A dynamic revenue sharing method, revenue sharing system, and related equipment based on multidimensional contribution.

Abstract

This invention discloses a dynamic revenue sharing method, system, and related equipment based on multi-dimensional contribution. The dynamic revenue sharing method includes the following steps: S1) receiving transaction order data from a cross-industry alliance platform; S2) extracting multiple contribution factors from the transaction order data; and S3) calculating the dynamic allocation ratio of each participant based on a preset dynamic weighting model and the multiple contribution factors. This application breaks through the limitations of traditional fixed-ratio revenue sharing. By extracting multi-dimensional contribution factors such as merchant discount ratios, consumer membership levels, and promoter channels, and combining them with a preset dynamic weighting model for weighted calculation and normalization, it can accurately measure the actual value of each participant in the transaction, ensuring a precise match between revenue and contribution, and fully protecting the rights and interests of high-contribution entities.

Description

Technical Field

[0001] This invention relates to the field of computer technology, specifically to a dynamic revenue sharing method, revenue sharing system, and related equipment based on multidimensional contribution. Background Technology

[0002] With the rapid development of the e-commerce industry, cross-industry alliances have become an important business model for merchants to integrate resources and share customer traffic. However, the core profit distribution process is limited by traditional revenue sharing technology, which has many industry shortcomings.

[0003] Traditional cross-industry alliance profit-sharing models generally suffer from three major drawbacks: First, profit sharing is opaque, with vaguely defined rules for profit distribution and a lack of publicly verifiable channels for fund flows, which can easily lead to trust rifts among alliance members; second, incentives are unfair, as the fixed-ratio profit-sharing mechanism makes it impossible to accurately quantify the actual contributions of different participants in a single transaction, and the value of high-contributing parties does not receive commensurate rewards, severely weakening their enthusiasm for participation; and third, settlement efficiency is low, relying on manual reconciliation and fund settlement, which is not only time-consuming but also prone to accounting errors due to human intervention, increasing the alliance's operating costs.

[0004] Therefore, the industry urgently needs a revenue-sharing technology solution that can achieve automatic accounting, fair allocation, and transparent processes, in order to solve the drawbacks of the traditional revenue-sharing model and ensure the healthy and sustainable development of cross-industry alliances. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic revenue sharing method, revenue sharing system and related equipment based on multidimensional contribution, so as to solve the problems of the existing cross-industry alliance revenue sharing model mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a dynamic revenue sharing method based on multidimensional contribution, comprising the following steps:

[0007] S1) Receive transaction order data from the cross-industry alliance platform;

[0008] S2) Extract multiple contribution factors from the transaction order data;

[0009] S3) Based on the preset dynamic weight model, calculate the dynamic allocation ratio of each participant according to the multiple contribution factors;

[0010] S4) Calculate the consumer rebate amount, platform operating fee and allocation pool amount based on the transaction amount and merchant discount ratio, and deduct the estimated tax from the allocation pool amount to obtain the net allocation pool amount;

[0011] S5) Distribute the net allocation pool amount to the corresponding participants according to the dynamic allocation ratio;

[0012] S6) Generate an immutable record of the revenue sharing rules, calculation process, and distribution results and store it in the blockchain.

[0013] Preferably, the plurality of contribution factors include at least two of the following: merchant discount ratio, consumer membership level, promoter promotion channel, transaction time, and product or service type.

[0014] Preferably, the calculation of the dynamic allocation ratio of each participant based on a preset dynamic weighting model specifically includes:

[0015] Assign basic weights to each of the contribution factors;

[0016] The basic weights are calculated by weighting the factors based on the specific values ​​of each contribution factor.

[0017] The weighted calculation results are normalized to obtain the final allocation ratio for each participant.

[0018] Preferably, the step of allocating the net allocation pool amount to the corresponding participants according to the dynamic allocation ratio specifically includes:

[0019] The amount allocated to participants will be distributed in the form of vouchers and points, and the allocation ratio of vouchers and points can be configured.

[0020] Preferably, the participants include at least three of the following categories: consumers, merchants, promoters, and platform operators.

[0021] A revenue-sharing system implementing any of the described dynamic revenue-sharing methods based on multidimensional contribution, comprising:

[0022] The order access module is used to receive and verify transaction order data from the cross-industry alliance platform;

[0023] The factor extraction module is used to extract multiple contribution factors from the transaction order data;

[0024] The revenue sharing calculation engine is used to run a preset dynamic weight model and calculate the dynamic allocation ratio of each participant.

[0025] The funds processing module is used to calculate the instant cashback amount for consumers, platform operating fees, allocation pool amount and net allocation pool amount, and to execute the allocation of funds to each participant.

[0026] The data storage module is used to generate immutable records of the revenue sharing rules, calculation process, and allocation results and store them in the blockchain.

[0027] Preferably, the system is communicatively connected to an intelligent weighing device, and the transaction order data is generated from the weight information collected in real time by the intelligent weighing device.

[0028] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the dynamic revenue sharing methods based on multidimensional contribution.

[0029] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of any of the dynamic revenue sharing methods based on multidimensional contribution.

[0030] Preferably, the computer device further includes a communication interface, which is used to establish a data transmission link between the computer device and a cross-industry alliance platform or intelligent weighing equipment.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] 1) This application breaks the limitations of the traditional fixed-ratio revenue sharing. By extracting multi-dimensional contribution factors such as merchant profit sharing ratio, consumer membership level, and promoter promotion channels, and combining them with a preset dynamic weight model for weighted calculation and normalization, it can accurately measure the actual value of each participant in the transaction, so that the income and contribution are accurately matched, and fully protect the rights and interests of high-contribution entities.

[0033] 2) This application generates an immutable hash value for the entire chain of information, including the revenue sharing rules, factor extraction data, weight calculation process, and final allocation results, and stores it on the blockchain. Any participant in the alliance can query and verify it, realizing full traceability of the revenue sharing process, completely breaking down information barriers, and building a trust system among the participants.

[0034] 3) The dynamic revenue sharing system built in this application can realize fully automated operation, from order data reception and verification, contribution factor extraction, to allocation ratio calculation and fund splitting and distribution, without manual intervention. This not only greatly shortens the settlement cycle, but also avoids the risk of errors in manual operation, effectively reducing the operating costs of cross-industry alliances.

[0035] 4) This application adopts a dual-track distribution system that combines consumption vouchers and points, and the ratio of the two can be flexibly configured. For consumers, the dual-track benefits can enhance their willingness to repurchase; for merchants, targeted consumption vouchers can achieve precise traffic generation; for the platform, the points system can accumulate user assets and form an ecological cycle of "transaction-revenue sharing-re-consumption", driving the continuous expansion of cross-industry alliances. Attached Figure Description

[0036] Figure 1 A flowchart illustrating the dynamic revenue sharing method of this application;

[0037] Figure 2 This is a structural framework diagram of the dynamic revenue sharing system of this application;

[0038] Figure 3 This is a schematic diagram illustrating the extraction and calculation of the contribution factor in this application.

[0039] Figure 4 This is a detailed diagram illustrating the interaction logic between the revenue sharing calculation engine and the anti-fraud rule module in Embodiment 2 of this application;

[0040] Figure 5 This is the state machine transition diagram of the cross-store pre-verification smart contract in Embodiment 2 of this application. Detailed Implementation

[0041] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] In the description of the invention, it should be noted that the terms "upper," "lower," "inner," "outer," "front end," "rear end," "both ends," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0043] In the description of the invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0044] In the description of the invention, it should be noted that the execution order of the steps is not limited by the sequence number. The possible changes in the order of some steps, the synchronous execution of steps, and the split execution of steps are all within the scope of protection of this application.

[0045] Please see Figure 1-5 This invention provides a technical solution: a dynamic revenue sharing method based on multidimensional contribution, comprising the following steps:

[0046] S1) Receive transaction order data from the cross-industry alliance platform;

[0047] S2) Extract multiple contribution factors from transaction order data;

[0048] S3) Based on a preset dynamic weighting model, calculate the dynamic allocation ratio of each participant according to multiple contribution factors;

[0049] S4) Calculate the consumer rebate amount, platform operating fee and allocation pool amount based on the transaction amount and merchant discount ratio, and deduct the estimated tax from the allocation pool amount to obtain the net allocation pool amount.

[0050] S5) Distribute the net allocation pool amount to the corresponding participants according to the dynamic allocation ratio;

[0051] S6) Generate an immutable record of the revenue sharing rules, calculation process, and distribution results and store it in the blockchain.

[0052] Specifically, traditional revenue-sharing models, lacking standardized dynamic allocation logic, either employ fixed ratios leading to unfair incentives, or suffer from vague fund accounting and opaque processes causing trust crises, and are inefficient due to reliance on manual operation. This application innovatively incorporates multi-dimensional contribution factors into the core allocation, replacing fixed ratios with a dynamic weighting model, directly linking the allocation ratio of participants to their actual contributions, fundamentally solving the unfair problem of high contribution resulting in low returns. Simultaneously, it explicitly includes "net allocation pool amount after deducting estimated taxes and fees" in the process, making fund splitting more rigorous and avoiding the accounting chaos caused by missing tax and fee accounting in traditional revenue-sharing. Furthermore, it uses blockchain notarization to ensure the immutability and traceability of data throughout the entire revenue-sharing process, completely breaking down information black boxes and resolving trust barriers among alliance members. In addition, this process standardizes and closes the loop in each stage of revenue sharing, providing core logical support for automated execution, significantly reducing manual intervention, and significantly improving revenue sharing efficiency, perfectly meeting the core demands of cross-industry alliances for fair, transparent, and efficient revenue sharing.

[0053] Multiple contribution factors include at least two of the following: merchant discount percentage, consumer membership level, promoter's promotion channel, transaction time, and product or service type. Specifically, traditional revenue-sharing models rely solely on transaction amount or use a fixed percentage, failing to capture the differentiated contributions of different participants. Key information such as merchant discount amounts, consumer loyalty levels, and the quality of promoter channels are often overlooked, leading to biased and unreasonable allocation results. The factors listed in this application are core dimensions influencing value distribution in cross-industry alliance transactions: the merchant discount percentage directly reflects the degree of profit concession made by the merchant to the alliance; consumer membership level reflects user stickiness and consumption potential; promoter's promotion channel determines the quality of customer traffic; and transaction time and product or service type adapt to the allocation needs of different scenarios. By selecting at least two factors for calculation, the actual contribution of each participant can be comprehensively and three-dimensionally measured. For example, merchants with high discounts can obtain higher allocation weights, and new customer transactions brought by high-quality promotion channels can give promoters additional rewards, thus making the revenue sharing results more in line with the actual transaction, fully mobilizing the enthusiasm of all parties to participate, and consolidating the collaborative foundation of cross-industry alliances.

[0054] The dynamic allocation ratio of each participant is calculated based on a pre-defined dynamic weighting model, specifically including:

[0055] Assign a basic weight to each contribution factor;

[0056] The basic weights are calculated by weighting the factors based on the specific values ​​of each contribution factor.

[0057] The weighted calculation results are normalized to obtain the final allocation ratio for each participant.

[0058] Specifically, the core flaw of traditional fixed-ratio revenue sharing is the lack of objective and quantifiable standards for the allocation ratio, which is often subjectively set by the platform. This leaves participants unaware of the basis for the allocation and prone to questioning the results. The calculation logic in this application ensures both flexibility and fairness: it assigns basic weights to each factor to ensure the core positioning of different factors in the allocation (e.g., a higher basic weight can be set for the merchant's profit-sharing ratio to highlight their direct contribution); it calculates by weighting based on the specific values ​​of the factors, accurately distinguishing the differences between different participants under the same factor (e.g., the consumer factor values ​​of gold members and ordinary members are different, resulting in a significant difference in contribution after weighting); and normalization ensures that the sum of the allocation ratios for all participants is 100%, avoiding imbalances in fund allocation. This calculation process is fully traceable and reproducible, transforming revenue sharing from vague estimation to precise quantification. Participants can clearly understand the relationship between their own contribution and the allocation results, solving the problem of unbased traditional revenue sharing ratios and further enhancing the fairness and transparency of revenue sharing.

[0059] The net allocation pool amount will be distributed to the corresponding participants according to a dynamic allocation ratio, specifically as follows:

[0060] The amount allocated to participants will be distributed in the form of vouchers and points, and the allocation ratio of vouchers and points can be configured.

[0061] Specifically, traditional cash-based revenue sharing only satisfies the immediate benefit needs of participants, failing to generate sustained motivation for participation and hindering secondary transactions within the alliance. This application's dual-track design offers significant ecosystem incentive advantages: vouchers can be directly used for subsequent consumption within the alliance, precisely driving traffic to merchants, boosting secondary transactions, and increasing consumer repurchase intentions; points can be accumulated as user assets, enhancing consumer loyalty and promoting long-term participation through redemption of designated goods / services and membership upgrades. Furthermore, the ratio of vouchers to points can be flexibly configured, allowing cross-industry alliances to adjust according to their development stages. For example, increasing the proportion of vouchers in the initial expansion phase to stimulate short-term consumption, and increasing the proportion of points in the stable phase to accumulate user assets, adapting to different operational needs. This distribution model transforms revenue sharing into a driving force for the "transaction-revenue sharing-re-consumption" cycle within the alliance, effectively improving alliance activity and sustainability, and solving the problem of traditional revenue sharing incentives being too simplistic and failing to promote ecosystem synergy.

[0062] The participants include at least three of the following: consumers, merchants, promoters, and platform operators. Specifically, traditional revenue-sharing models often focus solely on the profit distribution between merchants and platform operators, neglecting the contributions of key participants such as consumers (core traffic drivers) and promoters (customer acquisition agents). This leads to insufficient participation from these entities, hindering alliance expansion. This application includes consumers and promoters as core participants, clarifying their legitimate rights within the revenue-sharing system. Consumers' high membership levels and promoters' high-quality channel promotions can be transformed into a distribution ratio advantage through multi-dimensional contribution factors, ensuring precise returns on their contributions. This design guarantees that revenue sharing covers all core value creators, avoiding the problem of "disconnect between value creation and profit distribution," while also fully mobilizing the enthusiasm of all parties: consumers are willing to continue spending to upgrade their membership levels, promoters actively expand high-quality channels to acquire customers, merchants actively offer discounts to enhance competitiveness, and platform operators achieve ecosystem expansion by integrating resources from all parties, ultimately building a win-win alliance ecosystem.

[0063] Design of dynamic weight update rules:

[0064] The dynamic weight update rule designed in this invention balances the objectivity and accuracy of data-driven approaches with the flexibility and adaptability required for alliance operations. Through a combination of "automatic triggering of updates, manual intervention for adjustment, and multi-party consultation and confirmation," it ensures that the dynamic weight model can respond in real-time to business iterations and scenario changes within cross-industry alliances, maintaining the adaptability of the revenue-sharing logic. Simultaneously, a comprehensive "verification, public disclosure, transition, and evaluation" mechanism is established to guarantee the transparency and traceability of weight adjustments while preventing any impact on revenue-sharing stability, ultimately achieving both accuracy and sustainability in the revenue-sharing logic.

[0065] The specific rules are designed as follows:

[0066] 1. Automatic Update Trigger: The system has a built-in data monitoring module that collects real-time transaction data from cross-industry alliances (such as the distribution characteristics of each contribution factor, transaction frequency of participants, and revenue sharing satisfaction feedback). It also sets quantitative trigger thresholds (e.g., the standard deviation of a merchant's discount rate exceeds a preset threshold for 30 consecutive days, the correlation coefficient between consumer membership level and purchase frequency changes by more than 20%, and the conversion rate of traffic from promotional channels fluctuates by 30%). When the data meets the trigger conditions, the system automatically initiates a weight optimization process, iteratively adjusting the basic weights of each contribution factor based on machine learning algorithms (such as gradient descent) to ensure that the weight model conforms to the objective laws of the alliance's actual operational data.

[0067] 2. Manual Intervention Adjustment: For non-data-driven needs such as alliance strategy adjustments and special scenario operations (e.g., holiday promotions, new merchant onboarding support, preferential treatment for key promotional channels), alliance operators can initiate manual weight adjustment requests through the system backend, specifying the adjustment factors, target weights, and reasons for the adjustment. After the request is submitted, the system automatically verifies the adjustment range (to avoid a single adjustment exceeding a preset safety threshold, such as a single factor weight adjustment not exceeding 10%). Once the verification is successful, the system enters the transition process to ensure a smooth implementation of the adjustment.

[0068] 3. Multi-party consultation and confirmation: Significant adjustments to the weighting of core contribution factors (such as merchant discount ratios and platform operation fee coefficients) require initiating a multi-party consultation mechanism within the alliance. The system automatically pushes the adjustment plan (including the basis for the adjustment and simulated data on expected impact) to merchant representatives, promotion teams, consumer rights representatives, and platform operators, setting a consultation period of no less than 7 calendar days. All parties provide feedback through authorized interfaces. The adjustment plan only takes effect after a consensus is reached through consultation; if disputes arise, the alliance's deliberation committee will coordinate and make a decision based on objective data and the demands of all parties to ensure the fairness of the adjustment result.

[0069] Supporting guarantee procedures:

[0070] Verification process: Before the weight adjustment plan takes effect, simulated revenue sharing verification is conducted in the system sandbox environment. The revenue sharing results are replicated based on the transaction data of the past 3 months. The fairness of revenue sharing before and after the adjustment (such as the increase in the income of high-contribution participants) and the activity of the alliance (such as the prediction of changes in repurchase rate) are compared. The implementation will proceed only after it is verified that there is no significant negative impact.

[0071] Public Announcement Process: The adjustment plan (including the triggering reasons, adjustment content, negotiation results, and effective time) will be announced through the alliance bulletin board, back-end messages of each participating party, and other channels. The public announcement period shall not be less than 3 calendar days, during which time objections from all parties will be accepted.

[0072] Transition process: The "gradient transition" mode is adopted. After the new weight takes effect, a 15-day transition period is set. During the transition period, the revenue sharing is carried out in the manner of "gradually reducing the proportion of the old weight and gradually increasing the proportion of the new weight" to avoid sudden changes in the revenue sharing results.

[0073] Evaluation process: After the transition period, the system will automatically generate a weight adjustment evaluation report, which will quantify the effect of the adjustment from the dimensions of revenue sharing fairness, participant satisfaction, and alliance transaction growth rate. If the evaluation results do not meet expectations, a second optimization or rollback to the original weight scheme can be triggered.

[0074] Specifically, this invention innovatively adopts the core logic of "allocation by order number" in the design of the revenue sharing basis, replacing the traditional model of allocation based on the proportion of consumption amount. This ensures that the revenue sharing process better meets the fairness requirements of cross-industry alliances across multiple scenarios and scales of transactions. Here, "order number" refers to a unique identifier generated by the cross-industry alliance platform for each transaction, incrementing sequentially according to the transaction completion time. It possesses the characteristics of being non-repeatable and traceable. Its core logic as the allocation basis is that: each order is treated as an independent allocation unit, and the multi-dimensional contribution factors corresponding to each order are calculated and allocated according to unified rules, rather than determining allocation priority or share proportion based on the size of the consumption amount. This ensures that participants in both small and large orders receive fair returns commensurate with their actual contributions.

[0075] The specific implementation of this design logic is as follows: After the order access module receives transaction data, the system automatically generates a unique serial number for each order and associates it with all transaction information (including contribution factors, participant information, etc.). The revenue sharing calculation engine independently runs a dynamic weight model to calculate the allocation ratio of each participant for the transaction data corresponding to each order serial number, unaffected by the consumption amount of other orders. The funds processing module calculates the instant return amount, allocation pool amount, and net allocation pool amount for each order sequentially according to the order serial number, and executes a binary allocation system according to the dynamic allocation ratio corresponding to the order. For example, merchants within the alliance may have both small orders of 100 yuan and large orders of 1000 yuan. The system calculates these independently on a per-order-serial-number basis. Promoters and high-level members with high contributions in small orders receive the same proportion of distribution revenue as participants with the same contribution level in large orders. This avoids the problem of the contribution of participants in small orders being ignored and the revenue being diluted by large orders in the traditional distribution model based on consumption amount.

[0076] The core advantages of this design are as follows: First, it completely breaks down the distribution barrier of "amount determines revenue," protecting the legitimate rights and interests of participants such as small and medium-sized merchants, small-amount, high-frequency consumers, and high-quality small and medium-sized promotional channels, fully mobilizing the enthusiasm of various participants, and enriching the diversity of the alliance transaction ecosystem. Second, the uniqueness and sequence of order numbers make the accounting more organized, facilitating subsequent tracing of order details by number, and further improving the traceability and management efficiency of accounting with blockchain evidence storage. At the same time, this logic can be flexibly adapted to cross-industry alliance scenarios with different average order values, such as retail, catering, and local life services. Whether it is small-amount, high-frequency daily consumption or large-amount, low-frequency service procurement, fair distribution can be achieved through independent accounting by order number, significantly improving the scenario adaptability and practicality of the technical solution of this invention.

[0077] According to another aspect of this application, a revenue sharing system based on a dynamic revenue sharing method with multidimensional contribution is also provided, comprising:

[0078] The order access module is used to receive and verify transaction order data from the cross-industry alliance platform;

[0079] The factor extraction module is used to extract multiple contribution factors from transaction order data;

[0080] The revenue sharing calculation engine is used to run a preset dynamic weight model and calculate the dynamic allocation ratio of each participant.

[0081] The funds processing module is used to calculate the instant cashback amount for consumers, platform operating fees, allocation pool amount and net allocation pool amount, and to execute the allocation of funds to each participant.

[0082] The data storage module is used to generate immutable records of the revenue sharing rules, calculation process, and allocation results and store them in the blockchain.

[0083] Specifically, existing revenue sharing methods rely on manual processes for order collection, reconciliation, and fund allocation, which are not only time-consuming and error-prone but also require significant manpower. This application's system features clearly defined modules and a closed-loop collaborative mechanism: the order access module ensures the accuracy and completeness of order data, preventing subsequent calculation errors; the factor extraction module automatically filters and collects multi-dimensional contribution factors, replacing manual filtering and improving efficiency and accuracy; the revenue sharing calculation engine quickly runs a dynamic weight model, outputting precise allocation ratios, solving the problems of time-consuming and error-prone manual calculations; the fund processing module automatically calculates and allocates instant return amounts, operating expenses, and net allocation pool amounts, automating fund splitting and disbursement; and the data storage module completes blockchain storage, ensuring transparent and trustworthy revenue sharing. The entire process requires no manual intervention, shortening the settlement cycle to real-time completion, mitigating the risks of manual operation, significantly reducing alliance operating costs, and meeting the revenue sharing needs of large-scale, high-frequency transactions in cross-industry alliances.

[0084] The system communicates with intelligent weighing equipment, and transaction order data is generated from the weight information collected in real time by the intelligent weighing equipment. Specifically, in cross-industry alliance scenarios that rely on weight-based pricing, such as fresh food retail, catering ingredient procurement, and bulk commodity transactions, traditional order data is mostly manually entered, which is prone to errors and delays, leading to inaccurate calculation of revenue sharing amounts, such as calculating merchant discount ratios based on incorrect amounts. The design of this application allows the system to directly receive real-time weight data from intelligent weighing equipment and automatically generate transaction orders (including accurate amounts) without manual intervention. This ensures the authenticity and timeliness of order data (especially core data related to amounts) and avoids the error risks of manual entry, providing a reliable data foundation for subsequent revenue sharing calculations (instant return amount, allocation pool amount, net allocation pool amount, etc.). At the same time, this design enriches the application scenarios of the system, making the dynamic revenue sharing scheme of this application not only applicable to ordinary commodity transactions but also accurately adapted to special industry alliances that rely on weight-based pricing, significantly improving the practicality and promotional value of the technical solution.

[0085] According to another aspect of this application, a computer-readable storage medium is also provided, on which a computer program is stored. When the program is executed by a processor, it implements the steps of a dynamic revenue-sharing method based on multi-dimensional contribution. Specifically, traditional revenue-sharing schemes, if deployed on different devices or platforms, often require repeated development of core algorithms, which is time-consuming, labor-intensive, and prone to logical inconsistencies. This application stores the computer program implementing the revenue-sharing method in a readable medium. Cross-industry alliance platforms do not need to develop from scratch; they only need to deploy the program in the storage medium to their own computer devices to quickly enable the dynamic revenue-sharing function, significantly shortening the technology implementation cycle. Furthermore, this storage medium is easy to copy, transmit, and update. If subsequent optimization of the revenue-sharing algorithm or adjustment of the weight model is required, it can be achieved by updating the program in the storage medium, improving the maintainability and scalability of the technical solution and providing strong support for the widespread adoption of cross-industry alliance revenue-sharing technology.

[0086] According to another aspect of this application, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a dynamic revenue sharing method based on multidimensional contribution.

[0087] The computer equipment also includes a communication interface, which is used to establish a data transmission link between the computer equipment and a cross-industry alliance platform or intelligent weighing equipment.

[0088] Example 1

[0089] The dynamic revenue sharing method and system of this invention can be developed using Java or Python, built using a Spring Cloud microservice architecture, and achieves real-time data interaction with cross-industry alliance platforms, third-party payment systems, and intelligent weighing equipment through an API gateway, ensuring the automation and accuracy of the revenue sharing process. The following detailed implementation process of this invention is illustrated using a cross-industry alliance catering consumption scenario:

[0090] I. Implementation Scenario Setting

[0091] A cross-industry alliance platform connects multiple catering merchants, promotion teams, and membership systems. Consumer A is a gold member of the alliance and makes a purchase at merchant C (restaurant) within the alliance through promoter B (an exclusive channel for new customer promotion). Merchant C sets an alliance discount ratio of 10%. The system has preset basic weight rules for a dynamic weight model: merchant discount ratio weight 30%, consumer membership level weight 30%, promoter promotion channel weight 40%, and gold member corresponding factor value 1.2, new customer promotion channel corresponding factor value 1.1, estimated tax rate is 0.2% of the allocation pool amount, and the allocation amount adopts a configurable binary system of "30% vouchers + 70% points".

[0092] II. Specific Implementation Steps

[0093] 1. Order Data Collection and Verification: After consumer A makes a purchase and checks out at merchant C, the payment system synchronizes the transaction order data (including transaction amount of 100 yuan, merchant C ID, consumer A ID, promoter B ID, consumption time, etc.) to the cross-industry alliance platform in real time. After receiving the order data, the system's order access module automatically verifies the data integrity (confirming that no key fields are missing) and validity (verifying that the payment status is "completed"). After the verification is successful, the revenue sharing process is triggered.

[0094] 2. Multidimensional Contribution Factor Extraction: The factor extraction module accurately extracts core contribution factors from the verified order data: ① Merchant discount ratio factor: 10% (corresponding to a base weight of 30%); ② Consumer membership level factor: Gold Member (corresponding factor value 1.2, base weight 30%); ③ Promoter promotion channel factor: New customer promotion (corresponding factor value 1.1, base weight 40%).

[0095] 3. Dynamic Allocation Ratio Calculation: The revenue sharing calculation engine calls the preset dynamic weight model to perform the calculation.

[0096] The first step is to calculate the weighted scores for each factor: Merchant discount ratio score = 10% × 30% = 3%; Consumer membership level score = 1.2 × 30% = 0.36; Promoter promotion channel score = 1.1 × 40% = 0.44;

[0097] The second step is to summarize the total score: 3% + 0.36 + 0.44 = 0.83;

[0098] The third step is to normalize the distribution ratios of each participant: After model calculation, the distribution ratios of consumer A, merchant C, and platform operator are finally determined to be 50%, 30%, and 20%, respectively (the promotion revenue of promoter B has been weighted into the distribution ratio through the promotion channel factor and will be distributed after unified settlement by the platform operator).

[0099] 4. Funds Accounting and Allocation Execution: The funds processing module completes the splitting and allocation of funds according to preset rules.

[0100] Calculate the instant cashback amount for consumers: Transaction amount × Merchant discount percentage × Instant cashback coefficient = 100 yuan × 10% × 50% = 5 yuan (The instant cashback coefficient is a preset configuration of the alliance and will be returned to consumer A's payment account in cash immediately).

[0101] Calculate platform operating costs: Transaction amount × Merchant discount rate × Operating cost coefficient = 100 yuan × 10% × 10% = 1 yuan (The operating cost coefficient is calculated from the alliance's operating costs).

[0102] Calculate the allocation pool amount: Total merchant discount amount - instant rebate amount - platform operation fee = (100 yuan × 10%) - 5 yuan - 1 yuan = 4 yuan;

[0103] Calculate the net allocation pool amount: Allocation pool amount × (1 - estimated tax rate) = 4 yuan × (1 - 0.2%) = 3.992 yuan;

[0104] A dual-track allocation system is implemented: Consumer A receives 3.992 yuan × 50% = 1.996 yuan (of which 30% is a voucher, i.e., 0.5988 yuan; and 70% is alliance points, i.e., 1.3972 yuan, which can be used to redeem designated goods or services within the alliance); Merchant C receives 3.992 yuan × 30% = 1.1976 yuan (distributed according to the configuration of "30% voucher + 70% points", which can be used for procurement or traffic generation within the alliance); and the platform operator receives 3.992 yuan × 20% = 0.7984 yuan (used for alliance technical maintenance, operation management, etc.).

[0105] 5. Blockchain-based Record-Sharing Data: The data recording module automatically collects key data from the entire record-sharing process, including: record-sharing rules (basic weights, instant return coefficients, operating fee coefficients, etc.), extracted contribution factors and their values, dynamic ratio calculation process data, fund accounting details, and final allocation results. A unique hash value is generated using the SHA-256 algorithm and synchronously stored on the alliance's shared blockchain node. Consumer A, Merchant C, Promoter B, and platform operator can all query the hash value and corresponding record-sharing data of this transaction through their authorized accounts, achieving full traceability and tamper-proof integrity.

[0106] In addition, two key execution steps need to be explained to improve the entire revenue sharing process: First, in the initial consumption scenario, the merchant can immediately receive 90% of the transaction amount after the transaction is completed (i.e., in this example, Merchant C receives 100 yuan × 90% = 90 yuan). This portion of funds does not need to participate in revenue sharing and is directly used for the merchant's cash flow, ensuring the merchant's core revenue. Second, in the consumption scenario of instant cashback plus consumption vouchers, when a consumer holds alliance balance plus consumption vouchers and uses them to offset consumption first (e.g., a consumer holds 50 yuan of balance plus consumption vouchers, spends 100 yuan at an alliance merchant, uses the balance plus consumption vouchers to offset 50 yuan and pays 50 yuan in cash), the platform will immediately cash out to the merchant. The balance plus the corresponding 50 yuan consumption voucher deduction, plus the 90 yuan cash directly received by the merchant, means the merchant actually receives 90 yuan this time. At the same time, the 50 yuan cash paid by the consumer is split according to the merchant's preset discount ratio (such as 10%). Among them, 50 yuan * 10% = 5 yuan is used as the distribution base, the consumer immediately gets a balance of 5 * 50% = 2.5 yuan, the platform empowerment fee is 5 * 10% = 0.5 yuan, and the dividend pool is 5 * 40% = 2 yuan, which enters the distribution pool. The secondary distribution is completed according to the preset dynamic weight model and revenue sharing rules of this invention, ensuring that the cash payment part still follows the core logic of "contribution matching income", realizing a closed loop of full-link revenue sharing in the points consumption scenario.

[0107] III. Implementation Results Description

[0108] This embodiment verifies the feasibility and superiority of the dynamic revenue sharing method and system of the present invention through a specific scenario: On the one hand, by combining multi-dimensional contribution factors with a dynamic weight model, gold member consumers, high-discount merchants, and high-quality promotion channels all receive revenue sharing commensurate with their contributions, reflecting the fairness of revenue sharing; on the other hand, the entire process is automatically executed by the system module, taking only 2 seconds from order receipt to allocation completion, which effectively improves efficiency compared to traditional manual revenue sharing and eliminates human error; at the same time, the blockchain notarization function ensures the credibility of revenue sharing data, effectively avoiding revenue sharing disputes within the alliance and providing technical support for the long-term stable operation of cross-industry alliances.

[0109] Example 2

[0110] Referring to specifications 4-5, and according to another aspect of this application, this application also provides a cross-industry alliance intelligent anti-fraud order-sharing system and method based on blockchain and cross-store verification rules.

[0111] Existing cross-industry alliances or platform revenue-sharing systems generally suffer from the following shortcomings:

[0112] 1. Black box of revenue sharing and lack of trust: The revenue sharing rules of traditional centralized platforms are not transparent, data can be tampered with by one party, and the trust cost among participating merchants is high.

[0113] 2. Business Format Isolation and Data Fragmentation: Business systems of different business formats such as food delivery, retail, and services are independent of each other, and membership systems and consumption data cannot be connected, making it difficult to form a cross-business collaborative marketing and unified value distribution model.

[0114] 3. Low credibility of data sources: Promotional or transaction data (such as weighing and verification) rely on manual operation or can be easily forged, providing opportunities for order-brushing arbitrage.

[0115] 4. Inefficient and limited anti-fraud measures: Existing anti-fraud measures rely heavily on simple post-event data analysis and manual review, failing to address the root causes in business processes and technology. In particular, there is a lack of effective automated mechanisms to curb collusion among merchants within the alliance to fraudulently obtain subsidies and points.

[0116] In this embodiment, the dynamic rights ratio control rule means that the system divides the user rights generated by a single transaction into a first type of rights that can be directly paid for and deducted, and a second type of rights that need to be redeemed for physical goods or services, according to a preset ratio.

[0117] In this embodiment, the cross-store pre-verification and traffic-driving reward rules mean that when a user pre-verifies the second type of benefits obtained from the first merchant to the second merchant, the system automatically issues reward benefits to the first merchant, and the second type of benefits are only transferred to the second merchant after the real user completes the final verification at the second merchant.

[0118] This invention provides a blockchain-based intelligent revenue-sharing system and method for cross-industry alliances. The system automatically collects reliable transaction data through smart hardware (such as smart weighing), which is then calculated by a revenue-sharing engine according to preset rules. Key processes and results are stored on the blockchain to ensure immutability. Its core innovation lies in introducing a set of anti-fraud rules based on dynamic equity ratios and cross-store pre-verification, and solidifying these rules on the blockchain in the form of smart contracts. This ensures that any attempt to fraudulently obtain funds will be automatically transformed into a high-cost, traceable, genuine business activity or become unprofitable.

[0119] The following diagram, along with the accompanying illustrations and architecture diagram, uses the example of "users using smart weighing for shopping" to explain in detail how this system can automatically allocate revenue and effectively prevent fraudulent transactions in a complete process.

[0120] Detailed Explanation of Anti-Fraud Rules Module

[0121] Based on the flowchart, the anti-fraud function of this invention is mainly implemented by the following collaborative rules:

[0122] 1. Dynamic equity ratio control rules:

[0123] Rules: User benefits generated from any successful transaction will be distributed according to a fixed or dynamic ratio (e.g., 30% vouchers, 70% points). Vouchers can be used directly for payment deductions, while points must be redeemed for designated goods or services.

[0124] Technical Implementation: This ratio is managed by the "equity ratio controller" in the service layer and written as a key parameter into the "revenue sharing logic contract". After the revenue sharing engine performs the calculation, it calls the "points issuance and circulation contract" to generate assets on the chain according to this rule.

[0125] Anti-fraud measures: Significantly increases the threshold and cost of monetizing fraudulent transactions. Speculators primarily gain points, but must handle the redemption and logistics of physical goods, and cannot directly withdraw cash.

[0126] 2. Cross-store pre-redeem and traffic-driving reward rules:

[0127] Rule details: Allows users to pre-deduct points earned at one merchant (A) to another merchant (B) within the alliance for redeeming goods. The system automatically rewards the initiating merchant A with an additional "traffic referral points".

[0128] Technical Implementation: This process is driven by a "cross-store transfer and verification processor." When a user or merchant initiates pre-verification, the processor calls a smart contract. Contract Execution: a) Change the user's points status to "pre-verification to store B"; b) Distribute reward points to merchant A's account. All status changes are completed on-chain.

[0129] Anti-fraud measures: This rule cleverly redefines "fraudulent order placement" as "traffic generation." If Merchant A attempts to engage in fraudulent order placement, they must find a colluding Merchant B and transfer points to B's store. This immediately triggers publicly verifiable, on-chain cross-store transaction records. Ultimately, these points must be redeemed by a genuine user at B's store. Otherwise, the points are permanently locked, and the fraudster receives nothing; if there is collusion for redemption, the user must bear the cost of the actual goods, and the entire collusion chain is clearly traceable on the blockchain, posing a very high risk. Simultaneously, the reward given to Merchant A is also points, trapping them in a cycle from which they cannot directly profit.

[0130] The beneficial effects of this embodiment

[0131] 1. Building a Trustworthy Data Foundation: By combining smart hardware with blockchain, the authenticity and immutability of transaction data are ensured throughout the entire process from source to revenue sharing.

[0132] 2. Achieve automated and efficient revenue sharing: Utilize smart contracts to automatically execute complex revenue sharing and points distribution rules, greatly reducing manual intervention, improving efficiency, and reducing errors and disputes.

[0133] 3. Intelligent anti-fraud measures at the source: Through the design of two core rules, "equity ratio control" and "cross-store pre-verification and circulation", the traditional post-event audit is transformed into pre-event motivation guidance and process blocking, which greatly increases the operational complexity and economic cost of fraudulent orders.

[0134] 4. Promote a healthy alliance ecosystem: The anti-fraud rules themselves constitute a positive cycle business model that incentivizes merchants to drive traffic to each other, organically combining system security with business growth, and enhancing the stickiness and vitality of the alliance.

[0135] 5. Enhanced transparency and trust: All rule execution and asset transfers are recorded on the blockchain and are appropriately transparent to alliance members, thus building a brand-new trust mechanism.

[0136] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic revenue sharing method based on multidimensional contribution, characterized in that, Includes the following steps: S1) Receive transaction order data from the cross-industry alliance platform; S2) Extract multiple contribution factors from the transaction order data; S3) Based on the preset dynamic weight model, calculate the dynamic allocation ratio of each participant according to the multiple contribution factors; S4) Calculate the consumer rebate amount, platform operating fee and allocation pool amount based on the transaction amount and merchant discount ratio, and deduct the estimated tax from the allocation pool amount to obtain the net allocation pool amount; S5) Distribute the net allocation pool amount to the corresponding participants according to the dynamic allocation ratio; S6) Generate an immutable record of the revenue sharing rules, calculation process, and distribution results and store it in the blockchain.

2. The dynamic revenue sharing method based on multidimensional contribution as described in claim 1, characterized in that, The multiple contribution factors include at least two of the following: merchant discount rate, consumer membership level, promoter's promotion channel, transaction time, and product or service type.

3. The dynamic revenue sharing method based on multidimensional contribution as described in claim 1, characterized in that, The calculation of the dynamic allocation ratio of each participant based on the preset dynamic weighting model specifically includes: Assign basic weights to each of the contribution factors; The basic weights are calculated by weighting the factors based on the specific values ​​of each contribution factor. The weighted calculation results are normalized to obtain the final allocation ratio for each participant.

4. The dynamic revenue sharing method based on multidimensional contribution as described in claim 1, characterized in that, The specific steps for allocating the net allocation pool amount to the corresponding participants according to the dynamic allocation ratio are as follows: The amount allocated to participants will be distributed in the form of vouchers and points, and the allocation ratio of vouchers and points can be configured.

5. The dynamic revenue sharing method based on multidimensional contribution as described in claim 1, characterized in that, The participants include at least three of the following: consumers, merchants, promoters, and platform operators.

6. A revenue-sharing system implementing the dynamic revenue-sharing method based on multidimensional contribution as described in any one of claims 1-5, characterized in that, include: The order access module is used to receive and verify transaction order data from the cross-industry alliance platform; The factor extraction module is used to extract multiple contribution factors from the transaction order data; The revenue sharing calculation engine is used to run a preset dynamic weight model and calculate the dynamic allocation ratio of each participant. The funds processing module is used to calculate the instant cashback amount for consumers, platform operating fees, allocation pool amount and net allocation pool amount, and to execute the allocation of funds to each participant. The data storage module is used to generate immutable records of the revenue sharing rules, calculation process, and allocation results and store them in the blockchain.

7. The dynamic revenue sharing system based on multidimensional contribution as described in claim 6, wherein the system is communicatively connected to an intelligent weighing device, and the transaction order data is generated by the weight information collected in real time by the intelligent weighing device.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the dynamic revenue sharing method based on multidimensional contribution as described in any one of claims 1-5.

9. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the dynamic revenue sharing method based on multidimensional contribution as described in any one of claims 1-5.

10. The computer device according to claim 9, characterized in that, The computer device also includes a communication interface, which is used to establish a data transmission link between the computer device and a cross-industry alliance platform or intelligent weighing equipment.

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