A method for optimizing purchase cost applied in manufacturing supply chain management

Abstract

This invention discloses a procurement cost optimization method applied to manufacturing supply chain management, comprising the following steps: Purchaser nodes publish procurement demands containing dynamic green standard thresholds; supplier nodes register their green attribute proofs and dynamic performance capability data, cross-verified by third-party collaborative nodes and marked with confidence levels, generating a unique on-chain trusted dataset; based on procurement scale, industry green policy level, and market supply and demand fluctuation coefficients, adjust the objective weights and constraint priorities of a multi-objective optimization model, and input the unique trusted dataset into the adjusted multi-objective optimization model to solve for the Pareto optimal procurement decision scheme; compile the optimal procurement decision scheme into a layered programmable smart contract and deploy it to the supply chain blockchain network; obtain on-chain and off-chain performance trigger data in real time through oracles, driving the contract to automatically execute tiered payment, tiered incentive, or gradient penalty operations. This achieves automated performance and effectively reduces risk.

Description

Technical Field

[0001] This invention relates to the field of industrial internet and information technology intersection, specifically to a procurement cost optimization method applied to manufacturing supply chain management. Background Technology

[0002] Traditional manufacturing procurement cost control relies heavily on centralized negotiations, historical price analysis, and a single goal of minimizing costs. This approach suffers from information asymmetry, slow response times, and difficulty in balancing multiple objectives such as quality, delivery time, and cost. It is particularly challenging to quantify and manage the hidden costs and long-term benefits of "green" practices (e.g., environmentally friendly materials, low-carbon processes). Furthermore, green procurement requires reliable traceability and assessment of suppliers' environmental qualifications, material sources, and carbon emissions during production. Current authentication methods, relying on paper documents or centralized databases, suffer from high risks of data tampering, cumbersome verification processes, and low transparency, making accurate calculation and incentives for green costs difficult. Additionally, the isolated systems among buyers, suppliers, logistics providers, and financial institutions result in high costs for collaborative optimization and hinder automated collaborative processing, leading to overall supply chain inefficiency and preventing the realization of collaborative cost reduction potential. This ultimately restricts the deep synergistic optimization of procurement costs and green development. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, this invention provides a procurement cost optimization method applied to manufacturing supply chain management to solve the problems in existing technologies.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A procurement cost optimization method applied to manufacturing supply chain management includes: Construct a supply chain blockchain network jointly maintained by buyer nodes, supplier nodes, and third-party collaborative nodes to generate a unique and trustworthy dataset on the chain; Based on the procurement scale, the level of green policies in the industry, and the market supply and demand fluctuation coefficient, the objective weights and constraint priorities of the multi-objective optimization model are adjusted, and the unique credible dataset is input into the adjusted multi-objective optimization model. Combined with the dual-dimensional green benefit quantification coefficient, the Pareto optimal procurement decision scheme is solved. The optimal procurement decision scheme includes supplier combination, procurement volume allocation ratio, green ladder incentive clauses, and risk hedging clauses. The optimal procurement decision scheme is compiled into a layered programmable smart contract and deployed to the supply chain blockchain network. The on-chain and off-chain performance trigger data are obtained in real time through oracles to drive the contract to automatically execute tiered payment, tiered incentive or gradient penalty operations.

[0005] In one embodiment, a supply chain blockchain network is constructed, jointly maintained by buyer nodes, supplier nodes, and third-party collaborative nodes, to generate a unique, trusted on-chain dataset, including: The purchasing node publishes purchasing demands containing dynamic green standard thresholds; wherein, the dynamic green standard thresholds include at least a dynamic threshold for the use rate of environmentally friendly materials, a carbon emission tier ceiling, and green certification qualification level requirements, and the dynamic green standard thresholds are automatically iterated based on the green policy update frequency of the purchasing industry and the price fluctuation coefficient of the carbon trading market; The supplier node registration is cross-verified by the third-party collaborative nodes and includes green attribute certification and dynamic data on compliance capabilities with confidence level markings. The third-party collaborative nodes form a verification alliance and output cross-verification results associated with node reputation scores. The green attribute certification data includes a product lifecycle carbon footprint accounting report, ISO14001 green supply chain management system certification, full batch testing reports on the proportion of recycled materials, and closed-loop data on waste recycling. The third-party collaborative nodes include environmental certification bodies, quality inspection bodies, and carbon trading platforms. Each node forms a verification alliance. Environmental certification bodies verify the compliance of green qualifications, quality inspection bodies verify the authenticity of testing data, and carbon trading platforms connect to carbon emission quota data, outputting multi-node cross-verification results. The cross-verification results are associated with the reputation scores of each node. The procurement requirements, supplier data, and cross-validation results are collected and standardized to generate a unique on-chain trusted dataset containing supplier dynamic green scores, real-time performance capability assessments, and quotations; wherein, the dynamic green scores are dynamically updated based on supplier real-time performance data and the confidence level of the cross-validation.

[0006] In one embodiment, the objective function of the multi-objective dynamic optimization model is: Total procurement cost = Basic procurement cost + Default risk cost + Carbon quota usage cost - Green benefit subsidy - Green innovation subsidy; where, The green benefit subsidy is calculated by comprehensively considering the green benefit quantification coefficient, the green indicator compliance rate, and the green contribution of supply chain collaboration. The green benefit quantification coefficient is dynamically adjusted through policy dimensions, corporate goal dimensions, and supply chain collaboration dimensions. The green contribution of supply chain collaboration is generated based on the alignment of green goals between suppliers and purchasers. The carbon quota usage cost is dynamically calculated based on the total carbon footprint of the product, the current remaining carbon quota of the purchaser, and the real-time price of the carbon trading market. The green innovation subsidy is a special subsidy given by the purchaser to the supplier for the application of green patents and the upgrading of low-carbon processes. The amount of the subsidy is positively correlated with the green innovation capability score. The constraints include a minimum quality pass rate, a flexible delivery time threshold, a capacity matching threshold, a minimum green indicator score, and an upper limit on the total carbon footprint of the supply chain. Each constraint is dynamically adjusted through priority. When the market supply and demand fluctuation coefficient exceeds the preset threshold, the priority of the capacity matching threshold is automatically increased, and the constraint intensity of the flexible delivery time threshold is reduced.

[0007] In one embodiment, the adjustment formula for the green benefit quantification coefficient is: β = β0 × (1 + policy subsidy coefficient × policy adaptation weight) × (1 + emission reduction target completion rate × enterprise target weight) × (1 + supply chain green collaboration degree × collaboration weight); where, The policy subsidy coefficient is updated in real time based on national / local green policy subsidy standards and carbon emission reduction policy intensity, and the policy adaptation weight is determined according to the green category level of the purchased product. The emission reduction target completion rate is dynamically adjusted based on the purchaser's carbon emission reduction target completion progress and green supply chain construction stage, and the enterprise target weight is iterated with the purchaser's green strategy priority; The green synergy of the supply chain is generated based on the degree of alignment between green standards between suppliers and buyers and the degree of sharing of green data between upstream and downstream. The synergy weight is determined by negotiation between the buyer and the supplier and stored on the blockchain. The sum of the policy adaptation weight, the enterprise target weight, and the collaboration weight is 1, and the allocation ratio is dynamically optimized according to the procurement scenario.

[0008] In one embodiment, the constraints further include at least one of the following: supplier green supply chain resilience score, logistics low-carbon compliance rate, and green data sharing completeness rate; wherein, The supplier green supply chain resilience score is generated based on the supplier's emergency response plan for dealing with shortages of green raw materials and sudden changes in environmental policies, historical response records, and backup supplier resource storage data. Suppliers with scores below a preset threshold will have their bidding priority automatically reduced. The low-carbon compliance rate of logistics is calculated by comprehensively considering the carbon emissions of logistics transportation methods, transportation routes, and the recyclability of packaging materials, and must meet the low-carbon logistics standard threshold set by the purchaser. The green data sharing completeness rate is calculated based on the coverage of green attribute data uploaded by suppliers to the blockchain, the frequency of real-time updates, and the consistency with third-party verification data. Suppliers with a sharing completeness rate lower than the preset value will have their green score automatically reduced. The threshold of the constraint is dynamically adjusted according to the supplier's credit rating, and suppliers with high credit ratings can enjoy the incentive of a moderate relaxation of the constraint threshold.

[0009] In one embodiment, the smart contract includes a basic procurement performance contract, a green performance tiered incentive contract, an automatic payment tiered contract, a default tiered penalty contract, and a contract dynamic adjustment contract; The triggering data includes at least two of the following: real-time data from IoT sensors, data reported by third-party testing agencies, GPS positioning trajectory data of logistics nodes, and countdown data for deadlines. When the trigger data meets the preset conditions, the following corresponding operations will be executed automatically: Once the goods are delivered and pass quality inspection, different percentages of payment will be triggered based on the green indicator compliance rate range. Incentive payments will be transferred at different rates based on the degree to which green indicators are exceeded. And different proportions of penalties and default records will be triggered and recorded on the blockchain based on the length of the overdue delivery.

[0010] In one embodiment, it further includes: Collect full-process data generated during contract execution and update the supplier's dynamic credit profile; the full-process data includes real-time production progress data, logistics tracking data, detailed quality inspection and acceptance data, fund settlement voucher data, final calculation data of green indicators, and supplier green technology iteration data; Based on the updated archive data, the parameters and configuration of the multi-objective dynamic optimization decision model are iteratively optimized using machine learning algorithms. The supplier dynamic credit archive includes green performance score, quality compliance rate, on-time delivery rate, cost control accuracy, green innovation capability score, green supply chain resilience score, and default records. The weight of each score item is determined by the analytic hierarchy process to generate a comprehensive credit coefficient. The supplier's weight, green benefit quantification coefficient, and constraint threshold are adjusted based on the supplier's overall credit rating.

[0011] In one embodiment, it further includes: A hybrid encryption operation using hash encryption and symmetric encryption is employed to encrypt data interactions between nodes. Sensitive data such as procurement requirements and business quotations are uploaded to the blockchain after being symmetrically encrypted, and encryption keys are distributed among authorized nodes using asymmetric encryption.

[0012] In one embodiment, solving for the Pareto optimal purchasing decision includes: Based on the characteristics of the current procurement scenario, a target intelligent optimization algorithm is determined; The Pareto problem is solved based on the target intelligent optimization algorithm to obtain the optimal solution set; wherein, the intelligent optimization algorithm includes any one or a combination of genetic algorithm, particle swarm algorithm, simulated annealing algorithm, and ant colony algorithm, and the optimal solution set includes at least those emphasizing cost optimization, environmental optimization, delivery time optimization, and comprehensive balance optimization. The optimal solution set is dynamically stratified and filtered according to the procurement scenario, and then displayed on the interactive interface; The optimal procurement decision is obtained by the buyer selecting an operation based on the interactive interface.

[0013] A procurement cost optimization system applied to manufacturing supply chain management includes: The trusted data generation module is used to build a supply chain blockchain network jointly maintained by buyer nodes, supplier nodes, and third-party collaborative nodes, and generate a unique trusted dataset on the chain. The optimal solution module is used to adjust the objective weights and constraint priorities of the multi-objective optimization model based on the procurement scale, the level of industry green policies, and the market supply and demand fluctuation coefficient. The unique credible dataset is then input into the adjusted multi-objective optimization model, and combined with the two-dimensional green benefit quantification coefficient, the Pareto optimal procurement decision scheme is solved. The optimal procurement decision scheme includes supplier combination, procurement volume allocation ratio, green tiered incentive clauses, and risk hedging clauses. The contract execution module is used to compile the optimal procurement decision scheme into a layered programmable smart contract and deploy it to the supply chain blockchain network. It obtains on-chain and off-chain performance trigger data in real time through oracles to drive the smart contract to automatically execute tiered payment, tiered incentive or gradient penalty operations.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention ensures the authenticity and reliability of data throughout the entire procurement process by constructing a blockchain, significantly reducing the costs of reconciliation, auditing, and trust among multiple parties, improving collaborative efficiency, and achieving the effects of trustworthiness, transparency, cost reduction, and efficiency improvement. By quantifying difficult-to-measure green indicators into cost factors through models and automating rewards and penalties through smart contracts, green procurement transforms from a slogan into a measurable, executable, and incentivized economic behavior, truly driving the green transformation of the supply chain and achieving the effects of internalizing green costs and precise incentives. Based on real-time, reliable data, dynamic multi-objective optimization changes the traditional static and isolated procurement decision-making model, achieving dynamic collaborative optimization, improving the scientific nature of decision results, and reducing overall supply chain costs. By codifying contract terms through smart contracts, automatic execution and payment are achieved, reducing human error, delays, and disputes, lowering performance risks and legal costs, and automating performance to effectively reduce risks. The historical performance of all participants is immutably recorded on the blockchain, forming valuable supply chain reputation assets. Building a supply chain reputation system helps to weed out inferior players and establish a long-term healthy supply chain ecosystem. Attached Figure Description Figure 1A flowchart illustrating a procurement cost optimization method applied to manufacturing supply chain management provided by the present invention; Figure 2 This is a schematic diagram illustrating the working principle of a procurement cost optimization method applied to manufacturing supply chain management, as provided by the present invention. Detailed Implementation

[0015] 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.

[0016] like Figures 1 to 2 As shown, the present invention provides a procurement cost optimization method applied to manufacturing supply chain management, comprising: S100. On a blockchain network jointly maintained by all participants in the supply chain, purchasing nodes publish procurement demands containing dynamic green standard thresholds, and supplier nodes register their green attribute proofs and dynamic performance capability data, which are cross-verified by third-party collaborating nodes and accompanied by confidence level markers, generating a unique, trustworthy dataset on the chain; specifically, this includes: S110. Construct a supply chain blockchain network that includes buyer nodes, supplier nodes, and third-party collaborative nodes; and have buyer nodes record their procurement needs, including dynamic green standard thresholds, on the blockchain; have supplier nodes register their qualifications, green attribute certificates for the entire product lifecycle, and dynamic data on their performance capabilities on the blockchain; and have third-party collaborative nodes cross-validate the green attribute certificates based on historical data on the blockchain and mark the confidence level associated with their historical verification accuracy. In this embodiment, because existing technologies rely on centralized storage or paper documents for data such as procurement needs, supplier qualifications, and green attribute certificates, which are easily tampered with and difficult to verify, green procurement becomes a mere formality. This invention constructs a supply chain blockchain network to enable on-chain storage and third-party verification of data such as procurement needs, supplier qualifications, and green attribute certificates, ensuring that the data throughout the entire process is tamper-proof and traceable, greatly improving data credibility, and thus effectively solving the problem of unreliable data in traditional manufacturing green procurement.

[0017] S120. Collect and standardize on-chain procurement demands, supplier data, and verification results to generate a unique and reliable on-chain dataset containing supplier dynamic green scores, real-time performance capability assessments, and quotations. The dynamic green score is dynamically updated based on the supplier's real-time performance data. The formula for calculating the dynamic green score is: Dynamic Green Score = Basic Green Score × Cross-validation Confidence Weight × Real-time Performance Data Compliance Rate. The basic green score is calculated based on the integrity of green attribute proof data (e.g., weight 0.3), green certification level (weight 0.4), and the degree of low-carbon process application (weight 0.3). The cross-validation confidence weight is positively correlated with the historical verification accuracy of third-party collaborative nodes (e.g., weight 1.0 for verification accuracy ≥ 98%, weight 0.9 for 95%-98%, weight 0.8 for 90%-95%, and weight 0.6 for below 90%). The real-time performance data compliance rate is determined based on the ratio of the supplier's actual completion of current green indicators to the standard threshold.

[0018] In this embodiment, since procurement decisions in the prior art often rely on experience or a single cost target, and do not quantify and integrate multi-dimensional data such as green indicators, performance capabilities, and pricing data, it is difficult to balance multiple objectives such as cost, greenness, and quality, resulting in insufficient scientific decision-making. This invention generates a reliable dataset containing green scores, performance capability assessment values, and pricing data by standardizing the processing of on-chain data, thereby achieving a balance of multiple objectives and enhancing the scientific nature of decision-making.

[0019] In one embodiment, a supply chain blockchain network is constructed, jointly maintained by buyer nodes, supplier nodes, and third-party collaborative nodes, to generate a unique, trusted on-chain dataset, including: The purchasing node publishes purchasing demands containing dynamic green standard thresholds; wherein, the dynamic green standard thresholds include at least a dynamic threshold for the use rate of environmentally friendly materials, a carbon emission tier ceiling, and green certification qualification level requirements, and the dynamic green standard thresholds are automatically iterated based on the green policy update frequency of the purchasing industry and the price fluctuation coefficient of the carbon trading market; The supplier node registration is cross-verified by the third-party collaborative nodes and includes green attribute certification and dynamic data on compliance capabilities with confidence level labels. The third-party collaborative nodes form a verification alliance and output cross-verification results associated with node reputation scores. The green attribute certification data includes a product lifecycle carbon footprint accounting report, ISO14001 green supply chain management system certification, full batch testing reports on the proportion of recycled materials, and closed-loop data on waste recycling. The third-party collaborative nodes include environmental certification bodies, quality inspection bodies, and carbon trading platforms. Each node forms a verification alliance. Environmental certification bodies verify the compliance of green qualifications, quality inspection bodies verify the authenticity of testing data, and carbon trading platforms connect to carbon emission quota data, outputting multi-node cross-verification results. The cross-verification results are associated with the reputation scores of each node, such as: cross-verification pass rate = (number of nodes that passed verification / total number of verified nodes) × 100%. The pass rate is directly mapped to the cross-verification confidence weight (≥90% is 1.0, 70%-90% is 0.8, <70% is 0.6). The procurement requirements, supplier data, and cross-validation results are collected and standardized to generate a unique on-chain trusted dataset containing supplier dynamic green scores, real-time performance capability assessments, and quotations. The dynamic green scores are dynamically updated based on supplier real-time performance data and the confidence level of the cross-validation. For example, the dynamic green score = basic green score × cross-validation confidence weight × real-time performance data compliance rate. The basic green score is calculated based on the integrity of green attribute proof data, green certification level, and the degree of application of low-carbon processes, and is dynamically updated with supplier real-time performance data.

[0020] In this embodiment, by quantifying green standard requirements, clear green compliance criteria are provided to purchasers and suppliers, avoiding ambiguity in green procurement. Green attribute verification data enhances the comprehensiveness and completeness of green assessments. The introduction of specialized nodes such as carbon trading platforms connects green data verification with carbon reduction policies and the carbon trading market, improving the policy adaptability and market value of green procurement. By addressing the potential demand for green procurement that aligns with policies, covers the entire supply chain, and possesses market value, core data such as carbon footprint and recycled materials are combined with carbon trading platform verification. This resolves the overlapping issues of vague green standards, incomplete assessments, and insufficient verification authority, thereby achieving a deep understanding of the core needs of green procurement.

[0021] In step S100, the buyer uploads their procurement needs information, including product specifications, quantity, expected delivery date, and green standard requirements, to the blockchain. Potential suppliers register their company qualifications, production capacity, historical performance, and green attribute certification documents' hashes on the blockchain. The system automatically aggregates the certified data from each supplier on the blockchain. Third-party certification body nodes verify and confirm key green certifications on the blockchain. The buyer or the system calculates a dynamic green score for each supplier according to preset rules and publishes it on the blockchain.

[0022] S200. Based on the procurement scale, industry green policy level, and market supply and demand fluctuation coefficient, adjust the objective weights and constraint priorities of the multi-objective optimization model, and input the unique credible dataset into the adjusted multi-objective optimization model. Combined with the dual-dimensional green benefit quantification coefficient, solve the Pareto optimal procurement decision scheme. The optimal procurement decision scheme includes supplier combination, procurement volume allocation ratio, green ladder incentive clauses, and risk hedging clauses. In this embodiment, a multi-objective optimization model is used to integrate dimensions such as cost, environmental friendliness, and compliance capability to output multiple high-quality solutions, thereby achieving a balance between multiple objectives and enhancing the scientific nature of decision-making.

[0023] In step S200, the optimization decision engine is activated, retrieving on-chain quotes from various suppliers (or initiating on-chain bidding), historical quality data (from past contract records), estimated delivery dates, and the green score from step S100. Combining internal cost parameters, a multi-objective optimization model is run to solve for the supplier selection scheme, procurement volume allocation scheme, and recommended green incentive price that achieve the optimal overall cost while satisfying all constraints. Key parameters and a summary of results from the optimization process are stored on the blockchain.

[0024] In one embodiment, solving for the Pareto optimal purchasing decision includes: Based on the characteristics of the current procurement scenario, a target intelligent optimization algorithm is determined. These characteristics include the urgency of the procurement (e.g., urgent, routine, and relaxed), product type (e.g., high value-added, ordinary, and low value-added), and market competition pattern (e.g., fully competitive, moderately competitive, and monopolistic competition). The correspondence between different scenarios and algorithms is as follows: ① Urgent procurement, high value-added products, and fully competitive markets: Particle swarm optimization (fast convergence speed, suitable for rapid decision-making); ② Routine procurement, ordinary products, and moderately competitive markets: Genetic algorithm (strong global search capability, balancing multi-objective optimization); ③ Relaxed procurement, low value-added products, and monopolistic competition markets: Simulated annealing algorithm (excellent local search optimization capability, reducing optimization costs); ④ Complex scenarios (e.g., urgent procurement, ordinary products, and fully competitive markets): A combination of genetic algorithm and particle swarm optimization (combining the advantages of both). The Pareto problem is solved based on the target intelligent optimization algorithm to obtain the optimal solution set; wherein, the intelligent optimization algorithm includes any one or a combination of genetic algorithm, particle swarm algorithm, simulated annealing algorithm, and ant colony algorithm, and the optimal solution set includes at least those emphasizing cost optimization, environmental optimization, delivery time optimization, and comprehensive balance optimization. The optimal solution set is dynamically stratified and filtered according to the procurement scenario (in the case of urgent procurement, the optimal solution set with the best delivery time has the highest priority; in the case of high green requirements, the optimal solution set with the best green requirements has the highest priority; in the case of cost-sensitive scenarios, the optimal solution set with the best cost requirements has the highest priority), and then displayed on the interactive interface. The optimal procurement decision is obtained by the buyer selecting an operation based on the interactive interface.

[0025] S300. The optimal procurement decision scheme is compiled into a layered programmable smart contract and deployed to the supply chain blockchain network. On-chain and off-chain performance trigger data are obtained in real time through oracles to drive the contract to automatically execute tiered payment, tiered incentive or gradient penalty operations.

[0026] In this embodiment, by transforming the decision-making scheme into a smart contract and collecting trigger data through an oracle, the smart contract is driven to automatically execute payments, incentives, or penalties. This avoids situations where payment, incentive, and penalty operations are delayed due to the reliance on manual performance of procurement contract terms, as well as high collaboration costs and high performance risks. It effectively reduces human intervention, lowers the dispute rate, shortens the settlement cycle, and improves execution efficiency.

[0027] This invention addresses the combined pain points of unreliable data, unscientific decision-making, and inefficient execution. By deeply coupling blockchain, multi-objective optimization, and smart contracts, it breaks through the conventional thinking of solving a single problem with a single technology, thereby improving the feasibility and stability of the solution.

[0028] In step S300, based on the optimization results of S200, the smart contract factory automatically generates one or more related smart contracts. For example, the master procurement contract specifies the price, quantity, and delivery date; the subsidiary green incentive contract stipulates that if the final verified carbon emissions are lower than the standard, the supplier will receive an additional reward.

[0029] The purchaser and the successful supplier confirm the contract via digital signature and deploy it to the blockchain. Once the contract takes effect, the automatic execution process begins.

[0030] During order production and logistics, key node data (such as production start / end, key process quality inspection reports, and GPS location) are uploaded to the blockchain after being confirmed by IoT devices or manually. Oracles monitor this data and automatically trigger the next action (such as "releasing part of the payment" or "calculating green rewards") when the smart contract terms are met (such as "goods arrive at the port").

[0031] Once all orders are fulfilled and all contractual conditions are verified, the final payment smart contract is automatically executed, and funds are transferred via blockchain or a connected traditional payment system. Performance data throughout the entire procurement process (such as actual delivery deviations, quality pass rates, and final green indicators) is reliably recorded on the blockchain, serving as historical reputation data for the supplier's future optimization efforts, thus forming a closed loop.

[0032] For example, a car manufacturer (purchaser) needs to purchase a batch of environmentally friendly car seat fabrics.

[0033] Initial setup: An automaker, five fabric suppliers, an environmental materials certification center, a logistics company, and a bank jointly formed a supply chain alliance blockchain network.

[0034] In step S100: The automaker uploads its procurement requirements to the blockchain, explicitly requiring that the recycled fiber content in the fabric be ≥30%. Suppliers submit bids, uploading their quotes, production capacity data, and the hash value of the recycled content testing report issued by the certification center to the blockchain. The certification center node verifies the authenticity of the report (historical verification accuracy rate 99%, cross-validation confidence weight 1.0) and marks it "verified" on the blockchain. The system calculates the supplier's dynamic green score: Supplier A's base green score is 90 points (e.g., data integrity 85 points × 0.3 + certification level 95 points × 0.4 + low-carbon process application 90 points × 0.3 = 25.5 + 38 + 27 = 90.5 points, rounded to 91 points). With a real-time compliance rate of 100% (current recycled fiber content meets the standard), the dynamic green score = 91 × 1.0 × 1.0 = 91 points, and is published on the blockchain. In step S200: This procurement is a routine purchase, for ordinary products, and in a moderately competitive market. A genetic algorithm is chosen as the target intelligent optimization algorithm. The collaborative optimization decision engine is activated, acquiring data from five suppliers on the blockchain (such as price quotes, historical quality data, estimated delivery dates, and dynamic green scores). The model's objective is to minimize total procurement costs while meeting delivery deadlines. In cost calculation, the green benefit coefficient β is set as follows: β = β0 × (1 + policy subsidy coefficient × policy adaptation weight) × (1 + emission reduction target completion rate × enterprise target weight) × (1 + supply chain green synergy × synergy weight), where β0 = 10 yuan / percentage, policy subsidy coefficient = 0.15, policy adaptation weight = 0.3 (medium-level green category), emission reduction target completion rate = 0.9 (carbon emission reduction target completion progress 85%), enterprise target weight = 0.3 (green strategy is an important strategy), and supply chain green synergy = 0.9 (fitness 90%). With a sharing rate of 85% and a collaboration weight of 0.4 (determined through negotiation), β = 10 × (1 + 0.15 × 0.3) × (1 + 0.9 × 0.3) × (1 + 0.9 × 0.4) = 10 × 1.045 × 1.27 × 1.36 ≈ 10 × 1.045 × 1.7272 ≈ 10 × 1.805 ≈ 18.05 yuan / percentage. This means that for every 1% increase in recycled fiber content above the standard, it is equivalent to bringing the buyer 18.05 yuan / unit of green benefits (possibly brand value, carbon emission quota savings, etc.), which can be deducted from the total cost. After the engine runs, the output results are: Supplier A (not the lowest price, but with a high green score and stable delivery) and Supplier B (low price, passing green score) are selected for joint supply, and the specific allocation quantity and the "comprehensive cost" adjusted for green benefits are given. This result summary is stored on the blockchain.

[0035] In the S300 step: the system automatically generates two smart contracts: a "Procurement and Payment Contract" with A and B respectively, and a "Green Excess Incentive Contract," which stipulates that if the recycled content exceeds 35% in the final delivery inspection, the excess portion will be given an additional reward according to a coefficient. The contracts are deployed after being digitally signed by all three parties.

[0036] When Supplier A produces goods, it records the fiber input for key batches on the blockchain via the MES system interface. Upon shipment, RFID tags are activated, and logistics location information is periodically uploaded to the blockchain (70% of logistics transportation uses new energy vehicles, carbon emissions along transportation routes meet standards, 85% of packaging materials are recyclable, and the low-carbon logistics compliance rate is (1×0.4+1×0.3+1×0.3)×100%=100%). When goods arrive at the car manufacturer's warehouse, IoT scanning confirms receipt; this event is captured by an oracle and uploaded to the blockchain, automatically triggering a "Purchase and Payment Contract" to pay 70% of the payment (green indicator compliance rate is 95%, within the [90%, 100%) range). The quality inspection department conducts random inspections of the fabrics and hashes the test reports showing a 38% recycled content onto the blockchain.

[0037] The oracle reads 38% of the detection results (over-fulfillment by 3%), automatically triggering the "Green Excess Incentive Contract," calculating the 3% excess reward (incentive ratio 15%), and automatically paying it to supplier A along with the remaining 30% balance in the final settlement contract. The entire process concludes, and supplier A's excellent green performance record is permanently recorded on the blockchain, significantly improving its "green reputation" score (overall reputation coefficient = 91×0.25+98×0.2+99×0.15+95×0.1+92×0.15+88×0.1+0×0.05=22.75+19.6+14.85+9.5+13.8+8.8=99.3). This will result in a more favorable model weighting in future procurements (supplier weight increased by 20%, green benefit quantification coefficient increased by 15%, and constraint threshold relaxed by 10%).

[0038] In one embodiment, the objective function of the multi-objective dynamic optimization model is: Total procurement cost = Basic procurement cost + Default risk cost + Carbon quota usage cost - Green benefit subsidy - Green innovation subsidy; where, The green benefit subsidy is calculated by comprehensively considering the green benefit quantification coefficient, the green indicator compliance rate, and the green contribution of supply chain collaboration. The green benefit quantification coefficient is dynamically adjusted through policy dimensions, corporate goal dimensions, and supply chain collaboration dimensions. The green contribution of supply chain collaboration is generated based on the alignment of green goals between suppliers and purchasers. The carbon quota usage cost is dynamically calculated based on the total carbon footprint of the product, the current remaining carbon quota of the purchaser, and the real-time price of the carbon trading market. The green innovation subsidy is a special subsidy given by the purchaser to the supplier for the application of green patents and the upgrading of low-carbon processes. The amount of the subsidy is positively correlated with the green innovation capability score. The constraints include a minimum quality pass rate, a flexible delivery time threshold, a capacity matching threshold, a minimum green indicator score, and an upper limit on the total carbon footprint of the supply chain. Each constraint is dynamically adjusted through priority. When the market supply and demand fluctuation coefficient exceeds the preset threshold, the priority of the capacity matching threshold is automatically increased, and the constraint intensity of the flexible delivery time threshold is reduced.

[0039] In this embodiment, by converting green indicators into quantifiable benefit subsidies, green procurement is transformed from a policy requirement into an economic incentive, thereby stimulating suppliers' motivation for green transformation and achieving the effect of quantifying green value. Furthermore, by comprehensively covering the four core dimensions of quality, delivery time, capacity, and greenness, the optimization results are ensured to be both feasible and compliant. By incorporating default risk costs, the total procurement cost is made more realistic, avoiding decision-making errors caused by ignoring hidden costs and improving the accuracy of cost accounting.

[0040] This invention addresses the core issues of difficulty in quantifying green value and constraints that do not align with green procurement scenarios by incorporating green benefit subsidies and default risk costs into the objective function, while also integrating scenario-based constraints such as minimum scores for green indicators. Through objective function reconstruction and multi-dimensional constraint integration, it achieves a deep integration of green procurement and cost optimization.

[0041] In one embodiment, the adjustment formula for the green benefit quantification coefficient is: β = β0 × (1 + policy subsidy coefficient × policy adaptation weight) × (1 + emission reduction target completion rate × enterprise target weight) × (1 + supply chain green collaboration degree × collaboration weight); where, The policy subsidy coefficient is updated in real time based on national / local green policy subsidy standards and carbon emission reduction policy intensity, and the policy adaptation weight is determined according to the green category level of the purchased product. The emission reduction target completion rate is dynamically adjusted based on the purchaser's carbon emission reduction target completion progress and green supply chain construction stage, and the enterprise target weight is iterated with the purchaser's green strategy priority; The green synergy of the supply chain is generated based on the degree of alignment between green standards between suppliers and buyers and the degree of sharing of green data between upstream and downstream. The synergy weight is determined by negotiation between the buyer and the supplier and stored on the blockchain. The sum of the policy adaptation weight, the enterprise target weight, and the collaboration weight is 1, and the allocation ratio is dynamically optimized according to the procurement scenario.

[0042] In this embodiment, by adjusting the green quantification benefit coefficient in real time according to changes in policies and corporate goals, the rationality and effectiveness of green incentives are ensured, achieving the effect of dynamic adaptation of the incentive mechanism. Furthermore, by responding to national and local green policy guidance, the policy compliance of corporate green procurement and the efficiency of subsidy application are improved, the policy connection is enhanced, and carbon emission reduction targets are integrated into the incentive mechanism to promote the specific implementation of corporate green strategies and ensure the implementation of corporate goals.

[0043] It should be noted that β0 is the benchmark or initial coefficient of the green benefit quantification coefficient. The benchmark value β0 of the green benefit quantification coefficient can be determined by the purchaser based on the industry average green premium level, the company's annual green procurement budget and historical transaction data, and is recorded on the blockchain as a public parameter when the procurement demand is released.

[0044] This invention achieves dynamic optimization of the incentive mechanism through two-dimensional coupling, effectively solving the technical pain point of the disconnect between fixed coefficients and dynamic needs, and realizing the core need to accurately capture the differences between policy dynamics and corporate objectives in green procurement.

[0045] In one embodiment, the constraints further include at least one of the following: supplier green supply chain resilience score, logistics low-carbon compliance rate, and green data sharing completeness rate; wherein, The supplier green supply chain resilience score is generated based on the supplier's emergency response plan for dealing with shortages of green raw materials and sudden changes in environmental policies, historical response records, and backup supplier resource storage data. Suppliers with scores below a preset threshold will have their bidding priority automatically reduced. The low-carbon compliance rate of logistics is calculated by comprehensively considering logistics transportation methods (such as the proportion of new energy transportation vehicles), carbon emissions of transportation routes, and the recyclable proportion of packaging materials, and must meet the low-carbon logistics standard threshold set by the purchaser. The green data sharing completeness rate is calculated based on the coverage of green attribute data uploaded by suppliers to the blockchain, the frequency of real-time updates, and the consistency with third-party verification data. Suppliers with a sharing completeness rate lower than the preset value will have their green score automatically reduced. The threshold of the constraint is dynamically adjusted according to the supplier's credit rating, and suppliers with high credit ratings can enjoy the incentive of a moderate relaxation of the constraint threshold.

[0046] In this embodiment, a capacity elasticity threshold is used to ensure that suppliers can adapt to order fluctuations, thereby reducing the risk of supply chain disruptions. The upper limit of logistics carbon emissions ensures low carbon emissions across the entire product and logistics chain, guaranteeing green compliance throughout the entire chain. Suppliers with strong risk resistance are selected through emergency response capability scoring, improving procurement stability and ensuring that emergency risks are controllable.

[0047] This invention addresses the core needs of low carbon emissions and resilience across the entire green supply chain. By integrating multi-dimensional constraints from the product, logistics, and capability perspectives, it solves the pain points of existing models that are not comprehensive in their greenness and have weak resilience. It enables in-depth exploration of the needs of green procurement scenarios and breaks through the technical bias of focusing only on the product itself (quality, capacity, and green attributes).

[0048] In one embodiment, the smart contract includes a basic procurement performance contract, a green performance tiered incentive contract, an automatic payment tiered contract, a default tiered penalty contract, and a contract dynamic adjustment contract; The triggering data includes at least two of the following: real-time data from IoT sensors, data reported by third-party testing agencies, GPS positioning trajectory data of logistics nodes, and countdown data for deadlines. When the trigger data meets the preset conditions, the following corresponding operations will be executed automatically: Once the goods are delivered and pass quality inspection, different percentages of payment will be triggered based on the green indicator compliance rate range. For example, when the green indicator compliance rate is in the range of [80%, 90%), 60% of the payment is triggered; When the compliance rate is in the range of [90%, 100%), 70% of the payment will be triggered; when the compliance rate exceeds 100%, 80% of the payment will be triggered. Incentive payments will be transferred at different rates based on the degree to which green indicators are exceeded. For example: if the green indicator compliance rate exceeds the threshold by less than 10%, the incentive ratio is 5%; if it exceeds the threshold by 10%-20%, the incentive ratio is 10%; if it exceeds the threshold by more than 20%, the incentive ratio is 15%, and the incentive amount is automatically transferred to the supplier's on-chain account. And penalties and default records will be recorded on the blockchain at different rates based on the length of the overdue delivery; For example: if the delivery is late by 1-3 days, the penalty rate is 3%; if it is late by 3-7 days, the penalty rate is 8%; if it is late by more than 7 days, in addition to a 15% penalty, a default record will be automatically uploaded to the credit file. When a data anomaly is triggered (such as distortion of a single data source or data delay), the contract dynamically adjusts to automatically start, suspends core execution operations, and notifies relevant nodes to review them. Execution resumes only after the review results are uploaded to the blockchain.

[0049] In this embodiment, by covering the payment, incentive, and penalty requirements of the entire procurement and performance process, the green procurement rules are implemented automatically. Through cross-validation of multiple types of data, contract mis-execution caused by distortion of a single data source is avoided, thereby improving trigger reliability. By matching preset conditions with operations one by one, manual intervention and disputes are effectively reduced, and performance efficiency is improved.

[0050] By constructing an execution mechanism that accurately matches multiple contract types, multiple triggers, and preset conditions with corresponding operations, this mechanism addresses the core needs of precise incentives and timely penalties for green procurement. Through a combination of contract type adaptation to scenarios, reliable multi-source triggers, and clear corresponding conditions and operations, it solves the pain point of the disconnect between contract execution and green procurement scenarios.

[0051] In one embodiment, it further includes: Collect full-process data generated during contract execution and update the supplier's dynamic credit profile; the full-process data includes real-time production progress data, logistics tracking data, detailed quality inspection and acceptance data, fund settlement voucher data, final calculation data of green indicators, and supplier green technology iteration data; Based on the updated archive data, the parameters and configuration of the multi-objective dynamic optimization decision model are iteratively optimized using machine learning algorithms. The supplier dynamic credit archive includes green performance score, quality compliance rate, on-time delivery rate, cost control accuracy, green innovation capability score, green supply chain resilience score, and default records. The green innovation capability score is generated based on the supplier's on-chain green technology R&D investment data, environmental patent data, and green product iteration records. The weight of each scoring item is determined by the analytic hierarchy process to generate a comprehensive credit coefficient. The supplier's weight, green benefit quantification coefficient, and constraint threshold are adjusted based on the supplier's overall credit rating.

[0052] In this embodiment, by collecting data from the entire procurement and fulfillment process (production, logistics, quality inspection, settlement, etc.), and updating the supplier reputation profile (including green performance score, green innovation capability score, etc.) on the blockchain, an immutable, multi-dimensional supplier reputation profile is formed. This improves the reputation system, provides a reliable historical basis for procurement decisions, and incentivizes suppliers to increase investment in green technology research and development through the green innovation capability score, promoting the continuous green transformation of the supply chain and encouraging continuous green innovation. Based on the reputation profile, the input parameters and weight configurations (green benefit quantification coefficient, constraint threshold, supplier reputation weight) of the multi-objective dynamic optimization model are adjusted so that the model can be iteratively upgraded with performance data, ensuring the scientific nature and adaptability of long-term decisions and achieving dynamic optimization of the model.

[0053] By constructing a closed-loop mechanism of performance data → reputation update → model iteration, this addresses the core needs of green procurement, which require long-term optimization and incentives for continuous innovation. Through a combination of full-process data collection → multi-dimensional reputation assessment → dynamic model iteration, it solves the pain points of one-off decision-making, insufficient incentives, and static models.

[0054] In one embodiment, it further includes: A hybrid encryption operation using hash encryption and symmetric encryption is employed to encrypt data interactions between nodes. Sensitive data such as procurement requirements and business quotations are uploaded to the blockchain after being symmetrically encrypted, and encryption keys are distributed among authorized nodes using asymmetric encryption.

[0055] In this embodiment, a hybrid encryption method combining hash encryption and symmetric encryption is used to encrypt data interactions between nodes, ensuring that sensitive data cannot be leaked after being uploaded to the blockchain. Sensitive data (procurement requirements, commercial quotations) are uploaded to the blockchain after symmetric encryption, and the encryption key is distributed among authorized nodes using asymmetric encryption to control data access permissions. Symmetric encryption ensures efficient data encryption and decryption, adapting to high-frequency data interactions; asymmetric encryption ensures secure key distribution, addressing the shortcomings of single encryption methods and achieving a balance between efficiency and security. This prevents the leakage of core trade secrets of purchasers and suppliers, and enhances the trust among supply chain participants.

[0056] This invention addresses the characteristics of supply chain data being highly sensitive and frequently interacted with. It designs a hybrid symmetric and asymmetric encryption scheme, where symmetric encryption ensures efficiency for sensitive data encryption, while asymmetric encryption ensures security for key distribution. This achieves a precise match between the security and efficiency requirements of supply chain data interaction, solving the technical pain point that a single encryption method cannot balance privacy and efficiency.

[0057] This invention constructs a decentralized, tamper-proof supply chain blockchain network to achieve trusted storage and transparent sharing of data throughout the entire green procurement process (such as environmental certifications, carbon footprint, material quality inspection reports, and logistics information). Through a multi-objective cost collaborative optimization model, it quantifies and integrates green environmental indicators while considering traditional factors such as price, quality, and delivery time, achieving optimal overall cost decisions. Smart contracts automatically transform the optimization decision results (such as winning suppliers, procurement prices, and reward / penalty clauses) into programmable, self-executing business rules, enabling automated fulfillment, payment, and incentives for procurement contract terms, reducing human intervention and disputes. This promotes dynamic and trusted collaboration between buyers and multiple suppliers, jointly optimizing overall supply chain costs and green performance while protecting the privacy of their respective business data.

[0058] This invention ensures the authenticity and reliability of data throughout the entire procurement process by constructing a blockchain, significantly reducing the costs of reconciliation, auditing, and trust among multiple parties, improving collaborative efficiency, and achieving the effects of credibility, transparency, cost reduction, and efficiency improvement. By quantifying elusive green indicators into cost factors through models and automating rewards and penalties through smart contracts, green procurement transforms from a slogan into a measurable, executable, and incentivized economic behavior, truly driving the green transformation of the supply chain and achieving the effects of internalizing green costs and precise incentives. Based on real-time, reliable data, dynamic multi-objective optimization changes the traditional static and isolated procurement decision-making model, achieving dynamic collaborative optimization, improving the scientific nature of decision results, and reducing overall supply chain costs. By codifying contract terms through smart contracts, automatic execution and payment are achieved, reducing human error, delays, and disputes, lowering performance risks and legal costs, and automating performance to effectively reduce risks. The historical performance of all participants is immutably recorded on the blockchain, forming valuable supply chain reputation assets. Building a supply chain reputation system helps to weed out underperformers and establish a long-term healthy supply chain ecosystem.

[0059] A procurement cost optimization system applied to manufacturing supply chain management includes: The trusted data generation module is used to generate a unique trusted dataset on the blockchain network jointly maintained by nodes of all participants in the supply chain. The purchasing node publishes a procurement demand containing dynamic green standard thresholds, and the supplier node registers its green attribute proof and performance capability dynamic data that has been cross-verified by third-party collaborative nodes and attached with confidence level marks. The optimal solution module is used to adjust the objective weights and constraint priorities of the multi-objective optimization model based on the procurement scale, the level of industry green policies, and the market supply and demand fluctuation coefficient. The unique credible dataset is then input into the adjusted multi-objective optimization model, and combined with the two-dimensional green benefit quantification coefficient, the Pareto optimal procurement decision scheme is solved. The optimal procurement decision scheme includes supplier combination, procurement volume allocation ratio, green tiered incentive clauses, and risk hedging clauses. The contract execution module is used to compile the optimal procurement decision scheme into a layered programmable smart contract and deploy it to the supply chain blockchain network. It obtains on-chain and off-chain performance trigger data in real time through oracles to drive the smart contract to automatically execute tiered payment, tiered incentive or gradient penalty operations.

[0060] This is a permissioned or consortium blockchain jointly maintained by participants including buyer nodes, multiple supplier nodes, third-party certification body nodes (such as environmental certification bodies and quality inspection agencies), and financial institution nodes. Each node maintains a complete blockchain ledger, recording all consensus-based procurement-related transactions and events. Key data such as procurement needs, supplier qualifications, product green attribute certifications (such as carbon footprint certificates and renewable material ratio reports), production progress data, logistics tracking information, and quality inspection results are hashed and stored on the blockchain to ensure data authenticity and immutability.

[0061] The multi-objective optimization model aims to minimize total procurement cost, with constraints including but not limited to: minimum quality pass rate, longest delivery time, and minimum green score. A "green cost / benefit coefficient" is introduced into the objective function to quantify indicators such as carbon emissions and material environmental friendliness into calculable cost or subsidy items.

[0062] By employing intelligent optimization algorithms such as genetic algorithms and particle swarm optimization, the model is solved to obtain the Pareto optimal solution set or the optimal supplier allocation and pricing scheme.

[0063] The smart contract factory includes a series of predefined, configurable smart contract templates, such as "Bidding Contract," "Performance Bond Contract," "Green Performance Incentive Contract," and "Automatic Payment Contract." Contract logic is automatically instantiated and deployed to the blockchain based on the output of the optimization decision engine.

[0064] Oracles connect blockchain systems with trusted external data sources (such as IoT sensors and authoritative databases), securely feeding off-chain real-world state information (such as sensor signals of goods arriving at the warehouse and carbon emission prices published by third parties) into on-chain smart contracts, triggering contract execution.

[0065] It's important to clarify that "distributed ledger" refers to the technical name for the aforementioned "shared ledger." It emphasizes that the ledger's storage is "distributed" (each node has a copy), rather than "centralized" (stored only in a single company's central database). This is the core technology for achieving data transparency and tamper-proofing. All participants see consistent data, eliminating information silos and data version disputes.

[0066] A consensus mechanism is a set of rules governing how a blockchain network reaches an agreement on the validity of ledger records. Like a vote in a meeting, a resolution requires the consent of a majority to pass. Common mechanisms include Proof-of-Work and Proof-of-Stake. In consortium blockchains used for inter-enterprise applications like yours, efficient mechanisms such as Practical Byzantine Fault Tolerance are commonly employed. This ensures that only legitimate transactions verified by everyone (such as a valid digitally signed contract) are recorded on the chain, preventing malicious nodes from forging information.

[0067] Pareto optimal solution set: In multi-objective optimization, it is difficult to find a solution that is "best" for all objectives. Pareto optimality describes an equilibrium state where "no further improvement to any objective can be achieved without harming the others." The set of these solutions represents multiple feasible and excellent solutions.

[0068] The optimization engine does not calculate a unique answer, but rather provides a set of "high-quality alternatives" for the decision-maker to make the final decision. For example, option A has the lowest cost but is slightly less environmentally friendly, while option B is the most environmentally friendly but has a slightly higher cost; both are Pareto optimal solutions. A smart contract is a piece of automatically executing program code stored on a blockchain. It defines clear rules: "if...then...". Once the pre-set conditions are met, the code will automatically and forcibly execute the corresponding operation, and no one can interfere or prevent it.

[0069] The terms of the procurement contract (such as "If the goods are delivered and pass quality inspection by date X, 80% of the payment will be automatically paid") are coded. Once trusted data such as logistics information (delivery of goods) and quality inspection reports (passing inspection) are on the blockchain, the smart contract automatically triggers the payment, realizing "code is law" and greatly reducing human delays and disputes.

[0070] The foregoing description of specific exemplary embodiments of the present invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is obvious that many changes and variations can be made based on the above teachings. Although embodiments of the invention have been shown and described, these specific embodiments are merely explanations of the invention and are not intended to limit it. The specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. The purpose of selecting and describing exemplary embodiments is to explain the specific principles of the invention and its practical application, so that those skilled in the art, after reading this specification, can make modifications, substitutions, variations, and various choices and changes to the embodiments as needed without departing from the principles and spirit of the invention, provided that such modifications, substitutions, variations, and choices and changes are within the scope of the claims of the invention and are protected by patent law.

Claims

1. A procurement cost optimization method applied to manufacturing supply chain management, characterized in that, include: Construct a supply chain blockchain network jointly maintained by buyer nodes, supplier nodes, and third-party collaborative nodes to generate a unique and trustworthy dataset on the chain; Based on the procurement scale, the level of industry green policies, and the market supply and demand fluctuation coefficient, the objective weights and constraint priorities of the multi-objective optimization model are adjusted, and the unique credible dataset is input into the adjusted multi-objective optimization model. Combined with the dual-dimensional green benefit quantification coefficient, the Pareto optimal procurement decision scheme is solved. The optimal procurement decision-making scheme includes supplier combination, procurement volume allocation ratio, green tier incentive clauses and risk hedging clauses; The optimal procurement decision scheme is compiled into a layered programmable smart contract and deployed to the supply chain blockchain network. The on-chain and off-chain performance trigger data are obtained in real time through oracles to drive the contract to automatically execute tiered payment, tiered incentive or gradient penalty operations.

2. The procurement cost optimization method applied to manufacturing supply chain management according to claim 1, characterized in that, A supply chain blockchain network is constructed, jointly maintained by buyer nodes, supplier nodes, and third-party collaborative nodes, generating a unique and trusted on-chain dataset, including: The purchasing node publishes purchasing demands containing dynamic green standard thresholds; wherein, the dynamic green standard thresholds include at least a dynamic threshold for the use rate of environmentally friendly materials, a carbon emission tier ceiling, and green certification qualification level requirements, and the dynamic green standard thresholds are automatically iterated based on the green policy update frequency of the purchasing industry and the price fluctuation coefficient of the carbon trading market; The supplier node registration is cross-verified by the third-party collaborative nodes and includes green attribute certification and dynamic data on compliance capabilities with confidence level labels. The third-party collaborative nodes form a verification alliance and output cross-verification results associated with node reputation scores. The green attribute certification data includes a product lifecycle carbon footprint accounting report, ISO14001 green supply chain management system certification, full batch testing reports on the proportion of recycled materials, and closed-loop data on waste recycling. The third-party collaborative nodes include environmental certification bodies, quality inspection bodies, and carbon trading platforms. Each node forms a verification alliance. Environmental certification bodies verify the compliance of green qualifications, quality inspection bodies verify the authenticity of testing data, and carbon trading platforms connect to carbon emission quota data, outputting multi-node cross-verification results. The cross-verification results are associated with the reputation scores of each node. The procurement requirements, supplier data, and cross-validation results are collected and standardized to generate a unique on-chain trusted dataset containing supplier dynamic green scores, real-time performance capability assessments, and quotations; wherein, the dynamic green scores are dynamically updated based on supplier real-time performance data and the confidence level of the cross-validation.

3. The procurement cost optimization method applied to manufacturing supply chain management according to claim 1, characterized in that, The objective function of the multi-objective dynamic optimization model is: Total procurement cost = Basic procurement cost + Default risk cost + Carbon quota usage cost - Green benefit subsidy - Green innovation subsidy; where... The green benefit subsidy is calculated by comprehensively considering the green benefit quantification coefficient, the green indicator compliance rate, and the green contribution of supply chain collaboration. The green benefit quantification coefficient is dynamically adjusted through policy dimensions, corporate goal dimensions, and supply chain collaboration dimensions. The green contribution of supply chain collaboration is generated based on the alignment of green goals between suppliers and purchasers. The carbon quota usage cost is dynamically calculated based on the total carbon footprint of the product, the current remaining carbon quota of the purchaser, and the real-time price of the carbon trading market. The green innovation subsidy is a special subsidy given by the purchaser to the supplier for the application of green patents and the upgrading of low-carbon processes. The amount of the subsidy is positively correlated with the green innovation capability score. The constraints include a minimum quality pass rate, a flexible delivery time threshold, a capacity matching threshold, a minimum green indicator score, and an upper limit on the total carbon footprint of the supply chain. Each constraint is dynamically adjusted through priority. When the market supply and demand fluctuation coefficient exceeds the preset threshold, the priority of the capacity matching threshold is automatically increased, and the constraint intensity of the flexible delivery time threshold is reduced.

4. The procurement cost optimization method applied to manufacturing supply chain management according to claim 3, characterized in that, The adjustment formula for the green benefit quantification coefficient is as follows: β = β0 × (1 + policy subsidy coefficient × policy adaptation weight) × (1 + emission reduction target completion rate × enterprise target weight) × (1 + supply chain green collaboration degree × collaboration weight); where, The policy subsidy coefficient is updated in real time based on national / local green policy subsidy standards and carbon emission reduction policy intensity, and the policy adaptation weight is determined according to the green category level of the purchased product. The emission reduction target completion rate is dynamically adjusted based on the purchaser's carbon emission reduction target completion progress and green supply chain construction stage, and the enterprise target weight is iterated with the purchaser's green strategy priority; The green synergy of the supply chain is generated based on the degree of alignment between green standards between suppliers and buyers and the degree of sharing of green data between upstream and downstream. The synergy weight is determined by negotiation between the buyer and the supplier and stored on the blockchain. The sum of the policy adaptation weight, the enterprise target weight, and the collaboration weight is 1, and the allocation ratio is dynamically optimized according to the procurement scenario.

5. The procurement cost optimization method applied to manufacturing supply chain management according to claim 3, characterized in that, The constraints also include at least one of the following: supplier green supply chain resilience score, logistics low-carbon compliance rate, and green data sharing completeness rate; wherein... The supplier green supply chain resilience score is generated based on the supplier's emergency response plan for dealing with shortages of green raw materials and sudden changes in environmental policies, historical response records, and backup supplier resource storage data. Suppliers with scores below a preset threshold will have their bidding priority automatically reduced. The low-carbon compliance rate of logistics is calculated by comprehensively considering the carbon emissions of logistics transportation methods, transportation routes, and the recyclability of packaging materials, and must meet the low-carbon logistics standard threshold set by the purchaser. The green data sharing completeness rate is calculated based on the coverage of green attribute data uploaded by suppliers to the blockchain, the frequency of real-time updates, and the consistency with third-party verification data. Suppliers with a sharing completeness rate lower than the preset value will have their green score automatically reduced. The threshold of the constraint is dynamically adjusted according to the supplier's credit rating, and suppliers with high credit ratings can enjoy the incentive of a moderate relaxation of the constraint threshold.

6. The procurement cost optimization method applied to manufacturing supply chain management according to claim 1, characterized in that, The smart contracts include a basic procurement performance contract, a green performance tiered incentive contract, an automatic payment tiered contract, a default tiered penalty contract, and a contract dynamic adjustment contract; The triggering data includes at least two of the following: real-time data from IoT sensors, data reported by third-party testing agencies, GPS positioning trajectory data of logistics nodes, and countdown data of deadline nodes. When the trigger data meets the preset conditions, the following corresponding operations will be executed automatically: Once the goods are delivered and pass quality inspection, different percentages of payment will be triggered based on the green indicator compliance rate range. Incentive payments will be transferred at different rates based on the degree to which green indicators are exceeded. And different proportions of penalties and default records will be triggered and recorded on the blockchain based on the length of the overdue delivery.

7. The procurement cost optimization method applied to manufacturing supply chain management according to claim 1, characterized in that, Also includes: Collect full-process data generated during contract execution and update the supplier's dynamic credit profile; the full-process data includes real-time production progress data, logistics tracking data, detailed quality inspection and acceptance data, fund settlement voucher data, final calculation data of green indicators, and supplier green technology iteration data; Based on the updated archive data, the parameters and configuration of the multi-objective dynamic optimization decision model are iteratively optimized using machine learning algorithms. The supplier dynamic credit archive includes green performance score, quality compliance rate, on-time delivery rate, cost control accuracy, green innovation capability score, green supply chain resilience score, and default records. The weight of each score item is determined by the analytic hierarchy process to generate a comprehensive credit coefficient. The supplier's weight, green benefit quantification coefficient, and constraint threshold are adjusted based on the supplier's overall credit rating.

8. The procurement cost optimization method applied to manufacturing supply chain management according to claim 1, characterized in that, Also includes: A hybrid encryption operation using hash encryption and symmetric encryption is employed to encrypt data interactions between nodes. Sensitive data such as procurement requirements and business quotations are uploaded to the blockchain after being symmetrically encrypted, and encryption keys are distributed among authorized nodes using asymmetric encryption.

9. The procurement cost optimization method applied to manufacturing supply chain management according to claim 1, characterized in that, Solving for the Pareto optimal purchasing decision includes: Based on the characteristics of the current procurement scenario, a target intelligent optimization algorithm is determined; The Pareto problem is solved based on the target intelligent optimization algorithm to obtain the optimal solution set; wherein, the intelligent optimization algorithm includes any one or a combination of genetic algorithm, particle swarm algorithm, simulated annealing algorithm, and ant colony algorithm, and the optimal solution set includes at least those emphasizing cost optimization, environmental optimization, delivery time optimization, and comprehensive balance optimization. The optimal solution set is dynamically stratified and filtered according to the procurement scenario, and then displayed on the interactive interface; The optimal procurement decision is obtained by the buyer selecting an operation based on the interactive interface.

10. A procurement cost optimization system applied in manufacturing supply chain management, comprising: The trusted data generation module is used to build a supply chain blockchain network jointly maintained by buyer nodes, supplier nodes, and third-party collaborative nodes, and generate a unique trusted dataset on the chain. The optimal solution module is used to adjust the objective weights and constraint priorities of the multi-objective optimization model based on the procurement scale, the level of industry green policies, and the market supply and demand fluctuation coefficient. The module also inputs the unique credible dataset into the adjusted multi-objective optimization model and, in combination with the dual-dimensional green benefit quantification coefficient, solves the Pareto optimal procurement decision scheme. The optimal procurement decision-making scheme includes supplier combination, procurement volume allocation ratio, green tier incentive clauses and risk hedging clauses; The contract execution module is used to compile the optimal procurement decision scheme into a layered programmable smart contract and deploy it to the supply chain blockchain network. It obtains on-chain and off-chain performance trigger data in real time through oracles to drive the smart contract to automatically execute tiered payment, tiered incentive or gradient penalty operations.

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