Consumption allocation voucher-based closed ecosystem resource allocation method and system

Abstract

The invention provides a closed ecosystem resource allocation method and system based on a consumption allocation voucher. The core is that consumption data isolation processing is realized through a triple account binding mechanism (consumption account / right account / LTV account); distributing basic rights and interests according to the formula that a basic rights and interests distribution value = enterprise net profit * 60% * (user contribution value / total contribution value) by adopting a dynamic rights and interests distribution engine; the LTV voucher premium income is calculated according to the user standard contribution value * 10 (m-n), and m-n is smaller than or equal to 4 and smaller than or equal to 1 and smaller than or equal to 5, nlt; m < = 5; the integrated tax coprocessor deducts capital benefit tax in real time; and transaction risk grading interception is realized through a dynamic weight anti-fraud model (weight coefficient = Logistic regression output).

Description

Technical Field

[0001] This invention belongs to the field of resource allocation methods in the field of computer technology, and relates to an automated system for profit allocation through verifiable consumption vouchers, particularly an automated resource allocation method and system based on encrypted consumption vouchers. Specifically, it relates to profit allocation through verifiable consumption vouchers; dynamic premium realization of "LTV vouchers" (i.e., "Long-Term Consumer Rights Value-Added Vouchers") based on user baseline contribution values; and a comprehensive system and method for automated tax processing and consumer fraud prevention. Background Technology

[0002] The existing closed economic system has the following main technical defects: 1. Traditional membership mechanisms only offer discounts and cannot convert user spending into capital gains; 2. The LTV voucher allocation scheme requires users to actively subscribe, and fails to establish an automatic matching mechanism with users' baseline contribution values; 3. Electronic account systems generally do not embed modules for calculating and withholding capital gains tax; 4. The lack of real-time identification and defense mechanisms against consumer fraud leads to increased systemic risks. Therefore, there is an urgent need for innovative solutions that integrate the above functions. Patent Comparison Analysis: The prior art disclosed in patent CN114169548A (publication date March 11, 2022, invention title "Blockchain-based Consumer Rebate System") still has the following defects: ① The rebate model is limited to static points redemption and does not establish a dynamic rights appreciation mechanism with exponential growth (such as the $10^{(mn)}$ multiplication algorithm of LTV vouchers). ② The tax coprocessor is not integrated to enable real-time withholding of capital gains tax; ③ The passive fraud detection method (periodic audit) cannot generate a risk score in real time using dynamic weighting functions (such as a factor of 0.3 for amount fluctuation, 0.3 for time deviation, 0.2 for location deviation, and 0.2 for device fingerprint). The aforementioned defects prevent the system from coordinating the atomic operations of consumer data encryption, rights calculation, and tax withholding, resulting in low system collaboration efficiency. Existing technological limitations prevent the system from being implemented: (a) The automated conversion of consumption behavior into capital gains; (b) Real-time coordination between the dynamic premium of LTV certificates and the company's solvency; (c) Atomized operation of tax withholding and equity allocation; (d) Real-time fraud interception in high-concurrency scenarios; (e) The dynamic balance between exponential equity appreciation and system solvency is lacking; (f) Lack of a compliant exit channel design for equity certificates within a closed ecosystem; According to statistics from the National Commercial Data Monitoring Center (Report No. CNBDC-2025-001), existing closed systems suffer from insufficient liquidity of equity, resulting in an average user exit cycle of ≥18 months, and delayed fraud response causing an average annual loss of ¥1.27 billion (2024 Financial Security White Paper). Summary of the Invention

[0003] A Method and System for Resource Allocation in Closed Ecosystems Based on Consumption Distribution Certificates Technical issues to be resolved This invention aims to solve the following core technical problems: 1. Automatically convert consumption contribution value into capital gains. 2. The technology for automatically matching LTV certificate premium income is based on the user's baseline contribution value multiplied by 10^(mn), where n is the number of periods to enter and m is the number of periods to exit. - "Benchmark contribution value × 10^(mn)" is a technical design choice. The multiplication factor is constrained by the cash flow of the smart contract, and the actual return is limited by the company's solvency. - “Benchmark contribution value” refers to the portion of the consumption voucher designated as capital gains, and its value is equal to the retail price of the voucher card (excluding prepaid shopping credit). The calculation basis is shown in Example 1 (the ¥299 portion). - The upper limit of the LTV certificate doubling period is set at 4 (i.e., 10^4 = 10,000 times) to ensure that the premium income is within the company's solvency (see the cash flow verification function $Q_c \geq 1.1 \times Q_r$). 3. Embed a capital gains tax calculation and withholding module within the electronic account system. - By calling the tax authority's API through a tax coprocessor, the statutory tax rate can be obtained in real time with priority given to the tax coprocessor. - When there are no regional tax incentives, the tax shall be calculated according to the progressive tax rate for capital gains as stipulated in the Individual Income Tax Law. - The tax coprocessor manually executes Article 3, Paragraph 5 of the Individual Income Tax Law: Capital gains tax is levied at a rate of 20%, and the portion exceeding 500,000 yuan is taxed progressively at 30%; 4. Establish a real-time identification and defense mechanism for consumer fraud; 5. To address the rigid user exit mechanism caused by insufficient liquidity of equity certificates in closed ecosystems, a repurchase queuing mechanism with a holding period of ≥36 months will be established to balance system liquidity pressure; The core innovation of this invention lies in: ① It pioneered an automatic conversion mechanism from consumption contribution value to capital gains; ② Establish a dynamic premium model for LTV certificates based on user baseline contribution values; ③ Achieve atomized operations for tax calculation and equity allocation; Verified by the Financial Technology Certification Center (CFCA) of the People's Bank of China: - The LTV certificate premium model improves system liquidity by 47% (compared to traditional systems). - Dynamic fraud prevention mechanism reduces bad debt rate by 82%. (Testing standard: JR / T 0248-2024 "Financial Technology Innovation Risk Monitoring Specification") The comparative test data comes from the National Financial Technology Evaluation Center (NFEC) certified laboratory. The test environment is an Intel Xeon Platinum 8480C processor cluster with a stress test concurrency of 100,000 TPS.

[0004] Overview of Technical Solution A [User spends ¥300 (for example only) to purchase a spending allocation voucher] --> B [Activates a unique ID account] B --> C [Electronic account records raw data upon purchase] C --> D [Consumer Contribution Value = Σ(Consumer Amount × Product Weight)] D --> E [60% of net profit will be exchanged for "VCET Certificates" within the ecosystem, i.e., "Verifiable Cryptocurrency Equity Certificates (VCET)", based on the proportion of consumer contribution value] D -->F[LTV certificate premium returns are multiplied by 10^(mn) based on the user's baseline contribution value, where n is the entry period and m is the exit period; the enterprise's LTV certificate premium returns are distributed in a 10-fold increment and converted into equivalent VCET certificates within the ecosystem (the premium calculation function is activated only when mn≤4, and the holding period is ≥36 months to queue for repurchase; LTV certificate premium returns are subject to the enterprise's cash flow and smart contract conditions and do not constitute a guarantee of returns) - The total LTV certificate premium is deducted from the user's capital gains tax based on the statutory tax rate or the Individual Income Tax Law rate obtained in real time by the tax coprocessor in the location of the data operation company.

[0005] Core technology solutions To achieve the above objectives, the present invention adopts the following innovative solution: 1. Triple binding mechanism for consumption allocation voucher cards - Physical credential card / electronic account linked to user ID - Three separate accounts linked to the same ID: ▶ Consumer Account (Records raw data on purchases) ▶ Equity Account (Based on original shopping data, 60% of the company's net profit is allocated and converted into equivalent VCET vouchers within the ecosystem) ▶ LTV Certificate Account - The LTV certificate premium is multiplied by 10^(mn) based on the user's baseline contribution value, where n is the entry period and m is the exit period. The enterprise's LTV certificate premium is distributed in a 10-fold increment and converted into equivalent VCET certificates within the ecosystem (the premium calculation function is activated only when mn≤4, and the holding period is ≥36 months to queue for repurchase; the LTV certificate premium is subject to the enterprise's cash flow and smart contract conditions and does not constitute a guarantee of returns). - The total LTV certificate premium is deducted from the user's capital gains tax based on the statutory tax rate or the Individual Income Tax Law rate obtained in real time by the tax coprocessor in the location of the data operation company; 2. Closed-loop data flow verification system - Consumption allocation voucher card / electronic account consumption data → Only written to cloud server Data encryption uses the national standard SM4 algorithm + physical card private key signing. - All personal data processing complies with the anonymization standards stipulated in Article 24 of the Personal Information Protection Law, and noise data is added using differential privacy technology. 3. Data compliance measures: - Anonymization processing meets the requirements of Clause 6.3 of the Personal Information Security Specification GB / T 35273-2020. - Cross-border data transfers shall comply with Article 5 of the Measures for Security Assessment of Cross-border Data Transfers. 4. Dynamic Rights Allocation Logic # Definition of the rights allocation engine function Define the allocation engine (company net profit, total contribution value, user contribution value, entry period n, n minimum 1 maximum 5, exit period m, m minimum 2 maximum 5): # Step 1: Calculate VCET vouchers (representing 60% of net profit) User VCET voucher = Enterprise net profit × 0.6 × (User contribution value ÷ Total contribution value) # Step 2: Calculate the LTV premium (ensure mn ≤ 4) Actual doubling period = Exit period m - Entry period n If the actual doubling period is >4: Actual doubling period = 4 # Maximum doubling period is forcibly limited to 4 Multiplication factor = 10 raised to the power of (actual multiplication period) # Step 3: Calculate the LTV certificate premium (with a 10^4 times upper limit). Maximum earnings cap = User's baseline contribution value × 10000 # Maximum limit of 10000 times Pre-tax LTV premium = User baseline contribution value × Multiplier If the pre-tax LTV premium is greater than the maximum return cap: Pre-tax LTV certificate premium = Maximum yield cap # Step 4: Tax Processing ▶ 1. Verify taxpayer identity (ID number / corporate credit code) using the tax authority's API. ▶ 2. Automatically match regional tax incentive policies ▶ 3. When there is no regional preferential tax rate, the tax shall be withheld according to the real-time tax rate of the tax authority or the statutory tax rate. # Step 5: Calculate after-tax income Capital gains tax = Pre-tax LTV (Liquidity Value) × Applicable tax rate After-tax LTV premium gain = Before-tax LTV premium - Capital gains tax # Step 6: Summarize Total Equity Total equity = User VCET certificates + After-tax LTV certificate premium income # Return the encrypted VCET certificate Return VCET certificates (total equity) Implementation details of the cash flow verification function: Let the company's current cash flow be $Q_c$, the total amount of repurchase requests be $Q_r$, and the solvency verification result be: \text{Validation passed} = \begin{cases} \text{true}&\text{if} Q_c \geq 1.1 \times Q_r \\ \text{false}&\text{otherwise} \end{cases}Validation passed={true falseifQc≥1.1×Qrotherwise This function is executed via Hyperledger Fabric chaincode, and the trigger condition is embedded in Step 3.2 of the equity allocation smart contract. 5. Three-tiered dynamic anti-fraud mechanism: Response measures are triggered based on real-time risk scores: ┌────┬─────────┬────┐ │ Risk Dimension │ Detection Parameters │ Weighting Coefficient │ ├────┼─────────┼────┤ │ Amount Fluctuation │ Current Amount / Monthly Average Amount Ratio │ 0.3 │ │ Time Deviation │ Transaction Time Offset │ 0.3 │ Location Deviation | Geographical Distance Deviation | 0.2 | │ Device fingerprint │ Device conformance verification │ 0.2 │ └────┴─────────┴────┘ # Dynamic weight function upgrade def Dynamic weights (user credit rating, recent risk density): If a user's credit rating is "high net worth" and their recent risk density is less than 0.2: return [0.25, 0.25, 0.25, 0.25] # Balanced mode elif recent risk density > 0.5: return [0.4, 0.3, 0.2, 0.1] # Enhanced amount monitoring else: return [0.3, 0.3, 0.2, 0.2] # Standard mode The weighting coefficients were trained using a Logistic regression model, and the training dataset contained 100,000 historical fraudulent transaction samples. Attached Figure Description

[0006] Figure 1 System overall architecture diagram The overall architecture of the closed ecosystem based on consumption distribution vouchers is demonstrated, including: - Physical credentials / electronic accounts held by consumers - Cloud server (data storage and processing core) - Three-account binding relationship (consumption account, rights account, LTV certificate account) - Enterprise side (supermarket A / B / C / franchise / direct operation) - Data operation company (equity buyback and tax withholding) - Tax authorities (tax rate agreement port access, interface specification GB / T 39786-2021) Figure 2 Diagram illustrating the binding of three accounts for consumption allocation vouchers. Detailed display: 1. Physical voucher structure (Front: design, trademark, anti-counterfeiting features; Back: trademark, card number, password, company culture, philosophy, and values) 2. Electronic account three-account binding relationship: - Consumer Account: Records purchase time, amount, and product weight. - Equity Account: Displays the balance of available VCET certificates. - LTV Voucher Account: Displays the status of consumption allocation voucher card fees and LTV voucher premium earnings. Figure 3 Data Flow and Rights Allocation Flowchart The following technical process is demonstrated: 1. Consumption data records (encrypted and written to cloud server) 2. Calculation of Consumption Contribution Value (Consumption Amount × Product Weight) 3. Equity allocation engine operation (VCET certificate allocation + LTV certificate premium calculation) 4. Withholding tax (tax withheld on the premium of LTV voucher at the applicable tax rate) - The tax withholding module calls the Golden Tax System Phase IV API (interface specification GB / T 39786-2021) to obtain real-time tax rates. 5. Final distribution of rights (VCET certificates) Figure 4 Fraud Prevention Detection Response Flowchart Demonstrating the complete process of the three-tiered fraud prevention mechanism: 1. Real-time monitoring of transaction behavior 2. Risk scoring model calculation (amount fluctuation, time deviation, location deviation) 3. Trigger different response levels based on risk values. 4. Implement appropriate safety measures. Figure 5 Tax Coprocessor Workflow Diagram Process nodes: 1. Receive LTV certificate premium request 2. Verify taxpayer identity - Verify the taxpayer's eligibility to pay taxes (in accordance with Article 28 of the Law on the Administration of Tax Collection) 3. Check regional preferential policies 4. Enable segmented tax rate calculation - Generate electronic tax vouchers (compliant with GB / T 36632-2018 "Specifications for Electronic Tax Vouchers") 5. Generate tax payment certificate 6. Convert VCET vouchers Detailed Implementation

[0007] Legal Compliance Statement 1. Definition of LTV certificate attributes: - According to the "Announcement on Preventing Risks of Token Issuance Financing" (issued by the People's Bank of China and seven other ministries in 2017), the VCET vouchers in this scheme are only valid for consumption deduction within the closed ecosystem. - The repurchase of VCET certificates does not involve secondary market trading, thus circumventing the definition of "securities" in Article 2 of the Securities Law. - VCET certificates do not have monetary attributes and are prohibited from secondary market trading. When users exit, the transfer of rights can only be achieved through in-system consumption or corporate repurchase channels. 2. Basis for tax treatment: - The capital gains tax withholding module strictly follows Article 9 of the Individual Income Tax Law and State Taxation Administration Announcement No. 19 of 2020. - The capital gains tax withholding module complies with Article 32 of the "Detailed Rules for the Implementation of the Law of the People's Republic of China on the Administration of Tax Collection". When a user's accumulated income exceeds ¥500,000, the annual tax settlement procedure is automatically triggered. 3. The repurchase mechanism complies with Article 15 of the "Regulations on the Prevention and Handling of Illegal Fundraising": - The repurchase funds come from the company's operating cash flow, and there is no guarantee of principal and return. - The buyback queuing mechanism has a single-user limit (≤¥500,000 / month) 4. Basis for solvency supervision: The system monitors the company's asset-liability ratio (≤70%) and cash flow coverage ratio (≥110%) in real time, which complies with Article 15 of the "Regulations on Preventing and Handling Illegal Fundraising" and the requirements of the China Banking and Insurance Regulatory Commission's document No. 18 of 2023. System deployment environment: Blockchain node: Hyperledger Fabric 2.5 Encryption module: SM2 / 3 / 4 chip certified by the State Cryptography Administration Tax coprocessor: Certified at Level 3 of the "Tax Information System Security Specification"; All data within the ecosystem is stored within China; Example 1: Supermarket Ecosystem (Company A / B / C / Franchise / Direct Operation) 1. Activation of Consumption Allocation Certificate - A user pays ¥300 (for example only) to purchase a spending allocation voucher card (with ¥1 shopping credit pre-loaded on the card + ¥299 (for example only) the retail price of the voucher card). - The system generates a unique ID and binds it to three accounts. - KYC Real-Name Authentication Requirements: ▷ UnionPay Authentication: Bank Card + Mobile Number + Name ▷ Public Security Database Authentication: ID Card Scan + Facial Recognition - Restrictions on unverified accounts: ▶ Only activate the spending account ▶ Equity / LTV Certificate Account Freeze 2. Consumption data records | Consumer behavior | Technological implementation | |----------|----------| | Purchase ¥50 at Supermarket A | System reads electronic account information → Cloud server writes encrypted data packet | |Spend ¥1000 at Supermarket B | The system reads the ID electronic account information → The cloud server writes an encrypted data packet | 3. Month-end equity distribution - Basis for setting "Product Weighting Coefficient" The weighting coefficients are derived from historical sales data of the enterprise, using the formula: $W_i = \frac{\text{Net profit margin of product i}}{\text{Average net profit margin of all categories}}$ - Key fragmentation scheme: "Key fragmentation adopts the Shamir secret sharing scheme, with a polynomial:" $f(x)=a_0+a_1x+a_2x^2 \mod p$ Where $a_0$ is the original key, $s_i=(i,f(i))$ is the fragmentation, and $p$ is a 256-bit prime number. - User's monthly contribution of ¥50 → Company A's monthly net profit margin of 15% → System automatically extracts ¥4.5 (60%) → User's equity account receives ¥4.5 VCET vouchers. User's monthly contribution value of ¥1000 → Company B's monthly net profit margin of 20% → System automatically extracts ¥120 (60%) → User's equity account receives ¥120 VCET vouchers. 4. Realizing LTV certificate premium gains (1) Preconditions for repurchase: (a) When the enterprise-side blockchain node verifies cash flow data to the smart contract threshold; (b) Single-period repurchase amount ≤ 10% of the company's net cash flow (c) The Company may repurchase the shares at an opportune time; -For damage to national interests and reputation - Damage to national interests and reputation - Damaging the reputation and interests of the wealth distribution ecosystem of "Consumer Capitalization Certificates (CCC)". - Damaging the interests and reputation of employees, family members, and members in the wealth distribution ecosystem of "Consumer Capitalization Certificate (CCC)" - Users who seriously damage the interests and reputation of others are prohibited from premium repurchase. They can only be compulsorily repurchased at the retail price of the purchased consumption distribution voucher card, and their electronic accounts will be permanently blocked (d) LTV voucher account freezing and unfreezing mechanism Add a processing flow for special account status: graph TD A[Trigger freezing condition] --> B{Freezing type} B --> |Damaging ecological interests| C[Compulsory repurchase + permanent account block] B --> |Tax anomaly| D[Suspending LTV voucher income distribution] B --> |Fraudulent behavior| E[Freezing the permissions of three accounts] D --> F[Taxpayer pays the tax and late fees] F --> G[Verification by tax co-processor] G --> H[Unfreezing the LTV voucher account] (2)Legal basis for freezing: - Article 8 of the Consumer Rights and Interests Protection Law: Users have the right to know the reasons for freezing, and the system needs to push the "Account Status Notification Letter" to users within 24 hours after freezing; - Article 32 of the Tax Collection and Administration Law: For tax anomaly freezing, an electronic voucher of the "Tax Payment Notice" needs to be generated synchronously; (3)Example of realizing the theoretical value of the premium income of LTV vouchers: - The user holds the first-phase voucher card, with a benchmark contribution value in a 10-fold multiplication mode. It is cashed out at 10 times at the 2nd phase, 100 times at the 3rd phase, 1000 times at the 4th phase, and 10000 times at the 5th phase. Similarly, if the user holds the second-phase voucher, with a benchmark contribution value in a 10-fold multiplication mode, it is cashed out at 10 times at the 3rd phase, 100 times at the 4th phase, and 1000 times at the 5th phase. Similarly, if the user holds the third-phase voucher, with a benchmark contribution value in a 10-fold multiplication mode, it is cashed out at 10 times at the 4th phase and 100 times at the 5th phase. Similarly, if the user holds the fourth-phase voucher, with a benchmark contribution value in a 10-fold multiplication mode, it is cashed out at 10 times at the 5th phase; - Theoretical calculated value of the premium income of LTV vouchers: - Theoretical calculated value of the premium income of LTV vouchers = user's benchmark contribution value * 10^(m - n), entering at the 1st phase and exiting at the 4th phase (where 1 ≤ n ≤ 5, n < m ≤ 5, and m - n ≤ 4); The calculation is as follows: The theoretical calculated value of the LTV voucher premium income = 299 × 10^(4 - 1) = 299 × 10^3 = 299,000; Definition of core parameters: >1. User's benchmark contribution value: When the user purchases a consumption allocation voucher card, 1 yuan of shopping money + 299 yuan of the retail price of the voucher card. The 299 yuan of the retail price of the voucher card is designated as the benchmark contribution value for calculating the LTV voucher premium income; >2. Time node contribution value: The cycle doubling coefficient generated when the user enters from the nth period to the mth period (n < m) and exits (where 1 ≤ n ≤ 5, n < m ≤ 5, and m - n ≤ 4). The calculation formula is 10^(m - n); - Tax deduction processing (tax rate 12%): 299,000 × 0.12 = 35,880 yuan The tax deduction process for the theoretical calculated value of the LTV voucher premium income: >1. The data operation company calls the tax rate API through the tax co-processor; preferentially uses the tax co-processor to obtain the legal tax rate in real time >2. When preferentially using the tax co-processor to obtain the legal tax rate in real time is not activated, the capital gains tax is withheld by default according to the tax rate of the "Individual Income Tax Law"; >3. After tax deduction, the funds are automatically converted into equivalent VCET vouchers, and the source identifier of "after-tax LTV voucher income" is marked - Net income theoretical calculated value: 299,000 - 35,880 = 263,120 yuan - The user manually signs a transfer agreement through the e-account APP, and completes the transfer of rights through the blockchain NFT destruction protocol (the NFT destruction protocol follows Article 9 of the "Regulations on the Administration of Blockchain Information Services") - The data operation company completes the system registration of equivalent VCET vouchers after receiving the card; Note: The LTV voucher premium income in the example is a theoretical value. The actual payment is subject to the enterprise's current cash flow and the verification result of the smart contract, and does not constitute an income promise; The VCET voucher is only limited to consumption and shopping within the ecosystem; 5. Hierarchical encryption system - Data transmission: TLS 1.3 + rotation of dual certificates of national secret SM2 - Data storage: (1). The original data is encrypted by the SM4 algorithm (2). The encryption key is stored in fragments (5 out of 3 threshold scheme) (3). Key operation logs are stored on the blockchain for evidence (4) Personal data processing complies with Article 24 of the "Personal Information Protection Law", and the anonymization process adopts the ISO / IEC20889 standard 6. Anonymization is performed according to ISO / IEC 20889 standard, and the noise addition δ satisfies $\delta \geq \frac{\sqrt{2\ln(1.25 / \sigma)}}{\epsilon}$ ($\epsilon=0.1, \sigma=0.01$). Anonymization parameters: differential privacy budget $\epsilon=0.1$, confidence level $\sigma=0.01$, noise level $\delta\geq 3.04$; All performance data were supported by a test report issued by the National Financial Technology Evaluation Center (NFEC) (report number NFEC-PAT20250722-001), and the testing equipment complies with the ISO / IEC 17025 laboratory accreditation standard. |Test Items| Traditional Systems| This Invention| |---------------|-----------|---------------| Fraud detection speed: 8.2 hours | 0.17 seconds |Tax processing error rate| 12.7% | 0.33% | |Data encryption strength| AES-128 |SM4+SM2 dual authentication|.

[0008] Example 3: Tax Exception Handling Process 1. When the tax coprocessor detects an anomaly in the tax rate API (response code ≠ 200): a) Automatically switch to the backup tax rate database (manually updated quarterly). b) Trigger a blockchain alert transaction (TxType=TAX_ALERT) 2. Handling taxpayer objections: - Submit the Tax Review Application via electronic account - The tax coprocessor generates a difference report (Delta_Report). - Overpaid taxes will be manually reviewed and refunded within 7 business days (in the form of VCET vouchers).

[0009] Verified by the National Financial Technology Evaluation Center (NFEC): - The LTV voucher premium calculation module has a response latency of ≤47ms under a concurrent load of 100,000 requests. - The fraud prevention model has an accuracy rate of 98.2% (F1 score 0.963). - Tax withholding atomic operations have a success rate of 99.97%. Test report number: NFEC-PAT20250722-001.

[0010] The implementation of this patented solution must comply with the following laws and regulations. Financial Regulation: Article 18 of the Regulations on Non-bank Payment Institutions Tax compliance: Article 25 of the Law on the Administration of Tax Collection Data security: Article 24 of the Personal Information Protection Law Blockchain Specifications: Article 9 of the "Regulations on the Management of Blockchain Information Services".

[0011] Patent Compliance Declaration 1. The technical solution circumvents the restrictions of the "Announcement on Preventing Risks of Token Issuance Financing": - VCET tokens do not have an on-chain transfer interface. - The repurchase channel requires manual submission of a "Confirmation of Transfer of Rights" (in accordance with Article 543 of the Civil Code). 2. The tax module complies with State Taxation Administration Announcement No. 7 of 2023: - Tax rate database update cycle ≤ 15 calendar days - The format of the tax payment certificate meets the requirements of Article 12 of the "Administrative Measures for Tax Receipts". 3. Data security implementation standards: - Anonymization processing complies with GB / T 37964-2019 "Guidelines for De-identification of Personal Information" - Key sharding storage complies with GM / T 0054-2018 "Technical Specification for Cloud Server Cryptographic Machines" 4. Solvency monitoring mechanism: - Cash flow coverage monitoring frequency ≥ 1 time / day - When the debt-to-asset ratio exceeds the standard, the "Risk Disposal Plan" (Attachment 3 of Document No. 18 of the China Banking and Insurance Regulatory Commission

[2023] ) will be automatically triggered.

Claims

1. A method for resource allocation in a closed ecosystem based on consumption distribution vouchers, characterized in that... It includes the following steps: (1) The user purchases a consumption allocation voucher, completes KYC real-name authentication, activates the unique ID, and binds the consumption account, rights and interests account, and LTV voucher account; (2) Consume at affiliated merchants or directly-operated merchants through the consumption allocation voucher / electronic account. The consumption data is encrypted by SM4 and written into the cloud server. The commodity category weight coefficient is generated through the enterprise's historical sales data, and the formula is: $W_i=\frac{\text{Net profit rate of commodity}i}{\text{Average net profit rate of all categories}}$; (3) Calculate the user's consumption contribution value: Consumption contribution value = Σ (Consumption amount × Commodity category weight coefficient); (4) The rights and interests distribution engine executes: - VCET voucher distribution: User's VCET voucher = Enterprise net profit × 0.6 × (User contribution value ÷ Total contribution value). 60% of the enterprise net profit is distributed to the rights and interests account according to the proportion of the user contribution value; - LTV voucher premium distribution: Calculate the pre-tax income according to the user's benchmark contribution value × 10^(m - n), where n is the entry period (1 ≤ n ≤ 5), m is the exit period (n < m ≤ 5 and m ≤ 5), and m - n ≤ 4 and the holding period ≥ 36 months are satisfied; (5) Tax processing: Call the tax authority API through the tax co-processor, and preferentially use the tax co-processor to obtain the legal tax rate in real time; when the legal tax rate obtained by the tax co-processor in real time cannot be activated, calculate the tax according to the progressive tax rate of capital gains tax stipulated in the "Individual Income Tax Law" when there is no regional tax preference; (6) Convert the after-tax income into VCET vouchers within the ecosystem. The conversion process needs to meet: - The conversion ratio of the after-tax amount to the VCET voucher is 1:1 - The VCET voucher is only used for consumption deduction within the closed ecosystem.

2. The method according to claim 1, characterized in that: The consumption allocation voucher includes a dual-channel of physical voucher and electronic account. The physical voucher includes a card number and a password, and the electronic account adopts a three-account independent binding architecture.

3. The method according to claim 1, characterized in that: The calculation of the LTV voucher premium income needs to meet: - LTV voucher premium income = User's benchmark contribution value × 10^(m - n); max (User's benchmark contribution value × 10000), where 10^(m - n) is an exponential growth model; Note: The user's benchmark contribution value = The retail price part of the consumption voucher (excluding the pre-deposited shopping money) is the part designated as capital income in the consumption allocation voucher; - When m - n > 4, the actual doubling period is forcibly set to 4; when the user's holding period < 36 months, the LTV voucher premium calculation function is disabled; - The LTV voucher premium income is restricted by the enterprise's cash flow and smart contract conditions and does not constitute a guaranteed income; - The calculation of the LTV voucher premium income needs to meet the following constraints at the same time: a) The enterprise's current cash flow ≥ 110% of the total repurchase request amount, b) The user's holding period ≥ 36 months c) m - n ≤ 4 and 1 ≤ n ≤ 5, n < m ≤ 5 d) The LTV voucher premium income is only issued when the solvency verification is met. The verification conditions are: The company's current cash flow $Q_c$ ≥ the total amount of repurchase requests $Q_r$ × 110%. Where $Q_r$ = Σ(pre-tax profit of LTV certificates requested by the user for repurchase).

4. The method according to claim 1, characterized in that: The KYC real-name authentication includes: UnionPay authentication: Bank card binding + mobile phone number verification + name verification; - Public Security Database Authentication: ID card scanning + facial liveness detection; Users who have not passed authentication have only activated their consumption accounts; their benefit accounts and LTV voucher accounts are frozen.

5. The method according to claim 1, characterized in that: It also includes a three-tiered fraud prevention mechanism, which triggers response measures based on real-time risk score values: - Level 1 Response (1.5 ≤ Risk Value < 2.0): Biological Validation; - Level 2 Response (2.0 ≤ Risk Value < 2.5): Transaction paused + SMS verification; - Level 3 Response (Risk Value ≥ 2.5): Account Freeze + Regulatory Reporting; The risk value is calculated as follows: 0.3 × amount fluctuation coefficient + 0.3 × time deviation coefficient + 0.2 × location deviation coefficient + 0.2 × device fingerprint coefficient. The weight coefficients are dynamically adjusted using a Logistic regression model. The model training dataset contains 100,000 historical fraud transaction samples. - The dynamic weight adjustment module is deployed in a hardware security module (HSM) certified by the State Cryptography Administration; - Risk scores are calculated using the ISO / IEC 27005 certified risk assessment framework.

6. The method according to claim 5, characterized in that: - Amount Fluctuation Coefficient = Transaction Amount / Average Monthly Spending Amount of the User - Time Deviation Factor = |Actual Transaction Time - User's Typical Transaction Time| / 3 (hours) - Location deviation coefficient = min(distance between actual transaction location and commonly used location / 100 km, 1.0) - Device fingerprint coefficient = 0 (consistent with historical devices) or 1 (new device / inconsistent) The weighting coefficients are dynamically adjusted based on the user's credit rating and recent risk density. When the recent risk density is >0.5, [0.4, 0.3, 0.2, 0.1] is used; otherwise, [0.3, 0.3, 0.2, 0.2] is used.

7. The method according to claim 1, characterized in that: The realization of the premium income from the LTV certificate requires the fulfillment of a repurchase prerequisite: (a) When the enterprise-side blockchain node verifies cash flow data to the smart contract threshold; (b) The total amount of a single repurchase transaction shall not exceed 10% of the company's net cash flow; (c) The Company may repurchase and resell the shares at an opportune time; (d) The premium income from LTV certificates is subject to corporate cash flow and smart contract conditions and does not constitute a guarantee of income.

8. The method according to claim 1, characterized in that: The data encryption architecture uses the Shamir secret sharing scheme for key shard storage, ensuring that any three of the five shards can be reconstructed keys: - Transport layer: TLS 1.3 protocol + SM2 dual certificate rotation mechanism; - Storage layer: SM4 block encryption + key fragmentation storage (3 out of 5 threshold scheme).

9. A system for implementing the method according to any one of claims 1-8, characterized in that... include: - Consumption allocation vouchers (physical vouchers and electronic accounts); - Cloud server (including data encryption module, contribution value calculation engine, and rights and interests distribution engine); - Anti-fraud module (real-time risk scoring model and three-level response executor); - Tax coprocessor (tax rate storage and tax calculation module).

10. The system according to claim 9, characterized in that: The dynamic weight adjustment module uses a Logistic regression model trained with 100,000 fraudulent transaction samples; the risk dimension weight coefficients are dynamically adjusted. When the recent risk density ($D_r=\frac{\sum_{i=1}^{30}risk event_i}{30}$) > 0.5, the amount fluctuation weight is automatically increased to 0.4 and the device fingerprint weight is reduced to 0.1.

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