An internet-based cross-platform consumer green point subsidy coupon use method

Abstract

The application discloses an Internet cross-platform consumer green point subsidy coupon use method, relates to the technical field of low-carbon consumption incentive and electronic commerce settlement, and discloses the following technical scheme: a carbon right identifier is separated from user account binding, so that coupon rights and interests are attached to the goods itself; when the goods are resold, the system automatically splits the incentive to the buyer and seller, guarantees that the green value runs through the whole life cycle of the goods; the second-hand buyer can obtain the environmental protection subsidy without repeated consumption, and improves the willingness of idle resource circulation; the alliance chain coupon pool aggregates multiple types of transaction platforms, and based on the graph theory reduction algorithm, the complex debt network is compressed into net settlement; cross-platform coupon cancellation no longer depends on manual reconciliation, settlement delay is reduced from hours to near real-time, and platform fund occupation cost is reduced; the carbon footprint value of the goods is cross-verified by the database of the Ministry of Industry and Information Technology and the white list platform, and the coupon value is dynamically compressed for the virtual labeling behavior; the merchant cannot arbitrage by tampering with single-platform data, and the subsidy flow is real low-carbon goods.

Description

Technical Field

[0001] This invention relates to the field of low-carbon consumption incentives and e-commerce settlement technology, and in particular to a method for using consumer green points subsidy vouchers based on cross-platform internet. Background Technology

[0002] Currently, the consumer green points subsidy voucher system mainly relies on e-commerce and local life platforms. Users earn points by purchasing low-carbon products, such as energy-saving appliances and organic food, and can redeem them for discounts on goods or services within the platform. As the scale of the second-hand trading market expands, users are increasingly reselling green products to second-hand platforms. Existing solutions adopt a closed-loop design within the platform, with vouchers only linked to the initial purchaser's account, and the points automatically expire after the product is resold. Some emerging platforms are trying to introduce cross-platform mutual recognition alliances, allowing points issued by JD.com to be redeemed at partner merchants, such as specific chain supermarkets, but the alliance scope is limited to leading companies and does not cover the second-hand trading scenario.

[0003] In current resale scenarios, the value of points is fragmented: vouchers are strongly tied to the first-time buyer's account, and second-hand buyers cannot inherit any green benefits; even if the product itself has low-carbon attributes, such as an air conditioner with an energy efficiency label, the points incentive is zero in the new transaction; in addition, second-hand platforms such as Xianyu have not integrated with the green points system; when users resell goods, they cannot transfer the remaining points value to the buyer, nor can they receive rewards for promoting the recycling of green products; and if they try to bind the original voucher to the second-hand transaction, manual coordination is required for settlement among the original platform (such as JD.com), the seller, the buyer, and Xianyu; the existing cross-platform settlement agreement only supports B2C scenarios and does not define the rules for the transfer of points in C2C transactions.

[0004] In addition, some platforms use a traceability code reactivation scheme: a unique QR code is attached to the product packaging, and second-hand buyers can receive a small new coupon after scanning the code; however, this scheme has two limitations: it only applies to new products with packaging, and cannot be deployed for opened products such as large appliances; and the new buyer's coupon is not associated with the original seller, so the seller has no incentive to resell; some other schemes use cross-chain coupon protocols, which enable the exchange of points between JD.com and Meituan, but do not solve the problem of ownership transfer authentication in second-hand transactions, still requiring the original buyer to manually transfer the coupon. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] This invention provides a method for using consumer green points subsidy vouchers across internet platforms, solving the problems of existing green points vouchers being bound to user accounts, resulting in broken rights when reselling, and low efficiency due to reliance on manual coordination for cross-platform settlement.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] This invention provides a method for using consumer green points subsidy vouchers across internet platforms, comprising:

[0009] Step S1: In response to a user purchasing a low-carbon product on the platform for the first time, a unique carbon credit identifier is generated that is bound to the product. The carbon credit identifier is generated by hashing the product code, the buyer's identity identifier, and the initial carbon footprint value.

[0010] Step S2: Associate the voucher with the carbon credit identifier and store it in a cross-platform consortium blockchain voucher pool, which is connected to multiple heterogeneous trading platforms;

[0011] Step S3 is automatically triggered when a product carrying the carbon credit identifier is detected to have been resold on a second-hand trading platform:

[0012] A first proportion of the face value of the vouchers is released to resellers as a cyclical incentive;

[0013] A second percentage of the face value of the voucher is allocated to the purchaser as an inheritance right.

[0014] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on a cross-platform internet as described in this invention, the hash operation adopts the SHA-256 algorithm, and the input parameters include:

[0015] Unique product code, buyer identification, initial carbon footprint value;

[0016] In step S1, after the user completes payment on the platform for the first transaction, a CRID is generated in three stages: identity de-identification encryption, information concatenation, and hash digest. The steps include:

[0017] Perform identity desensitization and encryption:

[0018]

[0019] Among them, I e For encrypted identity verification, 64 bytes of ciphertext, K pub For the platform's public key, secp256k1 curve point, I u The buyer's identity is represented by a 32-byte UUID, t0 is the timestamp of the first transaction in milliseconds, and || indicates byte-level concatenation;

[0020] Perform footprint integerization and information concatenation:

[0021]

[0022] Where M is the string to be hashed, a byte sequence, and C...p A unique product code, 13-digit EAN, k f F0 represents the carbon footprint value after being amplified by a preset integer multiple. It is dimensionless. F0 is the initial carbon footprint value, expressed in kg CO2e. This indicates rounding down; BE8 means the result is converted to 8-byte big-endian encoding.

[0023] The hash digest forms the carbon weight identifier:

[0024] CRID = SHA256(M),

[0025] Wherein, CRID is the carbon weight identifier, a 256-bit hexadecimal string, and SHA256(·) represents the SHA256 digest function.

[0026] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on cross-platform internet as described in this invention, wherein: the method for monitoring product resale in step S3 is as follows:

[0027] The authenticity of the commodity transaction records corresponding to the carbon credit identifier is verified by calling the open transaction certificate interface of the second-hand trading platform.

[0028] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on cross-platform internet as described in this invention, the dynamic adjustment method of the first ratio and the second ratio is as follows:

[0029] The specific proportion value is calculated based on the length of the commodity resale cycle and the carbon footprint decay coefficient through a preset incentive allocation matrix.

[0030] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on a cross-platform internet platform as described in this invention, the construction of the incentive allocation matrix includes:

[0031] Define a resale cycle decay factor: based on the time difference between the resale time of the product and the first transaction, reduce the first proportion according to a preset decay curve;

[0032] Define carbon footprint gain weight: high-carbon emission reduction products have a higher allocation weight for the second proportion than low-carbon products;

[0033] Output ratio constraint: First ratio + Second ratio ≤ 100%.

[0034] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on cross-platform internet as described in this invention, the settlement mechanism of the cross-platform consortium blockchain voucher pool includes:

[0035] The system aggregates the debt relationships of vouchers across various platforms daily and uses a graph-based triangular reduction algorithm to merge multilateral debt flows.

[0036] Net settlement between platforms is performed based on the reduction results.

[0037] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on cross-platform internet as described in this invention, the settlement mechanism of the cross-platform consortium blockchain voucher pool includes triangular reduction settlement of the cross-platform consortium blockchain voucher pool, comprising:

[0038] Constructing a debt matrix:

[0039]

[0040] Among them, D ij This represents the total debt of platform i to platform j, in yuan, where i is the debtor index (1-N), j is the creditor index (1-N, j≠i), q is the serial number of the same debt, and m ij The amount of debt owed by platform i to platform j is dimensionless. The amount of transaction number q is in yuan.

[0041] The formula for calculating the net amount at each node is:

[0042]

[0043] Among them, b i The net income or net payment amount of platform i is expressed in yuan, and N is the total number of participating platforms, which is dimensionless.

[0044] Perform triangular cycle detection and determine the amount of offset:

[0045] Δ ijk =min(D ij D jk D ki ),

[0046] Where, Δ ijk The amount that can be offset by a three-node loop (i→j→k→i) is in yuan, where k is the index of the third node, 1 to N, and k ≠ i,j.

[0047] Update the debt-side rights:

[0048] D′ ij =D ij -Δ ijk ,

[0049] D′ jk =D jk -Δ ijk ,

[0050] D′ki =D ki -Δ ijk ,

[0051] Among them, D′ ij D′ represents the remaining debt of platform i to platform j after triangular reduction, expressed in yuan. jk D′ represents the remaining debt of platform j to platform k after the triangular reduction. ki This represents the remaining debt of platform k to platform i after the triangular reduction.

[0052] Generate a net settlement matrix:

[0053] S ij =max(D′) ij ,0),

[0054] Among them, S ij This represents the actual payment made by platform i to platform j in the final settlement matrix, expressed in yuan.

[0055] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on a cross-platform internet platform as described in this invention, the debt relationship is constructed as follows:

[0056] Based on the records of the vouchers being redeemed on non-issuing platforms, a triplet data table of creditor-debtor-amount is generated.

[0057] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on cross-platform internet as described in this invention, it further includes a step for verifying the authenticity of the product's carbon credit.

[0058] When the carbon footprint value of a merchant's products deviates from the median value of similar products across platforms by more than a threshold,

[0059] The actual redemption value of the voucher associated with the product is dynamically compressed according to the offset ratio.

[0060] As a preferred embodiment of the method for using consumer green points subsidy vouchers based on cross-platform internet as described in this invention, the method for determining whether the carbon footprint value deviates from the threshold is as follows:

[0061] Synchronize commodity carbon data from the Ministry of Industry and Information Technology's green product database and the pre-set whitelist platform.

[0062] Calculate the weighted absolute deviation between the merchant's labeled value and the median value from multiple data sources.

[0063] The beneficial effects of this invention are as follows: The carbon credit identifier is no longer bound to the user account, allowing the voucher benefits to be attached to the product itself. When a product is resold, the system automatically distributes incentives to both the buyer and seller, ensuring that green value permeates the entire product lifecycle; secondhand buyers can receive environmental subsidies without repeated purchases, increasing the willingness to circulate idle resources; the consortium blockchain voucher pool aggregates multiple types of trading platforms (e-commerce / secondhand / local services), compressing complex debt networks into net settlements based on graph theory reduction algorithms. Cross-platform voucher redemption no longer relies on manual reconciliation, reducing settlement latency from hours to near real-time, significantly lowering platform capital occupation costs.

[0064] The carbon footprint value of goods in this invention is cross-verified by the Ministry of Industry and Information Technology's database and whitelist platform, dynamically compressing coupon values ​​to prevent false labeling. Merchants cannot arbitrage by tampering with data from a single platform, ensuring that subsidy resources flow to genuine low-carbon products. Furthermore, it adopts the national standard coding (EAN) and a universal encryption algorithm (SHA-256 / ECC), eliminating the need to modify existing product identification systems. Small and medium-sized merchants can access the system simply by calling the open API, avoiding redundant development costs. Attached Figure Description

[0065] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating the method for using consumer green points subsidy vouchers across internet platforms in Example 1. Detailed Implementation

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0068] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0069] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0070] Example 1, referring to Figure 1 This embodiment provides a method for using consumer green points subsidy vouchers across internet platforms, including the following steps:

[0071] Step S1: In response to a user's first purchase of a low-carbon product on the trading platform, a unique carbon credit identifier is generated that is bound to the product. The carbon credit identifier is generated by hashing the product code, the buyer's identity identifier, and the initial carbon footprint value.

[0072] The hash operation uses the SHA-256 algorithm, and the input parameters include:

[0073] Unique product code (taken from the National Article Numbering Center database);

[0074] Purchaser identification (encrypted and anonymized using a public key);

[0075] Initial carbon footprint value (obtained by calling the Ministry of Industry and Information Technology's green product interface);

[0076] In step S1, after the user completes payment on the platform for the first transaction, a CRID is generated in three stages: identity de-identification encryption, information concatenation, and hash digest. The steps include:

[0077] Perform identity desensitization and encryption:

[0078]

[0079] Among them, I e For encrypted identity verification, 64 bytes of ciphertext, K pub For the platform's public key, secp256k1 curve point, I u The buyer's identity is represented by a 32-byte UUID, t0 is the timestamp of the first transaction in milliseconds, and || indicates byte-level concatenation;

[0080] Perform footprint integerization and information concatenation:

[0081]

[0082] Where M is the string to be hashed, a byte sequence, and C... p A unique product code, 13-digit EAN, k f The footprint magnification factor is set to 10. 3 Dimensionless, F0 is the initial carbon footprint value, in kg CO2e. This indicates rounding down; BE8 means the result is converted to 8-byte big-endian encoding.

[0083] The hash digest forms the carbon weight identifier:

[0084] CRID = SHA256(M),

[0085] Wherein, CRID is the carbon weight identifier, a 256-bit hexadecimal string, and SHA256(·) represents the SHA256 digest function;

[0086] Specifically, this step encrypts both transaction time and identity simultaneously, blocking external connection paths; the footprint value is amplified and rounded to avoid floating-point errors, ensuring consistent node parsing; the concatenated string length is fixed, reducing the risk of segmentation during multi-chain transmission; the SHA256 output space is huge, and the probability of replay and collision is negligible, maintaining the integrity of on-chain data; the three stages are completed on the same chain, effectively reducing average processing time and not increasing user waiting time; this carbon credit identifier is bound to both the product and the initial purchase behavior, providing a stable anchor for subsequent voucher issuance and traceability, and supporting inter-platform recognition;

[0087] Step S2: Associate the voucher with the carbon credit identifier and store it in the cross-platform consortium blockchain voucher pool, which is connected to multiple heterogeneous trading platforms;

[0088] The settlement mechanism for cross-platform consortium blockchain voucher pools includes:

[0089] The system aggregates the debt relationships of vouchers across various platforms daily and uses a graph-based triangular reduction algorithm to merge multilateral debt flows.

[0090] Net settlement between platforms will be performed based on the reduction results;

[0091] The debt relationship is constructed in the following ways:

[0092] Based on the records of voucher redemption on non-issuing platforms, generate a triplet data table of creditor-debtor-amount;

[0093] The settlement mechanism for cross-platform consortium blockchain voucher pools includes triangular reduction settlement, which comprises:

[0094] Constructing a debt matrix:

[0095]

[0096] Among them, D ij This represents the total debt of platform i to platform j, in yuan, where i is the debtor index (1-N), j is the creditor index (1-N, j≠i), q is the serial number of the same debt, and m ij The amount of debt owed by platform i to platform j is dimensionless. The amount of transaction number q is in yuan.

[0097] The formula for calculating the net amount at each node is:

[0098]

[0099] Among them, bi The net income (positive value) or net payment (negative value) of platform i is expressed in yuan, and N is the total number of participating platforms, which is dimensionless.

[0100] Perform triangular cycle detection and determine the amount of offset:

[0101] Δ ijk =min(D ij D jk D ki ),

[0102] Where, Δ ijk The amount that can be offset by a three-node loop (i→j→k→i) is in yuan, where k is the index of the third node, 1 to N, and k ≠ i,j.

[0103] Update the debt-side rights:

[0104] D′ ij =D ij -Δ ijk ,

[0105] D′ jk =D jk -Δ ijk ,

[0106] D′ ki =D ki -Δ ijk ,

[0107] Among them, D′ ij D′ represents the remaining debt of platform i to platform j after triangular reduction, expressed in yuan. jk D′ represents the remaining debt of platform j to platform k after the triangular reduction. ki This represents the remaining debt of platform k to platform i after the triangular reduction.

[0108] Generate a net settlement matrix:

[0109] S ij =max(D′) ij ,0),

[0110] Among them, S ij This represents the actual payment made by platform i to platform j in the final settlement matrix, expressed in yuan.

[0111] Specifically, graph theory triangular reduction decomposes multilateral debt into several three-node cycles, and successively reduces them using the minimum edge weight as the offset. After removing the cycles, the number of edges decreases significantly, and the network complexity is reduced from O(N) to O(N). 2Approaching O(N); only necessary net payments are retained between platforms, and the number of cross-chain transfers and transaction fees are reduced in tandem; the daily algorithm execution time can be controlled at the second level to meet the reconciliation frequency of the coupon pool; the settlement results are written into the consortium chain block in matrix form, and net settlement is completed after node consensus, reducing the platform's capital occupation and improving the efficiency of cross-platform flow of coupons.

[0112] Step S3 is automatically triggered when a product carrying a carbon credit identifier is detected to have been resold on a second-hand trading platform:

[0113] The first percentage of the face value of the vouchers released to resellers will be used as a cyclical incentive.

[0114] A second percentage of the voucher's face value will be allocated to the purchaser as inheritance rights.

[0115] The method for monitoring product resale in step S3 is as follows:

[0116] The authenticity of the commodity transaction records corresponding to the carbon credit identifier is verified by calling the open transaction certificate interface of the second-hand trading platform.

[0117] The dynamic adjustment method for the first and second ratios is as follows:

[0118] The specific proportion value is calculated based on the length of the commodity resale cycle and the carbon footprint decay coefficient through a preset incentive allocation matrix;

[0119] The construction of the incentive allocation matrix includes:

[0120] Define the resale cycle decay factor: based on the time difference between the resale time and the first transaction, reduce the first proportion according to the preset decay curve;

[0121] Define carbon footprint gain weight: high-carbon emission reduction products receive a second-highest allocation weight compared to low-carbon products;

[0122] Output ratio constraint: First ratio + Second ratio ≤ 100%;

[0123] It also includes steps for verifying the authenticity of the product's carbon credit:

[0124] When the carbon footprint value of a merchant's products deviates from the median value of similar products across platforms by more than a threshold,

[0125] The actual redemption value of the voucher associated with the product is dynamically compressed according to the offset ratio;

[0126] The method for determining whether a carbon footprint value deviates from a threshold is as follows:

[0127] Synchronize commodity carbon data from the Ministry of Industry and Information Technology's green product database and the pre-set whitelist platform.

[0128] Calculate the weighted absolute deviation between the merchant's labeled value and the median value from multiple data sources; example of weight allocation for the weighted absolute deviation:

[0129] The weighting of the Ministry of Industry and Information Technology's database is 0.6, the weighting of the median carbon value of the whitelist platform is 0.3, and the weighting of the merchant's historical carbon value deviation record is 0.1.

[0130] The weights or coefficients (such as 0.3, 0.6, 0.1, etc.) appearing in this specification are for illustrative purposes only. The specific values ​​can be flexibly adjusted by those skilled in the art based on historical data analysis or experimental calibration, and do not limit the scope of protection of this invention.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for using consumer green points subsidy vouchers across internet platforms, characterized in that, Includes the following steps: Step S1: In response to a user purchasing a low-carbon product on the platform for the first time, a unique carbon credit identifier is generated that is bound to the product. The carbon credit identifier is generated by hashing the product code, the buyer's identity identifier, and the initial carbon footprint value. Step S2: Associate the voucher with the carbon credit identifier and store it in a cross-platform consortium blockchain voucher pool, which is connected to multiple heterogeneous trading platforms; Step S3 is automatically triggered when a product carrying the carbon credit identifier is detected to have been resold on a second-hand trading platform: A first proportion of the face value of the vouchers is released to resellers as a cyclical incentive; A second percentage of the face value of the voucher is allocated to the purchaser as an inheritance right.

2. The method for using consumer green points subsidy vouchers based on cross-platform internet as described in claim 1, characterized in that, The hash operation uses the SHA-256 algorithm, and the input parameters include: Unique product code, buyer identification, initial carbon footprint value; In step S1, after the user completes payment on the platform for the first transaction, a CRID is generated in three stages: identity de-identification encryption, information concatenation, and hash digest. The steps include: Perform identity desensitization and encryption: Among them, I e For encrypted identity verification, 64 bytes of ciphertext, K pub For the platform's public key, secp256k1 curve point, I u The buyer's identity is represented by a 32-byte UUID, t0 is the timestamp of the first transaction in milliseconds, and || indicates byte-level concatenation; Perform footprint integerization and information concatenation: Where M is the string to be hashed, a byte sequence, and C... p A unique product code, 13-digit EAN, k f F0 represents the carbon footprint value after being amplified by a preset integer multiple. It is dimensionless. F0 is the initial carbon footprint value, expressed in kg CO2e. This indicates rounding down; BE8 means the result is converted to 8-byte big-endian encoding. The hash digest forms the carbon weight identifier: CRID = SHA256(M), Wherein, CRID is the carbon weight identifier, a 256-bit hexadecimal string, and SHA256(·) represents the SHA256 digest function.

3. The method for using consumer green points subsidy vouchers across internet platforms as described in claim 1, characterized in that, The method for monitoring product resale in step S3 is as follows: The authenticity of the commodity transaction records corresponding to the carbon credit identifier is verified by calling the open transaction certificate interface of the second-hand trading platform.

4. The method for using consumer green points subsidy vouchers across internet platforms as described in claim 1, characterized in that, The dynamic adjustment method for the first ratio and the second ratio is as follows: The specific proportion value is calculated based on the length of the commodity resale cycle and the carbon footprint decay coefficient through a preset incentive allocation matrix.

5. A method for using consumer green points subsidy vouchers across internet platforms as described in claim 4, characterized in that, The construction of the incentive allocation matrix includes: Define a resale cycle decay factor: based on the time difference between the resale time of the product and the first transaction, reduce the first proportion according to a preset decay curve; Define carbon footprint gain weight: high-carbon emission reduction products have a higher allocation weight for the second proportion than low-carbon products; Output ratio constraint: First ratio + Second ratio ≤ 100%.

6. The method for using consumer green points subsidy vouchers across internet platforms as described in claim 1, characterized in that, The settlement mechanism of the cross-platform consortium blockchain voucher pool includes: The system aggregates the debt relationships of vouchers across various platforms daily and uses a graph-based triangular reduction algorithm to merge multilateral debt flows. Net settlement between platforms is performed based on the reduction results.

7. A method for using consumer green points subsidy vouchers across internet platforms as described in claim 6, characterized in that, The settlement mechanism of the cross-platform consortium blockchain voucher pool includes triangular reduction settlement of the cross-platform consortium blockchain voucher pool, comprising: Constructing a debt matrix: Among them, D ij This represents the total debt of platform i to platform j, in yuan, where i is the debtor index (1-N), j is the creditor index (1-N, j≠i), q is the serial number of the same debt, and m ij The amount of debt owed by platform i to platform j is dimensionless. The amount of transaction number q is in yuan. The formula for calculating the net amount at each node is: Among them, b i The net income or net payment amount of platform i is expressed in yuan, and N is the total number of participating platforms, which is dimensionless. Perform triangular cycle detection and determine the amount of offset: Δ ijk <min(D ij ,D jk ,D ki ), Where, Δ ijk The amount that can be offset by a three-node loop (i→j→k→i) is in yuan, where k is the index of the third node, 1 to N, and k ≠ i,j. Update the debt-side rights: D′ ij =D ij -D ijk , D′ jk =D jk -D ijk , D′ ki =D ki -D ijk , Among them, D′ ij D′ represents the remaining debt of platform i to platform j after triangular reduction, expressed in yuan. jk D′ represents the remaining debt of platform j to platform k after the triangular reduction. ki This represents the remaining debt of platform k to platform i after the triangular reduction. Generate a net settlement matrix: S ij =max(D′ ij ,0), Among them, S ij This represents the actual payment made by platform i to platform j in the final settlement matrix, expressed in yuan.

8. A method for using consumer green points subsidy vouchers across internet platforms as described in claim 6, characterized in that, The debt relationship is constructed in the following way: Based on the records of the vouchers being redeemed on non-issuing platforms, a triplet data table of creditor-debtor-amount is generated.

9. A method for using consumer green points subsidy vouchers across internet platforms as described in claim 1, characterized in that, It also includes steps for verifying the authenticity of the product's carbon credit: When it is detected that the carbon footprint value of a merchant's product deviates from the median value of similar products across platforms by more than a threshold, the actual redemption value of the coupon associated with that product will be dynamically compressed according to the deviation ratio.

10. A method for using consumer green points subsidy vouchers based on a cross-platform internet platform as described in claim 9, characterized in that... The method for determining whether the carbon footprint value deviates from the threshold is as follows: Synchronize commodity carbon data from the Ministry of Industry and Information Technology's green product database and the pre-set whitelist platform. Calculate the weighted absolute deviation between the merchant's labeled value and the median value from multiple data sources.

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