Intelligent contract-driven supply chain economic optimization method

Abstract

The invention discloses a supply chain economic optimization method driven by a smart contract, and belongs to the technical field of supply chain management and block chains, the method is executed by a profit distribution smart contract deployed on a block chain, and the total cycle profit is calculated by obtaining real-time sales data of a product and contribution evaluation data of each participant from a credible data source on the chain; calling a preset contribution degree evaluation model to process the contribution data, generating a dynamic contribution coefficient of each participant, and determining a profit allocation weight according to the dynamic contribution coefficient; and finally, a profit distribution instruction is automatically generated and executed, and fund transfer is completed. According to the method, by establishing an automatic distribution mechanism with contribution and income dynamically linked, the problems that in a traditional supply chain, profit distribution is rigid, and value contributions of all links cannot be accurately reflected are solved, excitation compatibility is achieved, the overall economic benefits of the supply chain can be effectively optimized, and collaborative innovation is promoted.

Description

Technical Field

[0001] This invention relates to a supply chain economic optimization method, and more particularly to a supply chain economic optimization method driven by smart contracts. Background Technology

[0002] In modern industrial practice, supply chain profit distribution typically relies on pre-signed fixed-price or cost-plus agreements. This static model has inherent flaws: it fails to scientifically reflect the actual contribution of raw material technology innovation, production process improvement, and precise marketing investment to the final market competitiveness of the product (such as brand premium and sales growth), leading to a disconnect between value distribution and value creation, and inhibiting the enthusiasm of participants in each link to engage in long-term collaboration and collaborative innovation.

[0003] While blockchain technology and smart contracts offer a new technological paradigm for achieving transparent, trustworthy, and automated allocation logic, they still face significant technical bottlenecks when applied to complex supply chain scenarios. On one hand, existing blockchain-based solutions often attempt to store and process massive amounts of supply chain transaction data (such as individual sales records and detailed logistics trajectories) entirely on the blockchain. This is directly limited by the inherent characteristics of mainstream blockchain networks—long transaction confirmation times and limited throughput—leading to severe performance and cost challenges when processing high-frequency, large-scale commercial data. On the other hand, even if some data is processed, a systematic technical solution remains lacking for how to verifiable quantification and auditing of contributions from multiple parties without exposing trade secrets (such as the specific cost structure of each stage), and how to dynamically respond to real-time market signals. These technical obstacles keep many blockchain supply chain applications at the conceptual or partial evidence storage level, making it difficult to support an automated profit allocation system that covers the entire chain, responds in real time, and protects business privacy. Summary of the Invention

[0004] This invention overcomes the shortcomings of existing technologies and provides a smart contract-driven method for optimizing the supply chain economy.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a smart contract-driven supply chain economic optimization method, executed by a profit distribution smart contract deployed on a blockchain, comprising the following steps: S1. Obtain market performance data of the target product during the settlement period. Market performance data includes real-time sales data and contribution assessment data of each participant in the supply chain that has been recognized by consensus. An asynchronous parallel data processing mechanism is adopted. Sales data and contribution assessment data are batch-collected, compressed, and signed and verified through off-chain data preprocessing nodes. After verification, data summaries are batch-uploaded to the chain. The original data is stored in a distributed storage system and associated with the on-chain summary through content addressing identifiers. Smart contracts only trigger subsequent calculations based on the on-chain summary. The original data is verified and obtained off-chain as needed. S2. Calculate the total product profit for the settlement period based on the processed market performance data. S3. Based on the processed contribution assessment data, the dynamic contribution coefficients of each participant are obtained through the preset contribution assessment model, and the profit distribution weights are determined accordingly. The contribution assessment model adopts the DPRep-Prove framework, which deconstructs the processed contribution assessment data into three types of verifiable input data. The input data is processed through a contribution value sensor and a dynamic weight programmer, and outputs dynamic contribution coefficients and programmable profit distribution weights. S4. Combining the total product profit, dynamic contribution coefficient, and the weight of each profit distribution, generate and automatically execute profit distribution instructions to complete the fund transfer.

[0006] In a preferred embodiment of the present invention, the calculation process for the total product profit includes: S201. Obtain real-time sales data verified and signed by a decentralized oracle from the distributed storage system. The smart contract calls the preset aggregation function to calculate the total sales revenue of the product within the settlement period. S202. When each participant in the supply chain completes its respective stage, it simultaneously stores the cryptographic commitments of key cost elements on the blockchain. After the settlement cycle is triggered, each participant submits detailed cost data to the designated off-chain computing node. The off-chain computing node performs calculations according to the preset cost aggregation rules and generates a verifiable calculation proof document. The calculation proof document is submitted to the blockchain for verification by the smart contract, which then provides the total verifiable cost. S203. After the calculation proof document is verified, the smart contract executes the final calculation: total product profit = total product sales revenue - total verifiable cost, and records the result as an immutable on-chain state.

[0007] In a preferred embodiment of the present invention, the process of obtaining the market performance data includes: Obtain real-time sales data, including sales volume and unit profit, from the sales terminal system; obtain contribution assessment data quantifying the contribution of each participant to the product's market competitiveness from the corresponding assessment system, and the contribution assessment data must be verified by a valid digital signature.

[0008] In a preferred embodiment of the present invention, the three types of verifiable input data include: contribution claims, context state, and real-time market signals; The contribution value sensor quantifies the input contribution statement into a multi-dimensional value score under the context state and real-time market signals. The dynamic weight programmer consists of a programmable rule engine and a lightweight machine learning algorithm. The rule engine can temporarily inject or adjust evaluation rules according to different product lines and strategic goals. The lightweight machine learning algorithm tracks the behavior changes of each participant and the overall supply performance in the next cycle after each profit distribution, and adjusts the basic weights of each dimension.

[0009] In a preferred embodiment of the present invention, the step of determining the profit allocation weight includes: The smart contract reads the dynamic contribution coefficients of the current and historical settlement cycles, applies an algorithm based on the time decay factor to calculate the comprehensive contribution value of each participant, and obtains the final weight through normalization processing.

[0010] In a preferred embodiment of the present invention, before executing the profit distribution instruction, a verification and objection handling step is further included: the smart contract broadcasts the pre-execution result of the distribution, receives and processes the encrypted signed objection within a preset objection period, and triggers the on-chain dispute adjudication mechanism to correct or confirm the distribution result.

[0011] In a preferred embodiment of the present invention, the smart contract uses a pre-set anomaly detection model to determine whether market performance data is abnormal; if abnormal, the automatic allocation process is frozen and an emergency mechanism is activated, the emergency mechanism including triggering on-chain voting to decide whether to adopt a temporary allocation scheme or switch to manual processing.

[0012] In a preferred embodiment of the present invention, the smart contract stores the allocation results data of each period on the blockchain and generates optimization suggestions for the contribution evaluation model based on multi-period data correlation analysis. After approval by the on-chain governance process, the model parameters are automatically updated.

[0013] In a preferred embodiment of the present invention, the smart contract includes a top-level allocation master contract and multiple allocation sub-contracts for sub-supply chains; The top-level allocation master contract execution steps S1 to S4 are described, and the sub-supply chain as a whole is regarded as a participant, and the overall dynamic profit allocation weight and the total profit due are calculated. The top-level allocation master contract triggers the corresponding sub-supply chain allocation sub-contract and passes the total profit due to the sub-supply chain as input to the sub-contract; The allocation sub-contract executes steps S1 to S4 independently and in parallel to obtain market performance data within the sub-supply chain, calculate the contribution coefficient and allocation weight of each participant, and complete the secondary distribution of profits.

[0014] In a preferred embodiment of the present invention, before S1, there is a step of initializing the on-chain profit distribution fund pool; the smart contract receives the creation request, locks funds according to predefined initial rules, and forms an on-chain fund pool.

[0015] This invention addresses the shortcomings of the prior art and has the following beneficial effects: (1) By introducing off-chain data preprocessing nodes and a high-performance distributed storage system, this solution completes the collection, verification, and compression of massive amounts of raw data off-chain, and only uploads the immutable data digests (hash values) to the chain for evidence storage in batches. This effectively overcomes the bottleneck of blockchain network throughput and high transaction costs caused by directly uploading all data to the chain, enabling the system to process high-frequency, large-scale supply chain business data while maintaining the core trust anchoring function of the blockchain.

[0016] (2) The proposed DPRep-Prove contribution assessment model can combine multi-source, heterogeneous contribution data with real-time market signals and allow for dynamic injection of strategic objectives through programmable rules. This transforms profit distribution from a static, ex-post financial settlement into a dynamic, ex-ante value guidance mechanism, enabling a more scientific quantification of the marginal contribution of each link to the final market success. It allows profit distribution weights to reflect market preferences and strategic orientation in real time, effectively incentivizing supply chain members to engage in long-term value creation and collaborative innovation. This fundamentally changes the value distribution logic of the supply chain, deeply binding the interests of each link with the market performance of the end product, incentivizing all participants to transform from "cost centers" to "value creators," jointly maximizing profits and achieving the optimal solution for overall economic benefits.

[0017] (3) A verifiable computation process was designed using advanced cryptographic tools such as cryptographic commitments and zero-knowledge proofs. Each participant submits a cost commitment on-chain, and a zero-knowledge proof is generated after privacy-preserving computation off-chain. This enables smart contracts to verify the authenticity and compliance of the total cost calculation results with extremely high efficiency (milliseconds) without needing to know any sensitive business data such as the specific cost details of any participant. This fundamentally solves the core contradiction between data confidentiality and audit credibility in supply chain collaboration. Through the transparency and immutability of blockchain and the automatic enforcement of smart contracts, a trustworthy collaborative environment that does not rely on an intermediary coordinator is constructed, greatly reducing friction and trust costs in negotiation, reconciliation, and settlement.

[0018] (4) All rules are made public in advance, all data and calculation processes are traceable on the chain, the allocation results are highly transparent and can be audited in the whole process afterward, thus establishing unprecedented fairness and credibility.

[0019] (5) From trusted data collection, automatic profit calculation, dynamic contribution assessment to final fund transfer, the entire process is automatically driven and executed by smart contracts, and all key steps and results are recorded on the blockchain in a verifiable manner. This eliminates manual intervention, reconciliation disputes and execution delays in the traditional model, and establishes a highly automated, rule-transparent and tamper-proof supply chain economic collaborative infrastructure, which significantly reduces trust costs and collaboration frictions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a flowchart of the optimization method of a preferred embodiment of the present invention; Figure 2 This is a flowchart of step S1 of a preferred embodiment of the present invention; Figure 3 This is a flowchart illustrating the complete closed-loop process of generating and verifying a computational proof file according to a preferred embodiment of the present invention. Detailed Implementation

[0021] 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.

[0022] 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. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0023] like Figure 1 As shown, the smart contract-driven supply chain economic optimization method, executed by a profit-distribution smart contract deployed on the blockchain, includes the following steps: S1. Obtain market performance data of the target product during the settlement period. Market performance data includes real-time sales data and contribution assessment data of each participant in the supply chain that has been recognized by consensus. An asynchronous parallel data processing mechanism is adopted. Sales data and contribution assessment data are batch-collected, compressed and signed and verified through off-chain data preprocessing nodes. After verification, data summaries are batch-uploaded to the chain. The original data is stored in a distributed storage system and associated with the on-chain summary through content addressing identifiers. Smart contracts only trigger subsequent calculations based on the on-chain summary. The original data is verified and obtained off-chain as needed.

[0024] Furthermore, the process of obtaining market performance data includes: Obtain real-time sales data, including sales volume and unit profit, from the sales terminal system; obtain contribution assessment data quantifying the contribution of each participant to the product's market competitiveness from the corresponding assessment system, and the contribution assessment data must be verified by a valid digital signature.

[0025] Furthermore, the smart contract obtains market performance data of the target product during the settlement period from a trusted on-chain data source. The market performance data includes at least real-time sales data and market contribution assessment data. Real-time sales data is uploaded to the blockchain by the sales terminal system and includes unit product profit and total sales volume. Market contribution assessment data is uploaded to the blockchain by the contribution assessment systems of each participant in the supply chain, and is used to quantify the contribution of each participant to the product's market competitiveness.

[0026] In one embodiment, such as Figure 2 As shown, the implementation of step S1 begins with the real-time submission of data sources from each participating party. Taking a certain spring water brand as an example, real-time sales data comes from the POS system of the retail terminal. Whenever a product is sold, a record containing a timestamp, product ID, price, and quantity is automatically generated and pushed to the system interface. At the same time, contribution assessment data from each link in the supply chain is also generated simultaneously: the water source submits a water quality monitoring report with an automatic signature (such as pH value and mineral content) through IoT sensors; the manufacturer submits proof of low-carbon treatment processes and production qualification rate from its ERP system; and the logistics provider submits transportation temperature control records and on-time delivery vouchers based on GPS and temperature control equipment.

[0027] This raw data then enters the off-chain preprocessing and consensus verification stage. An off-chain preprocessing node (notary node) trusted by all parties will execute a series of pre-set automated verification rules. First, format and digital signature verification is performed to ensure the data source is authentic and has not been tampered with; then logical cross-verification is performed, such as comparing the delivery time of the logistics provider with the warehousing time of the retailer to ensure consistency. Only when the data passes all verification rules is it considered to have reached "consensus verification" and is packaged by the node into a structured contribution evaluation data package.

[0028] To achieve a balance between data trustworthiness and system efficiency, the system employs an "on-chain notarization, off-chain storage" strategy for data on-chain and trusted notarization. Preprocessing nodes calculate a unique cryptographic hash (equivalent to a digital fingerprint) for each complete contribution evaluation data packet and write this hash to the blockchain, leveraging its immutability to ensure the final state of the data packet is auditable. The complete original data packet is stored in a high-performance distributed storage system such as IPFS or an enterprise-grade database, with its storage address recorded and associated with the on-chain hash. Thus, smart contracts only need to read the on-chain hash to trigger subsequent processes, while the original data can be retrieved at any time by address and its integrity verified via the hash value, thereby completing the trusted acquisition of the entire market performance data.

[0029] S2. Calculate the total profit of the product during the settlement period based on market performance data.

[0030] Furthermore, the calculation process for total product profit includes: S201. Obtain real-time sales data verified and signed by a decentralized oracle from the distributed storage system. The smart contract calls the preset aggregation function to calculate the total sales revenue of the product within the settlement period. S202. When each participant in the supply chain completes its respective stage, it simultaneously stores the cryptographic commitments of key cost elements on the blockchain. After the settlement cycle is triggered, each participant submits detailed cost data to the designated off-chain computing node. The off-chain computing node performs calculations according to the preset cost aggregation rules and generates a verifiable calculation proof document. The calculation proof document is submitted to the blockchain for verification by the smart contract, which then provides the total verifiable cost. It needs to be explained that, such as Figure 3 As shown, the computation proof file is essentially a structured data packet generated by a zero-knowledge proof (ZKP) algorithm and conforming to a specific cryptographic protocol. It is not a plaintext, human-readable computation table, but rather a cryptographic object containing a series of mathematical relationship proofs.

[0031] Structured data packets typically appear as a compact block of binary data or a structured text string, including proofs, public inputs, circuit identifiers, or verification key indexes. The proof is the core of the structured data packet, a cryptographic byte string (a long, seemingly random combination of numbers and letters) encapsulating all the mathematical evidence regarding the correctness of the computation process. Public inputs are a set of publicly available parameters that need to be proven and computed, specifically including the hash value of the on-chain cost commitment and the total verifiable cost amount of the computation output. The circuit identifier or verification key index is an identifier pointing to the specific arithmetic circuit or corresponding verification key used, representing the mathematical representation of the cost aggregation rules.

[0032] Its main function is to prove to the validator (on-chain smart contract): "I know a set of cost details that meet specific rules, and based on this set of data, I have correctly executed the preset cost aggregation rules to obtain a certain total cost value." At the same time, the document does not disclose any specific numbers in this set of details (such as the purchase price of a single material).

[0033] S203. Once the verification of the proof document is passed, the smart contract executes the final calculation: Total product profit = Total product sales revenue - Total verifiable cost, and records the result as an immutable on-chain state.

[0034] In one embodiment, the entire process begins with trusted revenue aggregation. All retail POS systems push each digitally signed sales record in real time to a jointly supervised, high-performance off-chain database. This database periodically synchronizes the aggregated hash of the sales records to the blockchain, completing data anchoring. When settlement is triggered, the smart contract requests the aggregated results from the database, which returns the calculated total sales revenue (e.g., 1 million bottles multiplied by a unit price of 2 yuan, totaling 2 million yuan) and provides proof of data consistency. Once the contract verification is successful, the 2 million yuan is established as the undisputed "total sales revenue" among all parties on the chain.

[0035] The core phase then moves to verifiable cost backtracking and aggregation. This phase aims to resolve the conflict between cost data confidentiality and audit credibility. First, during production and logistics, each participant instantly records the cryptographic commitments (i.e., their hash values) of key cost elements (such as bottle cap procurement and transportation along specific routes) on the blockchain, establishing an immutable "timestamp anchor." Once the settlement cycle begins, each party submits a detailed cost breakdown, including specific amounts, to a neutral off-chain computing node in encrypted form. This node, in a secure environment, performs calculations according to pre-defined cost aggregation rules (such as activity-based costing) and simultaneously generates a zero-knowledge proof file. This file is a cryptographic object whose magic lies in its ability to prove to the blockchain that "the calculation process fully follows the established rules, and the detailed data used corresponds one-to-one with the cost commitments previously on the chain," without disclosing any original data. Subsequently, the node submits the calculated total cost amount along with this proof file to the blockchain.

[0036] The on-chain smart contract then quickly verifies the supporting documentation. The verification process involves only mathematical calculations, requires no access to plaintext cost data, and can be completed within seconds. Once verification is successful, it means that the submitted total verifiable cost (let's say 1.5 million yuan) is genuine, compliant, and accepted by all members.

[0037] The process culminates in on-chain profit verification. The smart contract automatically executes the final calculation: Total product profit = Total product sales revenue - Total verifiable costs, i.e., 2 million yuan - 1.5 million yuan = 500,000 yuan. This "500,000 yuan" serves as the final profit benchmark and is permanently and immutably recorded on the blockchain, providing an authoritative and reliable data foundation for subsequent dynamic profit distribution based on contributions.

[0038] S3. Based on the processed contribution assessment data, the dynamic contribution coefficients of each participant are obtained through a preset contribution assessment model, and the profit distribution weights are determined accordingly. The contribution assessment model adopts the DPRep-Prove framework, which deconstructs the processed contribution assessment data into three types of verifiable input data. The input data is processed through a contribution value perceiver and a dynamic weight programmer, and outputs dynamic contribution coefficients and programmable profit distribution weights.

[0039] Furthermore, the three types of verifiable input data include: contribution claims, context state, and real-time market signals; The contribution value sensor quantifies the input contribution statement into a multi-dimensional value score under contextual state and real-time market signals. The dynamic weight programmer consists of a programmable rules engine and a lightweight machine learning algorithm. The rules engine can temporarily inject or adjust evaluation rules according to different product lines and strategic goals. The lightweight machine learning algorithm tracks the behavior changes of each participant and the overall supply performance in the next cycle after each profit distribution, and adjusts the basic weights of each dimension.

[0040] It should be further explained that the DPRep-Prove (Dynamic Programmable Reputation Proof) model is a contribution evaluation and quantification system specifically designed for complex supply chain scenarios, operating in a hybrid on-chain and off-chain environment. Its design goal is to address the two core issues mentioned in the background: 1) Traditional static contracts cannot link profit distribution with the dynamic contribution of each stage to the final market value of the product.

[0041] 2) When using blockchain technology, how can we achieve credible quantification and auditing of contributions while ensuring the privacy of business data?

[0042] This model transforms subjective, multi-dimensional "contributions" into objective, verifiable "contribution coefficients" in a structured manner. Its core design philosophy lies in introducing "programmability" and "proof mechanisms," enabling evaluation rules to dynamically respond to market signals and strategic objectives, while ensuring that all evaluation processes and results are cryptographically verifiable.

[0043] The DPRep-Prove model mainly consists of the following three core logical modules, which form a closed-loop system through data flow and feedback mechanisms: The three modules mentioned above do not execute linearly, but rather form a cycle of coordination and feedback: The contribution evaluation data, processed and agreed upon in step S1, is first input into the contribution value sensor. Based on the currently active base evaluation matrix, the sensor calculates the initial multidimensional score for each contribution.

[0044] The dynamic weighted programmer operates in real time. It may generate a set of dynamic weighting coefficients based on external market oracle signals (such as trending topics) or pre-set strategic rules (such as promotional seasons), adjusting the value perceiver's evaluation matrix in real time. For example, when the market focuses on "sustainability," the programmer will increase the weight of the "environmental protection" dimension, making related contribution claims more valuable during that period.

[0045] The credit score manager receives a dynamically weighted stream of contribution value. It performs the following operations: The current contribution value is added to the long-term reputation score of each party, while a decay algorithm is applied to ensure that the score reflects recent activity.

[0046] This generates key outputs for the current settlement cycle, including programmable profit allocation weights and corresponding contribution proofs. The weights are functions of the current reputation score, dynamically weighted contribution value, etc.; while the contribution proof is a cryptographic credential used to prove to the on-chain smart contract that the weights were calculated through a legal and compliant process.

[0047] The generated contribution proofs and assigned weights are submitted to the blockchain. Smart contracts quickly verify the validity of the proofs. Once verified, the weights are used for profit distribution. Simultaneously, the results of this round of distribution (such as incentive effects) are recorded as feedback data for analysis by the learning algorithm in the dynamic weight programmer, enabling continuous optimization of future evaluation strategies.

[0048] The core innovation of the DPRep-Prove model lies in its creative integration of "programmable business rules," "machine learning optimization," and "cryptographically verifiable proofs" into a single evaluation framework. This distinguishes it from other models. It possesses the ability to dynamically respond to market and strategic changes.

[0049] It has moved from "data trustworthiness" to "computation process and logic trustworthiness", and can effectively protect sensitive business information.

[0050] By using an on-chain verifiable proof mechanism, the issues of eventual consistency and trusted arbitration of evaluation results in a distributed environment are resolved.

[0051] In one embodiment, the operational mechanism of the contribution assessment model (DPRep-Prove framework) can be explained as follows. Its core function is to transform the heterogeneous and multidimensional operational contributions of each node in the supply chain into a set of quantifiable, verifiable, and dynamically adjustable contribution coefficients, which are ultimately mapped to profit allocation weights. This process is not a static calculation, but a dynamic assessment system embedded with market awareness and strategic adaptability.

[0052] Within a settlement cycle, each participant must submit a structured, verifiable statement outlining their operational contributions. For example, a water source node's statement might include water quality parameters and third-party environmental certification fingerprints; a producer's statement might include production pass rates and identification of dedicated production lines activated to handle peak demand; and a logistics provider's statement might include on-time delivery rates and carbon emission data for specific routes. All statements are accompanied by the submitter's digital signature and the data fingerprint of the associated IoT device or management system to ensure the authenticity and integrity of the data source.

[0053] These verified claims, along with external market sentiment signals obtained through oracles (such as social media sentiment indices for specific environmental issues and promotional season time stamps), are input into the model. The contribution value perceiver within the model first normalizes this multi-source heterogeneous data and then performs initial value quantification based on a built-in multi-dimensional weight matrix. This quantification process is context-aware: when market signals indicate an increase in "low-carbon" attention, the model automatically increases the weight coefficients of environmentally related dimensions, giving the corresponding contribution claims a higher base value score.

[0054] Core enterprises can inject temporary rules aligned with their business strategies into the model, either directly or through on-chain governance protocols. For example, to address the 6.18 promotional period, a rule could be injected to increase the weighting of supply chain responsiveness and promotional synergy by 50% over the next 30 block cycles. This rule will change the weight matrix of the value perceiver in real time, significantly amplifying the current contribution of actions such as manufacturers activating high-speed production lines and logistics providers ensuring on-time delivery. Furthermore, the model integrates a lightweight machine learning module that continuously fine-tunes the basic weight parameters of each dimension by analyzing the correlation between historical allocation results and the overall supply chain performance in subsequent cycles (such as product sales growth rate and customer satisfaction index), achieving adaptive optimization of the evaluation strategy.

[0055] Following the dynamic evaluation process described above, the model outputs two types of results. The first is a dynamic reputation score, which represents the accumulated contribution capital of each participant across cycles. Its value is updated based on the current contribution value, and a time decay function is introduced to prevent reputational monopolies. Furthermore, a synergy gain coefficient is added to contributions that have been verified to produce synergistic effects (such as combining high-quality water sources with precise temperature-controlled logistics to improve product quality). The second is a programmable profit allocation weight for the current period. This weight is the result of a composite function of multiple factors, including the current dynamic reputation score of each party, the current contribution value adjusted according to strategic rules, and potential synergy gains.

[0056] This weighting is input into the profit distribution smart contract. Assuming the calculated weights for water source, producer, and logistics provider in this period are 30%, 45%, and 25%, respectively, this result reflects the marginal relative value of each party's contribution under the dual constraints of the current market environment (emphasizing environmental protection) and strategic orientation (ensuring sales promotion). Compared to traditional allocation methods based on fixed cost ratios or static contract terms, this model implements a flexible allocation mechanism based on real-time contribution value measurement, aligned with market dynamics and strategic goals. This transforms profit distribution from a post-event financial settlement tool into a real-time incentive engine driving supply chain collaborative optimization and the achievement of strategic objectives.

[0057] Further steps in determining profit allocation weights include: The smart contract reads the dynamic contribution coefficients of the current and historical settlement cycles, applies an algorithm based on the time decay factor to calculate the comprehensive contribution value of each participant, and obtains the final weight through normalization.

[0058] S4. Combine the total product profit with the weight of each profit distribution to generate and automatically execute profit distribution instructions to complete the fund transfer.

[0059] Furthermore, before executing the profit distribution instruction, there are also verification and objection handling steps: the smart contract broadcasts the pre-execution result of the distribution, receives and processes the cryptographically signed objections within the preset objection period, and triggers the on-chain dispute resolution mechanism to correct or confirm the distribution result.

[0060] Furthermore, the smart contract uses a pre-built anomaly detection model to determine whether market performance data is abnormal; if abnormal, it freezes the automatic allocation process and activates an emergency mechanism, which includes triggering on-chain voting to decide whether to adopt a temporary allocation scheme or switch to manual processing.

[0061] The smart contract stores the allocation results of each period on the blockchain and generates optimization suggestions for the contribution assessment model based on the correlation analysis of multi-period data. After approval by the on-chain governance process, the model parameters are automatically updated.

[0062] In one embodiment, a smart contract includes a top-level allocation master contract and multiple allocation sub-contracts for sub-supply chains; The top-level allocation of master contract execution steps S1 to S4, and the entire sub-supply chain as a participant, calculates the overall dynamic profit allocation weight and the total profit due; The top-level allocation master contract triggers the corresponding sub-supply chain allocation sub-contract, and passes the total profit due to the sub-supply chain as input to the sub-contract; The subcontracts execute steps S1 to S4 independently and in parallel to obtain market performance data within the sub-supply chain, calculate the contribution coefficient and allocation weight of each participant, and complete the secondary distribution of profits.

[0063] In one embodiment, a foundational blockchain platform is first required, providing distributed ledger, consensus mechanisms, and cryptographic security. This platform needs to be capable of running smart contracts with complex logic, typically employing a consortium blockchain architecture that meets the needs of enterprise-level applications, ensuring efficient collaboration among participating nodes with permission.

[0064] Secondly, smart contracts are the logical core of this invention. The most crucial of these is the "Dynamic Supply Chain Profit Allocation Master Contract," which encapsulates all the core rules from data collection and calculation to allocation and execution. Furthermore, auxiliary contracts such as the "Fund Pool Contract" responsible for fund custody and the "Dispute Resolution Contract" for handling disagreements can be deployed as needed, collectively forming a contract group.

[0065] The system needs to connect to reliable external data sources, commonly known as oracles. These oracles are responsible for securely and tamper-proofly inputting real-world off-chain data into the blockchain. They mainly fall into two categories: first, sales data oracles connected to the sales system, used to periodically obtain actual sales and net profit data for products; and second, contribution data interfaces for various participants, allowing suppliers, distributors, and others to submit contribution proof information such as quality reports and marketing effectiveness analyses in a standardized format.

[0066] Each company in the supply chain, as a participant, needs to run its own blockchain node. These nodes are used for identity authentication, data signing and submission, receiving smart contract status notifications, and participating in voting on certain governance decisions.

[0067] The system needs to bridge the blockchain with the traditional financial system through a secure "payment gateway." Once the on-chain smart contract generates a payment instruction, the gateway is responsible for completing the actual fund transfer in the bank or third-party payment system and sending the successful result back to the blockchain for final confirmation.

[0068] The entire system operates automatically according to a pre-set settlement cycle (such as monthly or quarterly), forming a fully closed and automated loop from data perception and intelligent calculation to value distribution.

[0069] The core steps of this invention are embodied in the automatic execution logic of the master contract for dynamic allocation of supply chain profits, which includes the following six sequentially performed stages: Phase 1: Multi-source data aggregation and trusted verification.

[0070] At the end of the settlement period, the main contract automatically initiates the data collection process. It first requests the sales data oracle to obtain encrypted sales data packets for the target product during that period, containing at least the total sales volume and average net profit per unit. The contract rigorously verifies the digital signature of this data packet to ensure it originates from a pre-defined list of trusted oracles.

[0071] Next, the main contract initiates a call to all registered supply chain participants to collect their contribution assessment data. For example, upstream component suppliers might submit data including performance test scores and quality ratings for specific batches of products; downstream distributors might submit data on the costs of a marketing campaign and the sales increase resulting from attribution analysis. Each piece of data must be accompanied by the submitter's digital signature, and the main contract verifies the signature's validity using cryptographic methods, thereby ensuring the uniqueness and non-repudiation of the data source. After all data is submitted, the system will allow a short consensus confirmation period for all parties to verify it. After the confirmation period ends, all data from that period will be locked, forming an immutable data foundation.

[0072] Phase Two: Calculation of the total profit pool.

[0073] Based on reliable sales data, the master contract performs a simple arithmetic calculation. It multiplies the "total sales quantity" extracted from the sales data package with the "average net profit per unit," and the resulting product is the total distributable profit generated by the product within the current settlement period. This amount forms the basis for all subsequent distribution calculations.

[0074] Phase 3: Calculation of dynamic contribution coefficient.

[0075] This stage is the key innovation of this invention. The main contract will invoke a pre-deployed, transparent "contribution evaluation model" on the blockchain. This model is essentially a public algorithm whose design can be determined through initial negotiation among all participants.

[0076] The model's processing involves three levels. First, the collected heterogeneous contribution data is standardized. For example, an "A+" quality rating is mapped to a numerical score, marketing ROI is converted into an efficiency coefficient, and improved logistics efficiency is quantified as a service score. Second, based on pre-agreed importance of each dimension, different weights are assigned to the scores of different categories, and the original contribution score for each participant is calculated comprehensively. Finally, the original scores of all participants are normalized to ensure that the sum of all scores is one. The normalized value obtained by each participant is its "dynamic contribution coefficient" for this period, which directly represents its relative contribution ratio to the total product value.

[0077] Phase 4: Determining the overall allocation weights.

[0078] To encourage long-term, stable, and high-quality cooperation, and to smooth out occasional fluctuations in single-period data, the weight ultimately used for profit distribution is not directly equal to the dynamic contribution coefficient of the current period. The main contract will incorporate historical performance data for comprehensive calculation.

[0079] Specifically, the system reads the dynamic contribution coefficients of each participant in the current period and several historical periods, forming a time series. Then, a weighted average algorithm with a time decay effect is applied for calculation. This algorithm assigns the highest weight to the contribution coefficient of the current period, while the weights of coefficients from historical periods decrease progressively over time. This calculation respects both current contribution and historical performance, ultimately generating a stable and fair "comprehensive profit distribution weight" for each participant.

[0080] Fifth stage: Generation of allocation instructions.

[0081] After determining the total profit pool and the allocation weights for each participant, the main contract performs a final deterministic calculation. It multiplies the "total profit amount" calculated in the second phase by the "comprehensive profit allocation weight" obtained by each participant in the fourth phase, thereby precisely calculating the specific profit amount each participant should receive in this period. Subsequently, the contract encapsulates these calculation results into structured payment instructions, which explicitly include the recipient's blockchain address and the amount to be allocated.

[0082] Phase 6: Automatic fund transfer execution.

[0083] After generating allocation instructions, the main contract automatically enters the execution phase. It first checks if the balance of the on-chain liquidity pool account associated with the contract is sufficient to pay all allocation instructions. Upon confirming sufficient funds, the contract triggers the underlying blockchain's transfer function or sends a payment instruction with an authoritative signature to the connected payment gateway. The payment gateway then executes batch transfer operations within the traditional financial system. Each successful transfer generates a transaction receipt, which is sent back to the blockchain and recorded. Once all allocation instructions have been executed, the main contract marks this settlement cycle as "completed," and the entire process concludes.

[0084] Furthermore, to ensure automated allocation, a dedicated funding pool managed by smart contracts needs to be established. During system initialization, core enterprises in the supply chain transfer initial capital to this funding pool contract. In each settlement cycle, if the funds required for profit distribution exceed the pool's balance, the main contract automatically sends a replenishment request to the core enterprise. Upon authorization, the shortfall is automatically transferred from the enterprise's account to the funding pool. All inflows, outflows, and balance changes are permanently recorded on the blockchain, ensuring full transparency and auditability.

[0085] To handle the diverse data submitted by different participants, the system defines a standardized data format template. For the production stage, the template requires fields such as supplier identification, production batch number, key performance parameters, quality rating, and associated material certification hash. For the marketing stage, the template requires activity identification, coverage area, investment cost, incremental effect data analyzed by third-party tools, and report hash. All data must be signed by the submitter using their private key before submission, and the smart contract verifies it using the corresponding public key to ensure that the data has not been tampered with during transmission and that its source is authentic.

[0086] The contribution assessment model is central to determining the fairness of allocation. Its implementation can be flexibly chosen. One approach is to directly incorporate the core algorithm into the main contract, running it as a public function to ensure complete transparency and on-chain verifiability. Another approach, suitable for exceptionally complex computational logic, employs an "off-chain computation, on-chain verification" model. This involves a designated trusted computing node running the model off-chain, calculating the result, and simultaneously generating a cryptographic credential (such as a zero-knowledge proof) proving the correctness of the computation process. The result and credential are then submitted to the blockchain. The main contract only needs to verify the validity of the credential to adopt the computation result, thus ensuring the usability of complex models while maintaining the trust foundation for on-chain verification.

[0087] The system is designed with robust negotiation and fault-tolerance mechanisms. During the data confirmation period or the allocation result publicity period, any participant who has objections to the authenticity of the data or the calculation results can initiate a dispute after paying a certain deposit. The dispute will trigger an independent on-chain adjudication process, in which multiple randomly selected arbitration nodes will review the evidence and vote, and the smart contract will automatically execute the final ruling based on the voting results.

[0088] In addition, the system has pre-built anomaly detection logic to monitor whether sales data fluctuates drastically beyond the normal range. Once such anomalies are detected, the main contract will automatically suspend the regular allocation process and activate the contingency response plan. The contingency plan typically includes initiating an on-chain emergency vote, whereby participants jointly decide whether to use historical average data for temporary allocation or to postpone the settlement of this cycle until the situation becomes clearer. This ensures the system's robustness under extreme market conditions.

[0089] It's important to note that the system supports privacy-preserving computation techniques for sensitive data involving trade secrets (such as precise costs and core formula parameters). Data holders do not need to expose the original data; instead, they can transform or encrypt it into a special "verifiable claim." For example, a supplier can apply for an encrypted "quality certificate" from an authoritative quality inspection agency and only need to present this certificate when submitting contributions. The smart contract can then use cryptographic methods to verify whether the certificate meets the condition of "quality rating exceeding the agreed standard" without knowing the specific quality score, thus completing the contribution assessment while protecting trade secrets.

[0090] For complex multi-level supply chain networks, this invention supports the cascading distribution of profits through a "master-slave contract" architecture. The distribution between the core enterprise and its first-tier partners is handled by the master contract. When a first-tier supplier receives a profit, the master contract automatically triggers a "sub-distribution contract" specific to that supplier and its downstream second-tier suppliers, injecting a portion of the profit into it. This sub-contract operates independently within its own collaborative network, running a complete contribution assessment and distribution process with potentially different parameters, enabling secondary or even multiple distributions of profits. In this way, the value incentives resulting from the market success of the end product can be precisely and progressively transmitted upstream along the supply chain network.

[0091] The system incorporates a blockchain-based governance framework. The weighting parameters of the contribution evaluation model, the data standardization templates, and even the upgrades to the contracts themselves are not determined by a single central authority. Any participant can initiate modification proposals. All proposals are publicly discussed on the blockchain and voted on by participants holding governance tokens or possessing voting rights. Proposals that reach a predetermined support rate will have their content automatically updated by the governance contract at a designated time, thereby achieving system democratization and gradual self-optimization, enabling it to continuously adapt to business development and changes.

[0092] Based on the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A smart contract-driven supply chain economic optimization method, characterized in that, Executed by a profit-sharing smart contract deployed on the blockchain, the process includes the following steps: S1. Obtain market performance data of the target product during the settlement period. Market performance data includes real-time sales data and contribution assessment data of each participant in the supply chain that has been recognized by consensus. An asynchronous parallel data processing mechanism is adopted. Sales data and contribution assessment data are batch-collected, compressed, and signed and verified through off-chain data preprocessing nodes. After verification, data summaries are batch-uploaded to the chain. The original data is stored in a distributed storage system and associated with the on-chain summary through content addressing identifiers. Smart contracts only trigger subsequent calculations based on the on-chain summary. The original data is verified and obtained off-chain as needed. S2. Calculate the total product profit for the settlement period based on the processed market performance data. S3. Based on the processed contribution assessment data, the dynamic contribution coefficients of each participant are obtained through the preset contribution assessment model, and the profit distribution weights are determined accordingly. The contribution assessment model adopts the DPRep-Prove framework, which deconstructs the processed contribution assessment data into three types of verifiable input data. The input data is processed through a contribution value sensor and a dynamic weight programmer, and outputs dynamic contribution coefficients and programmable profit distribution weights. S4. Combining the total product profit, dynamic contribution coefficient, and the weight of each profit distribution, generate and automatically execute profit distribution instructions to complete the fund transfer.

2. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: The calculation process for the total profit of the product includes: S201. Obtain real-time sales data verified and signed by a decentralized oracle from the distributed storage system. The smart contract calls the preset aggregation function to calculate the total sales revenue of the product within the settlement period. S202. When each participant in the supply chain completes its respective stage, it simultaneously stores the cryptographic commitments of key cost elements on the blockchain. After the settlement cycle is triggered, each participant submits detailed cost data to the designated off-chain computing node. The off-chain computing node performs calculations according to the preset cost aggregation rules and generates a verifiable calculation proof document. The calculation proof document is submitted to the blockchain for verification by the smart contract, which then provides the total verifiable cost. S203. After the calculation proof document is verified, the smart contract executes the final calculation: total product profit = total product sales revenue - total verifiable cost, and records the result as an immutable on-chain state.

3. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: The process of obtaining the market performance data includes: Obtain real-time sales data, including sales volume and unit profit, from the sales terminal system; obtain contribution assessment data quantifying the contribution of each participant to the product's market competitiveness from the corresponding assessment system, and the contribution assessment data must be verified by a valid digital signature.

4. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: The three types of verifiable input data include: contribution claims, context state, and real-time market signals; The contribution value sensor quantifies the input contribution statement into a multi-dimensional value score under the context state and real-time market signals. The dynamic weight programmer consists of a programmable rule engine and a lightweight machine learning algorithm. The rule engine can temporarily inject or adjust evaluation rules according to different product lines and strategic goals. The lightweight machine learning algorithm tracks the behavior changes of each participant and the overall supply performance in the next cycle after each profit distribution, and adjusts the basic weights of each dimension.

5. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: The steps for determining the profit allocation weights include: The smart contract reads the dynamic contribution coefficients of the current and historical settlement cycles, applies an algorithm based on the time decay factor to calculate the comprehensive contribution value of each participant, and obtains the final weight through normalization processing.

6. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: Before executing the profit distribution instruction, the process also includes verification and objection handling steps: the smart contract broadcasts the pre-execution result of the distribution, receives and processes encrypted and signed objections within a preset objection period, and triggers the on-chain dispute resolution mechanism to correct or confirm the distribution result.

7. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: The smart contract uses a pre-built anomaly detection model to determine whether market performance data is abnormal; if abnormal, it freezes the automatic allocation process and activates an emergency mechanism, which includes triggering on-chain voting to decide whether to adopt a temporary allocation scheme or switch to manual processing.

8. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: The smart contract stores the allocation results of each period on the blockchain and generates optimization suggestions for the contribution evaluation model based on multi-period data correlation analysis. After approval by the on-chain governance process, the model parameters are automatically updated.

9. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: The smart contract includes a top-level allocation master contract and multiple allocation sub-contracts for sub-supply chains; The top-level allocation master contract execution steps S1 to S4 are described, and the sub-supply chain as a whole is regarded as a participant, and the overall dynamic profit allocation weight and the total profit due are calculated. The top-level allocation master contract triggers the corresponding sub-supply chain allocation sub-contract and passes the total profit due to the sub-supply chain as input to the sub-contract; The allocation sub-contract executes steps S1 to S4 independently and in parallel to obtain market performance data within the sub-supply chain, calculate the contribution coefficient and allocation weight of each participant, and complete the secondary distribution of profits.

10. The smart contract-driven supply chain economic optimization method according to claim 1, characterized in that: Before S1, there is also a step of initializing the on-chain profit distribution fund pool; the smart contract receives the creation request, locks funds according to the predefined initial rules and forms the on-chain fund pool. Smart contracts support a state channel mechanism for handling high-frequency, small-amount real-time profit distribution. Participants negotiate and confirm the distribution multiple times off-chain through the state channel, and only submit the final state to the chain at the end of the settlement cycle.

Images