Integrated dynamic cataloguing and trading system based on medical health data capitalization

Abstract

The invention relates to an integrated dynamic cataloguing and trading system based on medical health data capitalization, and belongs to the technical field of medical health data capitalization and trading. The method comprises the following steps: determining an ownership subject and a splitting rule through a medical health data capitalization element definition unit, and constructing a capitalization framework comprising ownership, value and compliance layers; the dynamic cataloguing unit analyzes and marks the multi-source medical health data, generates a dynamic cataloguing list with feature tags, and iterates along with scene and data changes; the asset value linkage transaction unit evaluates the asset value through a multi-dimensional algorithm, generates a pricing interval, and performs profit sharing and transaction track evidence storage based on an intelligent contract; and the compliance verification unit verifies ownership splitting, privacy protection, transaction qualification and profit sharing compliance in a whole process, marks risk points and generates a report. Integrated management of medical and health care data from capitalization conversion and dynamic cataloguing to compliance transaction is realized, and the problems that ownership is fuzzy, cataloguing is static, value is difficult to quantify, transaction is not compliant and the like are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of medical and health data assetization and transaction, and particularly relates to an integrated dynamic cataloging and transaction system based on medical and health data assetization. BACKGROUND

[0002] In the field of medical and health data assetization and transaction, the traditional medical and health data management and transaction mode has obvious limitations. On the operation, it depends on manual definition of data ownership and evaluation of data value, and needs manual arrangement of data dimensions and profit sharing rules, which is tedious and time-consuming. Due to the difference in professional level of personnel, the consistency of asset definition is poor. The data processing and transaction scheme is mostly a fixed template, which cannot be dynamically adjusted according to the type of medical and health data and the application scene, and the matching of data value in different scenes is insufficient, and the asset transaction is weak in pertinence. In the data management and asset report generation link, medical and health data are stored in different systems, and need to be manually summarized and analyzed, which is low in efficiency and easy to make mistakes, and the report lacks visualization of asset value, ownership split, etc., which is not convenient for quickly grasping the asset dynamics and compliance state. SUMMARY

[0003] To solve the above problems in the prior art, the application provides an integrated dynamic cataloging and transaction system based on medical and health data assetization, The object of the application can be achieved by the following technical scheme: including a medical and health data assetization element definition unit, a dynamic cataloging unit, an asset value linkage transaction unit and a compliance verification unit; The medical and health data assetization element definition unit generates an assetization constituting element framework and defines data ownership split rules by mapping the corresponding relationship between data sources and ownership subjects based on the asset value dimension through a dynamic linkage module; The dynamic cataloging unit dynamically converts the medical and health data in the assetization constituting element framework into tradable assets, synchronously accesses and processes real-time health monitoring data and electronic medical record data to generate medical texts; extracts clinical information in the medical texts through a data analysis and labeling module, identifies trend characteristics of assetizable clinical index data in the clinical information, and labels asset feature labels for the assetizable clinical index data; automatically adjusts the cataloging dimension and synchronously updates the asset feature labels through a dynamic cataloging and labeling iteration module to generate a dynamic cataloging list; The asset value linkage transaction unit is used for correlating the cataloging results and transaction values, taking the asset feature labels as input, obtaining real-time asset value scores through an asset evaluation module, automatically triggering a transaction process through an intelligent contract module based on the real-time asset value scores and the qualifications of both parties, including presetting a profit sharing ratio according to the ownership split rules, generating a pricing interval according to the value scores, and synchronously recording asset flow tracks; The compliance verification unit splits the asset characteristic label output by the dynamic cataloging unit based on the data right split rule to obtain the data source, the right subject and the corresponding right range; for the asset value linkage transaction unit process, the qualifications and income distribution of the transaction parties are verified; after the verification is completed, the equity risk points are marked, and the asset equity compliance report is generated.

[0004] Specifically, the definition mechanism of the asset value dimension is used to construct a value evaluation benchmark in the system, and the definition process is: connecting medical information systems and health management platforms, collecting data clinical application scene labels; extracting core value indicators from clinical needs based on scene characteristics, including drug research and development scene extracting clinical relevance, data integrity, and case scarcity; setting quantitative thresholds for each dimension; and assigning dynamic weights to the dimensions of different scenes, storing the dimensions, thresholds and weights into the basic rule library, and finally obtaining the asset value evaluation benchmark.

[0005] Specifically, the data source is the producer or collector of medical and health data, including medical institutions and equipment manufacturers; the right subject is the subject who has legal rights to the data, including patients and hospitals; the layered right split rule is defined, including: splitting the medical and health data rights commonly owned by multiple subjects according to right type and scene range, and clearly defining the right boundary of the right subject. Specifically, the basic rule library is the core carrier of the asset value evaluation benchmark, which is used to structurally store clinical application scene labels, core value indicators, quantitative thresholds and scenario-based dynamic weight key elements; through the API interface, real-time data calling is provided to the medical and health data assetization element definition unit, the dynamic cataloging unit and the asset value linkage transaction unit.

[0006] Specifically, the dynamic linkage module is used to generate an assetization constituent element framework and define data right split rules, interface with hospital electronic medical record systems, intelligent equipment manufacturer server data terminals, collect source subject identification and collection scenes, form a source subject, data type, and collection scene association table; match the legal right subject from the association table and write the association information into the blockchain storage unit; based on the association information, the assetization constituent element framework including the right subject layer, the value layer and the compliance layer is constructed; based on the assetization constituent element framework, the right split rule is defined by layering according to the right type, combined with the right subject, the data source and the medical data right specification, the data rights are split by layering according to the core rights and derivative rights; the use right is split according to the application scene; the income right is split according to the contribution of the right subject.

[0007] Specifically, the data parsing and labeling module is used to extract clinical information from the medical text. The extraction process is as follows: receiving the medical text through a multi-protocol interface, converting device manufacturer-defined fields into system standard fields, and performing numerical verification, marking fields exceeding the clinically reasonable value range as abnormal; extracting entity information from unstructured text data and mapping it to standard medical terminology codes; extracting lesion feature parameters from image data and converting them into structured descriptive text; performing field completion verification on the parsed structured information based on the asset-based constituent element framework; extracting trend features of clinical indicators using a sliding window algorithm and calculating the fluctuation amplitude and rate of change per unit time; comparing the parsing results with preset thresholds, and labeling abnormal states when clinical indicator values ​​exceed preset ranges.

[0008] Specifically, when the data source and ownership entity are consistent, ownership compliance tags are marked through the data parsing and tagging module; when privacy-sensitive fields in unstructured text of the medical text are anonymized and the generated public content does not contain preset privacy-sensitive protection content, corresponding privacy protection tags are marked through the data parsing and tagging module; the generated tag combination is hash-bound with the unique data identifier, synchronously stored in the distributed database, and finally pushed to the dynamic cataloging and tag iteration module.

[0009] Specifically, the dynamic cataloging and tag iteration module is used to automatically process the dynamic changes of healthcare data assets. This includes: capturing data update timestamps and feature fields (including blood glucose levels and medication records) in real time via the API interface of the data acquisition terminal, and calculating the update frequency and feature fluctuation amplitude per unit time; filtering the collected signals for validity, verifying whether the update frequency duration and outlier percentage meet preset thresholds; calling the verification interface of the assetization component framework to verify whether newly added data contains the ownership identifier and basic clinical tag fields required by the framework; executing operations on the basic rule base according to the principles of addition, deletion, modification, and combination for the filtered valid signals; and connecting to the healthcare data asset tag rule base through the tag mapping engine, comparing data attribute values ​​with dimension thresholds to generate tag combinations, binding them with unique data IDs, and finally generating a dynamic cataloging list.

[0010] Specifically, the principles of adding, deleting, modifying, and merging are used to automatically perform cataloging dimension operations. The specific execution process is as follows: When adding a dimension, the engine calls the gene data processing interface to obtain the site information output by the sequencer, and automatically creates gene site coverage, mutation type correlation fields, and calculation logic; when merging dimensions, it calculates the daily and weekly average blood pressure fields in the chronic disease data. If the correlation exceeds a preset threshold, the two fields are merged into the blood pressure periodic average, and the original calculation log is retained; when adjusting dimension weights, the dimension weight values ​​are automatically updated after detecting changes in scene labels.

[0011] Specifically, the asset assessment module is used to output an asset value score, receive the asset feature tags output by the dynamic cataloging and tag iteration module, extract the completeness, timeliness, and clinical relevance assessment dimensions through a field parser, apply an interval mapping algorithm to each dimension, and set a base score according to the data attribute compliance, load the dynamic linkage module of the medical and health data assetization element definition unit, call the interface of the basic rule base, and allocate the weight of each dimension according to the current application scenario, convert the privacy protection level and ownership compliance status into dynamic coefficients, monitor the update signal of asset feature tags in real time, trigger recalculation, call the outlier correction mechanism to handle dimension score anomalies, and finally output the asset value score.

[0012] Specifically, the outlier correction mechanism works as follows: when the difference between the score of a single dimension and the average score of other dimensions exceeds a preset threshold, a dimension correlation check is initiated; the correlation between the dimension and the core dimension of the scenario is calculated; if the correlation exceeds the preset threshold, a weighted correction is performed; if the correlation is lower than the preset threshold, the original score remains unchanged; the correction result is updated to the asset evaluation module in real time.

[0013] Specifically, the smart contract module is used to automatically trigger the medical and health data asset transaction process. The process is as follows: deploy a smart contract instance based on a consortium blockchain architecture, connect to the API interface of the dynamic cataloging and tagging iteration module and the off-chain database of the compliance verification unit through an off-chain oracle, and synchronize the asset feature tags, digital certificates of both parties to the transaction, and compliance verification results in real time; when the condition judgment logic built into the contract detects that the preset conditions are met, the transaction process is automatically triggered, and an immutable transaction start block is generated; call the profit-sharing interface of the basic rule base to extract the ownership subject and the corresponding proportion and write it into on-chain storage; receive the asset value score pushed by the asset evaluation module, generate a pricing range through a pricing algorithm and lock it on-chain; automatically capture timestamps, account addresses, and data ID information at the transaction node; and generate on-chain transaction logs.

[0014] Specifically, the pricing algorithm is used to generate a dynamic pricing range based on the asset value score and scenario characteristics, call the basic rule base stored on the chain, extract the benchmark price corresponding to the current application scenario as the pricing calculation base, receive the asset value score output by the asset evaluation module, and perform tiered adjustments according to the preset range; call the transaction records of the same type of data in the blockchain distributed ledger through the smart contract module, calculate the historical average transaction price, and perform secondary calibration of the tiered range according to the preset volatility coefficient; confirm the calibrated pricing range through the consensus of the consortium chain nodes, preset the lock-in time, and automatically trigger recalculation when the timeout expires.

[0015] As a preferred technical solution of the present invention, the asset value linkage transaction unit includes an asset evaluation module, a pricing algorithm module, a smart contract module, and a transaction trajectory storage module. The asset evaluation module receives the asset feature tags output by the dynamic cataloging unit and generates a real-time asset value score through multi-dimensional quantitative calculation and outlier correction. The pricing algorithm module generates a dynamic pricing range through a tiered adjustment algorithm and a historical average price calibration algorithm. The smart contract module synchronizes the asset feature tags of the dynamic cataloging unit, the compliance status of the compliance verification unit, and the digital certificate verification results of both parties to the transaction through an off-chain oracle. It automatically triggers the transaction process based on preset logical conditions (compliance passed, qualification met, value score met). It calculates the profit-sharing amount for each entity (patient, medical institution, platform) according to the ownership splitting rules stored on-chain and executes the on-chain transfer. The transaction trajectory storage module captures key node information of the entire transaction process, generates a unique transaction information hash value through hash encryption technology, and writes it into the blockchain distributed ledger in timestamp order, forming a chain storage structure that associates the hash of the previous block with the hash of the current block, thus constructing an immutable transaction trajectory record.

[0016] Specifically, the compliance verification unit is used to verify the compliance of the transaction process of the asset feature tags and the asset value linkage transaction unit: for the asset feature tags, it verifies the compliance of ownership splitting, including comparing the ownership subject with the hierarchical ownership splitting rules; it verifies the compliance of privacy protection, checking whether the corresponding protection measures meet the standards based on the privacy protection level in the tag, and checking the encrypted logs and access permission records of data transmission and storage; for the asset value linkage transaction unit process, it verifies the qualifications of both parties to the transaction, including verifying whether the buyer has the qualifications matching the data application scenario and whether the seller has the corresponding data transaction permissions; it verifies the compliance of profit sharing, comparing whether the profit sharing ratio preset by the smart contract module is consistent with the agreed ratio in the hierarchical ownership splitting rules, and checking whether the profit sharing object is the legal ownership subject marked in the tag; it marks abnormal issues as equity risk points and generates an asset equity compliance report.

[0017] The beneficial effects of this invention are as follows: (1) By setting up a closed-loop linkage structure with medical and health data assetization element definition unit and dynamic cataloging unit, the automatic splitting of medical and health data ownership and dynamic adaptation of cataloging dimensions are realized: replacing the traditional mode of manually defining ownership and manually sorting data dimensions, reducing the problem of poor consistency of asset definition caused by differences in the professional level of personnel; at the same time, the dynamic cataloging unit adopts medical entity recognition algorithm and addition, deletion, modification and combination rules to effectively filter out redundant interference in multi-source medical and health data, accurately extract effective asset feature tags, and make the asset definition and value dimension match more accurately, providing stable and reliable technical support for the transformation of medical and health data from resources to assets; (2) By setting up an asset value linkage transaction unit and a built-in scenario-based basic weight library, the automated pricing and compliant circulation of medical and health data asset transactions have been realized: The asset value linkage transaction unit, based on the asset feature tags output by the dynamic cataloging unit, automatically generates value scores and pricing ranges matching the scenario through interval mapping algorithms and pricing algorithms, replacing the traditional manual negotiation pricing mode and reducing subjective bias; At the same time, the smart contract module, based on the consortium blockchain architecture, records key links such as profit sharing and transaction trajectory on the blockchain, effectively avoiding the risks of data tampering and profit sharing disputes. Combined with the real-time qualification verification and risk labeling of the compliance verification unit, it ensures that the transaction process is always synchronized with regulatory requirements; Combined with historical transaction data and asset value trend analysis, the rationality of pricing and profit sharing has been further improved, which helps the efficient and compliant circulation of medical and health data elements. Attached Figure Description

[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0019] Figure 1 This is a system architecture diagram of an integrated dynamic cataloging and transaction system based on the assetization of medical and health data. Detailed Implementation

[0020] Please see Figure 1 A unified dynamic cataloging and trading system based on the assetization of medical and health data; This includes a medical and health data assetization element definition unit, a dynamic cataloging unit, an asset value linkage and transaction unit, and a compliance verification unit; The medical and health data assetization element definition unit generates an assetization component framework and defines data ownership splitting rules by mapping the correspondence between data sources and ownership subjects through a dynamic linkage module based on the asset value dimension. The dynamic cataloging unit dynamically transforms the healthcare data in the assetization framework into tradable assets, synchronously accesses and processes real-time health monitoring data and electronic medical record data to generate medical text; it extracts clinical information from the medical text through a data parsing and tagging module, identifies the trend characteristics of the assetizable clinical indicator data in the clinical information, and labels the assetizable clinical indicator data with asset feature tags; and it automatically adjusts the cataloging dimensions and synchronously updates the asset feature tags through a dynamic cataloging and tagging iteration module to generate a dynamic cataloging list. The asset value linkage transaction unit is used to associate the cataloging results with the transaction value. It takes the asset feature tags as input and obtains a real-time asset value score through the asset evaluation module. Based on the real-time asset value score and the qualifications of the two parties to the transaction, the transaction process is automatically triggered through the smart contract module, including preset profit sharing ratio according to the ownership split rules, generating a pricing range according to the value score, and synchronously recording the asset transfer trajectory. The compliance verification unit performs ownership separation on the asset feature tags output by the dynamic cataloging unit based on the data ownership separation rules to obtain the data source, the ownership subject, and the corresponding scope of rights; for the asset value linkage transaction unit process, it verifies the qualifications of the two parties to the transaction and the profit sharing; after verification, it marks the equity risk points and generates an asset equity compliance report.

[0021] Specifically, the data source is the party that generates or collects medical and health data, including medical institutions and equipment manufacturers; the ownership subject is the subject that has legal rights to the data, including patients and hospitals; the hierarchical ownership splitting rules are defined, including: splitting medical and health data rights shared by multiple subjects according to the type of rights and the scope of the scenario, and clarifying the rights boundaries of the ownership subjects; Specifically, the basic rule base is the core carrier of the asset value assessment benchmark, used to structure and store clinical application scenario tags, core value indicators, quantitative thresholds, and key elements of scenario-based dynamic weights; and provides real-time data access to the medical and health data asset element definition unit, the dynamic cataloging unit, and the asset value linkage transaction unit through API interfaces.

[0022] Specifically, the dynamic linkage module is used to generate an assetization component framework and define data ownership splitting rules. It connects to the hospital's electronic medical record system and the server data terminal of the smart device manufacturer, collects the source entity identifier and collection scenario, and forms a correlation table of source entity, data type, and collection scenario. It matches the legitimate ownership entity from the correlation table and writes the correlation information into the blockchain storage unit. Based on the correlation information, it constructs the assetization component framework, which includes an ownership layer, a value layer, and a compliance layer. Based on the assetization component framework, it defines ownership splitting rules and layers the data rights according to the right type. Combining the ownership entity, the data source, and the medical data ownership confirmation standard, it splits the data rights into core rights and derivative rights. It splits the right of use according to the application scenario and the right of income according to the contribution of the ownership entity.

[0023] In this embodiment, the dynamic linkage module connects to the hospital's HIS system and the server of a home blood glucose meter manufacturer. It collects the source entity identifier and the collection scenario. The hospital scenario involves diabetes outpatient treatment, and data types include electronic medical records, fundus images, and biochemical test reports. The manufacturer's scenario involves home blood glucose monitoring, and data types include daily fasting / postprandial blood glucose values. The module matches the legal ownership entities: patient Li, the tertiary hospital, and the blood glucose meter manufacturer. The associated information is used to generate a unique identifier via SHA-256 hash calculation. This identifier is then bound to the unique data ID and written into the consortium blockchain's notarization unit. Nodes include the hospital, the manufacturer, and local health commission regulatory nodes. Notarization requires consensus from at least 2 / 3 of the nodes.

[0024] Specifically, the data parsing and labeling module is used to extract clinical information from the medical text. The extraction process is as follows: receiving the medical text through a multi-protocol interface, converting device manufacturer-defined fields into system standard fields, and performing numerical verification, marking fields exceeding the clinically reasonable value range as abnormal; extracting entity information from unstructured text data and mapping it to standard medical terminology codes; extracting lesion feature parameters from image data and converting them into structured descriptive text; performing field completion verification on the parsed structured information based on the asset-based constituent element framework; extracting trend features of clinical indicators using a sliding window algorithm and calculating the fluctuation amplitude and rate of change per unit time; comparing the parsing results with preset thresholds, and labeling abnormal states when clinical indicator values ​​exceed preset ranges.

[0025] Specifically, when the data source and ownership entity are consistent, ownership compliance tags are marked through the data parsing and tagging module; when privacy-sensitive fields in unstructured text of the medical text are anonymized and the generated public content does not contain preset privacy-sensitive protection content, corresponding privacy protection tags are marked through the data parsing and tagging module; the generated tag combination is hash-bound with the unique data identifier, synchronously stored in the distributed database, and finally pushed to the dynamic cataloging and tag iteration module.

[0026] Specifically, the dynamic cataloging and tag iteration module is used to automatically process the dynamic changes of healthcare data assets. This includes: capturing data update timestamps and feature fields (including blood glucose levels and medication records) in real time via the API interface of the data acquisition terminal, and calculating the update frequency and feature fluctuation amplitude per unit time; filtering the collected signals for validity, verifying whether the update frequency duration and outlier percentage meet preset thresholds; calling the verification interface of the assetization component framework to verify whether newly added data contains the ownership identifier and basic clinical tag fields required by the framework; executing operations on the basic rule base according to the principles of addition, deletion, modification, and combination for the filtered valid signals; and connecting to the healthcare data asset tag rule base through the tag mapping engine, comparing data attribute values ​​with dimension thresholds to generate tag combinations, binding them with unique data IDs, and finally generating a dynamic cataloging list.

[0027] Specifically, the principles of adding, deleting, modifying, and merging are used to automatically perform cataloging dimension operations. The specific execution process is as follows: When adding a dimension, the engine calls the gene data processing interface to obtain the site information output by the sequencer, and automatically creates gene site coverage, mutation type correlation fields, and calculation logic; when merging dimensions, it calculates the daily and weekly average blood pressure fields in the chronic disease data. If the correlation exceeds a preset threshold, the two fields are merged into the blood pressure periodic average, and the original calculation log is retained; when adjusting dimension weights, the dimension weight values ​​are automatically updated after detecting changes in scene labels.

[0028] Specifically, the asset assessment module is used to output an asset value score, receive the asset feature tags output by the dynamic cataloging and tag iteration module, extract the completeness, timeliness, and clinical relevance assessment dimensions through a field parser, apply an interval mapping algorithm to each dimension, and set a base score according to the data attribute compliance, load the dynamic linkage module of the medical and health data assetization element definition unit, call the interface of the basic rule base, and allocate the weight of each dimension according to the current application scenario, convert the privacy protection level and ownership compliance status into dynamic coefficients, monitor the update signal of asset feature tags in real time, trigger recalculation, call the outlier correction mechanism to handle dimension score anomalies, and finally output the asset value score.

[0029] Specifically, the outlier correction mechanism works as follows: when the difference between the score of a single dimension and the average score of other dimensions exceeds a preset threshold, a dimension correlation check is initiated; the correlation between the dimension and the core dimension of the scenario is calculated; if the correlation exceeds the preset threshold, a weighted correction is performed; if the correlation is lower than the preset threshold, the original score remains unchanged; the correction result is updated to the asset evaluation module in real time.

[0030] Specifically, the smart contract module is used to automatically trigger the medical and health data asset transaction process. The process is as follows: deploy a smart contract instance based on a consortium blockchain architecture, connect to the API interface of the dynamic cataloging and tagging iteration module and the off-chain database of the compliance verification unit through an off-chain oracle, and synchronize the asset feature tags, digital certificates of both parties to the transaction, and compliance verification results in real time; when the condition judgment logic built into the contract detects that the preset conditions are met, the transaction process is automatically triggered, and an immutable transaction start block is generated; call the profit-sharing interface of the basic rule base to extract the ownership subject and the corresponding proportion and write it into on-chain storage; receive the asset value score pushed by the asset evaluation module, generate a pricing range through a pricing algorithm and lock it on-chain; automatically capture timestamps, account addresses, and data ID information at the transaction node; and generate on-chain transaction logs.

[0031] In this embodiment, the cataloging of multi-source data (including pathology reports, gene sequencing data, and chemotherapy records) of Zhang, a lung cancer patient at a certain oncology hospital, is taken as an example: Data update monitoring: On June 15th, blood routine data of patient Zhang was retrieved via API interface, revealing that the white blood cell count had increased from 4.5 × 10⁻⁶. 9 / L decreased to 2.8×10 9 / L (the manifestation of myelosuppression after chemotherapy), the data update frequency increased from once a week to twice a day; Validity screening: Verify that the update frequency lasts for 48 hours (preset threshold ≥ 24 hours) and the percentage of outliers is 2% (preset threshold ≤ 5%), and it is determined to be a valid signal; call the asset composition element verification interface to confirm that the newly added blood routine data contains the ownership identifier (Zhang's ID) and the basic fields of clinical label (chemotherapy for lung adenocarcinoma); Cataloging Dimension Adjustment: Following the principles of addition, deletion, modification, and merger, a new dimension for monitoring chemotherapy toxic side effects has been added. A field index has been created: White blood cell count fluctuation range = (previous value - current value) / previous value × 100%. The weight of this dimension is set to 35%, while the weight of the gene target dimension has been simultaneously reduced to 30%. Label iteration: By connecting to the tumor data label rule base through the label mapping engine, the white blood cell count of 2.8 × 10⁻⁶ was compared. 9 / L and the threshold for chemotherapy toxic side effects (≤3.0×10 9 The data is classified as Grade II myelosuppression. A Grade II myelosuppression tag is added, and the dynamic catalog list is updated, including data ID, tag combination, catalog dimensions (tumor markers, gene targets, toxic side effect indicators) and weights (35%, 30%, 35%). This information is then simultaneously pushed to the asset value linkage trading unit.

[0032] Specifically, the pricing algorithm is used to generate a dynamic pricing range based on the asset value score and scenario characteristics, call the basic rule base stored on the chain, extract the benchmark price corresponding to the current application scenario as the pricing calculation base, receive the asset value score output by the asset evaluation module, and perform tiered adjustments according to the preset range; call the transaction records of the same type of data in the blockchain distributed ledger through the smart contract module, calculate the historical average transaction price, and perform secondary calibration of the tiered range according to the preset volatility coefficient; confirm the calibrated pricing range through the consensus of the consortium chain nodes, preset the lock-in time, and automatically trigger recalculation when the timeout expires.

[0033] As a preferred technical solution of the present invention, the asset value linkage transaction unit includes an asset evaluation module, a pricing algorithm module, a smart contract module, and a transaction trajectory storage module. The asset evaluation module receives the asset feature tags output by the dynamic cataloging unit and generates a real-time asset value score through multi-dimensional quantitative calculation and outlier correction. The pricing algorithm module generates a dynamic pricing range through a tiered adjustment algorithm and a historical average price calibration algorithm. The smart contract module synchronizes the asset feature tags of the dynamic cataloging unit, the compliance status of the compliance verification unit, and the digital certificate verification results of both parties to the transaction through an off-chain oracle. It automatically triggers the transaction process based on preset logical conditions (compliance passed, qualification met, value score met). It calculates the profit-sharing amount for each entity (patient, medical institution, platform) according to the ownership splitting rules stored on-chain and executes the on-chain transfer. The transaction trajectory storage module captures key node information of the entire transaction process, generates a unique transaction information hash value through hash encryption technology, and writes it into the blockchain distributed ledger in timestamp order, forming a chain storage structure that associates the hash of the previous block with the hash of the current block, thus constructing an immutable transaction trajectory record.

[0034] Specifically, the calculation formula for the multi-dimensional quantization is as follows: Where f() is the interval mapping function, Sᵢ is the single-dimensional basic score, and W... i For the dimension weights of the application scenario, T is the weighted total score, and K is the dynamic coefficient. When the dimensional attribute compliance is Aᵢ, the corresponding Sᵢ is directly output through interval matching, combined with the dynamic coefficient K and the application scenario dimension weight W. i Calculate the weighted total score.

[0035] Specifically, the algorithm related to outlier correction is as follows: Where Sⱼ' is the score corrected for the anomaly dimension, and W core As the weight of the core dimension of the scene, T final To score the final asset value.

[0036] Specifically, the formula for calculating the historical average transaction price is as follows: Among them, P avg P represents the historical average price of similar data over the past 30 days. k The actual transaction amount of the k-th historical transaction (k=1,2,...N, where N is the total number of transactions in the last 30 days), and N is the total number of transactions of the same type in the last 30 days.

[0037] Specifically, the formula for calculating the profit-sharing amount for the ownership entity is as follows: Among them, M j Let P be the profit share of the j-th ownership entity. trade R represents the actual transaction amount. j Let be the profit-sharing ratio of the j-th ownership entity.

[0038] In this embodiment, the tag combination <ownership compliance, high privacy, SMAI type, SMN1 gene homozygous deletion, muscle strength score grade 3, nusinersen treatment for 3 months> output by the dynamic cataloging unit is received, and the evaluation dimensions and attribute compliance are extracted: Basic score calculation: In the drug development scenario, clinical relevance A1=92%, the patient is SMA type I, matching the pharmaceutical company's SMN I gene drug development needs, corresponding to S1=10 points; data completeness A2=88% (including 6 months of treatment follow-up data, missing 1 muscle strength assessment, corresponding to S2=8 points); case scarcity A3=95% (the incidence of SMA type I is about 1 / 10000, corresponding to S3=10 points); Weighted total score calculation: Scene weights W1=40%, W2=30%, W3=30%, dynamic coefficient K=1.05, then the uncorrected weighted total score T=9.85; Outlier Correction: Data integrity dimension score S2 = 3 (due to missing 3 follow-up data, the difference between the average score of other dimensions and the score of 10 is 7 ≥ 4, which is considered outlier). Calculate its cosine similarity with the core dimension's clinical relevance = 88% (≥ 80%, preset threshold), and correct the score S2. ’ =3=0.96 points; Final score output: Adjusted weighted total score T final ≈7.65, after being multiplied 10 times, the final asset value score is 77 points (out of 100).

[0039] Specifically, the compliance verification unit is used to verify the compliance of the transaction process of the asset feature tags and the asset value linkage transaction unit: for the asset feature tags, it verifies the compliance of ownership splitting, including comparing the ownership subject with the hierarchical ownership splitting rules; it verifies the compliance of privacy protection, checking whether the corresponding protection measures meet the standards based on the privacy protection level in the tag, and checking the encrypted logs and access permission records of data transmission and storage; for the asset value linkage transaction unit process, it verifies the qualifications of both parties to the transaction, including verifying whether the buyer has the qualifications matching the data application scenario and whether the seller has the corresponding data transaction permissions; it verifies the compliance of profit sharing, comparing whether the profit sharing ratio preset by the smart contract module is consistent with the agreed ratio in the hierarchical ownership splitting rules, and checking whether the profit sharing object is the legal ownership subject marked in the tag; it marks abnormal issues as equity risk points and generates an asset equity compliance report.

[0040] In this embodiment, taking a transaction between a medical examination institution and a health management company involving the medical examination data (including electrocardiogram, blood lipid report, and blood pressure monitoring records) of a patient with coronary heart disease, Mr. Zhao, as an example, the compliance verification unit implements the process according to the asset-side verification, transaction-side verification, and risk labeling procedures: Asset Feature Tag Compliance Verification: Ownership Splitting Verification: The blockchain hierarchical ownership splitting rule base was invoked to query the list of legal owners (patients, medical examination institutions) corresponding to the cardiovascular disease physical examination data. This list was compared with the ownership entity list (Zhao, medical examination institution) in the asset feature tag to confirm that there were no additional illegal entities. Zhao's electronic signature was verified to be consistent with the on-chain evidence, thus determining that the ownership splitting was compliant. Privacy Protection Verification: The tag indicated a privacy protection level of "Medium" (excluding genes and core medical records). This was verified against compliance standards. The data transmission log showed compliance with medium privacy transmission requirements, and the access permission list showed that only 3 authorized administrators could view the original data (the access period was 15 days, which met the medium privacy storage requirements). The storage access logs for the past 7 days were captured, and there were no unauthorized access records, thus determining that privacy protection was compliant.

[0041] Transaction process compliance verification: Verification of the qualifications of both parties: Connecting to the API of the National Medical Data Transaction Filing Platform to verify the qualification documents of the buyer's health management company and confirm that it has the qualifications to apply data in the cardiovascular disease risk assessment scenario; verifying the scope of authority of the seller's physical examination institution and matching it with the current data type of the transaction to determine the compliance of the qualifications of both parties; Compliance verification of revenue sharing: Through the query interface on the smart contract chain, extract the preset revenue sharing ratio (patient Zhao 30%, physical examination institution 25%, platform 45%) and compare it with the revenue sharing range of physical examination data in the hierarchical ownership splitting rules (patient 25%-35%, institution 20%-30%, platform 35%-50%). The ratio is within the range.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A unified dynamic cataloging and trading system based on medical and health data assetization, characterized in that, This includes a medical and health data assetization element definition unit, a dynamic cataloging unit, an asset value linkage and transaction unit, and a compliance verification unit; The medical and health data assetization element definition unit generates an assetization component framework and defines data ownership splitting rules by mapping the correspondence between data sources and ownership subjects through a dynamic linkage module based on the asset value dimension. The dynamic cataloging unit dynamically transforms the medical and health data in the assetization component framework into tradable assets, synchronously accesses and processes real-time health monitoring data and electronic medical record data to generate medical text; and extracts clinical information from the medical text through the data parsing and tagging module, identifies the trend characteristics of the assetizable clinical indicator data in the clinical information, and tags the assetizable clinical indicator data with asset feature labels. The dynamic cataloging and tag iteration module automatically adjusts the cataloging dimensions and synchronously updates the asset feature tags to generate a dynamic cataloging list. The asset value linkage transaction unit is used to associate the cataloging results with the transaction value. It takes the asset feature tags as input and obtains a real-time asset value score through the asset evaluation module. Based on the real-time asset value score and the qualifications of the two parties to the transaction, the transaction process is automatically triggered through the smart contract module, including preset profit sharing ratio according to the ownership split rules, generating a pricing range according to the value score, and synchronously recording the asset transfer trajectory. The compliance verification unit performs ownership separation on the asset feature tags output by the dynamic cataloging unit based on the data ownership separation rules to obtain the data source, the ownership subject, and the corresponding scope of rights; for the asset value linkage transaction unit process, it verifies the qualifications of both parties to the transaction and the profit sharing; After verification, identify the risk points and generate an asset rights compliance report.

2. The system according to claim 1, characterized in that, The asset value dimension definition mechanism is used to build a value assessment benchmark within the system. The definition process is as follows: connecting with the medical information system and health management platform, and collecting clinical application scenario tags from the data. Based on the characteristics of the scenario, core value indicators are extracted from clinical needs, including clinical relevance, data completeness, and case scarcity in drug development scenarios; quantitative thresholds are set for each dimension. Dynamic weights are assigned to dimensions for different scenarios, and dimensions, thresholds, and weights are stored in the basic rule base to ultimately obtain the asset value assessment benchmark.

3. The system according to claim 2, characterized in that, The basic rule base serves as the carrier of asset value assessment benchmarks, used for structured storage of clinical application scenario tags, core value indicators, quantitative thresholds, and key elements of scenario-based dynamic weights; and provides real-time data access to the medical and health data asset element definition unit, the dynamic cataloging unit, and the asset value linkage transaction unit through API interfaces.

4. The system according to claim 1, characterized in that, The dynamic linkage module is used to generate an asset-based component framework and define data ownership splitting rules. It connects to the hospital's electronic medical record system and the server data terminal of the smart device manufacturer, collects the source entity identifier and collection scenario, and forms a relationship table of source entity, data type, and collection scenario. It matches the legitimate ownership entity from the relationship table and writes the relationship information into the blockchain storage unit. Based on the aforementioned related information, an assetization component framework is constructed, comprising an ownership layer, a value layer, and a compliance layer. Based on this framework, ownership splitting rules are defined, stratified by right type. Combining the ownership entity, the data source, and medical data ownership confirmation standards, data rights are split into core rights and derivative rights. Usage rights are split according to application scenarios, and revenue rights are split according to the contribution of the ownership entity.

5. The system according to claim 1, characterized in that, The data parsing and labeling module is used to extract clinical information from the medical text. The extraction process is as follows: receiving the medical text through a multi-protocol interface, converting device manufacturer-defined fields into system standard fields, and performing numerical verification; fields exceeding the clinically reasonable value range are marked as abnormal. For unstructured text data, entity information is extracted and mapped to standard medical terminology codes. For image data, lesion feature parameters are extracted and converted into structured descriptive text. For the parsed structured information, field completion verification is performed based on the asset-based component framework. The trend characteristics of clinical indicators are extracted using a sliding window algorithm, and the fluctuation amplitude and rate of change per unit time are calculated. The parsing results are compared with preset thresholds, and when the clinical indicator values ​​exceed the preset range, an abnormal status label is marked.

6. The system according to claim 5, characterized in that, When the data source and ownership entity are consistent, the ownership compliance label is marked through the data parsing and labeling module; when the privacy-sensitive fields in the unstructured text of the medical text are anonymized and the generated public content does not contain the preset privacy-sensitive protection content, the corresponding privacy protection label is marked through the data parsing and labeling module; the generated label combination is hash-bound with the unique data identifier, synchronously stored in the distributed database, and finally pushed to the dynamic cataloging and label iteration module.

7. The system according to claim 1, characterized in that, The dynamic cataloging and tagging iteration module is used to automatically process the dynamic changes of healthcare data assets. Specifically, it includes: capturing data update timestamps and feature fields in real time, including blood glucose levels and medication records, through the API interface of the data acquisition terminal, and calculating the update frequency and feature fluctuation amplitude per unit time; filtering the collected signals for validity, verifying whether the update frequency duration and outlier ratio meet preset thresholds; calling the verification interface of the assetization component framework to verify whether the newly added data contains the ownership identifier and basic clinical tag fields required by the framework; calling the basic rule base to perform operations on the filtered valid signals according to the principles of addition, deletion, modification, and combination; and connecting to the healthcare data asset tag rule base through the tag mapping engine, comparing data attribute values ​​with dimension thresholds to generate tag combinations, binding them with unique data IDs, and finally generating a dynamic cataloging list.

8. The system according to claim 7, characterized in that, The aforementioned addition, deletion, modification, and merging principles are used to automatically execute cataloging dimension operations. The specific execution process is as follows: When adding a dimension, the engine calls the gene data processing interface to obtain the site information output by the sequencer, and automatically creates gene site coverage, mutation type correlation fields, and calculation logic; when merging dimensions, it calculates the daily and weekly average blood pressure fields in the chronic disease data. If the correlation exceeds a preset threshold, the two fields are merged into the blood pressure periodic average, and the original calculation log is retained; when adjusting dimension weights, the dimension weight values ​​are automatically updated after detecting changes in scene labels.

9. The system according to claim 1, characterized in that, The asset assessment module is used to output asset value scores, receive asset feature tags output by the dynamic cataloging and tag iteration module, extract completeness, timeliness, and clinical relevance assessment dimensions through a field parser, use an interval mapping algorithm for each dimension, and set a basic score according to the data attribute compliance. The module loads the dynamic linkage module of the medical and health data assetization element definition unit, calls the interface of the basic rule base, and allocates the weight of each dimension according to the current application scenario. The privacy protection level and ownership compliance status are converted into dynamic coefficients; the update signals of asset feature tags are monitored in real time to trigger recalculation; the outlier correction mechanism is invoked to handle dimensional score anomalies, and finally the asset value score is output.

10. The system according to claim 9, characterized in that, The outlier correction mechanism works as follows: when the difference between the score of a single dimension and the average score of other dimensions exceeds a preset threshold, dimension correlation verification is initiated; the correlation between the dimension and the core dimension of the scene is calculated; if the correlation exceeds the preset threshold, a weighted correction is performed; if the correlation is lower than the preset threshold, the original score remains unchanged. The correction results are updated to the asset valuation module in real time.

11. The system according to claim 1, characterized in that, The smart contract module is used to automatically trigger the medical and health data asset transaction process. The process is as follows: deploy a smart contract instance based on a consortium blockchain architecture, connect to the API interface of the dynamic cataloging and tagging iteration module and the off-chain database of the compliance verification unit through an off-chain oracle, and synchronize the asset feature tags, digital certificates of both parties to the transaction, and compliance verification results in real time; when the condition judgment logic built into the contract detects that the preset conditions are met, the transaction process is automatically triggered, and an immutable transaction start block is generated; call the profit-sharing interface of the basic rule base to extract the ownership subject and the corresponding proportion and write it into on-chain storage; receive the asset value score pushed by the asset evaluation module, generate a pricing range through a pricing algorithm and lock it on-chain; automatically capture timestamps, account addresses, and data ID information at the transaction node; and generate on-chain transaction logs.

12. The system according to claim 11, characterized in that, The pricing algorithm is used to generate a dynamic pricing range based on the asset value score and scenario characteristics, call the basic rule library stored on the chain, extract the benchmark price corresponding to the current application scenario as the pricing calculation base, and receive the asset value score output by the asset evaluation module and perform step adjustment according to the preset range. The smart contract module calls the transaction records of the same type of data in the blockchain distributed ledger to calculate the historical average transaction price and performs secondary calibration on the tiered range according to the preset volatility coefficient; the calibrated pricing range is confirmed through the consensus of the consortium blockchain nodes, the preset lock-in time is set, and recalculation is automatically triggered when the timeout expires.

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