A method and system for full-process compliance audit of digital asset issuance

Abstract

The application discloses a kind of digital asset issuing whole-process compliance auditing method and system, it is related to data processing technical field, comprising: obtaining the project data of to-be-audited project;Determine degree index and state index based on project data;Call the historical project data stored in historical auditing database;Determine the first similarity of to-be-audited project and historical project on degree index, and the second similarity on state index;Based on the first similarity and the second similarity, determine the target historical project with the highest similarity to to-be-audited project;Based on the historical compliance auditing result corresponding to target historical project, determine whether the production information of to-be-audited project conforms to preset compliance rule;In the case where it conforms to preset compliance rule, generate compliance pass result and allow the digital asset issuance of to-be-audited project.Can improve auditing efficiency and accuracy, unify auditing standard, optimize resource allocation, realize the traceability and intelligentization of auditing decision.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method and system for full-process compliance review of digital asset issuance. Background Technology

[0002] With the rapid development of blockchain technology and the digital economy, digital asset issuance has become an important way for enterprises to raise funds and for the development of the digital economy. However, digital asset issuance involves complex compliance requirements, including the qualification review of the issuing entity, the security review of smart contracts, investor suitability management, and anti-money laundering compliance.

[0003] Currently, existing technologies mainly rely on manual review. Reviewers need to verify the issuer's information item by item, examine smart contract code, and compare it with the white paper commitments. The entire review process is time-consuming and cannot meet the high-frequency and rapid market demand for digital asset issuance. Moreover, different reviewers have different judgment standards for the same type of project, resulting in the problem of "different judgments for the same case", which affects the fairness and predictability of the review results. The review experience and judgment rules contained in a large number of historical review cases have not been fully explored and utilized. Each project needs to be reviewed from scratch, and it is impossible to learn from the review experience of similar projects, resulting in a waste of resources.

[0004] Therefore, this invention provides a method and system for full-process compliance review of digital asset issuance. Summary of the Invention

[0005] This application provides a method and system for end-to-end compliance review of digital asset issuance, which addresses the technical problems of low review efficiency and inconsistent standards in existing technologies.

[0006] On the one hand, this invention provides a method for end-to-end compliance review of digital asset issuance, including: Step 1: Obtain project data for the project to be reviewed. The project data includes information on the issuing entity, smart contracts, and white paper documents. Step 2: Determine the degree index and the status index based on the project data; the degree index is used to characterize the audit elements in the project to be audited that are quantifiable and have continuous value characteristics; the status index is used to characterize the audit elements in the project to be audited that have binary qualitative characteristics and whose values ​​are only two mutually exclusive states. Step 3: Retrieve several historical project data stored in the historical audit database. The historical project data includes historical degree indicator vectors, historical status indicator vectors, and historical compliance audit results. Step 4: Determine the first similarity between the project to be reviewed and the historical project on the degree indicator, and the second similarity on the status indicator; based on the first similarity and the second similarity, determine the target historical project with the highest similarity to the project to be reviewed; Step 5: Based on the historical compliance audit results corresponding to the target historical project, determine whether the production information of the project to be audited complies with the preset compliance rules; if it complies with the preset compliance rules, generate a compliance pass result and allow the digital asset issuance of the project to be audited.

[0007] According to the full-process compliance review method for digital asset issuance provided by the present invention, the step of determining the first similarity between the project to be reviewed and historical projects on the degree indicator includes: for any degree indicator, obtaining the current value of the current project on the degree indicator, and the historical values ​​of each historical project on the degree indicator; The current value and each of the historical values ​​are normalized to obtain the normalized current value and each of the historical values. The first similarity is calculated based on the difference between the current normalized value and each of the historical normalized values, combined with the first weight of the degree index; wherein the first weight is determined based on the distribution characteristics of the historical values ​​of each historical item on the degree index, and the distribution characteristics include the quantity distribution and dispersion of the historical values.

[0008] According to the full-process compliance review method for digital asset issuance provided by the present invention, the first weight is determined in the following manner: Obtain the historical values ​​of each historical item on the target degree index, and count the frequency of occurrence of each historical value; The maximum frequency value corresponding to the historical value with the highest frequency of occurrence is determined; the maximum frequency value refers to the maximum number of times each historical value appears among all historical values ​​corresponding to the target degree index; the maximum frequency value is used to reflect the degree of concentration of the historical value distribution of the target degree index. Calculate the standard deviation between historical values; The initial first weight of the target severity index is obtained by multiplying the ratio of the frequency of occurrence of the target historical value to the maximum frequency value by the standard deviation. Obtain the time difference between the generation time of each historical item and the current time; A time decay factor is determined based on the time difference; the time decay factor is negatively correlated with the time difference. The initial first weight is obtained by weighting and adjusting the initial first weight according to the time decay factor.

[0009] According to the full-process compliance review method for digital asset issuance provided by the present invention, the step of determining the second similarity between the project to be reviewed and historical projects on the status indicator includes: for any status indicator, obtaining the current Boolean value of the current project on the status indicator, and the historical Boolean values ​​of each historical project on the status indicator; When the current Boolean value is the same as the historical Boolean value, the difference is set to a first preset value; when the current Boolean value is different from the historical Boolean value, the difference is set to a second preset value; the difference is used to quantify the degree of difference between the current project and the historical project in the status indicator. Based on the difference, and in conjunction with the second weight of the status index, the second similarity is calculated; wherein, the second weight is determined based on the distribution characteristics of the historical Boolean values ​​of each historical item on the status index; the distribution characteristics include the frequency of occurrence and the degree of dispersion of each historical Boolean value.

[0010] According to the present invention, a full-process compliance review method for digital asset issuance is provided, wherein determining the target historical project with the highest similarity to the project to be reviewed based on the first similarity and the second similarity includes: determining the first project similarity between the current project and each historical project based on the first similarity, the second similarity and the third weight; Using a preset radial basis function, the first vector distance corresponding to the first similarity is mapped to a first similarity component, and the second vector distance corresponding to the second similarity is mapped to a second similarity component; the radial basis function is configured to map the vector distance to a monotonically decreasing similarity value. The harmonic mean of the first similarity component and the second similarity component is calculated as the second item similarity; wherein the harmonic mean is used to comprehensively evaluate the similarity between the degree index and the state index. The overall project similarity is determined based on the first project similarity, the second project similarity, and the fourth weight. The target historical item is determined based on the comprehensive similarity score.

[0011] According to the full-process compliance review method for digital asset issuance provided by the present invention, the third weight is determined in the following manner: Obtain the confidence score of the historical compliance audit results corresponding to each historical project. The confidence score is determined based on the audit pass rate, risk event occurrence rate, and time difference between the audit time and the current time of the historical project. The third weight corresponding to each historical item is determined based on the ratio of the confidence score of each historical item to the sum of the confidence scores of all historical items.

[0012] According to the full-process compliance review method for digital asset issuance provided by the present invention, the step of determining the target historical project based on the comprehensive similarity score includes: Calculate the overall project similarity between the current project and all historical projects to obtain an overall project similarity set; The historical project corresponding to the maximum value in the comprehensive project similarity set is determined as the candidate target historical project; When the overall similarity of the candidate target historical item is greater than a preset similarity threshold, the candidate target historical item is determined as the target historical item. When the overall similarity of the candidate target historical project is less than or equal to the preset similarity threshold, a manual review process is triggered, and the historical project specified after manual review is determined as the target historical project.

[0013] According to the present invention, a full-process compliance review method for digital asset issuance includes determining whether the production information of the project to be reviewed conforms to preset compliance rules based on the historical compliance review results corresponding to the target historical project, comprising: Obtain the historical compliance audit results corresponding to the target historical project. The historical compliance audit results include: the audit judgment results of the target historical project at each stage of the pre-issuance audit stage, the issuance monitoring stage, and the post-issuance supervision stage. The review and judgment results of each stage of the target historical project are compared with the review elements of the corresponding stage of the project to be reviewed. When there is a difference between the review elements of the project to be reviewed at any stage and the review elements of the corresponding stage of the target historical project, the difference items are extracted and a difference risk analysis is performed. When the historical compliance audit result of the target historical project is compliant and the differences between the project to be audited and the target historical project do not exceed the preset risk threshold after risk analysis, the production information of the project to be audited is determined to comply with the preset compliance rules; the preset risk threshold is a dynamic risk threshold determined based on the risk score distribution of differences of all historical projects in the historical audit database. When the historical compliance audit result of the target historical project is non-compliant, or the difference between the project to be audited and the target historical project exceeds a preset risk threshold after risk analysis, it is determined that the production information of the project to be audited does not comply with the preset compliance rules.

[0014] On the other hand, this invention provides a full-process compliance review system for digital asset issuance, including: The acquisition module acquires project data for projects to be reviewed, including information on the issuing entity, smart contracts, and white paper documents. The determination module determines degree indicators and status indicators based on the project data; the degree indicators are used to characterize the audit elements in the project to be audited that are quantifiable and have continuous value characteristics; the status indicators are used to characterize the audit elements in the project to be audited that have binary qualitative characteristics and take two mutually exclusive states. The calling module calls several historical project data stored in the historical audit database. The historical project data includes historical degree indicator vectors, historical status indicator vectors, and historical compliance audit results. The calculation module determines the first similarity between the project to be reviewed and the historical project on the degree index and the second similarity on the status index. Based on the first similarity and the second similarity, it determines the target historical project with the highest similarity to the project to be reviewed. The judgment module determines whether the production information of the project to be reviewed complies with preset compliance rules based on the historical compliance review results corresponding to the target historical project. If it complies with the preset compliance rules, it generates a compliance pass result and allows the digital asset issuance of the project to be reviewed.

[0015] Compared with the prior art, the beneficial effects of this application are as follows: By classifying and processing audit elements and employing differentiated similarity calculation methods, combined with dynamic weights, radial basis function mapping, time decay factors, and multi-stage comparisons, historical audit data can be fully utilized to reuse experience, significantly improving audit efficiency and accuracy, unifying audit standards, optimizing resource allocation, and achieving traceability and intelligence in audit decisions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a full-process compliance review method for digital asset issuance provided by an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the structure of a full-process compliance review system for digital asset issuance provided by an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] Example 1: This invention provides a method for end-to-end compliance review of digital asset issuance, such as... Figure 1 As shown, it includes: Step 1: Obtain project data for the project to be reviewed. The project data includes information on the issuing entity, smart contracts, and white paper documents. Step 2: Determine the degree index and the status index based on the project data; the degree index is used to characterize the audit elements in the project to be audited that are quantifiable and have continuous value characteristics; the status index is used to characterize the audit elements in the project to be audited that have binary qualitative characteristics and whose values ​​are only two mutually exclusive states. Step 3: Retrieve several historical project data stored in the historical audit database. The historical project data includes historical degree indicator vectors, historical status indicator vectors, and historical compliance audit results. Step 4: Determine the first similarity between the project to be reviewed and the historical project on the degree indicator, and the second similarity on the status indicator; based on the first similarity and the second similarity, determine the target historical project with the highest similarity to the project to be reviewed; Step 5: Based on the historical compliance audit results corresponding to the target historical project, determine whether the production information of the project to be audited complies with the preset compliance rules; if it complies with the preset compliance rules, generate a compliance pass result and allow the digital asset issuance of the project to be audited.

[0021] In this embodiment, the compliance review server obtains project data for the project to be reviewed. This project data includes information on the issuing entity, smart contracts, and a white paper. The issuing entity information includes the issuer's business registration information, legal representative information, and qualification certificates. The smart contract is an automatically executable computer program deployed on the blockchain, defining the core logic of the digital asset issuance, such as the total amount, price, allocation rules, and lock-up mechanism. The white paper is a project description document for investors, containing the project's technical solutions, economic model, risk warnings, and other commitments.

[0022] In this embodiment, based on the business characteristics of the audit elements in the project data, each audit element is divided into degree indicators and status indicators. Degree indicators are used to characterize quantifiable audit elements in the project to be audited that have continuous value characteristics, including smart contract complexity, contract call frequency, contract modification frequency, etc. Status indicators are used to characterize audit elements in the project to be audited that have binary qualitative characteristics and only two mutually exclusive states, including entity qualification compliance status (compliant / non-compliant), sanctions list hit status (hit / not hit), white paper commitment consistency status (consistent / inconsistent), etc.

[0023] In this embodiment, contract call frequency refers to the total number of times a smart contract is triggered and executed by a user or other smart contracts through blockchain transactions within a unit of time period (such as a day, a week, or a month), reflecting the activity level of smart contract usage. Contracts with high-frequency calls involve a large amount of fund transfers and user interactions, resulting in a greater risk exposure and requiring close attention.

[0024] In this embodiment, the contract modification frequency refers to the number of times a smart contract is modified, updated, or iterated within a preset time period, reflecting the contract's stability and iteration activity. Contracts that are frequently modified may have issues requiring bug fixes, requirement changes, or security vulnerabilities, and require close review; contracts with low modification frequencies are relatively mature and stable, and have lower risks.

[0025] In this embodiment, the entity's compliance status refers to the determination of whether the issuing entity possesses the legal and compliant qualifications for issuing digital assets. This status is a Boolean value, including "compliant" and "non-compliant." The determination criteria include whether the entity has completed business registration, obtained relevant financial licenses, passed regulatory filing, and has any records of illegal or irregular activities.

[0026] In this embodiment, the "sanctions list hit status" refers to the determination result of whether the issuing entity or related responsible person is included in various sanctions lists, blacklists, or restricted lists. This status is a Boolean value, including "hit" and "not hit". The determination criteria include whether it appears on the sanctions lists issued by the United Nations, the European Union, the US OFAC, China, and other organizations.

[0027] In this embodiment, the white paper commitment consistency status refers to the determination result of whether the actual execution logic of the smart contract is consistent with the business commitments and technical solutions described in the white paper document. This status is a Boolean value, including "consistent" and "inconsistent". Key commitment clauses in the white paper are extracted using natural language processing technology, and semantically compared with the contract code logic to determine whether there are any discrepancies.

[0028] In this embodiment, the historical audit database is a database that stores historical project audit data. Each historical project data includes a historical degree indicator vector (containing the historical project's values ​​on each degree indicator), a historical status indicator vector (containing the historical project's Boolean values ​​on each status indicator), and historical compliance audit results (including audit judgment results at each stage, risk event records, rectification measures, etc.). This database provides the data foundation for similarity matching in this application.

[0029] In this embodiment, the historical audit database pre-stores historical project data for multiple historical projects and the corresponding historical compliance audit results for each historical project. Each historical project data includes a historical degree indicator vector (containing the historical project's values ​​for each degree indicator), a historical status indicator vector (containing the historical project's Boolean values ​​for each status indicator), and historical compliance audit results (including audit judgment results at each stage, risk event records, rectification measures, etc.).

[0030] In this embodiment, when it is determined that the production information of the project to be reviewed meets the preset compliance rules, the compliance review server generates a compliance pass result and allows the project to be reviewed to carry out digital asset issuance operations; when it does not meet the requirements, a non-compliance result is generated, and rectification suggestions are output, and issuance is rejected.

[0031] In this embodiment, a smart contract is an automatically executable computer program deployed on the blockchain. It defines all the rules for the issuance and circulation of digital assets in code form, including the total issuance amount, issuance price, allocation ratio, lock-up period, and trading permissions. When preset conditions are met, the smart contract automatically executes the corresponding operation without human intervention, and is characterized by transparency, immutability, and automatic execution. In digital asset issuance projects, smart contracts are a core object of compliance review, and their code logic must be consistent with the commitments and terms in the white paper document.

[0032] In this embodiment, the review elements refer to the specific evaluation dimensions that need to be reviewed, assessed, and judged one by one when conducting compliance reviews of digital asset issuance projects. These elements together constitute the indicator system for measuring the compliance of the project. In this application, the review elements are divided into two main categories based on their business characteristics: degree indicators and status indicators, each using different similarity calculation methods.

[0033] The beneficial effects of the above technical solution are as follows: By classifying and processing audit elements and employing differentiated similarity calculation methods, combined with dynamic weights, radial basis function mapping, time decay factors, and multi-stage comparisons, historical audit data can be fully utilized to reuse experience, significantly improving audit efficiency and accuracy, unifying audit standards, optimizing resource allocation, and achieving traceability and intelligence in audit decisions. This enables efficient, accurate, and traceable compliance audits.

[0034] Example 2: This invention provides a full-process compliance review method for digital asset issuance. The step of determining the first similarity between the project to be reviewed and historical projects on the degree indicator includes: for any degree indicator, obtaining the current value of the current project on the degree indicator, and the historical values ​​of each historical project on the degree indicator; The current value and each of the historical values ​​are normalized to obtain the normalized current value and each of the historical values. The first similarity is calculated based on the difference between the current normalized value and each of the historical normalized values, combined with the first weight of the degree index; wherein the first weight is determined based on the distribution characteristics of the historical values ​​of each historical item on the degree index, and the distribution characteristics include the quantity distribution and dispersion of the historical values.

[0035] In this embodiment, the degree index refers to a quantifiable index with continuous value among the audit elements. Its value can reflect the degree of a certain feature. For example, smart contract complexity reflects the complexity of the contract, contract call frequency reflects the activity level of the contract, and contract modification frequency reflects the stability level of the contract. The degree index is suitable for measuring the similarity between projects by calculating numerical distance.

[0036] In this embodiment, for degree indicators, a measurement method based on numerical differences is used to calculate the first similarity. For any degree indicator, the current value of the current item on that degree indicator and the historical values ​​of each historical item on that degree indicator are obtained; the current value and each historical value are subjected to min-max normalization processing to obtain the normalized current normalized value and each historical normalized value; based on the difference between the current normalized value and each historical normalized value, combined with the first weight of the degree indicator, the first similarity is calculated.

[0037] In this embodiment, normalization is a data preprocessing method that transforms numerical data with different dimensions to the same scale range (usually the [0,1] interval). This application uses the min-max normalization formula to eliminate the influence of dimensions between different review elements, making the values ​​of different dimensions comparable, thereby accurately calculating the similarity between projects.

[0038] In this embodiment, smart contract complexity is an indicator of the complexity of smart contract code, typically calculated by combining multiple dimensions such as the number of lines of contract code, the number of functions, the depth of nested loops, and the number of branch conditions. Contracts with higher complexity have more intricate logic and a higher potential risk of security vulnerabilities, requiring closer review.

[0039] In this embodiment, the first weight is determined based on the distribution characteristics of the historical values ​​of each historical item on the target level indicator. These distribution characteristics include the quantity distribution and dispersion of the historical values. The specific method for determining the first weight is as follows: Historical values ​​of each historical item on the target level indicator are obtained, and the frequency of occurrence of each historical value is counted; the maximum frequency value corresponding to the historical value with the highest frequency is determined; the standard deviation between each historical value is calculated; the initial first weight of the target level indicator is obtained by multiplying the ratio of the frequency of occurrence of the target historical value to the maximum frequency value by the standard deviation; the time difference between the generation time of each historical item and the current time is obtained; a time decay factor is determined based on the time difference, and the time decay factor is negatively correlated with the time difference; the initial first weight is adjusted by weighting based on the time decay factor to obtain the final first weight. By introducing the time decay factor, historical items closer to the current time contribute more to the weight calculation, while items farther away contribute less, thus improving the timeliness of the weight. The time decay factor is a coefficient used to adjust the weight of historical data. Its value is negatively correlated with the time difference between the generation time of the historical item and the current time; that is, the larger the time difference, the smaller the time decay factor. The time decay factor can be calculated using a preset decay function, such as an exponential decay function. ; in Let λ be the time difference and λ be the decay rate parameter.

[0040] The beneficial effects of the above technical solution are: determining the first similarity between the project to be reviewed and the historical project on the degree indicator can improve the accuracy and rationality of the similarity calculation of the degree indicator dimension, and provide reliable quantitative dimension support for the subsequent matching of target historical projects.

[0041] Example 3: This invention provides a method for full-process compliance review of digital asset issuance, wherein the first weight is determined in the following way: Obtain the historical values ​​of each historical item on the target degree index, and count the frequency of occurrence of each historical value; The maximum frequency value corresponding to the historical value with the highest frequency of occurrence is determined; the maximum frequency value refers to the maximum number of times each historical value appears among all historical values ​​corresponding to the target degree index; the maximum frequency value is used to reflect the degree of concentration of the historical value distribution of the target degree index. Calculate the standard deviation between historical values; The initial first weight of the target severity index is obtained by multiplying the ratio of the frequency of occurrence of the target historical value to the maximum frequency value by the standard deviation. Obtain the time difference between the generation time of each historical item and the current time; A time decay factor is determined based on the time difference; the time decay factor is negatively correlated with the time difference. The initial first weight is obtained by weighting and adjusting the initial first weight according to the time decay factor.

[0042] In this embodiment, for status indicators, a Boolean matching-based metric is used to calculate the second similarity. For any status indicator, the current Boolean value of the current project on that status indicator and the historical Boolean values ​​of each historical project on that status indicator are obtained. When the current Boolean value is the same as the historical Boolean value, the difference is set to a first preset value; when they are different, the difference is set to a second preset value. Based on the difference and combined with the second weight of the status indicator, the second similarity is calculated. The aforementioned Boolean value refers to the logical value used to represent the binary value of the status indicator, including two mutually exclusive states: "yes" and "no," "compliant" and "non-compliant," and "consistent" and "inconsistent," which correspond to the first and second values ​​respectively in the numerical processing.

[0043] In this embodiment, the first preset value refers to a preset numerical value used to quantify the degree of difference in the state index. When the current Boolean value is the same as the historical Boolean value, the difference is set to the first preset value, which indicates that the two items have no difference in this state index. In one embodiment of this application, the first preset value is set to 0, indicating complete consistency and no difference contribution.

[0044] In this embodiment, the second preset value refers to a preset numerical value used to quantify the degree of difference in the status indicator. When the current Boolean value is different from the historical Boolean value, the difference is set as the second preset value to indicate that there is a difference between the two items in this status indicator. In one embodiment of this application, the second preset value is set to 1, indicating that there is a significant difference and the largest difference value is contributed.

[0045] The beneficial effects of the above technical solution are: determining the first weight can adapt to the latest review requirements in real time, further ensuring the scientific nature and timeliness of the similarity calculation of the degree index.

[0046] Example 4: This invention provides a full-process compliance review method for digital asset issuance. The step of determining the second similarity between the project to be reviewed and historical projects on the status indicator includes: for any status indicator, obtaining the current Boolean value of the current project on the status indicator, and the historical Boolean values ​​of each historical project on the status indicator; When the current Boolean value is the same as the historical Boolean value, the difference is set to a first preset value; when the current Boolean value is different from the historical Boolean value, the difference is set to a second preset value; the difference is used to quantify the degree of difference between the current project and the historical project in the status indicator. Based on the difference, and in conjunction with the second weight of the status index, the second similarity is calculated; wherein, the second weight is determined based on the distribution characteristics of the historical Boolean values ​​of each historical item on the status index; the distribution characteristics include the frequency of occurrence and the degree of dispersion of each historical Boolean value.

[0047] In this embodiment, status indicators refer to indicators among the audit elements that have a binary qualitative nature and only two mutually exclusive values. Their values ​​reflect a "yes / no" judgment of a certain feature, such as the compliance status of the entity's qualifications (compliant / non-compliant), the status of being on the sanctions list (hit / not hit), and the consistency status of the white paper commitment (consistent / inconsistent). Status indicators are suitable for measuring the similarity between projects by judging whether they are the same.

[0048] In this embodiment, the second weight is determined based on the distribution characteristics of historical Boolean values ​​for each historical item on the status indicator. These distribution characteristics include the frequency and dispersion of each historical Boolean value. The second weight is determined as follows: Historical Boolean values ​​for each historical item on the target status indicator are obtained, and the frequency of each historical Boolean value (e.g., "compliant" vs. "non-compliant", "yes" vs. "no") is statistically analyzed. The maximum frequency value corresponding to the historical Boolean value with the highest frequency is determined; this maximum frequency value reflects the concentration of the historical distribution of the status indicator. The standard deviation between each historical Boolean value is calculated; a larger standard deviation indicates a more dispersed distribution of historical Boolean values ​​and a higher distinguishability of the status indicator. The initial second weight of the status indicator is obtained by multiplying the ratio of the frequency of the target historical Boolean value to the maximum frequency value by the standard deviation. Furthermore, a time decay factor is introduced, and the initial second weight is adjusted by weighting the time difference between the generation time of each historical item and the current time to obtain the final second weight. Through this method, status indicators with dispersed distribution and high distinguishability receive higher weights and play a greater role in similarity calculation.

[0049] The beneficial effects of the above technical solution are: determining the second similarity between the project to be reviewed and the historical project on the status indicator can improve the accuracy and flexibility of matching the target historical project, and fundamentally avoid the review deviation caused by the distortion of benchmark cases.

[0050] Example 5: This invention provides a full-process compliance review method for digital asset issuance. The step of determining the target historical project with the highest similarity to the project to be reviewed based on the first similarity and the second similarity includes: determining the first project similarity between the current project and each historical project based on the first similarity, the second similarity, and the third weight. Using a preset radial basis function, the first vector distance corresponding to the first similarity is mapped to a first similarity component, and the second vector distance corresponding to the second similarity is mapped to a second similarity component; the radial basis function is configured to map the vector distance to a monotonically decreasing similarity value. The harmonic mean of the first similarity component and the second similarity component is calculated as the second item similarity; wherein the harmonic mean is used to comprehensively evaluate the similarity between the degree index and the state index. The overall project similarity is determined based on the first project similarity, the second project similarity, and the fourth weight. The target historical item is determined based on the comprehensive similarity score.

[0051] In this embodiment, the radial basis function is configured to map the vector distance to a monotonically decreasing similarity value, that is, the larger the vector distance, the smaller the similarity component obtained by mapping.

[0052] In this embodiment, the harmonic mean of the first similarity component and the second similarity component is calculated as the second item similarity. The harmonic mean is used to comprehensively evaluate the similarity between the degree and state indicators, ensuring that a low similarity in either dimension significantly lowers the overall score, thus preventing a high similarity in one dimension from masking significant differences in the other.

[0053] In this embodiment, the radial basis function is a real-valued function with distance as its independent variable, and its value monotonically decreases as the distance increases. This application uses the radial basis function to map vector distances to similarity components, so that items with smaller distances receive larger similarity components, which aligns with the intuitive understanding of similarity measurement.

[0054] In this embodiment, the harmonic mean is a statistic describing the average level of a numerical value, characterized by its significant influence from extreme low values. This application uses the harmonic mean to comprehensively evaluate the similarity of two dimensions: degree index and state index. This ensures that a low similarity in either dimension significantly lowers the overall score, preventing a single high similarity from masking a significant difference in the other dimension. In this embodiment, the formula for calculating the harmonic mean of the first similarity component and the second similarity component is: ; Where S1 is the first similarity component and S2 is the second similarity component. In this embodiment, the harmonic mean is significantly affected by extremely small values. When either the first or second similarity component is small, the harmonic mean will decrease significantly. This avoids the overall score being excessively inflated by a single high value when the similarity of the degree index is high while the similarity of the state index is low (or vice versa). A higher overall score can only be obtained when the similarity of both dimensions reaches a high level.

[0055] In this embodiment, the comprehensive item similarity is determined based on the first item similarity, the second item similarity, and the fourth weight.

[0056] In this embodiment, the overall item similarity is determined by weighted summation. The fourth weight is a coefficient used to balance the proportions of the first and second item similarities in the overall item similarity calculation, with a value range of [0,1]. Optionally, the fourth weight can be dynamically determined based on the optimal matching results of historical review data, or a fixed value can be preset according to business needs. When the fourth weight is large, the first item similarity contributes more to the overall score, meaning the direct weighted summation of similarity results dominates. When the fourth weight is small, the second item similarity calculated by radial basis function mapping plus harmonic mean dominates, making the overall score more focused on the balance between degree and state indicators. By setting the fourth weight, this application can adaptively adjust the similarity calculation strategy, improving the accuracy and flexibility of target historical item matching.

[0057] The beneficial effects of the above technical solution are as follows: Based on the first similarity and the second similarity, the target historical project with the highest similarity to the project to be reviewed is determined, which can filter out the interference of low-quality and high-risk historical cases on the review results from the source, improve the reliability and reference value of benchmarking historical projects, and make the final compliance judgment result more traceable and reasonable.

[0058] Example 6: This invention provides a method for end-to-end compliance review of digital asset issuance, wherein the third weight is determined in the following manner: Obtain the confidence score of the historical compliance audit results corresponding to each historical project. The confidence score is determined based on the audit pass rate, risk event occurrence rate, and time difference between the audit time and the current time of the historical project. The third weight corresponding to each historical item is determined based on the ratio of the confidence score of each historical item to the sum of the confidence scores of all historical items.

[0059] In this embodiment, the third weight is determined as follows: The confidence score of the historical compliance audit results for each historical project is obtained. The confidence score is determined based on the historical project's approval rate, risk event occurrence rate, and the time difference between the audit time and the current time. The third weight for each historical project is determined based on the ratio of its confidence score to the sum of all historical project confidence scores. It should be understood that a higher confidence score indicates a more reliable audit result for that historical project, and thus carries a higher weight in the similarity calculation.

[0060] The confidence score for the historical compliance audit results of each historical project is obtained, including: obtaining the approval rate of the historical project, which is the proportion of all audit elements of the historical project that pass on the first attempt during the audit process; obtaining the risk event occurrence rate of the historical project, which is the ratio of the number of risk warnings triggered in the entire process of pre-issuance audit, in-issuance monitoring and post-issuance supervision to the total number of audit items; obtaining the time difference between the audit time of the historical project and the current time, and determining the timeliness factor based on the time difference, wherein the timeliness factor is negatively correlated with the time difference, and the smaller the time difference, the larger the timeliness factor; and weighted summing the approval rate, the complement of the risk event occurrence rate (i.e., 1 minus the risk event occurrence rate), and the timeliness factor to obtain the confidence score of the historical project.

[0061] The beneficial effects of the above technical solution are: determining the third weight can improve the efficiency of the review while firmly safeguarding the bottom line of compliance review risks and ensuring the accuracy and security of the review results in extreme scenarios.

[0062] Example 7: This invention provides a method for end-to-end compliance review of digital asset issuance, wherein determining the target historical project based on the comprehensive similarity score includes: Calculate the overall project similarity between the current project and all historical projects to obtain an overall project similarity set; The historical project corresponding to the maximum value in the comprehensive project similarity set is determined as the candidate target historical project; When the overall similarity of the candidate target historical item is greater than a preset similarity threshold, the candidate target historical item is determined as the target historical item. When the overall similarity of the candidate target historical project is less than or equal to the preset similarity threshold, a manual review process is triggered, and the historical project specified after manual review is determined as the target historical project.

[0063] In this embodiment, the target historical item is determined based on comprehensive similarity. The comprehensive item similarity between the current item and each historical item is calculated to obtain a comprehensive item similarity set. The historical item corresponding to the maximum value in the comprehensive item similarity set is determined as a candidate target historical item. When the comprehensive item similarity of a candidate target historical item is greater than a preset similarity threshold, the candidate target historical item is determined as the target historical item. When the comprehensive item similarity of a candidate target historical item is less than or equal to the preset similarity threshold, it indicates that there are not enough similar historical items for reference. At this time, a manual review process is triggered, and the historical item specified after manual review is determined as the target historical item.

[0064] The beneficial effects of the above technical solution are as follows: determining the target historical project based on the comprehensive similarity score allows the compliance judgment result to align with historical review standards, accurately adapts to the personalized characteristics of the project to be reviewed, and improves the rigor, comprehensiveness, and risk control capabilities of the compliance review.

[0065] Example 8: This invention provides a method for end-to-end compliance review of digital asset issuance. The method involves determining whether the production information of the project to be reviewed complies with preset compliance rules based on the historical compliance review results corresponding to the target historical project. Obtain the historical compliance audit results corresponding to the target historical project. The historical compliance audit results include: the audit judgment results of the target historical project at each stage of the pre-issuance audit stage, the issuance monitoring stage, and the post-issuance supervision stage. The review and judgment results of each stage of the target historical project are compared with the review elements of the corresponding stage of the project to be reviewed. When there is a difference between the review elements of the project to be reviewed at any stage and the review elements of the corresponding stage of the target historical project, the difference items are extracted and a difference risk analysis is performed. When the historical compliance audit result of the target historical project is compliant and the differences between the project to be audited and the target historical project do not exceed the preset risk threshold after risk analysis, the production information of the project to be audited is determined to comply with the preset compliance rules; the preset risk threshold is a dynamic risk threshold determined based on the risk score distribution of differences of all historical projects in the historical audit database. When the historical compliance audit result of the target historical project is non-compliant, or the difference between the project to be audited and the target historical project exceeds a preset risk threshold after risk analysis, it is determined that the production information of the project to be audited does not comply with the preset compliance rules.

[0066] In this embodiment, the pre-issuance review stage refers to the compliance review stage conducted before the formal issuance of digital assets. It mainly includes verifying the qualifications of the issuing entity, conducting automated static analysis and formal verification of smart contracts, and comparing the consistency between the white paper document and the execution logic of the smart contracts.

[0067] In this embodiment, the monitoring phase during issuance refers to the real-time monitoring phase carried out during the issuance of digital assets. This mainly includes real-time monitoring of the blockchain network to capture transaction data, on-chain behavior analysis of investor addresses, and monitoring the execution status of smart contracts.

[0068] In this embodiment, the post-issuance supervision phase refers to the continuous supervision phase carried out after the digital asset issuance is completed. It mainly includes regularly acquiring on-chain circulation data and secondary market transaction data, detecting market manipulation and deviations in asset use, dynamically adjusting compliance scores, and generating continuous regulatory reports.

[0069] In this embodiment, the historical compliance audit results corresponding to the target historical project are obtained. The results include the audit judgment results of the target historical project at each stage of the pre-issuance audit stage, the issuance monitoring stage, and the post-issuance supervision stage. The audit judgment results of each stage of the target historical project are compared with the audit elements of the corresponding stage of the project to be audited. When there is a difference between the audit elements of the project to be audited at any stage and the audit elements of the corresponding stage of the target historical project, the difference items are extracted and the difference risk analysis is performed.

[0070] In this embodiment, the review results of each stage of the target historical project are compared with the review elements of the corresponding stage of the project to be reviewed. This includes: constructing a stage review element vector for the project to be reviewed and a stage review element vector for the target historical project. The stage review element vector includes the degree indicator value and status indicator Boolean value corresponding to the pre-issuance review stage, the contract call frequency and abnormal transaction identifier corresponding to the issuance monitoring stage, and the on-chain data flow characteristics and information disclosure completeness status corresponding to the post-issuance supervision stage. The difference between the stage review element vector of the project to be reviewed and the stage review element vector of the target historical project in each stage corresponding dimension is calculated to form a difference vector. When the absolute value of the difference in any dimension of the difference vector exceeds a preset dimension threshold, the review element corresponding to that dimension is determined as a difference item. The difference item is extracted, including the stage to which the difference item belongs, the name of the review element, the element value of the project to be reviewed, the element value of the target historical project, and the size of the difference, to generate a list of difference items. When the historical compliance review result of the target historical project is compliant and the difference items between the project to be reviewed and the target historical project do not exceed a preset risk threshold after risk analysis, the production information of the project to be reviewed is determined to comply with the preset compliance rules. The preset risk threshold refers to a dynamic risk threshold determined based on the distribution of risk scores for all historical projects in the historical audit database. Specifically, it can be determined by calculating the weighted sum of the mean and standard deviation of the risk scores for the difference items.

[0071] In this embodiment, the preset risk threshold refers to a dynamic risk threshold determined based on the distribution of difference item risk scores for all historical projects in the historical review database. Specifically, the difference item risk scores generated by all historical projects in the historical review database during their respective similarity matching processes are obtained. The difference item risk scores are calculated by weighting the importance weight of the stage to which the difference item belongs, the risk level weight of the review element corresponding to the difference item, and the quantitative value of the difference degree. The arithmetic mean μ and standard deviation σ of the difference item risk scores for all historical projects are calculated. In this embodiment, a preset risk threshold T = μ + k·σ, where k is a preset adjustment coefficient with a value range of [0.5, 3], used to dynamically adjust the threshold size according to the rigor of the audit. When the risk score of the difference between the project to be audited and the target historical project obtained after risk analysis is less than or equal to the preset risk threshold T, it is determined that the difference does not exceed the preset risk threshold; when the risk score of the difference is greater than T, it is determined that the difference exceeds the preset risk threshold. Through the above method, the preset risk threshold can be adaptively and dynamically determined according to the distribution characteristics of historical audit data, which avoids the problem that a fixed threshold cannot adapt to different project types, and makes the audit standard statistically reasonable and interpretable.

[0072] When the historical compliance audit result of the target historical project is non-compliant, or the differences between the project to be audited and the target historical project exceed the preset risk threshold after risk analysis, it is determined that the production information of the project to be audited does not comply with the preset compliance rules.

[0073] The beneficial effects of the above technical solution are: based on the historical compliance audit results corresponding to the target historical project, it can be determined whether the production information of the project to be audited complies with preset compliance rules.

[0074] Example 9: This invention provides a full-process compliance review system for digital asset issuance, such as... Figure 2 As shown, it includes: The acquisition module acquires project data for projects to be reviewed, including information on the issuing entity, smart contracts, and white paper documents. The determination module determines degree indicators and status indicators based on the project data; the degree indicators are used to characterize the audit elements in the project to be audited that are quantifiable and have continuous value characteristics; the status indicators are used to characterize the audit elements in the project to be audited that have binary qualitative characteristics and take two mutually exclusive states. The calling module calls several historical project data stored in the historical audit database. The historical project data includes historical degree indicator vectors, historical status indicator vectors, and historical compliance audit results. The calculation module determines the first similarity between the project to be reviewed and the historical project on the degree index and the second similarity on the status index. Based on the first similarity and the second similarity, it determines the target historical project with the highest similarity to the project to be reviewed. The judgment module determines whether the production information of the project to be reviewed complies with preset compliance rules based on the historical compliance review results corresponding to the target historical project. If it complies with the preset compliance rules, it generates a compliance pass result and allows the digital asset issuance of the project to be reviewed.

[0075] The beneficial effects of the above technical solution are as follows: By classifying and processing audit elements and employing differentiated similarity calculation methods, combined with dynamic weights, radial basis function mapping, time decay factors, and multi-stage comparisons, historical audit data can be fully utilized to reuse experience, significantly improving audit efficiency and accuracy, unifying audit standards, optimizing resource allocation, and achieving traceability and intelligence in audit decisions. This enables efficient, accurate, and traceable compliance audits.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for end-to-end compliance review of digital asset issuance, characterized in that, include: Step 1: Obtain project data for the project to be reviewed. The project data includes information on the issuing entity, smart contracts, and white paper documents. Step 2: Determine the degree index and the status index based on the project data; the degree index is used to characterize the audit elements in the project to be audited that are quantifiable and have continuous value characteristics; the status index is used to characterize the audit elements in the project to be audited that have binary qualitative characteristics and whose values ​​are only two mutually exclusive states. Step 3: Retrieve several historical project data stored in the historical audit database. The historical project data includes historical degree indicator vectors, historical status indicator vectors, and historical compliance audit results. Step 4: Determine the first similarity between the project to be reviewed and the historical project on the degree indicator, and the second similarity on the status indicator; based on the first similarity and the second similarity, determine the target historical project with the highest similarity to the project to be reviewed; Step 5: Based on the historical compliance audit results corresponding to the target historical project, determine whether the production information of the project to be audited complies with the preset compliance rules; if it complies with the preset compliance rules, generate a compliance pass result and allow the digital asset issuance of the project to be audited.

2. The method for full-process compliance review of digital asset issuance according to claim 1, characterized in that, Determining the first similarity between the project to be reviewed and historical projects on the degree indicator includes: for any degree indicator, obtaining the current value of the current project on the degree indicator, and the historical values ​​of each historical project on the degree indicator; The current value and each of the historical values ​​are normalized to obtain the normalized current value and each of the historical values. The first similarity is calculated based on the difference between the current normalized value and each of the historical normalized values, combined with the first weight of the degree index; wherein the first weight is determined based on the distribution characteristics of the historical values ​​of each historical item on the degree index, and the distribution characteristics include the quantity distribution and dispersion of the historical values.

3. The method for full-process compliance review of digital asset issuance according to claim 2, characterized in that, The first weight is determined in the following way: Obtain the historical values ​​of each historical item on the target degree index, and count the frequency of occurrence of each historical value; Determine the maximum frequency value corresponding to the historical value that appears most frequently; the maximum frequency value refers to the maximum number of times each historical value appears among all historical values ​​corresponding to the target degree index; The maximum frequency value is used to reflect the degree of concentration in the historical numerical distribution of the target level indicator; Calculate the standard deviation between historical values; The initial first weight of the target severity index is obtained by multiplying the ratio of the frequency of occurrence of the target's historical values ​​to the maximum frequency value by the standard deviation. Obtain the time difference between the generation time of each historical item and the current time; A time decay factor is determined based on the time difference; the time decay factor is negatively correlated with the time difference. The initial first weight is obtained by weighting and adjusting the initial first weight according to the time decay factor.

4. The method for full-process compliance review of digital asset issuance according to claim 1, characterized in that, Determining the second similarity between the project to be reviewed and historical projects on the status indicator includes: for any status indicator, obtaining the current Boolean value of the current project on the status indicator, and the historical Boolean values ​​of each historical project on the status indicator; When the current Boolean value is the same as the historical Boolean value, the difference is set to a first preset value; when the current Boolean value is different from the historical Boolean value, the difference is set to a second preset value; the difference is used to quantify the degree of difference between the current project and the historical project in the status indicator. Based on the difference, and in conjunction with the second weight of the status index, the second similarity is calculated; wherein, the second weight is determined based on the distribution characteristics of the historical Boolean values ​​of each historical item on the status index; the distribution characteristics include the frequency and dispersion of each historical Boolean value.

5. The method for full-process compliance review of digital asset issuance according to claim 1, characterized in that, The step of determining the target historical project with the highest similarity to the project to be reviewed based on the first similarity and the second similarity includes: determining the first project similarity between the current project and each historical project based on the first similarity, the second similarity and the third weight; Using a preset radial basis function, the first vector distance corresponding to the first similarity is mapped to a first similarity component, and the second vector distance corresponding to the second similarity is mapped to a second similarity component; the radial basis function is configured to map the vector distance to a monotonically decreasing similarity value; The harmonic mean of the first similarity component and the second similarity component is calculated as the second item similarity; wherein the harmonic mean is used to comprehensively evaluate the similarity between the degree index and the state index. The overall project similarity is determined based on the first project similarity, the second project similarity, and the fourth weight. The target historical item is determined based on the comprehensive similarity score.

6. The method for full-process compliance review of digital asset issuance according to claim 1, characterized in that, The third weight is determined in the following way: Obtain the confidence score of the historical compliance audit results corresponding to each historical project. The confidence score is determined based on the audit pass rate, risk event occurrence rate, and time difference between the audit time and the current time of the historical project. The third weight corresponding to each historical item is determined based on the ratio of the confidence score of each historical item to the sum of the confidence scores of all historical items.

7. The method for full-process compliance review of digital asset issuance according to claim 1, characterized in that, Determining the target historical item based on the comprehensive similarity score includes: Calculate the overall project similarity between the current project and all historical projects to obtain an overall project similarity set; The historical project corresponding to the maximum value in the comprehensive project similarity set is determined as the candidate target historical project; When the overall similarity of the candidate target historical item is greater than a preset similarity threshold, the candidate target historical item is determined as the target historical item. When the overall similarity of the candidate target historical project is less than or equal to the preset similarity threshold, a manual review process is triggered, and the historical project specified after manual review is determined as the target historical project.

8. The method for full-process compliance review of digital asset issuance according to claim 1, characterized in that, The step of determining whether the production information of the project to be audited complies with preset compliance rules based on the historical compliance audit results corresponding to the target historical project includes: Obtain the historical compliance audit results corresponding to the target historical project. The historical compliance audit results include: the audit judgment results of the target historical project at each stage of the pre-issuance audit stage, the issuance monitoring stage, and the post-issuance supervision stage. The review and judgment results of each stage of the target historical project are compared with the review elements of the corresponding stage of the project to be reviewed. When there is a difference between the review elements of the project to be reviewed at any stage and the review elements of the corresponding stage of the target historical project, the difference items are extracted and a difference risk analysis is performed. When the historical compliance audit result of the target historical project is compliant and the differences between the project to be audited and the target historical project do not exceed the preset risk threshold after risk analysis, the production information of the project to be audited is determined to comply with the preset compliance rules; the preset risk threshold is a dynamic risk threshold determined based on the risk score distribution of differences of all historical projects in the historical audit database. When the historical compliance audit result of the target historical project is non-compliant, or the difference between the project to be audited and the target historical project exceeds a preset risk threshold after risk analysis, it is determined that the production information of the project to be audited does not comply with the preset compliance rules.

9. A full-process compliance review system for digital asset issuance, characterized in that, A method for conducting a full-process compliance audit of digital asset issuance as described in any one of claims 1 to 8, comprising: The acquisition module acquires project data for projects to be reviewed, including information on the issuing entity, smart contracts, and white paper documents. The determination module determines degree indicators and status indicators based on the project data; the degree indicators are used to characterize the audit elements in the project to be audited that are quantifiable and have continuous value characteristics; the status indicators are used to characterize the audit elements in the project to be audited that have binary qualitative characteristics and take two mutually exclusive states. The calling module calls several historical project data stored in the historical audit database. The historical project data includes historical degree indicator vectors, historical status indicator vectors, and historical compliance audit results. The calculation module determines the first similarity between the project to be reviewed and the historical project on the degree index and the second similarity on the status index. Based on the first similarity and the second similarity, it determines the target historical project with the highest similarity to the project to be reviewed. The judgment module determines whether the production information of the project to be reviewed complies with preset compliance rules based on the historical compliance review results corresponding to the target historical project. If it complies with the preset compliance rules, it generates a compliance pass result and allows the digital asset issuance of the project to be reviewed.

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