Blockchain-based service type manufacturing resource matching method and system

Abstract

The application discloses a kind of service type manufacturing resource matching method and system based on blockchain, comprising: establishing supply-demand matching platform based on blockchain, for all the manufacturing service providers and resource suppliers on chain are given credit points;Receive manufacturing service task, and decompose into several subtasks, each subtask can be completed by single manufacturing service provider;Filter a part of manufacturing service providers and resource suppliers in front of credit points, respectively into alternative manufacturing service pool and alternative resource pool;Select manufacturing service provider from alternative manufacturing service pool and subtask to carry out task matching;Select resource provider from alternative resource pool and manufacturing service provider to carry out resource matching with successful task matching;After matching, it is transferred into manufacturing service task execution phase.The application can solve the problem of service type manufacturing resource matching under blockchain, to enhance the trust degree between each participating subject in service type manufacturing system, improve the matching and scheduling efficiency of qualified service resources.

Description

Technical Field

[0001] This invention belongs to the field of intelligent manufacturing technology, specifically, it relates to a resource matching method and system for service-oriented manufacturing. Background Technology

[0002] Service-embedded manufacturing (SEM) refers to a manufacturing model where enterprises, in order to add value for all stakeholders in the manufacturing value chain, integrate decentralized manufacturing resources and achieve a high degree of synergy among their core competencies through the integration of products and services, full customer participation, and the mutual provision of productive and service-oriented production by different enterprises. This results in highly efficient innovation. SEM emphasizes focusing on the personalized needs of users, achieving efficient collaboration between enterprises by integrating production and services across different companies, and jointly providing customers with manufacturing services and products covering the entire product lifecycle.

[0003] Currently, service-oriented manufacturing supply and demand matching platforms commonly suffer from insufficient platform credibility and difficulty in trusting trading partners. Enterprises need to exchange information through manufacturing service platforms, which act as third-party intermediaries. Enterprise users face risks such as data leaks and unreliable trading partners. The platforms also lack effective technical means to ensure the authenticity and integrity of user data, and their centralized management model raises security and reliability issues, leading users to question the platforms. Furthermore, the key to successful supply and demand matching and reaching cooperative consensus among enterprises lies in their thorough understanding and trust in their partners. The efficiency, security, and trustworthiness of blockchain technology align perfectly with these needs. The development of blockchain technology offers a new solution to these demand matching problems. Intelligent resource matching and scheduling built on blockchain can securely and efficiently achieve results satisfactory to the demanding parties under pre-set conditions.

[0004] Meanwhile, most service-oriented manufacturing platforms still rely on a centralized framework architecture at their underlying system level. The defining characteristic of this framework is that system decisions depend on a small number of nodes, making it inherently prone to single points of failure. Currently, redundancy and backup are primarily used to address single points of failure, but this incurs significant maintenance costs and fails to fundamentally resolve the issue. Furthermore, the excessive privileges granted to a few nodes make them vulnerable to hacking, posing a risk of confidential data leakage. Summary of the Invention

[0005] The purpose of this invention is to provide a service-oriented manufacturing resource matching method based on blockchain to solve the service-oriented manufacturing resource matching problem under blockchain, thereby enhancing the trust between the participating entities in the service-oriented manufacturing system and improving the matching and scheduling efficiency of qualified service resources.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] In one aspect, the present invention proposes a blockchain-based service-oriented manufacturing resource matching method, comprising:

[0008] Establish a supply and demand matching platform based on blockchain, wherein the blockchain adopts a Byzantine fault-tolerant consensus mechanism based on service quality points, and assigns reputation points to all manufacturing service providers and resource suppliers on the chain.

[0009] Receive manufacturing service tasks;

[0010] The received manufacturing service tasks are broken down into several sub-tasks, each of which can be completed by a single manufacturing service provider.

[0011] A portion of the manufacturing service providers and resource suppliers with high credit scores are selected and placed into the alternative manufacturing service pool and alternative resource pool, respectively.

[0012] Select a manufacturing service provider from the pool of alternative manufacturing services and match it with the sub-task;

[0013] Resource suppliers are selected from the pool of available resources and manufacturing service providers that have successfully matched the task are matched for resource allocation.

[0014] Once a match is successful, the process transitions to the manufacturing service task execution phase.

[0015] In some embodiments of this application, the Byzantine fault-tolerant consensus mechanism based on quality of service scores can be configured by the following processes:

[0016] Based on the credit score from high to low, the enterprise nodes of manufacturing service providers and resource suppliers in the blockchain are classified into three clusters: trusted node cluster, ordinary node cluster, and suspicious node cluster.

[0017] Select an enterprise node from the trusted node cluster as the master node and respond to the enterprise nodes that apply to join the chain;

[0018] Enterprise nodes in trusted node clusters and ordinary node clusters participate in consensus, while enterprise nodes in suspicious node clusters only back up the consensus results.

[0019] Consensus results are generated using the Byzantine Fault Tolerance consensus algorithm;

[0020] After each round of consensus is completed, the service quality score of each enterprise node is recalculated using the service quality score model to update the reputation score of each enterprise node.

[0021] In some embodiments of this application, manufacturing service providers entering the alternative manufacturing service pool and resource suppliers entering the alternative resource pool are preferably selected from trusted node clusters and ordinary node clusters, so as to improve the trust between the participating entities in the system and the success rate of the smooth completion of manufacturing service tasks.

[0022] In some embodiments of this application, the service quality score model can be configured as follows:

[0023]

[0024] in, Let δ represent the service quality score, static score, and dynamic score of the i-th enterprise node in round t of consensus, respectively; t For time-biased influencing factors, δ t The value range is (0,1).

[0025] In some embodiments of this application, the expression for the static integral can be configured as follows:

[0026]

[0027] in, These represent the number of CPU cores, memory capacity, hard disk capacity, and collateral amount of the i-th enterprise node, respectively. These represent the maximum and minimum values ​​of the j-th indicator, respectively; w j represents the weight of the j-th indicator; N is the total number of enterprise nodes participating in the consensus.

[0028] In some embodiments of this application, the expression for the dynamic integral can be configured as follows:

[0029]

[0030] in, Let represent the node activity score and node contribution score of the i-th enterprise node, respectively. W represents the maximum and minimum values ​​of the j-th indicator, respectively; j This represents the weight of the j-th indicator.

[0031] In some embodiments of this application, the formula for calculating the node activity score can be configured as follows:

[0032]

[0033] Among them, hi H i Let α1 and α2 represent the actual and expected communication volumes of the i-th enterprise node, respectively; α1 and α2 are used to adjust the growth rate of the score, and α1 < α2 is set; α3 and α4 are used to control the degree of penalty for the enterprise node, and α3 > α4 is set.

[0034] In some embodiments of this application, the formula for calculating the node contribution integral can be configured as follows:

[0035]

[0036] Where, N F N represents the number of incorrect consensus actions. T The number of correct consensus actions; I a (N F ) and I b (N F ) is an indicator function, and when N F When ≠0, I a (N T ) = 0, I b (N F ) = 1; when N F When I = 0, a (N T ) = 1, I b (N F )=0; α5 is the bias coefficient, and α5∈[0,1]; α6 and α7 are the reward coefficient and the penalty coefficient, respectively.

[0037] In some embodiments of this application, in order to incentivize the enthusiasm of enterprise nodes at different levels to reach consensus on parameters, a points-based reward and punishment mechanism can be set up. For example, after each round of consensus, Byzantine nodes can be penalized by deducting some reputation points; if the consensus is successful in this round, non-Byzantine nodes can be rewarded by increasing their reputation points. In addition, in order to optimize the system environment and enhance users' trust in the platform, enterprise nodes that have been in suspicious node clusters for a long time or have repeatedly become Byzantine nodes can be marked as malicious nodes, and malicious nodes can be removed from the blockchain after each round of consensus to eliminate the impact of malicious nodes on the system environment.

[0038] In some embodiments of this application, the following reward and penalty mechanism may be implemented in each round of consensus:

[0039] If the master node does not respond, deduct E reputation points from the master node;

[0040] 0.5E reputation points will be deducted from the Byzantine nodes that participate in the consensus.

[0041] Enterprise nodes and Byzantine nodes in suspicious node clusters that do not have backup consensus results will have 0.2E reputation points deducted.

[0042] After each round of consensus is successfully executed, the master node is rewarded with F reputation points, and non-Byzantine nodes in the trusted node cluster, ordinary node cluster, and suspicious node cluster are rewarded with 0.4F, 0.2F, and 0.05F reputation points, respectively.

[0043] in, α8 is the penalty coefficient for master nodes; α9 is the reward coefficient for master nodes.

[0044] In another aspect, the present invention also proposes a blockchain-based service-oriented manufacturing resource matching system, comprising:

[0045] The front-end consumer interaction module is used to interact with consumers and determine manufacturing service tasks;

[0046] The service-oriented manufacturing resource matching and decomposition module is used to decompose the manufacturing service task into several sub-tasks, each of which can be completed by a single manufacturing service provider; based on reputation scores, a portion of manufacturing service providers and resource suppliers are selected and placed into the alternative manufacturing service pool and alternative resource pool, respectively.

[0047] The blockchain-based supply and demand matching platform includes a supply and demand matching chain and a blockchain. The supply and demand matching chain is used to select manufacturing service providers from the alternative manufacturing service pool and match them with sub-tasks, and to select resource suppliers from the alternative resource pool and match them with manufacturing service providers whose tasks have been successfully matched. The blockchain adopts a Byzantine fault-tolerant consensus mechanism based on service quality points to assign reputation points to all manufacturing service providers and resource suppliers in the system.

[0048] The post-enterprise interaction module is used to broadcast the information of enterprise users requesting to join the system to the blockchain for consensus, and to broadcast the resource information or manufacturing service information of enterprise users who have successfully reached consensus to the supply and demand matching chain.

[0049] In some embodiments of this application, the front-end consumer interaction module can be configured to first show the consumer the products of successful cases for the consumer to order when interacting with the consumer; if the products of successful cases do not meet the consumer's needs, then receive the product requirements submitted by the consumer and determine the manufacturing service task.

[0050] In some embodiments of this application, in order to improve the success rate and credibility of consensus, multiple types of blockchains can be configured according to service type, such as R&D chain, supply chain, manufacturing chain, logistics chain, sales chain, and after-sales chain. The back-end enterprise interaction module broadcasts the information of enterprise users requesting to join the system to their respective blockchains, and the same type of enterprise users reach a consensus on it, which can improve the accuracy of the consensus result.

[0051] In some embodiments of this application, a product service module and a supervision and access management module can also be configured in the service-oriented manufacturing resource matching system; wherein, the product service module is used to provide users with transportation, sales, after-sales and other services after the product is manufactured; the supervision and access management module is used to supervise the entire process of the product from research and development design to sales and after-sales, and to review and manage the access of enterprise users.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are mainly reflected in:

[0053] (1) In response to the common problems of insufficient platform credibility, difficulty in trusting user trading partners, and information silos in traditional service manufacturing supply and demand matching platforms, this invention utilizes the traceability, decentralization, and immutability of blockchain, combined with the business information of service manufacturing, to propose a blockchain-based service manufacturing resource matching framework. This framework uses blockchain technology as an effective tool to build a trust bridge between supply and demand parties, ensuring data authenticity and platform security, and solving the trust problem among participating enterprises in each link of the system.

[0054] (2) Consensus algorithms are the core of blockchain and directly affect its performance. However, the currently used Byzantine Fault-Tolerant (PBFT) consensus algorithm suffers from high communication complexity and low consensus efficiency. Considering the characteristics of numerous participants and frequent information interaction in the entire process of service manufacturing, this invention proposes to construct an improved PBFT scheme based on service quality points. At the same time, a consensus mechanism-driven, publicly transparent, and commonly recognized reputation score is used as an indicator for enterprise evaluation, which is used for preliminary screening of manufacturing services and resources. Compared with the manually constructed enterprise credit score under the traditional architecture, it is more credible. By reducing the number of enterprise nodes participating in the consensus, the consensus efficiency can be improved.

[0055] (3) In the service-oriented manufacturing system built on blockchain, in order to address the problems of information asymmetry and lack of transparency between supply and demand sides and untimely information updates in the traditional architecture, the present invention applies the resource scheduling mechanism to the supply and demand matching chain, automatically performs task matching and resource matching, and improves the efficiency of resource scheduling.

[0056] Other features and advantages of the present invention will become clearer after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. Attached Figure Description

[0057] Figure 1 This is an overall architecture diagram of an embodiment of the blockchain-based service-oriented manufacturing resource matching system proposed in this invention;

[0058] Figure 2 A flowchart for enterprise users' on-chain process;

[0059] Figure 3 Flowchart for the release of manufacturing service information and manufacturing resource information;

[0060] Figure 4 A flowchart for calculating the reputation score of enterprise nodes;

[0061] Figure 5 Flowchart of the product customization process for consumers;

[0062] Figure 6 A flowchart for matching service-oriented manufacturing resources. Detailed Implementation

[0063] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0064] To achieve service-oriented manufacturing resource matching, this invention first combines blockchain with service-oriented manufacturing operations, proposing a blockchain-based service-oriented manufacturing resource matching system. Second, by improving the consensus mechanism, a trusted service quality score model is established, which not only improves the consensus efficiency of the blockchain but also provides a reference for selecting suitable enterprises in resource matching. Finally, on the blockchain-based service-oriented manufacturing resource matching system, the service quality score model driven by the consensus mechanism is used to calculate the reputation score of enterprise users, screen trusted manufacturing service providers and resource suppliers, and complete the automatic matching and scheduling of service-oriented manufacturing resources.

[0065] The following is combined with Figure 1 First, the overall architecture of the service-oriented manufacturing resource matching system in this embodiment will be described in detail.

[0066] The service-oriented manufacturing resource matching system in this embodiment mainly includes a front-end consumer interaction module, a blockchain-based supply and demand matching platform, a back-end enterprise interaction module, a service-oriented manufacturing resource matching and decomposition module, a product service module, and a supervision and permission management module.

[0067] The pre-consumer interaction module within the service-oriented manufacturing resource matching system is responsible for interacting with consumers. It showcases successful product case studies to consumers. Consumers can select satisfactory products to order, or submit product requirements and engage in continuous communication with R&D personnel to finalize their order. Consumers can also view open-source data throughout the entire product lifecycle; relevant data from each stage of the process is gradually presented as the product flows through the system, enabling reliable traceability.

[0068] A blockchain-based supply and demand matching platform is used to evaluate and admit new enterprise users who request to join the blockchain, and to automatically match manufacturing services and resources. It mainly consists of two parts: the blockchain and the supply and demand matching chain.

[0069] A blockchain is a chain of blocks. Each block stores specific information, and these blocks are linked together in chronological order of their creation. This chain is stored on all servers, and as long as one server in the entire system is operational, the entire blockchain is secure. These servers are called nodes in the blockchain system, and they provide storage space and computing power for the entire system. To modify information in the blockchain, the consent of more than half of the nodes must be obtained, and the information in all nodes must be modified. Since these nodes are usually controlled by different entities, tampering with information in the blockchain is extremely difficult. Compared to traditional networks, blockchain has two core characteristics: data is difficult to tamper with, and it is decentralized. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable, helping to solve the problem of mutual distrust and avoiding the problem of power concentration caused by centralized organizations.

[0070] To address the issues of high communication complexity and low consensus efficiency inherent in blockchain, this embodiment employs a Byzantine fault-tolerant consensus mechanism based on service quality points, assigning reputation points to all manufacturing service providers and resource suppliers on the blockchain.

[0071] Considering the different types of services provided by manufacturing service providers, this embodiment preferably forms multiple types of blockchains based on the service types of enterprise users, such as including but not limited to R&D chains, supply chains, manufacturing chains, logistics chains, sales chains, and after-sales chains.

[0072] A supply-demand matching chain is a module used to automatically match consumer demand with manufacturing services provided by suppliers. In this embodiment, the supply-demand matching chain is primarily used to select manufacturing service providers from a pool of alternative manufacturing services for task matching, and to select resource providers from a pool of alternative resources for resource matching with manufacturing service providers whose tasks have been successfully matched.

[0073] The post-enterprise interaction module is a unit where enterprise users join the service-oriented manufacturing resource matching system, maintain relevant enterprise information (including adding, modifying, and querying basic enterprise information), publish and query enterprise resources, and publish and query enterprise manufacturing services. The post-enterprise interaction module broadcasts the information of enterprise users applying to join the system to the blockchain for consensus, and broadcasts the resource information or manufacturing service information of successfully consensus-reached enterprise users to the supply and demand matching chain for task and resource matching.

[0074] Enterprises applying to join the system can be from various service types, such as R&D departments, resource suppliers, manufacturing service providers, logistics providers, distributors, and after-sales service providers. The subsequent enterprise interaction module broadcasts the enterprise information to the blockchain of its respective service type for consensus based on the service type of the requesting enterprise. For example, for an R&D department, it can convene enterprises in the R&D chain to reach a consensus on the requesting enterprise; for a resource supplier, it can convene enterprises in the supply chain; for a manufacturing service provider, it can convene enterprises in the manufacturing chain, and so on. Utilizing existing enterprises of the same service type to reach a consensus on new users requesting to join the system can improve the accuracy of the consensus results.

[0075] The service-oriented manufacturing resource matching and decomposition module primarily reads the manufacturing service tasks uploaded by the R&D design department in the front-end consumer interaction module and decomposes the entire manufacturing service task into several sub-tasks. Each sub-task can be completed independently by a manufacturing service provider. Then, based on the enterprise user's reputation score, a certain proportion of reputable enterprises are selected from the manufacturing service providers and resource manufacturers that can meet the manufacturing service task requirements. These are placed into the alternative manufacturing service pool and alternative resource pool, respectively, to provide to the supply and demand matching chain for task matching and resource matching. If a manufacturing service provider possesses the manufacturing raw materials / personnel / service resources to meet the manufacturing task, there is no need to match resource suppliers for that manufacturing service provider; otherwise, resource suppliers need to be matched for the manufacturing service provider to procure resources.

[0076] Once the entire process from product design to manufacturing is completed, the manufacturing process can be traced through the service-oriented manufacturing resource matching and decomposition module. The manufacturing and resource supply at each stage have reliable records, achieving full-process traceability from design to manufacturing.

[0077] The product service module mainly provides users with transportation, sales, and after-sales functions after the product is manufactured, and can provide full-process tracking services from transportation to after-sales.

[0078] The supervision and access control module primarily involves regulatory agencies and a regulatory alliance comprised of select high-quality enterprises to oversee the entire product lifecycle, from R&D and design to sales and after-sales service. Users can view information at each stage and monitor its execution. It also includes enterprise access approval and access control functions.

[0079] The following section elaborates on the specific architecture of the service-oriented manufacturing resource matching system in this embodiment, including the on-chain process for enterprise users, the statistical process of credit scores, and the resource matching process for ordering products.

[0080] I. On-chain process for enterprise users

[0081] In service-oriented manufacturing, participating companies are mainly divided into R&D and design departments, resource suppliers, manufacturing service providers, logistics providers, sales providers, and after-sales service providers. Each company uploads relevant information through the post-enterprise interaction module and broadcasts it to the R&D chain, supply chain, manufacturing chain, logistics chain, sales chain, and after-sales chain respectively.

[0082] like Figure 2 As shown, for enterprise users applying to join the blockchain, their information must first be verified by the enterprise acting as the master node in their respective blockchain. Only after the enterprise user's information is verified and confirmed to be correct will it be allowed to be written into the system and broadcast across the entire network. Then, a consensus mechanism is initiated, allowing other enterprise nodes in the blockchain to participate in the consensus process. If the consensus is successful, the enterprise user is allowed to join the blockchain; otherwise, the joining process fails.

[0083] If an enterprise user on the blockchain needs to change its enterprise information, it can submit an application to modify the enterprise information through the back-end enterprise interaction module. After being broadcast across the entire network and agreed upon by other nodes, the enterprise information can be modified.

[0084] See Figure 3 In the service-oriented manufacturing supply chain, participating enterprises can fill in manufacturing service information or manufacturing resource information through the post-interaction module and then publish it. The post-interaction module will broadcast the information published by the enterprise to the blockchain of the node to achieve consensus. If the consensus is successful, it will be recorded on the chain.

[0085] After successful on-chain processing, the nodes of the blockchain will broadcast manufacturing service information or manufacturing resource information containing the information of the enterprise to the supply and demand matching chain for verification. If the verification passes, the supply and demand matching chain will store the relevant information; otherwise, it will be removed from the chain.

[0086] II. The Statistical Process of Credit Score

[0087] In order to select reputable companies to participate in the manufacturing services of ordered products, this embodiment assigns reputation points to on-chain companies. By constructing a service quality point model driven by a consensus mechanism and in conjunction with a point reward and penalty mechanism, the reputation points of each company node are calculated scientifically and objectively.

[0088] In service-oriented manufacturing, with numerous participating enterprises and frequent information exchange, the existing Byzantine Fault-Tolerant (PBFT) consensus algorithm suffers from excessively high communication complexity. When the number of participating nodes is large, the communication volume of the PBFT algorithm increases dramatically, leading to longer system latency, higher communication overhead, network congestion, and a sharp decline in consensus efficiency. Furthermore, different enterprise nodes hold varying amounts of resources and should not have an equal probability of becoming the master node; otherwise, enterprise node investment will decrease, degrading overall network performance. However, the existing PBFT algorithm selects master nodes arbitrarily and lacks a master node election evaluation mechanism, potentially resulting in the selection of Byzantine nodes, causing frequent switching of the protocol view and impacting consensus efficiency.

[0089] Based on the existing PBFT algorithm, this embodiment proposes an improved PBFT scheme based on service quality points. At the same time, service quality points can provide an indicator for enterprise evaluation of supply and demand matching, which is more credible than third-party evaluation and self-assessment.

[0090] The following section details the specific construction process of the service quality points model.

[0091] The service quality score model can not only provide a reference indicator for selecting companies in supply and demand matching, but also classify companies through scores, providing ideas for improving the PBFT consensus algorithm.

[0092] Service quality score is a comprehensive evaluation of the performance, reliability, stability and other factors of each enterprise node in the blockchain. This embodiment defines the service quality score of enterprise nodes from both static and dynamic dimensions by studying the attributes and interactions of the enterprise nodes participating in the consensus in the blockchain.

[0093] In this embodiment, the service quality score model can be expressed as:

[0094]

[0095] in, Let δ represent the service quality score, static score, and dynamic score of the i-th enterprise node in round t of consensus, respectively; t For time-biased influencing factors, δ t The value range of δ is (0, 1). t It is mainly used to adjust the growth rate of the integral, δ t The larger the value, the more a node's score is determined by its behavior in previous rounds compared to the current round. The smaller the impact of the behavior in the current round, the less likely it is to inhibit the growth of the node's score in this round.

[0096] Static integrals of enterprise nodes The evaluation primarily focuses on the basic configuration and collateral amount of enterprise nodes. During the consensus process, enterprise nodes with higher basic configurations transmit information faster and more efficiently, improving transaction processing speed and reducing consensus latency. Collateral is the amount of security deposited by an enterprise upon joining the system. If an enterprise engages in malicious activities and is detected, there is a risk of having a portion of its collateral confiscated. Therefore, the higher the collateral amount deposited by an enterprise, the greater its node credibility. Simultaneously, a maximum collateral threshold is set to prevent some enterprises from maliciously depositing large amounts of collateral, thereby disrupting the system's balance.

[0097] In this embodiment, the expression for static integration is configured as follows:

[0098]

[0099] in, These represent the number of CPU cores (unit: number of cores), memory capacity (unit: GB), hard disk capacity (unit: GB), and collateral amount (unit: yuan) of the i-th enterprise node, respectively. and represent the maximum and minimum values ​​of the j-th indicator, respectively. Here, the first indicator is the number of CPU cores, the second is the memory capacity, the third is the hard disk capacity, and the fourth is the collateral amount; w j represents the weight of the j-th indicator; N is the total number of enterprise nodes participating in the consensus.

[0100] Dynamic points of enterprise nodes It is mainly derived from a comprehensive consideration of various factors such as the historical consensus process and interaction of nodes, and is mainly reflected in two aspects: node activity and node contribution.

[0101] In this embodiment, the expression for dynamic integration is configured as follows:

[0102]

[0103] in, Let represent the node activity score and node contribution score of the i-th enterprise node, respectively. Let W represent the maximum and minimum values ​​of the j-th indicator, where the first indicator is the node activity score and the third indicator is the node contribution score; j This represents the weight of the j-th indicator.

[0104] Node activity refers to the frequency of a node's participation within a certain period. The node's activity level is dynamically evaluated based on its actual communication volume in the consensus process. The formula for calculating the node activity score is as follows:

[0105]

[0106] Among them, hi H i α1 and α2 represent the actual and expected communication volume of the i-th enterprise node, respectively; α1 and α2 are used to adjust the growth rate of the score, usually set to α1 < α2, with default values ​​of α1 = 0.2 and α2 = 0.1; α3 and α4 are used to control the degree of penalty for enterprise nodes, usually set to α3 > α4, with default values ​​of α3 = 20 and α4 = 10.

[0107] Node contribution score measures a node's contribution to the consensus process. The formula for calculating the node contribution score is as follows:

[0108]

[0109] Where, N F N represents the number of incorrect consensus actions. T The number of correct consensus actions; I a (N F ) and I b (N F ) is an indicator function, and when N F When ≠0, I a (N T ) = 0, I b (N F ) = 1; when N F When I = 0, a (N T ) = 1, I b (N F ) = 0; α5 is the bias coefficient, α5∈[0,1]. The larger α5 is, the smaller the proportion of the impact on normal behavior in the current round, which will inhibit the growth of reward points for normal behavior in this round. Since the long-term behavior of a node is more meaningful than the behavior in the current round, it is generally set to α5>0.5; α6 and α7 are the reward coefficient and the penalty coefficient, respectively. Generally, α6 is much smaller than α7. The purpose is to prevent the contribution points of a single node from increasing too quickly and causing excessive concentration of power, while increasing the penalty for malicious nodes so that the contribution points decrease rapidly.

[0110] The following is combined with Figure 4 The process of calculating the credit score for each enterprise node is described in detail, including the following steps:

[0111] S401. After each round of consensus, the scores of each enterprise node are calculated based on the service quality scoring model. A recalculation will be performed. To incentivize enterprise nodes to participate in consensus, a points-based reward and penalty mechanism can be implemented to reward enterprise nodes with points (adding points) or penalize them with points (deducting points). Specific reward and penalty methods will be described in detail later.

[0112] Utilizing service quality points The credit score for each enterprise node is determined by the reward and punishment points.

[0113] S402. Based on the reputation score of each enterprise node, sort and classify the enterprise nodes.

[0114] Specifically, after each round of consensus, the reputation scores of enterprise nodes are updated, and the enterprise nodes are ranked in descending order of reputation score, dividing the enterprise nodes in the system into the following three levels:

[0115] The first level is a trusted node cluster: In a blockchain with N nodes, nodes ranked in the range [1, μ1] can be grouped into a trusted node cluster; in this embodiment, the default value of μ1 can be configured as... That is, the top 25% of enterprise nodes in terms of points are assigned to the trusted node cluster; the nodes in this cluster have good static and dynamic performance, with low probability of malicious behavior and failure, and fast data processing and transmission efficiency.

[0116] The second level is a normal node cluster: nodes ranked in the (μ1μ2) range are grouped into a normal node cluster; in this embodiment, the default value of μ2 can be configured as follows. That is, enterprise nodes with scores ranking between 25% and 75% will be assigned to ordinary node clusters;

[0117] The third level is the suspicious node cluster: nodes with scores ranked in (μ2, N) are classified as suspicious node clusters. In this embodiment, enterprise nodes in the suspicious node cluster are configured not to participate in consensus, but can only back up the consensus results.

[0118] S403. When an enterprise user applies to join the blockchain, a single enterprise node is randomly selected from the trusted node cluster to serve as the master node and respond to the enterprise node that applied to join the blockchain.

[0119] S404. If the master node fails to respond within the specified time, deduct E points from the master node and randomly generate a new master node from the trusted node cluster to respond to the enterprise node applying to join the chain.

[0120] In this embodiment, it can be configured Where α8 is the penalty coefficient for the master node, and the default value of α8 is 20.

[0121] S405. If the master node responds within the specified time, the master node will verify the information of the enterprise applying to join the chain. After the verification is successful, the consensus process will be executed.

[0122] In this embodiment, only enterprise nodes in trusted node clusters and ordinary node clusters are allowed to participate in consensus. Enterprise nodes in suspicious node clusters can only back up the consensus results and cannot participate in the consensus process. During the consensus process, the PBFT consensus algorithm is used to generate the consensus result, which determines whether the enterprise user applying to join the blockchain can do so.

[0123] S406. If consensus fails, investigate Byzantine nodes and punish them.

[0124] In this embodiment, if a participating enterprise node is found to be acting maliciously, the malicious node will be designated as a Byzantine node and 0.5E points will be deducted from it.

[0125] Nodes in the suspicious node cluster that do not have backup consensus results or malicious nodes are designated as Byzantine nodes and 0.2E points are deducted from their scores.

[0126] S407. If consensus is successful, enterprise nodes will be rewarded with points.

[0127] In this embodiment, after each round of consensus is successfully executed, the master node can be rewarded with F points, and the enterprise nodes in the first, second and third levels can be rewarded with 0.4F, 0.2F and 0.05F points respectively.

[0128] In this embodiment, it can be configured Among them, α9 is the reward coefficient for the master node, and the default value of α9 is 5.

[0129] S408, Update the reputation scores of each enterprise node.

[0130] S409, Check for malicious nodes.

[0131] In this embodiment, enterprise nodes that have been in a cluster of suspicious nodes for a long time and nodes that have been detected to have committed malicious acts multiple times can be marked as malicious nodes.

[0132] S410, malicious node removal.

[0133] After each round of consensus, enterprise nodes marked as malicious nodes are removed to purify the system environment and improve the success rate of subsequent consensus processes.

[0134] III. Resource Matching Process for Ordered Products

[0135] Resource matching for ordered products mainly includes two parts: the consumer product customization process and the manufacturing resource matching process.

[0136] like Figure 5As shown, during the consumer product customization process, consumers can view customized products from publicly available success stories through the consumer front-end interaction module. If they find a suitable product, they can place an order; if they don't find a satisfactory product, they can describe their needs and submit a product design request. The R&D department then iterates and develops the product based on the consumer's needs, through continuous communication and discussion, to determine the final product. Afterwards, the consumer front-end interaction module completes the manufacturing service task and broadcasts the manufacturing service task information to the supply and demand matching chain, which stores the relevant information.

[0137] Simultaneously, the consumer pre-interaction module broadcasts the manufacturing service task to the service-oriented manufacturing resource matching and decomposition module for task decomposition, such as... Figure 6 As shown.

[0138] The service-oriented manufacturing resource matching and decomposition module breaks down the manufacturing service tasks uploaded by the R&D and design departments into several sub-tasks, which can be represented by the set Task = {Task...} i |i=1,2,3,...,N i} represents, where N i Indicates the total number of manufacturing service sub-tasks, Task i This represents the i-th manufacturing service subtask of Task. Each subtask can be completed independently by a single manufacturing service provider. The resources used by the manufacturing service provider during the manufacturing or service process can be self-sufficient or provided by a resource supplier.

[0139] The service-oriented manufacturing resource matching and decomposition module selects a certain proportion of manufacturing service providers from the manufacturing service supply pool based on the reputation scores of each enterprise node and places them into a candidate manufacturing service pool. In this embodiment, the selected manufacturing service providers should belong to both trusted node clusters and ordinary node clusters. Simultaneously, the service-oriented manufacturing resource matching and decomposition module selects a certain proportion of resource suppliers from the resource supply pool and places them into a candidate resource pool. In this embodiment, the selected resource suppliers should also belong to both trusted node clusters and ordinary node clusters. Manufacturing service providers and resource suppliers in suspicious node clusters do not participate in resource allocation.

[0140] Manufacturing service provider (MSB) selection technology involves choosing a suitable MSB for each subtask from a vast pool of candidate MSBs that share similar functional attributes but differ in functional attributes. Assume SW... i Subtask i The set of candidate service providers, then Among them, M i This represents the total number of candidate manufacturing service providers for the i-th subtask. Indicates SW i The j-th candidate service provider, candidate service provider Alternative manufacturing service pool (SW) from the supply and demand matching chain.

[0141] The supply and demand matching chain selects manufacturing service providers from the alternative manufacturing service pool and matches them with sub-tasks, broadcasting the matching results across the network. If a manufacturing service provider agrees to execute the sub-task, the provider and the sub-task enter the service and task resolution pool. If the provider disagrees, another suitable manufacturing service provider is selected from the alternative manufacturing service pool for the sub-task matching.

[0142] Once all subtasks have been assigned to manufacturing service providers, if a manufacturing service provider assigned to a particular subtask does not have enough on-chain resources, a resource matching process is triggered to match a resource supplier from the alternative resource supply pool for that manufacturing service provider. If the manufacturing service provider has sufficient on-chain resources, then resources owned by the company are directly matched to that manufacturing service provider.

[0143] Assuming each subtask is a Task i The set of candidate resource suppliers is represented as Among them, T i This represents the total number of candidate resource suppliers for the i-th subtask; MS i The j-th candidate resource supplier, candidate resource supplier The resource pool MS comes from the supply and demand matching chain. If the manufacturing service provider and the resource supplier agree on the matching result, it is written to the service and resource solution pool; if they disagree on the matching result, a suitable resource supplier needs to be selected from the alternative resource pool for resource matching.

[0144] Once the task decomposition is completed and all sub-tasks are matched with manufacturing service providers and resource suppliers, the supply and demand matching and combination process is triggered. This process integrates all sub-tasks and their matched manufacturing service providers and resource suppliers, and stores the integration results in the product manufacturing result pool.

[0145] If there is no missing data in any stage of the manufacturing service task in the product manufacturing results pool, then the product manufacturing is completed; otherwise, return to the product manufacturing service task and start the decomposition and matching of the manufacturing service task again.

[0146] Once product manufacturing is complete, a recalculation process for services and manufacturing resources is triggered. That is, the manufacturing service provider that has completed its manufacturing service task returns to the manufacturing service pool and enters an idle state, while the quantities of various manufacturing resources in the manufacturing service pool and resource supply pool are re-verified. The entire production process is tailored to diverse consumer needs, thus satisfying the different product requirements of various consumers. Simultaneously, the entire product design, manufacturing, and resource usage are recorded on the blockchain for product traceability.

[0147] Of course, the above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A service-oriented manufacturing resource matching method based on blockchain, characterized in that, include: Establish a supply and demand matching platform based on blockchain, wherein the blockchain adopts a Byzantine fault-tolerant consensus mechanism based on service quality points, and assigns reputation points to all manufacturing service providers and resource suppliers on the chain. Receive manufacturing service tasks; The received manufacturing service tasks are broken down into several sub-tasks, each of which can be completed by a single manufacturing service provider. A portion of the manufacturing service providers and resource suppliers with high credit scores are selected and placed into the alternative manufacturing service pool and alternative resource pool, respectively. Select a manufacturing service provider from the pool of alternative manufacturing services and match it with the sub-task; Resource suppliers are selected from the pool of available resources and manufacturing service providers that have successfully matched the task are matched for resource allocation. Once a match is successful, the process transitions to the manufacturing service task execution phase. The Byzantine fault-tolerant consensus mechanism based on service quality scores includes: Based on the credit score from high to low, the enterprise nodes of manufacturing service providers and resource suppliers in the blockchain are classified into three clusters: trusted node cluster, ordinary node cluster, and suspicious node cluster. Select an enterprise node from the trusted node cluster as the master node and respond to the enterprise nodes that apply to join the chain; Enterprise nodes in trusted node clusters and ordinary node clusters participate in consensus, while enterprise nodes in suspicious node clusters only back up the consensus results. Consensus results are generated using the Byzantine Fault Tolerance consensus algorithm; After each round of consensus, the service quality score of each enterprise node is recalculated using a service quality score model to update the reputation score of each enterprise node; among which, The service quality score model is as follows: ; in, , , Let these represent the service quality score, static score, and dynamic score of the i-th enterprise node in round t of consensus, respectively. For time-dependent influencing factors, The range of values ​​for is (0,1); The expression for the static integral is: ; in, , , , These represent the number of CPU cores, memory capacity, hard disk capacity, and collateral amount of the i-th enterprise node, respectively. , These represent the maximum and minimum values ​​in the j-th indicator, respectively; This represents the weight of the j-th indicator; N is the total number of enterprise nodes participating in the consensus. The expression for the dynamic integral is: ; in, , Let represent the node activity score and node contribution score of the i-th enterprise node, respectively. , These represent the maximum and minimum values ​​in the j-th indicator, respectively; This represents the weight of the j-th indicator; The formula for calculating the node activity integral is as follows: ； in, , Let these represent the actual communication volume and the expected communication volume of the i-th enterprise node, respectively. , Used to adjust the rate of increase in points, and set ; , Used to control the severity of penalties imposed on enterprise nodes, and to set... ; The formula for calculating the node contribution integral is as follows: ； in, The number of incorrect consensus actions; The number of correct consensus actions; and It is an indicator function, and when hour, ;when hour, 0; It is the bias coefficient, and ; , These are the reward coefficient and the penalty coefficient, respectively.

2. The blockchain-based service-oriented manufacturing resource matching method according to claim 1, characterized in that, The manufacturing service providers placed in the alternative manufacturing service pool and the resource suppliers entering the alternative resource pool belong to the trusted node cluster and the ordinary node cluster, respectively.

3. The blockchain-based service-oriented manufacturing resource matching method according to claim 1, characterized in that, After each round of consensus, Byzantine nodes are penalized by deducting some of their reputation points; if the consensus is successful in this round, non-Byzantine nodes are rewarded by increasing their reputation points. Enterprise nodes that are in suspicious node clusters for a long time or that have repeatedly become Byzantine nodes are marked as malicious nodes, and malicious nodes are removed from the blockchain after each round of consensus.

4. The blockchain-based service-oriented manufacturing resource matching method according to claim 3, characterized in that, In each round of consensus, the following reward and punishment mechanism will be implemented: If the master node does not respond, deduct E reputation points from the master node; 0.5E reputation points will be deducted from the Byzantine nodes that participate in the consensus. Enterprise nodes and Byzantine nodes in suspicious node clusters that do not have backup consensus results will have 0.2E reputation points deducted. After each round of consensus is successfully executed, the master node is rewarded with F reputation points, and non-Byzantine nodes in the trusted node cluster, ordinary node cluster, and suspicious node cluster are rewarded with 0.4F, 0.2F, and 0.05F reputation points, respectively. in, ; ; Penalty coefficient for master node; The main node reward coefficient.

5. A blockchain-based service-oriented manufacturing resource matching system, characterized in that, include: The front-end consumer interaction module is used to interact with consumers and determine manufacturing service tasks; The service-oriented manufacturing resource matching and decomposition module is used to decompose the manufacturing service task into several sub-tasks, each of which can be completed by a single manufacturing service provider. Based on credit scores, a number of manufacturing service providers and resource suppliers are selected and placed into the alternative manufacturing service pool and alternative resource pool, respectively. Blockchain-based supply and demand matching platforms include: The supply and demand matching chain is used to select manufacturing service providers from the alternative manufacturing service pool and match them with sub-tasks, and to select resource suppliers from the alternative resource pool and match them with manufacturing service providers whose tasks have been successfully matched. Blockchain employs a Byzantine fault-tolerant consensus mechanism based on service quality points, assigning reputation points to all manufacturing service providers and resource suppliers in the system. The post-enterprise interaction module is used to broadcast the information of enterprise users requesting to join the system to the blockchain for consensus, and to broadcast the resource information or manufacturing service information of enterprise users who have successfully reached consensus to the supply and demand matching chain. The Byzantine fault-tolerant consensus mechanism based on service quality points adopted by the blockchain includes: Based on the credit score from high to low, the enterprise nodes of manufacturing service providers and resource suppliers in the blockchain are classified into three clusters: trusted node cluster, ordinary node cluster, and suspicious node cluster. Select an enterprise node from the trusted node cluster as the master node and respond to the enterprise nodes that apply to join the chain; Enterprise nodes in trusted node clusters and ordinary node clusters participate in consensus, while enterprise nodes in suspicious node clusters only back up the consensus results. Consensus results are generated using the Byzantine Fault Tolerance consensus algorithm; After each round of consensus, the service quality score of each enterprise node is recalculated using a service quality score model to update the reputation score of each enterprise node; among which, The service quality score model is as follows: ; in, , , Let these represent the service quality score, static score, and dynamic score of the i-th enterprise node in round t of consensus, respectively. For time-dependent influencing factors, The range of values ​​for is (0,1); The expression for the static integral is: ; in, , , , These represent the number of CPU cores, memory capacity, hard disk capacity, and collateral amount of the i-th enterprise node, respectively. , These represent the maximum and minimum values ​​in the j-th indicator, respectively; This represents the weight of the j-th indicator; N is the total number of enterprise nodes participating in the consensus. The expression for the dynamic integral is: ; in, , Let represent the node activity score and node contribution score of the i-th enterprise node, respectively. , These represent the maximum and minimum values ​​in the j-th indicator, respectively; This represents the weight of the j-th indicator; The formula for calculating the node activity integral is as follows: ； in, , Let these represent the actual communication volume and the expected communication volume of the i-th enterprise node, respectively. , Used to adjust the rate of increase in points, and set ; , Used to control the severity of penalties imposed on enterprise nodes, and to set... ; The formula for calculating the node contribution integral is as follows: ； in, The number of incorrect consensus actions; The number of correct consensus actions; and It is an indicator function, and when hour, ;when hour, 0; It is the bias coefficient, and ; , These are the reward coefficient and the penalty coefficient, respectively.

6. The blockchain-based service-oriented manufacturing resource matching system according to claim 5, characterized in that, When interacting with consumers, the front-end consumer interaction module first displays successful case products for consumers to order; if the successful case products do not meet the consumer's needs, it receives the product requirements submitted by the consumer and determines the manufacturing service task. The blockchain includes R&D chain, supply chain, manufacturing chain, logistics chain, sales chain, and after-sales chain. The back-end enterprise interaction module broadcasts the information of enterprise users requesting to join the system to their respective blockchains for consensus.

7. The blockchain-based service-oriented manufacturing resource matching system according to claim 5 or 6, characterized in that, Also includes: The product service module is used to provide users with transportation, sales, and after-sales services after the product is manufactured. The supervision and access control module is used to supervise the entire process of product development and design, sales and after-sales service, and to review and manage the access rights of enterprise users.

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