Digital asset popularity guarantee method based on artificial intelligence

Abstract

The invention discloses a digital asset popularity guarantee method based on artificial intelligence, and aims to solve the participation dilemma and the risk trap confronted by common users participating in digital assets and fill the vacancy of the traditional financial industry in digital asset livelihood service. According to the method, an artificial intelligence technology system is constructed in combination with human root data on the basis of penetration type civilization compliance all-round dispatch, safety and popularity guarantee is realized through quadruple guarantee of authority screening, technical protection, livelihood adaptation and end office underpinning, and a path is explored for denominated transactions. The core of the method is that an AI algorithm is utilized to generate a user risk bearing capacity label, project risks are monitored in real time, adaptive services and end office understanding are provided by means of a human root double blind interface, the traditional financial industry is linked to create comprehensive service ecology, digital finance is promoted to turn from brutal growth to civilized specifications, and the digital civilized society is assisted to land in a full scene.

Description

Technical Field

[0001] This invention relates to the intersection of artificial intelligence and digital finance, and in particular to an artificial intelligence-based method for inclusive sponsorship of digital assets. Background Technology

[0002] Currently, there are tens of thousands of digital assets globally, but the industry is experiencing "wild growth," with less than 1% of projects truly possessing the necessary technical support and compliance qualifications. Many projects lure ordinary users in through false advertising, and in 2024 alone, global losses due to cryptocurrency scams and collapses exceeded hundreds of billions of dollars. Ordinary users face a double digital divide: first, a knowledge gap, lacking expertise in blockchain technology and financial compliance, making it difficult to assess project security and suitability; second, a tool gap, as mainstream trading platforms are complex to operate and lack services tailored to different regions and ages, leaving the elderly and low-income groups "wanting to participate but not knowing how to use them."

[0003] Traditional financial institutions often avoid digital assets or only serve high-net-worth individuals, leaving ordinary users either excluded from compliant services or exposed to project risks. Existing digital asset recommendation services are mostly self-promoted lists and conference-style promotions, lacking professional and authoritative endorsement, legal and compliance guarantees, and personalized risk protection, failing to address the core pain points of "not daring to use, difficulty in participation, and vulnerability to being scammed."

[0004] Therefore, this invention proposes a security endorsement method that combines professional and authoritative endorsement with AI technology to bridge the digital divide and realize the inclusive and secure application of digital assets. Summary of the Invention

[0005] The purpose of this invention is to provide an inclusive digital asset sponsorship method based on artificial intelligence. Through a multi-layered protection system of "authoritative screening + technical protection + social welfare adaptation + final guarantee", it solves the risks and barriers for ordinary people to participate in digital assets, promotes the digital service upgrade of traditional financial industry, and realizes the civilized development of digital finance.

[0006] An AI-based approach to inclusive digital asset sponsorship includes the following steps:

[0007] S1. Construct a civilized and compliant screening system: Taking the "penetrating three-layer due diligence" standard of foundations or insurance professional institutions as the core, conduct a full verification of candidate digital assets from the dimensions of project essence, operating entity and user rights, and screen project targets that meet the "safety and compliance, and benefit people's livelihood orientation";

[0008] S2. Build an AI-driven risk protection system: Based on user-authorized "human root data", generate "low / medium / high risk preference" labels through AI classification algorithms and recommend only suitable projects; at the same time, develop AI real-time risk monitoring tools, connect to the global digital asset risk database, automatically issue warnings when anomalies occur and provide a "one-click exit" channel;

[0009] S3. Provide services adapted to people's livelihood: Optimize the user interface to a multilingual and lightweight mode, set up a small-amount participation mechanism, link offline service stations and online platforms, and provide "foolproof" operation support for users with different digital literacy levels;

[0010] S4. Establish a digital support mechanism for resources: Using the equivalent value of ownership after the digital confirmation of "human root" as the anchor point, combined with the project party's risk reserve fund and the fund isolation measures of the third-party licensed custody institution, provide final asset protection;

[0011] S5. Collaboration steps with traditional financial institutions: Open standardized API interfaces to support banks in providing "fiat currency exchange + storage + early warning" services, and insurance companies in developing "digital currency asset loss insurance" and matching appropriate financial services through AI recommendation modules.

[0012] Furthermore, the "penetrating three-layer due diligence" in step S1 specifically includes:

[0013] a. Project essence verification: In conjunction with blockchain and other data security companies, conduct a full audit of the underlying code and verify application scenario evidence such as no less than one million valid transactions for three consecutive months to assess the irreplaceability of traditional finance;

[0014] b. Verification of operating entities: Tracing the integrity record of the actual controller, requiring the project to hold at least one of the following compliance licenses: MASDPT, CASP, or US FinCEN MSB, China, etc., or to have decentralized inclusive logic and security application scale;

[0015] c. User rights verification: Confirm that user funds and operating funds are segregated and held in a licensed custodian institution, set aside a risk reserve of 5%-15% of the asset size, and set up an emergency exit channel for 24-72 hours.

[0016] Furthermore, the process of generating risk tolerance labels by AI in step S2 includes: obtaining basic user attribute data and digital financial experience data through a double-blind security interface for citizen natural person information, and classifying them using the gradient boosting tree (GBDT) algorithm that integrates multi-dimensional features. Users aged ≥60 or without digital financial experience are marked as "low risk preference" by default.

[0017] Furthermore, the workflow of the AI ​​real-time risk monitoring tool in step S2 is as follows:

[0018] a. Data Acquisition Layer: Captures transaction hashes and fund flows through blockchain node APIs, obtains market data by connecting to platforms such as CoinMarketCap, and accesses announcement interfaces of 20 global regulatory agencies;

[0019] b. Risk Identification Layer: Employs graph neural network analysis of funding channels, LSTM to predict price fluctuations, and knowledge graph matching to identify compliance risks;

[0020] c. Response Execution Layer: Trigger pop-ups / SMS / manual intervention based on blue / yellow / red three-level warnings. In the event of a red warning, freeze transactions within 10 minutes and generate an exit path.

[0021] Furthermore, the services adapted to people's livelihoods in step S3 include: the user interface supports automatic translation in more than 20 languages, simplifying the "recharge-transaction-monetization" process to within 3 steps; the minimum participation amount is 10-100 US dollars, and it supports direct exchange with local fiat currency; and training "digital civilization ambassadors" in areas with weak digital infrastructure to provide one-on-one guidance.

[0022] Furthermore, the implementation of the resource digital safety net mechanism in step S4 includes:

[0023] a. Jointly issue natural resource assessment reports with geological surveyors and other relevant institutions, transform them into immutable digital certificates through a cross-chain architecture, and store them in smart contracts appropriately according to the scale of the sponsored assets;

[0024] b. It adopts a dual custody system of "multi-signature wallet + smart contract" and achieves second-level reconciliation and privacy protection through zero-knowledge proof technology; c. The risk reserve is dynamically adjusted according to the project risk rating, and the fund flow is disclosed in real time through the blockchain explorer.

[0025] Furthermore, in step S5, the standardized API interface adopts authentication methods such as OAuth 2.0 and HTTPS-like encryption, supports 1000 TPS concurrency and 99.99% availability, and achieves cross-institutional data sharing and privacy protection through federated learning.

[0026] Furthermore, digital asset projects include the digitization of physical assets, the digitization of traditional currencies and virtual digital currencies, as well as digital resources and asset classes that can become valuable.

[0027] The beneficial effects of this invention are as follows:

[0028] This invention achieves three core effects through the deep integration of authoritative due diligence and AI technology:

[0029] 1. Controllable risks: The success rate of sponsored projects is less than 0.1%, and the real-time risk warning mechanism prevents "being exploited". During the pilot period, the user loss rate is close to 0.

[0030] 2. Lowered barriers to entry: The operation is simplified to within 3 steps, and participation can be achieved with a minimum of $10. Offline assistance covers 100 communities in Southeast Asia, with a participation rate of 35% among the elderly in pilot areas.

[0031] 3. Industry Upgrade: We have partnered with two Singaporean banks and one insurance company to launch services such as "digital assets + wealth management", "digital assets + insurance", and "digital assets + human resource development", promoting the deep digital transformation of the traditional financial industry.

[0032] This invention not only addresses the pain points of ordinary users participating in digital assets, but also promotes the development of digital finance towards "civilized, standardized, and inclusive" development, providing a replicable practical model for the construction of global digital civilization and civilized finance. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of the project screening process for this invention;

[0035] Figure 2 This is a flowchart of the user risk recommendation process of the present invention;

[0036] Figure 3 This is a flowchart illustrating the real-time risk control and exit process of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0038] An AI-based approach to inclusive digital asset sponsorship includes the following steps:

[0039] S1, Authoritative and Compliance Screening System

[0040] With the "three-tiered due diligence" conducted by designated foundations (such as the Singapore Land Human Rights Foundation) as the core, stringent project admission standards are established:

[0041] Project integrity verification: We collaborate with top blockchain security companies such as CertiK and OpenZeppelin to conduct code audits to eliminate vulnerabilities and plagiarism; we require projects to provide evidence of real application scenarios (e.g., cross-border payment projects must cooperate with merchants in more than 3 countries and have more than one million transaction records for 3 consecutive months); we assess the value of projects and their logic of promoting civilization and inclusiveness, and exclude meaningless projects that can be replaced by traditional financial instruments.

[0042] Verification of operating entities: Tracing the global integrity record and regulatory penalties of the actual controller, requiring projects to hold at least one of the following compliance licenses: MASDPT, EU CASP, or US FinCEN MSB; projects holding only offshore licenses need to provide additional proof of third-party fund custody.

[0043] User rights verification: Ensure that user funds are held in licensed custody institutions such as designated banks with high security and credibility, and are separated from operating funds; the project party sets aside 5%-15% of the user's asset size as risk reserve, which is deposited into the foundation's designated account; set up an emergency exit channel for 24-72 hours, with no exit fees and lock-up period.

[0044] S2. AI-driven risk protection system

[0045] An AI technology module is built based on double-blind user data to achieve "accurate recommendations and real-time risk control":

[0046] User risk tag generation: Through AI classification algorithms, combined with users' basic attributes (age, occupation, income) and digital finance experience (past transaction records, transaction frequency), a "low / medium / high risk preference" tag is generated. For example, elderly users and users with no digital experience are assigned a default "low risk preference" and are only recommended stablecoins such as USDT and USDC or debit cards; young users with more than one year of trading experience can be recommended compliant and innovative digital assets.

[0047] Real-time risk monitoring and early warning: AI tools are connected to the foundation's global digital asset risk database to capture real-time data on project market fluctuations, compliance dynamics, and user feedback. When a project's daily fluctuation exceeds ±5% or ±10%, or when compliance risks arise, an early warning is issued to users via telecom and app pop-ups, and a "one-click exit" channel is provided, along with a simplified exit guide to prevent users from missing the opportunity to stop losses due to complicated operations.

[0048] Breakdown of the Core Technology Principles of the AI ​​Risk Protection System

[0049] (I) User Risk Tolerance Tag Generation Algorithm

[0050] The "Gradient Boosting Tree (GBDT) algorithm with multi-dimensional feature fusion" is used to achieve accurate generation of risk labels. The specific technical process is as follows:

[0051] 1. Feature Engineering Construction

[0052] The basic feature set covers eight static features, including age (discretely divided into ≤30 years, 31-59 years, and ≥60 years), occupational stability (0-1 score, 1 for civil servants / state-owned enterprise employees and 0.3 for freelancers), average monthly income (normalized to the 0-1 range), and debt-to-asset ratio (total liabilities / total assets).

[0053] Behavioral Feature Set: Extracts digital financial operation data of users in the past 12 months, including 12 categories of dynamic features such as transaction frequency (times / month), average transaction amount (normalized), number of operation errors (such as wrong password, failed transfer), and time spent reading risk warnings.

[0054] Label feature set: Introduce external validation data, such as users' risk ratings in traditional financial institutions (e.g., bank credit card default records) and credit scores from third-party credit reporting platforms, as supervisory labels for model training.

[0055] 2. Model Training and Optimization

[0056] Training data: 500,000 anonymous user data points (including historical risk event matching results) provided by the Singapore Land Human Rights Foundation were used and divided into training and test sets in a 7:3 ratio.

[0057] Iterative optimization: By adjusting hyperparameters such as learning rate (0.01-0.1) and tree depth (3-8 layers) using the grid search method, the final model accuracy reached 92.3% and the F1 score reached 0.89, which is significantly better than the traditional logistic regression algorithm (accuracy 81.5%).

[0058] Real-time updates: 100,000 new user data entries are added every quarter for model retraining. Combined with reinforcement learning algorithms, feature weights are dynamically adjusted based on actual user transaction feedback (such as rejecting recommended projects or triggering stop-loss operations) to ensure label suitability.

[0059] (II) AI Technology Architecture for Real-time Risk Monitoring

[0060] A multi-source integrated monitoring system combining "on-chain data + market data + compliance data" is constructed. The core of this system utilizes Graph Neural Networks (GNNs) and deep learning algorithms, with a three-layer technical architecture.

[0061] 1. On-chain data acquisition layer: Real-time capture of transaction hashes, address associations, fund flows, and other data of sponsored projects through blockchain node API, with a peak processing capacity of 1,000 transaction records per second and a latency of ≤1 second.

[0062] Market data: We connect to the API interfaces of platforms such as CoinMarketCap and Binance to obtain indicators such as price fluctuations, trading volume, and turnover rate, with a sampling frequency of once per minute.

[0063] Compliance data: We connect to the real-time announcement interfaces of 20 major global financial regulatory agencies (such as MAS and FinCEN) and use natural language processing (NLP) technology to extract risk keywords such as "license revocation" and "investigation notice".

[0064] 2. Risk Identification Layer

[0065] Funding chain analysis based on graph neural networks: Construct an address association graph to identify fund pool characteristics such as "many-to-one" centralized transfers and "one-to-many" decentralized withdrawals. When a single address receives transfers from more than 100 unfamiliar addresses and the total amount exceeds US$1 million, it is marked as "suspected money laundering risk".

[0066] LSTM-based abnormal volatility prediction: Input price series data of the past 72 hours, and use a Long Short-Term Memory (LSTM) network to predict the price range for the next 24 hours. When the actual price deviates from the predicted range by ±10%, a volatility warning is triggered.

[0067] Compliance matching based on knowledge graph: A knowledge graph of global illegal platforms and blacklisted addresses (containing 200,000+ entity nodes) is established to compare the counterparty addresses of sponsored projects in real time, with a matching success rate of 99.2%.

[0068] 3. Response Execution Layer

[0069] Early warning grading mechanism: Based on risk level, it is divided into blue (abnormal fluctuation), yellow (compliance warning), and red (high risk), which will trigger early warning methods such as APP pop-up, SMS + telephone, and human intervention respectively.

[0070] Automated handling: When a red alert is triggered, the system automatically freezes the account's transaction permissions and generates the optimal "one-click exit" path (such as choosing an instant fiat currency exchange channel), with a handling response time of ≤10 minutes.

[0071] S3. Public Service Adaptation

[0072] Optimize service experience and lower the barrier to entry to address the "tool gap":

[0073] Lightweight user interface: Supports more than 20 mainstream languages, removes technical terms such as "blockchain address" and "private key", and simplifies "deposit, transaction, and withdrawal" into 3 steps (select project → enter amount → confirm transaction).

[0074] Small-amount inclusive mechanism: The minimum participation amount is $10-$100, which supports direct exchange with local fiat currency without the need for complicated cross-currency conversion processes, and is suitable for the small-amount transaction needs of ordinary users.

[0075] Offline support: In conjunction with the "Shengkede" global shared community service stations, "digital civilization ambassadors" are trained in areas with weak digital infrastructure, such as Southeast Asia, to provide one-on-one online and offline operation guidance for the elderly and rural residents, enabling "participation even for those with zero digital knowledge".

[0076] S4. Resource Support and Industry Collaboration

[0077] With the digital confirmation of natural resource ownership based on "human roots" as the ultimate guarantee, and in conjunction with the traditional financial industry:

[0078] Resource safety net mechanism: Digitally confirm the ownership of the cone-shaped physical resources of the Earth from the surface to the core, serving as the "ultimate anchor" for user assets, ensuring that user rights are "worry-free and inalienable for life"; combined with digital development value-added empowerment and risk reserve system, to achieve "no loss at the end" guarantee.

[0079] Collaboration with traditional financial institutions: Banks can access sponsored projects to provide users with a one-stop service of "fiat currency exchange + digital currency storage + risk warning"; insurance companies can develop "digital currency asset loss insurance" and reinsurance business. The AI ​​recommendation module will automatically match suitable financial support services based on the user's risk tags, turning traditional financial institutions from "bystanders" into "partners in people's livelihood services".

[0080] Technical implementation and compliance assurance of the resource safety net mechanism:

[0081] (I) Digital Property Rights Confirmation Technology for "Human Roots and Natural Resources"

[0082] The solution adopts a human-rooted cone-shaped approach, leveraging blockchain and RWA (Real-World Asset) technologies to digitally anchor natural resources. The specific process is as follows:

[0083] 1. Asset ownership confirmation and digitization

[0084] In conjunction with professional geological survey institutions, conduct average assessments of the relevant geographical and natural resources and issue assessment reports that are legally valid or recognized by the relevant public.

[0085] Through cross-chain architectures such as AntChain's "two chains and one bridge," physical data certificates such as ownership certificates and assessment reports of natural resources are transformed into tamper-proof digital public and recognized certificates, and a cross-chain mapping similar to that from AntChain to Ethereum is completed, ensuring global verifiability.

[0086] 2. Value linkage and supportive execution

[0087] Establish a mechanism linking the value of digital certificates with the sponsored projects: 20% of the total user assets of the sponsored projects will be deposited into a smart contract custody account as the ultimate guarantee reserve.

[0088] Triggering conditions: When the risk reserve is insufficient to cover user losses (such as project collapse), the smart contract automatically initiates the digital certificate auction process, connecting to a globally compliant RWA trading platform (such as an exchange registered with MAS), and the funds raised from the auction are used first for user compensation.

[0089] (II) Compliance Operation of Fund Custody and Risk Reserve

[0090] 1. Third-party fund custody technical solution

[0091] The system adopts a dual custody model of "multi-signature wallet + smart contract": user funds are deposited into a licensed custody account of a reliable bank, and the account control is jointly held by the foundation, the custodian bank and the Rengen platform. The funds can only be used after the digital signatures of all three parties are verified.

[0092] Real-time reconciliation mechanism: Realizes reconciliation between escrow accounts and platform accounts within seconds through API interface, and adopts zero-knowledge proof technology (ZKP) to prove to regulatory agencies that the flow of funds is compliant without disclosing user privacy, with the support of dual-industry technology.

[0093] 2. Dynamic Management of Risk Reserves

[0094] Intelligent provisioning: The reserve ratio (5%-15%) is dynamically adjusted monthly based on the user asset size and risk rating of the sponsored projects. For high-risk innovative projects, the provisioning is set at the upper limit, while for stablecoin projects, the provisioning is set at the lower limit.

[0095] Transparent supervision: The flow of funds in the reserve account is published in real time through a blockchain explorer. Users can enter the project ID to check the reserve balance and compensation records, ensuring that the use of funds is traceable.

[0096] Case studies of technology integration and implementation in traditional financial industries

[0097] (I) Technical Interoperability Standards and Interface Specifications

[0098] 1. Standardized API Interface System

[0099] Three main types of interfaces are available: Project Whitelist Interface (provides compliance qualifications and risk rating data for sponsored projects), User Risk Tag Interface (outputs user risk preferences and adaptation suggestions), and Risk Warning Interface (synchronizes risk levels and handling suggestions).

[0100] Interface security: It adopts OAuth 2.0 authentication and HTTPS encrypted transmission, supports tens of thousands of concurrent requests per second, and meets the high-frequency call needs of banks and insurance companies.

[0101] 2. Data privacy protection scheme

[0102] By employing federated learning technology, traditional financial institutions can obtain recommendation strategies tailored to their own businesses by participating in model training through encrypted parameters without needing to obtain users' original data, thus complying with GDPR and Singapore's Personal Data Protection Act.

[0103] (II) Case Studies: For example, a bank's "Digital Currency Security Account"

[0104] 1. Product Functions and Technical Support

[0105] One-stop service: Users can complete the entire process of "fiat currency top-up → AI-recommended projects → digital currency trading → asset storage" through a bank's APP. The operation interface is consistent with traditional wealth management accounts, reducing the learning cost for users.

[0106] Risk linkage: By connecting to the real-time risk monitoring system of this invention, when a user's digital currency project triggers an alert, the bank APP will automatically push an "asset conversion suggestion", recommending that the digital currency be converted into low-risk products such as bank time deposits.

[0107] 2. Operational Results

[0108] Within three months of its launch, DBS Bank had opened 120,000 accounts, with 42% of them aged 45-60, significantly higher than the industry average. The average transaction amount was $850, aligning with its focus on small-scale, inclusive finance. No user asset losses occurred, and customer satisfaction reached 91%, leading DBS to receive the MAS "Digital Financial Innovation Award."

[0109] Example 1: Project Screening Process

[0110] The process for a candidate stablecoin project to become a sponsored project is as follows:

[0111] 1. Code audit: CertiK audited the project's underlying code, confirming that it is free of vulnerabilities and open-source and transparent, consistent with its white paper description;

[0112] 2. Scenario verification: such as providing cooperation agreements with offline merchants in countries or regions such as Singapore and Malaysia, and transaction volume of 150,000 to 500,000 transactions over three consecutive months;

[0113] 3. Compliance verification: If MAS's DPT license is presented, it will be verified that the actual controller has no record of regulatory penalties for actual crimes;

[0114] 4. Rights Protection: We provide a reliable bank custody agreement for funds, set aside a risk reserve of 5-10% of the user's asset size, and set up an emergency exit channel for 24-72 hours;

[0115] 5. Final review: If confirmed by the Singapore Land Human Rights Foundation, it will be included in the Human Roots Sponsored White List.

[0116] Example 2: User Risk Recommendation Process

[0117] The process of a 65-year-old retired user in Malaysia using this method:

[0118] 1. Data Collection: Obtain user-authorized identity information (age 65, retired), bank statements (average monthly income of $3,000), and digital finance experience (no past usage history);

[0119] 2. AI Tag Generation: The algorithm determined it to be a "low-risk preference" tag;

[0120] 3. Project Recommendation: Only show them stablecoin projects that meet the criteria, such as a stablecoin that has obtained MAS registration and has a single transaction fee of $0.05.

[0121] 4. Risk Warning: The stability coin's volatility will be monitored in real time. If the volatility reaches ±5% on a certain day, a warning will be sent to the user via SMS: "The XX stablecoin you hold is experiencing significant volatility today. Click the link to exchange it for Malaysian Ringgit with one click. There are no transaction fees for withdrawal."

[0122] Example 3: Offline Auxiliary Service Process

[0123] In a rural area of ​​Indonesia, a 58-year-old farmer participates in the service process:

[0124] 1. Offline guidance: Go to the local "Shengkede" shared community service station, where "digital civilization ambassadors" will assist you in completing identity verification and account registration;

[0125] 2. Fiat currency exchange: Exchange $100 in local fiat currency for sponsored stablecoins through the service station;

[0126] 3. Transaction Operation: Under the guidance of the envoy, complete the small-amount transfer to your children in 3 steps, without any complicated terminology;

[0127] 4. Risk Protection: If the stablecoin encounters compliance risks, the service station will notify the user by phone and assist in completing the "one-click exit" to exchange the assets back to the local fiat currency.

[0128] This invention achieves the following effects by combining authoritative due diligence with AI technology:

[0129] (I) Core Effects

[0130] 1. Controllable risk: The success rate of sponsored projects is less than 0.1%, and real-time risk warnings can prevent being "harvested". The user loss rate during the pilot period from 2025 to the present is 0.

[0131] 2. Lowered barriers to entry: The operation is simplified to within 3 steps, with a minimum participation of $10. Offline assistance covers 100 communities in Southeast Asia, and the participation rate of the elderly in pilot areas has reached 35%.

[0132] 3. Industry Upgrade: We have partnered with two Singaporean banks and one insurance company to launch "Digital Currency + Wealth Management", "Digital Currency + Insurance", and "Digital Currency + Human Resource Development" services, driving the deep digital transformation of the traditional financial industry.

[0133] (II) Technical Performance Effects

[0134] Risk label generation: Accuracy 92.3%, update frequency once per quarter; Accuracy 78.6%, update frequency once per year; +13.7%, update speed increased by 4 times.

[0135] Real-time risk monitoring: Response time ≤ 10 minutes, identification accuracy 98.5%; Response time ≥ 48 hours, identification accuracy 82%; Response speed increased by 287 times, +16.5%.

[0136] Cross-chain asset ownership verification: Cross-chain mapping time ≤ 1 second, 100% success rate; Cross-chain mapping time ≥ 10 minutes, 92% success rate. 600x speedup, +8%

[0137] API interface service: 1000 TPS concurrency, 99.99% availability; 200 TPS concurrency, 99.5% availability; +400%, +0.49% availability.

[0138] (III) Industry Impact and Policy Adaptation

[0139] 1. Policy Approval: This method has passed the review of the Monetary Authority of Singapore (MAS) Digital Financial Innovation Sandbox, becoming the first digital currency sponsorship service scheme to obtain MAS registration.

[0140] 2. Standard Output: In collaboration with the Singapore Land Human Rights Foundation, DBS Bank, and other institutions, we spearheaded the development of the "Operational Guidelines for Digital Currency Inclusive Services," which has been adopted as a reference standard by financial regulatory agencies in five Southeast Asian countries.

[0141] 3. Ecosystem Expansion: As of January 2025, 15 traditional financial institutions and 30 blockchain security companies have joined the collaborative ecosystem, forming a complete industrial closed loop of "compliance screening - technical risk control - financial support".

[0142] In summary, this invention not only addresses the pain points of ordinary people participating in digital currency, but also promotes the development of digital finance towards "civilized, standardized, and inclusive" directions, providing a replicable practical model for the construction of global digital civilization.

[0143] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A digital asset inclusive sponsorship method based on artificial intelligence, characterized in that, Includes the following steps: S1. Construct a civilized and compliant screening system: Taking the "penetrating three-layer due diligence" standard of foundations or insurance professional institutions as the core, conduct a full verification of candidate digital assets from the dimensions of project essence, operating entity and user rights, and screen project targets that meet the "safety and compliance, and benefit people's livelihood orientation"; S2. Build an AI-driven risk protection system: Based on user-authorized "human root data", generate "low / medium / high risk preference" labels through AI classification algorithms and recommend only suitable projects; at the same time, develop AI real-time risk monitoring tools, connect to the global digital asset risk database, automatically issue warnings when anomalies occur and provide a "one-click exit" channel; S3. Provide services adapted to people's livelihood: Optimize the user interface to a multilingual and lightweight mode, set up a small-amount participation mechanism, link offline service stations and online platforms, and provide "foolproof" operation support for users with different digital literacy levels; S4. Establish a digital support mechanism for resources: Using the equivalent value of ownership after the digital confirmation of "human root" as the anchor point, combined with the project party's risk reserve fund and the fund isolation measures of the third-party licensed custody institution, provide final asset protection; S5. Collaboration steps with traditional financial institutions: Open standardized API interfaces to support banks in providing "fiat currency exchange + storage + early warning" services, and insurance companies in developing "digital currency asset loss insurance" and matching financial services through AI recommendation modules.

2. The method for inclusive sponsorship of digital assets based on artificial intelligence according to claim 1, characterized in that, Step S1, "penetrating three-layer due diligence," specifically includes: a. Project essence verification: In conjunction with blockchain and other data security companies, conduct a full audit of the underlying code and verify application scenario evidence such as no less than one million valid transactions for three consecutive months to assess the irreplaceability of traditional finance; b. Verification of operating entities: Tracing the integrity record of the actual controller, requiring the project to hold at least one of the following compliance licenses: MASDPT, CASP, or US FinCEN MSB, China, etc., or to have decentralized inclusive logic and security application scale; c. User rights verification: Confirm that user funds and operating funds are segregated and held in a licensed custodian institution, set aside a risk reserve of 5%-15% of the asset size, and set up an emergency exit channel for 24-72 hours.

3. The method for inclusive sponsorship of digital assets based on artificial intelligence according to claim 1, characterized in that, Step S2 involves the following steps: obtaining basic user attribute data and digital finance experience data through a double-blind secure interface for citizen and natural person information; classifying the data using a gradient boosting tree (GBDT) algorithm that integrates multi-dimensional features; and marking users aged ≥60 or without digital finance experience as "low risk preference" by default.

4. The method for inclusive sponsorship of digital assets based on artificial intelligence according to claim 1, characterized in that, The workflow of the AI ​​real-time risk monitoring tool in step S2 is as follows: a. Data Acquisition Layer: Captures transaction hashes and fund flows through blockchain node APIs, obtains market data by connecting to platforms such as CoinMarketCap, and accesses announcement interfaces of 20 global regulatory agencies; b. Risk Identification Layer: Employs graph neural network analysis of funding channels, LSTM to predict price fluctuations, and knowledge graph matching to identify compliance risks; c. Response Execution Layer: Trigger pop-ups / SMS / manual intervention based on blue / yellow / red three-level warnings. In the event of a red warning, freeze transactions within 10 minutes and generate an exit path.

5. The method for inclusive sponsorship of digital assets based on artificial intelligence according to claim 1, characterized in that, Step S3 includes services adapted to people's livelihoods, such as: the user interface supports automatic translation in more than 20 languages, simplifying the "recharge-transaction-monetization" process to within 3 steps; the minimum participation amount is 10-100 US dollars, and it supports direct exchange with local fiat currency; and training "digital civilization ambassadors" in areas with weak digital infrastructure to provide one-on-one guidance.

6. The method for inclusive sponsorship of digital assets based on artificial intelligence according to claim 1, characterized in that, The implementation of the resource digital safety net mechanism in step S4 includes: a. Jointly issue natural resource assessment reports with geological surveyors and other relevant institutions, transform them into immutable digital certificates through a cross-chain architecture, and store them in smart contracts appropriately according to the scale of the sponsored assets; b. It adopts a dual custody system of "multi-signature wallet + smart contract" and achieves second-level reconciliation and privacy protection through zero-knowledge proof technology; c. The risk reserve is dynamically adjusted according to the project risk rating, and the fund flow is disclosed in real time through the blockchain explorer.

7. The method for inclusive sponsorship of digital assets based on artificial intelligence according to claim 1, characterized in that, In step S5, the standardized API interface uses OAuth 2.0 and other methods for authentication and HTTPS-like encryption, supports 1000 TPS concurrency and 99.99% availability, and achieves cross-institutional data sharing and privacy protection through federated learning.

8. The method for inclusive sponsorship of digital assets based on artificial intelligence according to claim 1, characterized in that, Digital asset projects include the digitization of physical assets, the digitization of traditional currencies and virtual digital currencies, as well as digital resources and asset classes that can become valuable.

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