transaction to calculate the matching confidence level. The results are fed back to the intelligent matching engine to fine-tune the weight coefficients; if the weight fine-tuning effect is not good, the fine-tuning rules are calibrated; at the same time, the value evaluation model is updated collaboratively through the federated learning framework to achieve dynamic optimization throughout the entire process.

[0065] By transforming the functions of the system's four-layer architecture into implementable full-process operational specifications, and through a progressive closed-loop design of data governance, value quantification, intelligent matching, and transaction optimization, the system systematically addresses the technical pain points in traditional data processing, such as the difficulty in integrating multi-source heterogeneous data, the one-sided and static value assessment, the insufficient accuracy of supply and demand matching, the rigidity of transaction pricing, the weak protection of privacy and security, and the lack of continuous optimization mechanisms. The data standardization and governance steps rely on cloud-edge collaboration and four-dimensional quality assessment to ensure the standardization and high quality of data assets from the source, breaking the data silo dilemma. The dual-end profiling and value quantification steps achieve accurate characterization of data value and supply and demand characteristics through dynamic comprehensive value index calculation and structured profile construction, while relying on the federated learning framework to balance model optimization and data privacy and security. The intelligent matching and recommendation steps integrate value fit and historical feedback to improve the accuracy of supply and demand matching, and with interpretable explanations and dynamic pricing mechanisms, reduce the decision-making costs of demanders and balance the interests of both supply and demand sides. The transaction execution and closed-loop optimization steps ensure the security and efficiency of transactions through smart contracts, and rely on confidence quantification and model collaborative updates to achieve dynamic iteration of parameters throughout the process, ensuring that the method can continuously adapt to changes in data value, market dynamics, and demand upgrades. Ultimately, this reduces the threshold and cost of data transactions, improves data flow efficiency, and provides a standardized and intelligent implementation path for the transformation of data elements from decentralized storage to efficient utilization, strongly supporting the high-quality development of the digital economy.

[0066] This embodiment takes a user credit behavior data transaction scenario of a fintech company as an example, and the specific implementation process of the method is as follows: Data Standardization and Governance: This fintech company utilizes a cloud-edge collaborative architecture for data access and governance, adapting to the multi-source heterogeneous data transmission protocols and formats of its own credit system, partner banks, and third-party credit reporting platforms. The edge receives raw user credit data in real time, performs protocol parsing and initial filtering, removing invalid data with format errors and excessive null values ​​(filtering approximately 20,000 redundant data entries daily). The cloud performs a three-tiered progressive governance process on valid data: semantic alignment (unifying the semantics of core fields such as "overdue days" and "repayment status"), dimensional alignment (standardizing data field formats and precision), and entity alignment (linking the same user's credit records across different data sources). A four-dimensional quality assessment unit scores data based on completeness, accuracy, consistency, and timeliness, setting a quality threshold of 0.85. If a batch of data after governance achieves a comprehensive quality score of 0.82 (below the threshold), the system automatically triggers a secondary cleaning (correcting 3% of erroneous data) and data supplementation (adding 5% of missing fields), ultimately outputting standardized and unified data assets with a comprehensive quality score of 0.91.

[0067] Dual-end profiling and value quantification: The value quantification and profiling layer is based on standardized data assets and uses a multi-dimensional coupling algorithm with attenuation and gain mechanisms to calculate the comprehensive value index; an initial benchmark value index is set for the data. Dynamic data attenuation coefficient Normalized value of market access frequency at a certain moment Application effect rating Using default weighting coefficients The comprehensive value index at that moment was calculated. Simultaneously, a supply-side data asset profile (core features: high value, financial and credit sector, real-time updates, completeness 0.92, accuracy 0.95) and a dynamic demand profile for the demand side are constructed. (Core requirements: financial industry data, user credit behavior data, real-time updates, completeness ≥ 0.9, accuracy ≥ 0.9, used for training credit risk assessment models); System verification revealed that the requesting party had added a dimension of credit application amount, causing the profile fit to drop to 0.82 (below the threshold of 0.85). Following preset steps, the update ratio of metadata, the type of change in the request dimension, and the newly added feature tags were integrated to complete the dual-end profile update, improving the fit to 0.93. Furthermore, in collaboration with two similar data providers, relying on a federated learning framework, the global value assessment model was collaboratively optimized without leaving the data domain. A collaborative update cycle of 7 days was set, and after the update, the model's evaluation accuracy improved from 87% to 92%.

[0068] Intelligent Matching and Recommendation: The intelligent matching and recommendation layer calculates the supply-demand matching degree through a hybrid intelligent matching engine; the demand side consists of enterprise users, who are assigned dynamic weight coefficients. The cosine similarity between the data asset value vector and the demand profile vector was calculated. Historical transaction confidence factor Final matching degree A Top-5 recommendation list is generated based on descending matching scores. For the top 3 key features contributing to the positioning of this data asset ("real-time updates" 65%, "accuracy ≥ 0.9" 22%, "financial industry tags" 13%), an explainable description is generated. The client's approval rating for the description is 7 points (below the threshold of 8 points). The system optimizes the description by supplementing the feature descriptions, raising the approval rating to 9 points. Simultaneously, the transaction operation and security protection layers are based on... and Calculate personalized transaction reference prices; base pricing 10,000 yuan, average comprehensive value index of similar data Scarcity data value elasticity coefficient High matching premium coefficient Reference Price 10,000 yuan; combined with market heat adjustment factors Final transaction suggested price The price was set at 10,000 yuan, but the deviation rate from the actual transaction price of 500,000 yuan was 7.56% (higher than the threshold of 5%), triggering the price adjustment calibration module. After calibration, the price deviation rate was reduced to 0.4%.

[0069] Transaction execution and closed-loop optimization: The data transaction settlement is completed based on smart contracts, and feedback scores from the requesting party are collected after the transaction. Score (out of 5), after normalization Combined with matching degree Relative value index Using default weighting coefficients The matching confidence level was calculated. After accumulating 10 similar transactions, the dynamic weight coefficients of the hybrid intelligent matching engine are fine-tuned based on a mean confidence level of 0.89; Adjusted from 0.75 to 0.78 (fine adjustment of 0.03, not exceeding ±0.05). The corresponding adjustment is 0.22; because the matching accuracy improvement rate after the fine-tuning is 3.4% (lower than the threshold of 5%), the weight fine-tuning rules are calibrated and supplemented with enterprise demand side information. With a constraint not exceeding 0.8, the matching accuracy improved by 6.8% after calibration. Simultaneously, the global value assessment model is updated collaboratively every 7 days through a federated learning framework, achieving dynamic optimization across the entire process and continuously improving data mining and transaction efficiency.

[0070] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-source heterogeneous data value mining and intelligent matching system, characterized in that, It includes a data access and governance layer, a value quantification and profiling layer, an intelligent matching and recommendation layer, and a transaction operation and security protection layer, which are linked in sequence. Each layer realizes the bidirectional flow of data and instructions through standardized interfaces. The data access and governance layer is used to adapt to the communication protocols and data formats of multi-source heterogeneous data sources, perform real-time cleaning, entity alignment and knowledge enhancement on the raw data, and output standardized and unified data assets that meet the quality threshold. The value quantification and profiling layer, based on standardized data assets, calculates the dynamic comprehensive value index of data assets through a multi-dimensional coupling algorithm, and constructs a data asset profile on the supply side and a dynamic demand profile on the demand side. The intelligent matching and recommendation layer integrates value matching degree and semantic similarity to calculate the two-way matching degree between supply and demand, and generates a personalized recommendation list with interpretable explanations; The transaction operation and security protection layer achieves dynamic intelligent pricing based on matching results and value index, completes transaction settlement through smart contracts, and relies on privacy computing technology to ensure data flow security. At the same time, it optimizes the model parameters of each preceding layer through a closed-loop feedback mechanism.

2. The multi-source heterogeneous data value mining and intelligent matching system according to claim 1, characterized in that, The value quantification and profiling layer includes a data value multidimensional quantification unit and a dual-end profiling construction unit; The data value multidimensional quantification unit is used to calculate data assets. exist Comprehensive value index of time This index integrates the intrinsic quality of data with its external market value, and introduces a value decay and gain mechanism, as shown in the following formula: ； in, For data assets At the initial moment The benchmark value index is based on preset data collection costs and processing complexity; The value decay coefficient is the static data range. The dynamic data value range is ; for Moment data assets The normalized value of market access frequency, ranging from 0 to 1; for Moment data assets The application effectiveness score is calculated based on user feedback and ranges from 0 to 1. Let be the weighting coefficient, satisfying The default value is It can be dynamically adjusted according to the data application scenario; The dual-end portrait construction unit is based on, on the one hand, On the one hand, data metadata, domain tags, and quality scores are used to generate a multi-dimensional data asset profile for the supply side; on the other hand, natural language processing technology is used to analyze the textual intent input by the demand side, extract core demand dimensions, and construct a structured dynamic demand profile. ; The dual-end profile building unit also includes a profile update module, which updates the corresponding profile when the fit of the data asset profile or the dynamic demand profile of the demand side is less than a set fit threshold. The specific steps are as follows: S01. Confirm the update ratio of metadata, domain tags, and quality scores of data assets as components of profile update c1; S02. Confirm the types of changes in the core needs of the demanders, and use them as components of the profile update c2. S03. Perform a judgment on the need to supplement portrait features and output the judgment result; S04. When the judgment result indicates that supplementation is needed, confirm the newly added portrait feature labels as c3, a component of the portrait update. S05. When the result of S03 is that it is needed, integrate the portrait update components c1, c2 and c3 to complete the update of the corresponding portrait; when the result of S03 is that it is not needed, integrate the portrait update components c1 and c2 to complete the update of the corresponding portrait.

3. The multi-source heterogeneous data value mining and intelligent matching system according to claim 2, characterized in that, The intelligent matching and recommendation layer includes a hybrid intelligent matching engine for calculating supply-side assets. With demand side exist Time-of-fact matching The formula is as follows: ； in, is a cosine similarity function used to calculate the degree of fit between the data asset value vector and the demand profile vector in the feature space, with a value range of 0-1; The confidence factor for historical transactions is based on the relationship between the demand side and the historical transaction confidence factor over the past 90 days. Similar user groups on assets The feedback score is calculated using a weighted average, with a value range of 0-1. For dynamic weighting coefficients, satisfying The range of values ​​for enterprise demand is: The range of values ​​for individual demanders is: It can be dynamically adjusted according to the type of demand.

4. The multi-source heterogeneous data value mining and intelligent matching system according to claim 3, characterized in that, The intelligent matching and recommendation layer also includes an interpretable recommendation unit; The explainable recommendation unit is based on Sort the candidate data assets in descending order to generate a Top-N recommendation list; For each recommended item, a demand profile is calculated. Matching degree of each core dimension partial derivatives ,in Profiling Needs Analyze the top 3 key demand characteristics in terms of contribution across 10 dimensions; The explainability recommendation unit also includes a recommendation description optimization module, which optimizes the explainability description when the user approval rating of the recommendation description is less than a set approval rating threshold. The specific steps are as follows: S11. Confirm the deviation in the contribution calculation of key demand features, as an explanation for optimizing component d1; S12. Confirm the score for the accessibility of the natural language expression, as an explanation of the optimization component d2; S13. Perform supplementary judgment on the description of requirements features and output the judgment result; S14. When the judgment result indicates that supplementation is needed, confirm the newly added feature descriptions as part of the explanation of the optimized component d3. S15. When the result of S13 is required, the optimized components d1, d2 and d3 will be integrated to generate an optimized interpretable description. When the S13 judgment result is that no supplementation is needed, it will explain the integration of optimized components d1 and d2 to generate an optimized interpretable explanation.

5. The multi-source heterogeneous data value mining and intelligent matching system according to claim 4, characterized in that, The transaction operation and security protection layer includes a dynamic intelligent pricing unit for generating data assets. For the demand side Personalized transaction reference price The formula is as follows: ； in, For assets The base price is calculated based on a comprehensive assessment of data collection costs, processing costs, and industry benchmark prices. for The average comprehensive value index of similar data assets at any given time is the average of the top 100 assets in the same field within the platform; The value elasticity coefficient, with a range of values ​​of [value range missing]. For scarce data, the upper limit is used; for general data, the lower limit is used. To match the premium factor, the value range is: The higher the matching degree, the higher the premium ratio.

6. The multi-source heterogeneous data value mining and intelligent matching system according to claim 5, characterized in that, The dynamic intelligent pricing unit also includes a market popularity adjustment factor. This is used to smooth out the impact of market supply and demand fluctuations on prices. The final recommended trading price formula is as follows: ； The market heat adjustment factor is defined as follows: ； in, for Time platform and assets The number of relevant active demanders; for The number of similar valid data assets within the platform; To prevent extremely small positive numbers from being divided by zero, the value is taken as... ; The hyperbolic tangent function is used to make... To avoid drastic price fluctuations; The dynamic intelligent pricing unit also includes a price adjustment calibration module, used to calibrate price adjustment parameters when the deviation between the final suggested transaction price and the actual transaction price exceeds a set deviation threshold. The specific steps are as follows: S21. Confirm market heat adjustment factors The calculated deviation is used as a calibration component e1; S22, Confirming the value elasticity coefficient Matching premium coefficient The adaptation deviation is used as a calibration component e2; S23. Perform supplementary calibration parameter judgment and output the judgment result; S24. When the judgment result indicates that supplementation is required, confirm the newly added calibration parameter item as calibration component e3. S25. When the result of S23 indicates that it is necessary, integrate the calibration components e1, e2 and e3 to complete the calibration of the price adjustment parameters; when the result of S23 indicates that it is not necessary to supplement, integrate the calibration components e1 and e2 to complete the calibration of the price adjustment parameters.

7. The multi-source heterogeneous data value mining and intelligent matching system according to claim 6, characterized in that, The intelligent matching and recommendation layer also includes a matching quality closed-loop optimization unit; After the transaction is completed, the data asset requester will collect the data assets. Actual user feedback rating and normalized to ; Calculate the confidence level of this match. The formula is as follows: ； in, Weighting coefficients, satisfying The default value is ; For every 10 transactions accumulated, the hybrid intelligent matching engine, based on confidence level... For dynamic weighting coefficients Fine-tuning is performed, with the adjustment range not exceeding ±0.05, to achieve online iterative optimization of the recommendation model; The matching quality closed-loop optimization unit also includes a weight fine-tuning calibration module, which is used to calibrate the weight fine-tuning rules when the improvement rate of the matching degree after fine-tuning is less than a set improvement rate threshold. The specific steps are as follows: S31. Confirm Confidence Level The calculated deviation is used as calibration component f1; S32. Confirm dynamic weighting coefficients The fine-tuning amplitude deviation is used as a calibration component f2; S33. Perform fine-tuning rule supplementation judgment and output the judgment result; S34. When the judgment result indicates that supplementation is needed, confirm the newly added fine-tuning constraints as calibration component f3. S35. When the result of S33 indicates that it is necessary, integrate the calibration components f1, f2 and f3 to generate the calibrated weight fine-tuning rules; when the result of S33 indicates that it is not necessary to supplement, integrate the calibration components f1 and f2 to generate the calibrated weight fine-tuning rules.

8. The multi-source heterogeneous data value mining and intelligent matching system according to claim 7, characterized in that, The value quantification and profiling layer also includes a value collaborative evaluation unit, which is deployed within the federated learning framework of the transaction operation and security protection layer. Each data provider calculates the gradient update information of the comprehensive value index model locally using its own data characteristics, and encrypts the gradient using a homomorphic encryption algorithm. The encrypted gradient information is uploaded to the federated aggregation server, which uses a secret-sharing secure aggregation algorithm to aggregate the gradients and update the parameters of the global value assessment model. Global model parameters are encrypted and distributed to all suppliers, ensuring that data remains within the domain and models are jointly optimized, thereby improving the objectivity and accuracy of value assessment, and the collaborative update cycle does not exceed 7 days; The value collaborative evaluation unit also includes a collaborative update verification module, which is used to verify the collaborative update process when the value evaluation accuracy after the global model parameter update is less than a set accuracy threshold. The specific steps are as follows: S41. Confirm the encryption deviation of the gradient update information of each supplier as a verification component g1; S42. Confirm the gradient aggregation deviation of the federated aggregation server as a verification component g2; S43. Perform supplementary judgments in the update process and output the judgment results; S44. When the judgment result indicates that supplementation is required, confirm the newly added process verification node as a verification component element g3. S45. When the result of S43 indicates that it is required, the verification components g1, g2 and g3 are integrated to complete the verification and correction of the collaborative update process; when the result of S43 indicates that it is not required to supplement, the verification components g1 and g2 are integrated to complete the verification and correction of the collaborative update process.

9. A multi-source heterogeneous data value mining and intelligent matching system according to claim 8, characterized in that, The data access and governance layer includes a cloud-edge collaborative processing unit and a four-dimensional quality assessment unit; The cloud-edge collaborative processing unit is responsible for receiving multi-source data in real time at the edge, performing protocol parsing and preliminary filtering, and for a three-layer progressive governance approach at the cloud, which includes semantic alignment, dimension alignment, and entity alignment. The four-dimensional quality assessment unit scores the data from four dimensions: completeness, accuracy, consistency, and timeliness, resulting in a quality score. ; Set quality threshold ,when When this happens, the system automatically triggers a data replenishment or secondary cleaning process until the quality requirements are met. The four-dimensional quality assessment unit also includes a quality control optimization module, which is used to optimize the quality control process when the quality compliance rate after data supplementation or secondary cleaning is less than a set compliance rate threshold. The specific steps are as follows: S51. Confirm the scoring deviations of each dimension of the four-dimensional quality assessment and use them as optimization component h1; S52. Confirm the efficiency of the data supplementation or secondary cleaning process as an optimization component h2; S53. Perform supplementary judgments on the control process and output the judgment results; S54. When the judgment result indicates that supplementation is needed, confirm the newly added quality control node as an optimization component h3. S55. When the result of S53 is that it is necessary, the optimized components h1, h2 and h3 will be integrated to generate an optimized quality control process; when the result of S53 is that it is not necessary to supplement, the optimized components h1 and h2 will be integrated to generate an optimized quality control process.

10. A method for value mining and intelligent matching of multi-source heterogeneous data, applied to a multi-source heterogeneous data value mining and intelligent matching system as described in claim 9, characterized in that, Includes the following steps: Data standardization and governance: Through the cloud-edge collaborative architecture of data access and governance layer, it adapts to the protocols and formats of multi-source heterogeneous data, and outputs standardized and unified data assets through three-layer alignment and four-dimensional quality assessment; If the quality score fails to meet the standard, a data supplementation or secondary cleaning process will be triggered until the quality requirements are met. Dual-end profiling and value quantification: The value quantification and profiling layer is based on standardized data assets and calculates the comprehensive value index through a multi-dimensional coupling algorithm with attenuation and gain mechanisms. They also constructed supply-side data asset profiles and demand-side dynamic demand profiles, respectively. ; If the profile does not meet the requirements, update the corresponding profile according to the preset steps; at the same time, through the federated learning framework, coordinate with various suppliers to optimize the value assessment model. Intelligent Matching and Recommendation: The intelligent matching and recommendation layer calculates the supply-demand matching degree through a hybrid intelligent matching engine. Generate a Top-N recommendation list and its interpretability description; If the recommended explanation does not meet the acceptance criteria, optimize the explanatory details; simultaneously, transaction operations and security protection layers are based on... and The final suggested trading price is generated by combining market sentiment adjustment factors. If the price deviation is too large, calibrate the price adjustment parameters; Transaction execution and closed-loop optimization: Transaction settlement is completed based on smart contracts, and user feedback ratings are collected after the transaction to calculate the matching confidence level. The results are fed back to the intelligent matching engine to fine-tune the weight coefficients; if the weight fine-tuning effect is not good, the fine-tuning rules are calibrated; at the same time, the value evaluation model is updated collaboratively through the federated learning framework to achieve dynamic optimization throughout the entire process.

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