CN116629510A - Service type manufacturing resource matching method and system based on block chain - Google Patents
Service type manufacturing resource matching method and system based on block chain Download PDFInfo
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Abstract
The application discloses a service type manufacturing resource matching method and system based on a block chain, comprising the following steps: establishing a supply and demand matching platform based on a block chain, and endowing reputation points to all uplink manufacturing service providers and resource suppliers; receiving a manufacturing service task and decomposing the manufacturing service task into a plurality of subtasks, wherein each subtask can be completed by a single manufacturing service provider; screening a part of manufacturing service providers and resource suppliers with credit points in front, and respectively putting the manufacturing service providers and the resource suppliers into an alternative manufacturing service pool and an alternative resource pool; selecting a manufacturing service provider from the alternative manufacturing service pool to perform task matching with the subtasks; selecting a resource provider from the alternative resource pool to perform resource matching with a manufacturing service provider successfully matched with the task; after the matching is successful, the manufacturing service task execution stage is shifted to. The application can solve the problem of service type manufacturing resource matching under the block chain, so as to enhance the trust degree among all participating subjects in the service type manufacturing system and improve the matching and scheduling efficiency of qualification service resources.
Description
Technical Field
The application belongs to the technical field of intelligent manufacturing, and particularly relates to a resource matching method and system for service type manufacturing.
Background
Service-oriented manufacturing (Service-Embedded Manufacturing) is a manufacturing mode in which enterprises realize integration of distributed manufacturing resources and high coordination of core competitiveness of the enterprises in order to realize value increment of stakeholders in a manufacturing value chain, and realize efficient innovation by fusing products and services, participating in the whole process of clients and providing productive services and Service production for the enterprises. Service type manufacturing emphasizes taking the personalized demands of users as cores, and by integrating production and service among different enterprises, efficient coordination among the enterprises is realized, and manufacturing service and products covering the whole life cycle of the products are provided for customers together.
At present, the problem that the platform public trust is insufficient, and the user transaction object is difficult to trust and the like exists in the service type manufacturing supply and demand matching platform generally. The enterprises need to interact information through a manufacturing service platform which is an organization of a third party intermediary, and enterprise users face risks in terms of data leakage, unreliable transaction objects and the like. The platform also lacks effective technical means to ensure the authenticity and integrity of the data used by the user, and the centralized management mode of the manufacturing service platform also brings problems in the aspects of platform safety and reliability, and the problems all cause the user to question the platform. Meanwhile, the key of matching supply and demand among enterprises and achieving cooperative consensus is that the enterprises fully know cooperative objects and can give trust. The efficient, safe and reliable characteristics of the blockchain are highly matched with the requirements of the blockchain, the development of the blockchain technology provides a new solution to the problem of matching the requirements, and the intelligent matching and scheduling of resources erected on the blockchain can safely and efficiently obtain the result satisfying the requirements under the condition of meeting the preset conditions.
At the same time, the underlying system architecture of most service-oriented manufacturing platforms remains a centralized framework. Under this framework, the biggest feature is that decisions in the system depend on a small number of nodes, and therefore, it is essentially unavoidable to have a single point of failure. At present, the problem of single-point failure is mainly solved through redundant backup, but expensive maintenance cost is required, and the problem of single-point failure cannot be fundamentally solved. In addition, a few nodes in the system master excessive rights, which is easy to be a target of hacking, and the risk of secret data leakage exists.
Disclosure of Invention
The application aims to provide a service type manufacturing resource matching method based on a block chain, which aims to solve the problem of service type manufacturing resource matching under the block chain, further enhance the trust degree among all participating subjects in a service type manufacturing system and improve the matching and scheduling efficiency of qualification service resources.
In order to solve the technical problems, the application is realized by adopting the following technical scheme:
in one aspect, the present application provides a blockchain-based service type manufacturing resource matching method, comprising:
establishing a supply and demand matching platform based on a blockchain, wherein the blockchain adopts a Bayesian-busy-court fault-tolerant consensus mechanism based on quality of service integration, and endows credit integration to all uplink manufacturer service providers and resource suppliers;
receiving a manufacturing service task;
decomposing the received manufacturing service task into a plurality of subtasks, each of which may be completed by a single manufacturing service provider;
screening out a part of the credit points from the manufacturer service provider and the resource provider in front of the credit points, and correspondingly putting the credit points into an alternative manufacturing service pool and an alternative resource pool;
selecting a manufacturing service provider from the alternative manufacturing service pool to perform task matching with the subtasks;
selecting a resource provider from the alternative resource pool to perform resource matching with a manufacturing service provider successfully matched with the task;
after the matching is successful, the manufacturing service task execution stage is shifted to.
In some embodiments of the present application, the bayer tolerant consensus mechanism based on quality of service integration may be configured to include the following processes:
and (3) carrying out enterprise node classification on the manufacturer service providers and the resource suppliers in the blockchain according to the order of the credit score from high to low to form three clusters, wherein the three clusters are respectively: trusted node clusters, normal node clusters, and suspicious node clusters;
selecting an enterprise node from the trusted node cluster as a master node, and responding to the enterprise node applying for the uplink;
enabling enterprise nodes in the trusted node cluster and the common node cluster to participate in consensus, wherein the enterprise nodes in the suspicious node cluster only backup the consensus result;
generating a consensus result by using a Bayesian fault-tolerant consensus algorithm;
and after each round of consensus is finished, recalculating the service quality scores of all enterprise nodes by using the service quality score model so as to update the credit scores of all enterprise nodes.
In some embodiments of the present application, it is preferable for the manufacturer service provider that enters the alternative manufacturing service pool and the resource provider that enters the alternative resource pool to select from a trusted node cluster and a normal node cluster, so as to improve the trust level between the participating entities in the system and the success rate of successful completion of the manufacturing service task.
In some embodiments of the present application, the quality of service integration model may be configured to:
wherein ,respectively representing the service quality integral, static integral and dynamic integral of the ith enterprise node in t round consensus; delta t Delta as a time bias influencing factor t The value range of (1) is (0).
In some embodiments of the application, the expression of static integration may be configured to:
wherein ,respectively representing the CPU core number, the memory capacity, the hard disk capacity and the mortgage amount of the ith enterprise node; />Respectively representing the maximum value and the minimum value in the j index; w (w) j A weight representing the j-th index; n is the total number of enterprise nodes participating in the consensus.
In some embodiments of the application, the expression of the dynamic integration may be configured as:
wherein ,node liveness scores and node contribution scores of the ith enterprise node are respectively represented; /> Respectively representing the maximum value and the minimum value in the j index; w (W) j And the weight of the j-th index is represented.
In some embodiments of the present application, the calculation formula of the node liveness integral may be configured as:
wherein ,hi 、H i Representing actual traffic and expected traffic of the ith enterprise node, respectively; alpha 1 、α 2 For adjusting the magnitude of the increase in the integral and setting alpha 1 ＜α 2 ；α 3 、α 4 For controlling punishment degree of enterprise node and setting alpha 3 ＞α 4 ；
In some embodiments of the present application, the calculation formula of the node contribution degree integral may be configured as:
wherein ,NF The number of actions to make error consensus; n (N) T The number of acts to make the correct consensus; i a (N F) and Ib (N F ) As a function of the readiness, and when N F When not equal to 0, I a (N T )＝0，I b (N F ) =1; when N is F When=0, I a (N T )＝1，I b (N F )＝0；α 5 Is a weight coefficient and alpha 5 ∈[0,1]；α 6 、α 7 A bonus coefficient and a penalty coefficient, respectively.
In some embodiments of the present application, to motivate the aggressiveness of node parameter consensus for different layers of enterprises, an integrating rewards and punishment mechanism may be set, for example: punishment can be carried out on the Bayesian and the horrible nodes after each round of consensus is finished, and part of credit points are deducted; if the round of consensus is successful, rewarding the non-Bayesian-family nodes, and increasing credit points; in addition, in order to optimize the system environment, the trust degree of the user on the platform is improved, enterprise nodes which are in suspicious node clusters for a long time or enterprise nodes which become Bayesian nodes for many times can be marked as malicious nodes, and after each round of consensus is finished, the malicious nodes are removed from the block chain, so that the influence of the malicious nodes on the system environment is eliminated.
In some embodiments of the present application, the following punishment mechanisms may be performed in each round of consensus:
if the main node does not respond, E credit points of the main node are deducted;
deducting 0.5E credit points from the Bayesian node participating in the consensus;
deducting 0.2E credit points from enterprise nodes and Bayesian nodes without backup consensus results in the suspicious node cluster;
after each round of consensus is successfully executed, F reputation points are rewarded to the main node, and 0.4F reputation points, 0.2F reputation points and 0.05F reputation points are rewarded to non-Bayesian nodes in the trusted node cluster, the common node cluster and the suspicious node cluster respectively;
wherein ,α 8 punishment coefficients for the master node; alpha 9 Coefficients are awarded to the master node.
In another aspect, the present application also provides a blockchain-based service-type manufacturing resource matching system, comprising:
a pre-consumer interaction module for interacting with a consumer, determining a manufacturing service task;
a service-type manufacturing resource matching decomposition module for decomposing the manufacturing service task into a number of sub-tasks, each sub-task being completable by a single manufacturing service provider; screening out a part of manufacturing service providers and resource suppliers according to the credit points, and respectively putting the manufacturing service providers and the resource suppliers into an alternative manufacturing service pool and an alternative resource pool;
a supply-demand matching platform based on a block chain, which comprises a supply-demand matching chain and a block chain; the supply and demand matching chain is used for selecting a manufacturing service provider from the alternative manufacturing service pool and performing task matching on the subtasks, and selecting a resource provider from the alternative resource pool and performing resource matching on the manufacturing service provider with successful task matching; the blockchain adopts a Bayesian fault tolerance consensus mechanism based on quality of service points to endow credit points to all manufacturing service providers and resource suppliers in the system;
and the post enterprise interaction module is used for broadcasting the information of the enterprise users requesting to join the system into the blockchain for consensus, and broadcasting the resource information or manufacturing service information of the enterprise users with successful consensus into the supply and demand matching chain.
In some embodiments of the present application, the pre-consumer interaction module may be configured to first display the product of the successful case to the consumer for the consumer to order when interacting with the consumer; if the product of the successful case does not meet the consumer demand, receiving the product demand submitted by the consumer, and determining the manufacturing service task.
In some embodiments of the present application, in order to improve success rate and reliability of consensus, multiple types of blockchains may be configured according to service types, such as a research and development chain, a supply chain, a manufacturing chain, a logistics chain, a sales chain, and an after-sales chain, and the post-enterprise interaction module broadcasts information of enterprise users requesting to join the system to the blockchain to which the enterprise users belong, and the enterprise users of the same type consensus the information, so that accuracy of consensus results may be improved.
In some embodiments of the present application, a product service module, a administration and rights management module may also be configured in a service type manufacturing resource matching system; the product service module is used for providing transportation, sales, after-sales and other services for users after the product is manufactured; the supervision and authority management module is used for carrying out full-flow supervision on products from research and development design to sales and after-sales, and checking and authority management on users who enter enterprises.
Compared with the prior art, the application has the advantages and positive effects that:
(1) Aiming at the problems that a service type manufacturing supply and demand matching platform under the traditional architecture is generally insufficient in platform public confidence, a user transaction object is difficult to trust, information is isolated and the like, the application provides a service type manufacturing resource matching framework based on a blockchain by utilizing the characteristics of traceability, decentralization and non-falsification of the blockchain and combining service information of service type manufacturing, and takes the blockchain technology as an effective tool for establishing a supply and demand trust bridge, thereby ensuring the true reliability and platform safety of data and solving the trust problem between internal parameters of each link of a system and enterprises.
(2) The common identification algorithm is used as the core of the block chain to directly influence the performance of the block chain, but the currently used Bayesian fault-tolerant common identification algorithm (PBFT) has the problems of high communication complexity and low common identification efficiency. In consideration of the characteristics of numerous participation subjects and frequent information interaction in the whole process of service type manufacturing, the application provides a PBFT (physical broadcast transmission) improvement scheme based on service quality score, and simultaneously, the public transparent and commonly accepted credit score of the whole network driven by a consensus mechanism is used as an index of enterprise evaluation for preliminary screening of manufacturing service and manufacturing resources, so that compared with the credit score of an enterprise artificially constructed under the traditional architecture, the method has higher credibility, and the consensus efficiency can be improved by reducing the number of enterprise nodes participating in consensus.
(3) In the service type manufacturing system based on block chain construction, the application applies a resource scheduling mechanism to a supply and demand matching chain to automatically execute task matching and resource matching, thereby improving the resource scheduling efficiency, aiming at the problems of difficult demand matching caused by asymmetric and opaque information of both supply and demand parties, untimely information updating and the like in the traditional architecture.
Other features and advantages of the present application will become more apparent from the following detailed description of embodiments of the present application, which is to be read in connection with the accompanying drawings.
Drawings
FIG. 1 is an overall architecture diagram of one embodiment of a blockchain-based service manufacturing resource matching system in accordance with the present application;
FIG. 2 is a flow chart of the uplink of enterprise users;
FIG. 3 is a flow chart of the distribution of manufacturing service information and manufacturing resource information;
FIG. 4 is a statistical flow chart of reputation scores of enterprise nodes;
FIG. 5 is a flow chart of a consumer product customization process;
FIG. 6 is a flow chart of service type manufacturing resource matching.
Detailed Description
The following describes specific embodiments of the present application in detail with reference to the drawings.
In order to realize service type manufacturing resource matching, firstly, combining a block chain with service type manufacturing business, and providing a service type manufacturing resource matching system based on the block chain; secondly, by improving a consensus mechanism, a trusted quality of service integral model is established, so that the consensus efficiency of a blockchain can be improved, and references can be provided for screening proper enterprises in resource matching; finally, on the service type manufacturing resource matching system based on the blockchain, the credit points of enterprise users are calculated by utilizing a service quality point model driven by a consensus mechanism, trusted manufacturing service providers and resource suppliers are screened, and automatic matching and scheduling of service type manufacturing resources are completed.
The overall architecture of the service type manufacturing resource matching system of the present embodiment will be specifically described with reference to fig. 1.
The service type manufacturing resource matching system of the embodiment mainly comprises a front consumer interaction module, a supply and demand matching platform based on a block chain, a rear enterprise interaction module, a service type manufacturing resource matching and decomposing module, a product service module, a supervision and authority management module and the like.
The pre-consumer interaction module is a module responsible for interaction with consumers in the service type manufacturing resource matching system, and can be a product for showing successful cases to the consumers. The consumer may choose to order the product satisfactorily, or may submit the product requirements, by communicating with personnel in the development and design department on a continuous basis, to determine the final ordered product. Consumers can also check full-period open source data of ordered products, and related data of each stage of process can be gradually presented along with circulation of product links so as to realize credible tracing.
The supply and demand matching platform based on the block chain is used for evaluating and admitting new enterprise users requesting the uplink and realizing automatic matching of manufacturing services and resources, and mainly comprises two parts of the block chain and the supply and demand matching chain.
A blockchain is a chain of blocks. Each block holds certain information which is linked in a chain according to the time sequence of their respective generation. This chain is kept in all servers, and the entire blockchain is secure as long as one server in the entire system can work. These servers, referred to as nodes in the blockchain system, provide storage space and computational support for the entire blockchain system. If the information in the blockchain is to be modified, it is necessary to sign consent of more than half of the nodes and modify the information in all the nodes, which are usually held in different subject hands, so it is an extremely difficult thing to tamper with the information in the blockchain. Compared with the traditional network, the blockchain has two main core characteristics: firstly, the data is difficult to tamper, and secondly, the data is decentralised. Based on the two characteristics, the information recorded by the blockchain is more real and reliable, so that the problem that people are mutually not trusted can be solved, and the problem of rights centralization brought by centralization organization is avoided.
In order to solve the problems of high communication complexity and low consensus efficiency of the blockchain, the blockchain of the embodiment adopts a Bayesian fault-tolerant consensus mechanism based on quality of service integration, and gives credit integration to all uplink manufacturing service providers and resource suppliers in the system.
In view of the different types of services provided by the manufacturer, the present embodiment preferably forms a plurality of types of blockchains in the blockchain according to the types of services of the enterprise users, including, for example, but not limited to, a research and development chain, a supply chain, a manufacturing chain, a logistics chain, a sales chain, an after-sales chain, and the like.
A supply and demand matching chain is a module for automatically matching consumer demand with manufacturing services provided by suppliers. The supply and demand matching chain of the embodiment is mainly used for selecting a manufacturing service provider from an alternative manufacturing service pool to perform task matching with a subtask, and selecting a resource provider from an alternative resource pool to perform resource matching with a manufacturing service provider with successful task matching.
The post-enterprise interaction module is a module unit for enterprise users to join the service type manufacturing resource matching system, and for performing related enterprise information maintenance (including addition, modification, inquiry and the like of basic enterprise information), enterprise resource release and inquiry, enterprise manufacturing service release and inquiry and other operations. The post enterprise interaction module broadcasts the information of the enterprise users applying for joining the system to the block chain for consensus, and broadcasts the resource information or manufacturing service information of the enterprise users with successful consensus to the supply and demand matching chain for matching tasks and resources.
The enterprise users applying for joining the system may be enterprises of different service types, such as development and design departments, resource suppliers, manufacturing service providers, logistics providers, sellers, after-sales providers, and the like. And broadcasting the enterprise information to a block chain of the service type to which the enterprise information belongs for consensus by the rear enterprise interaction module according to the service type of the enterprise user requesting to join the system. For example, for a development design department, enterprises in the development chain may be summoned to consensus enterprise users requesting a chaining; for a resource provider, enterprises in the supply chain can be summoned to consensus enterprise users requesting for uplink; for a manufacturing service provider, enterprises in the manufacturing chain can be summoned to make consensus on enterprise users requesting uplink; and so on. The accuracy of the consensus result can be improved by utilizing the co-generic type in-link enterprises to consensus new users requesting uplink.
The service type manufacturing resource matching and decomposing module mainly reads manufacturing service tasks uploaded by a research and development design department in the front consumer interaction module, and decomposes the whole manufacturing service tasks into a plurality of subtasks, wherein a single subtask can be independently completed by a certain manufacturing service provider. And then, according to the credit points of enterprise users, selecting a certain proportion of credit enterprises from manufacturer service providers and resource manufacturers capable of meeting the requirements of manufacturing service tasks, and respectively putting the credit enterprises into an alternative manufacturing service pool and an alternative resource pool to provide the credit points and the credit points for supply and demand matching chains for task matching and resource matching. If a manufacturer service provider holds manufacturing raw materials/personnel/service resources that meet manufacturing tasks, there is no need to match the resource provider for that manufacturer service provider; otherwise, the resource provider needs to be matched for the manufacturing service to purchase the resource.
After the whole process from design to manufacture of the product is completed, the manufacturing process of the product can be traced through the service type manufacturing resource matching and decomposing module, and the manufacturing and resource supply of each link are provided with credible records, so that the whole process tracing from design to manufacture is realized.
The product service module mainly provides the functions of transportation, sales and after-sales for users after the product is manufactured, and can provide the full-flow tracking service from transportation to after-sales of the product.
The supervision and authority management module is mainly a supervision agency and a supervision alliance consisting of a part of high-quality enterprises, so that the whole process supervision from research and development design to sales and after-sales of products is realized, the information of each link can be checked, and the execution of each link is supervised. Meanwhile, the system has the functions of enterprise access auditing and authority management.
The following describes the uplink flow of enterprise users, the statistics process of credit points and the resource matching flow of ordered products in detail based on the specific architecture of the service type manufacturing resource matching system of the embodiment.
1. Uplink flow for enterprise users
The enterprises involved in the service type manufacturing mainly comprise research and development design departments, resource suppliers, manufacturing service providers, logistics providers, sellers and after-sales providers, and the enterprises upload related information of the enterprises through the post-enterprise interaction modules and broadcast the related information to a research and development chain, a supply chain, a manufacturing chain, a logistics chain, a sales chain and an after-sales chain respectively.
As shown in fig. 2, the user of the enterprise applying for the uplink first needs to write the information of the user of the enterprise into the system and broadcast the information over the whole network after checking the information of the enterprise by the enterprise serving as the master node in the blockchain to which the user belongs. Then, starting a consensus mechanism to enable other enterprise nodes in the affiliated blockchain to participate in consensus; and if the consensus is successful, allowing the enterprise user to uplink, otherwise, failing to uplink.
If the enterprise information of the enterprise users in the chain needs to be changed, the application for modifying the enterprise information can be submitted through the rear-mounted enterprise interaction module, and the enterprise information can be modified after the enterprise users are subjected to whole-network broadcasting and other node agreements.
Referring to fig. 3, each participating enterprise entity in the service type manufacturing supply chain may perform filling in of manufacturing service information or manufacturing resource information through the post-interaction module and then issue. The post interaction module broadcasts information issued by an enterprise to a block chain to which the node belongs for consensus, and the link is up if the consensus is successful.
After the uplink is successful, the nodes of the block chain broadcast manufacturing service information or manufacturing resource information containing the enterprise information to the supply and demand matching chain for verification, and if the verification passes, the supply and demand matching chain stores related information; otherwise, the link is down.
2. Statistical process for reputation score
In order to screen out that credit enterprises participate in the manufacturing service of ordered products, the embodiment endows credit points for the uplink enterprises, and scientifically and objectively counts the credit points of all enterprise nodes by constructing a service quality point model driven by a consensus mechanism and matching with a point reward and penalty mechanism.
In service type manufacturing, a plurality of enterprises participate in, information interaction is frequent, and the problem of too high communication complexity exists in the use of the existing Bayesian fault-tolerant consensus algorithm (PBFT algorithm). When the number of nodes participating in consensus is large, the traffic of the PBFT algorithm increases sharply, which results in system delay lengthening, communication overhead increasing, network congestion and sharp reduction of consensus efficiency. Meanwhile, different enterprise nodes hold different amounts of resources and should not have the same probability of becoming a master node, otherwise, the investment of the enterprise nodes is reduced, and the whole network performance is deteriorated. However, in the existing PBFT algorithm, the main node is selected randomly, and a main node election evaluation mechanism is not available, so that the Bayesian node can be selected, the protocol view is frequently switched, and the consensus efficiency is affected.
On the basis of the existing PBFT algorithm, the embodiment provides a PBFT improvement scheme based on the service quality integral, and meanwhile, the service quality integral can provide an index for enterprise evaluation for supply and demand matching, and compared with third party evaluation and self-identification, the method has higher credibility.
The specific construction process of the qos integration model is described in detail below.
The service quality integral model not only can provide reference indexes for enterprises for supply and demand matching selection, but also can grade the enterprises through integral, and provides ideas for the improvement of the PBFT consensus algorithm.
The service quality integral is the comprehensive evaluation of the performance, reliability, stability and other factors of each enterprise node in the blockchain, and the embodiment defines the service quality integral of the enterprise node from two static and dynamic dimensions by researching the self attribute and interaction condition of the enterprise nodes participating in consensus in the blockchain.
In this embodiment, the quality of service integration model can be expressed as:
wherein ,respectively representing the service quality integral, static integral and dynamic integral of the ith enterprise node in t round consensus; delta t Delta as a time bias influencing factor t The value range of (1) is (0). Delta t Mainly used for regulating the integral growth speed delta t The larger the value, the more the point integral of the node looks at the behavior from the previous round than the current round, and the smaller the influence duty ratio of the current round behavior is, the increment of the point integral of the node of the current round is inhibited.
Static integration of enterprise nodesThe evaluation is mainly performed from the basic configuration of the enterprise node and the collateral deposit quantity. In the consensus process, the higher the basic configuration is, the faster the speed and the higher the efficiency of information transmission are, so that the transaction processing speed can be improved, and the consensus delay can be reduced. Collateral deposit is the amount of deposit submitted after the enterprise joins the system, and there is a risk of being withdrawn from a portion of collateral deposit when the enterprise is wrongly detected. Therefore, the greater the number of collateral deposit that an enterprise delivers, the greater its node trustworthiness. Meanwhile, the highest threshold value of collateral deposit is set, so that some enterprises are prevented from maliciously paying a large amount of deposit, and the balance of the system is destroyed.
The present embodiment configures the expression of static integration as:
wherein ,the CPU core number (unit: number), the memory capacity (unit: GB), the hard disk capacity (unit: GB) and the mortgage amount (unit: element) of the ith enterprise node are respectively represented; />Respectively representing the maximum value and the minimum value in the j index, wherein the 1 index is the CPU core number, the 2 index is the memory capacity, the 3 index is the hard disk capacity and the 4 index is the mortgage amount; w (w) j A weight representing the j-th index; n is the total number of enterprise nodes participating in the consensus.
Dynamic integration of enterprise nodesThe method is mainly obtained by comprehensively considering the aspects of the history consensus process, interaction condition and the like of the nodes, and is mainly embodied in two aspects of node liveness and node contribution.
The present embodiment configures the expression of dynamic integration as:
wherein ,node liveness scores and node contribution scores of the ith enterprise node are respectively represented; /> Respectively representing the maximum value and the minimum value in the j-th index, wherein the 1-th index is node liveness integral, and the 3-th index is node contribution integral; w (W) j And the weight of the j-th index is represented.
The node liveness refers to the participation frequency of the evaluated node in a certain time, the active state of the node is evaluated according to the actual traffic dynamic state of the node in the consensus, and the calculation formula of the node liveness integral is as follows:
wherein ,hi 、H i Representing actual traffic and expected traffic of the ith enterprise node, respectively; alpha 1 、α 2 For adjusting the magnitude of the increase in the integral, alpha is usually set 1 ＜α 2 Default value is alpha 1 ＝0.2、α 2 ＝0.1；α 3 、α 4 For controlling the degree of punishment to enterprise nodes, typically by setting α 3 ＞α 4 Default value is alpha 3 ＝20、α 4 ＝10。
The node contribution degree is used for measuring the contribution of a node to the process of consensus of the message, and the calculation formula of the node contribution degree integral is as follows:
wherein ,NF The number of actions to make error consensus; n (N) T The number of acts to make the correct consensus; i a (N F) and Ib (N F ) As a function of the readiness, and when N F When not equal to 0, I a (N T )＝0，I b (N F ) =1; when N is F When=0, I a (N T )＝1，I b (N F )＝0；α 5 As a weight bias coefficient alpha 5 ∈[0,1]，α 5 The larger the effect duty ratio on the normal behavior of the current round is, the smaller the effect duty ratio on the normal behavior of the current round is, the inhibition isThe bonus points of the normal behavior of this round increase. Since the long-term behavior of the node has a reference meaning relative to the behavior of the current round, an alpha is generally set 5 >0.5；α 6 、α 7 A prize coefficient and a penalty coefficient, respectively, generally alpha 6 Far less than alpha 7 The aim is to prevent excessive concentration of rights caused by too fast increase of contribution degree integral of a single node, and increase the penalty of a disqualified node so as to enable the contribution degree integral to be fast reduced.
The following describes the statistical flow of reputation scores of each enterprise node in detail with reference to fig. 4, including the following procedures:
s401, after each round of consensus is finished, integrating each enterprise node according to the service quality integral modelAnd (5) performing recalculation. In order to encourage the enterprise nodes to participate in the enthusiasm of consensus, a point reward and punishment mechanism can be set to carry out point reward (adding part of points) or point punishment (deducting part of points) on the enterprise nodes, and a specific reward and punishment mode is specifically described in the following.
Using quality of service integrationAnd determining credit points of all enterprise nodes by using the credit points and the punishment points.
S402, sorting and grading the enterprise nodes according to the credit points of the enterprise nodes.
Specifically, after each round of consensus is finished, updating the credit points of the enterprise nodes, ranking the enterprise nodes according to the order of the credit points from high to low, and dividing the enterprise nodes in the system into the following three stages:
the first level is a trusted node cluster: in a blockchain with N nodes, points can be ranked at [1, μ 1 ]The nodes of the (a) are classified into a trusted node cluster; in the present embodiment, μmay be configured 1 Default value of (2) isThat is, the top 25% of the enterprise nodes with points ranked are assigned to a cluster of trusted nodes; the nodes of the cluster have better static and dynamic performances, and have low probability of disfigurement and faults and faster data processing and transmission efficiency.
The second level is a common node cluster: rank points at (μ) 1 μ 2 ]The nodes of the node (a) are classified into a common node cluster; in the present embodiment, μmay be configured 2 Default value of (2) isNamely, enterprise nodes with the score ranking of 25% -75% are classified into common node clusters;
the third level is suspicious node clusters: rank points at (μ) 2 ,N]Is classified as a suspicious node cluster. In this embodiment, enterprise nodes in the suspicious node cluster cannot participate in consensus, and only the consensus result can be backed up.
S403, when the enterprise user applies for the uplink, an enterprise node is randomly generated from the trusted node cluster to serve as a master node, and the response is performed to the enterprise node applying for the uplink.
S404, if the master node does not respond within the specified time, E points of the master node are deducted, and one master node is randomly generated from the trusted node cluster again to respond to the enterprise node applying for the uplink.
In the present embodiment, it is possible to configure wherein ,α8 Punishment coefficients for master node, defaults to alpha 8 ＝20。
And S405, if the master node responds within a specified time, the master node performs information verification on the enterprise applying for the uplink, and after the verification is passed, the common identification process is executed.
In this embodiment, only the enterprise nodes in the trusted node cluster and the normal node cluster are allowed to participate in the consensus, and the enterprise nodes in the suspicious node cluster only can backup the consensus result and cannot participate in the consensus. In the consensus process, a PBFT consensus algorithm is adopted to generate a consensus result, and whether the enterprise user applying for uplink can be uplink is determined.
And S406, if the consensus fails, checking the Bayesian node, and punishing the Bayesian node.
In this embodiment, if an enterprise node participating in consensus is found to be bad, the bad node is taken as a Bayesian node, and 0.5E points are deducted.
And taking the node or the disqualified node without the backup consensus result in the suspicious node cluster as a Bayesian node, and deducting 0.2E points.
And S407, if the consensus is successful, carrying out point rewards on the enterprise nodes.
In this embodiment, after each round of consensus is successfully executed, F points may be awarded to the master node, and 0.4F,0.2F, and 0.05F points may be awarded to the enterprise nodes in the first, second, and third stages, respectively.
In the present embodiment, it is possible to configure wherein ,α9 Awarding coefficients to the master node, defaulting alpha 9 ＝5。
S408, updating the credit points of the enterprise nodes.
S409, checking malicious nodes.
In this embodiment, enterprise nodes that are long-term in the suspicious node cluster and nodes that are detected to be numerous wrought may be marked as malicious nodes.
S410, malicious node elimination.
After each round of consensus is finished, the enterprise nodes marked as malicious nodes are cleared, so that the system environment is purified, and the success rate of the subsequent consensus process is improved.
3. Resource matching process for ordered products
The resource matching of ordered products mainly comprises two parts, namely a consumer product customizing process and a manufacturing resource matching process.
As shown in FIG. 5, in the consumer product customization process, a consumer can view customized products in a publicly available successful case through a consumer pre-interaction module, and can place an order if a suitable product is found; if no satisfactory product is found, a description of the need can be made and a product design application filed. The research and development design department determines the final product according to the requirements of consumers and through the iterative research and development of the product through continuous communication and discussion with the consumers. And then, the customer pre-interaction module fills in the manufacturing service task and broadcasts the manufacturing service task information to a supply and demand matching chain, and the supply and demand matching chain stores related information.
Meanwhile, the customer pre-interaction module broadcasts the manufacturing service task to the service type manufacturing resource matching decomposition module for task decomposition, as shown in fig. 6.
The service type manufacturing resource matching and decomposing module decomposes the manufacturing service Task uploaded by the research and development design department into a plurality of subtasks, and can use the set task= { Task i |i＝1,2,3,...,N i Represented by, N i Representing the total number of manufacturing service subtasks, task i Representing the ith manufacturing service subtask of the Task. Each sub-task may be independently performed by a single manufacturer service, and the resources used by the manufacturer service during manufacture or service may be self-sufficient or may be provided by a matching resource provider.
The service type manufacturing resource matching and decomposing module screens out a certain proportion of manufacturing service providers from the manufacturing service supply pool according to the credit points of all enterprise nodes, and puts the manufacturing service providers into an alternative manufacturing service pool. In this embodiment, the screened manufacturer service providers are categorized into trusted node clusters and common node clusters. Meanwhile, the service type manufacturing resource matching and decomposing module screens out a certain proportion of resource suppliers from the resource supply pool and puts the resource suppliers into the alternative resource pool. In this embodiment, the selected resource providers are categorized into trusted node clusters and common node clusters. The manufacturing servers and resource providers in the suspicious node cluster do not participate in the resource allocation.
The manufacturer selection technique is to select, for each sub-from a plurality of candidate manufacturer service providers having the same functional attribute but different non-functional attributesThe task selects the appropriate manufacturer. Suppose SW i Representing subtask Task i Candidate service manufacturer set of (2) then wherein ,Mi Representing the total number of candidate manufacturer servers for the ith subtask,/->Representing SW i Is the j candidate facilitator, candidate facilitator->From an alternative manufacturing service pool SW in the supply and demand matching chain.
And the supply and demand matching chain selects a manufacturer service provider from the alternative manufacturing service pool to perform task matching with the subtasks, and performs full-network broadcasting on the matching result. If the manufacturer service agrees to execute the subtask, the manufacturer service and the subtask enter a service and task solution pool. If the manufacturer service does not agree to perform the sub-task, then other suitable manufacturer service providers are reselected from the alternative manufacturing service pool for the sub-task to perform task matching.
After all the subtasks have been assigned to the manufacturer service provider, if the manufacturer service provider to which a subtask is matched does not have enough resources already in the chain, a resource matching process is triggered to match the resource provider for the manufacturer service provider from the alternative resource supply pool. If a manufacturer service has enough resources in the chain, the manufacturer service is directly matched with the resources owned by the enterprise.
Assume that each subtask Task is to be Task i Is represented as a set of candidate resource providers wherein ,Ti Representing the total number of candidate resource suppliers for the ith subtask; />Representing MS i Is the j-th candidate resource provider of the resource provider, candidate resource provider->From the alternative resource pool MS in the supply and demand matching chain. If the manufacturer service provider agrees with the resource provider to match the result, writing into a service and resource solution pool; if the matching result is not agreed, then the appropriate resource provider needs to be reselected from the candidate resource pool for resource matching.
And after completing the task decomposition to match the manufacturing service provider and the resource provider for all the subtasks, triggering a supply and demand matching combination process, integrating all the subtasks and the matched manufacturing service provider and resource provider, and storing the integration result into a product manufacturing result pool.
If the data of each link in the manufacturing service task in the product manufacturing result pool is not missing, the product manufacturing is completed, otherwise, the product manufacturing service task is returned to again, and the decomposition and matching of the manufacturing service task are restarted.
After the product is manufactured, the service and manufacturing resource recalculation process is triggered. That is, the manufacturer who completed the manufacturing service task returns to the manufacturing service pool and enters the idle state, and re-checks the number of various manufacturing resources in the manufacturing service pool and the resource supply pool. The whole production process is formed according to the diversified demands of consumers, which also meets the different demands of different consumers on products. Meanwhile, the design, manufacture and resource use of the whole product can be recorded on a block chain, so that the tracing of the product is facilitated.
The foregoing is, of course, merely a preferred embodiment of the application, and it should be noted that modifications and adaptations of the application will occur to one skilled in the art and are intended to be comprehended within the scope of the application without departing from the principles of the application.
Claims (10)
1. A blockchain-based service type manufacturing resource matching method, comprising:
establishing a supply and demand matching platform based on a blockchain, wherein the blockchain adopts a Bayesian-busy-court fault-tolerant consensus mechanism based on quality of service integration, and endows credit integration to all uplink manufacturer service providers and resource suppliers;
receiving a manufacturing service task;
decomposing the received manufacturing service task into a plurality of subtasks, each of which may be completed by a single manufacturing service provider;
screening out a part of the credit points from the manufacturer service provider and the resource provider in front of the credit points, and correspondingly putting the credit points into an alternative manufacturing service pool and an alternative resource pool;
selecting a manufacturing service provider from the alternative manufacturing service pool to perform task matching with the subtasks;
selecting a resource provider from the alternative resource pool to perform resource matching with a manufacturing service provider successfully matched with the task;
after the matching is successful, the manufacturing service task execution stage is shifted to.
2. The blockchain-based service manufacturing resource matching method of claim 1, wherein the bezier fault tolerance consensus mechanism based on quality of service credits comprises:
and (3) carrying out enterprise node classification on the manufacturer service providers and the resource suppliers in the blockchain according to the order of the credit score from high to low to form three clusters, wherein the three clusters are respectively: trusted node clusters, normal node clusters, and suspicious node clusters;
selecting an enterprise node from the trusted node cluster as a master node, and responding to the enterprise node applying for the uplink;
enabling enterprise nodes in the trusted node cluster and the common node cluster to participate in consensus, wherein the enterprise nodes in the suspicious node cluster only backup the consensus result;
generating a consensus result by using a Bayesian fault-tolerant consensus algorithm;
and after each round of consensus is finished, recalculating the service quality scores of all enterprise nodes by using the service quality score model so as to update the credit scores of all enterprise nodes.
3. The blockchain-based service type manufacturing resource matching method of claim 2, wherein the manufacturing service provider entering the alternative manufacturing service pool and the resource provider entering the alternative resource pool belong to a trusted node cluster and a normal node cluster.
4. The method for matching a blockchain-based service manufacturing resource of claim 2 or 3,
the service quality integral model is as follows:
wherein ,respectively representing the service quality integral, static integral and dynamic integral of the ith enterprise node in t round consensus; delta t Delta as a time bias influencing factor t The value range of (1, 0);
the expression of the static integral is:
wherein ,respectively representing the CPU core number, the memory capacity, the hard disk capacity and the mortgage amount of the ith enterprise node; />Respectively representing the maximum value and the minimum value in the j index; w (w) j A weight representing the j-th index; n is the total number of enterprise nodes participating in consensus;
the expression of the dynamic integral is:
wherein ,node liveness scores and node contribution scores of the ith enterprise node are respectively represented; /> Respectively representing the maximum value and the minimum value in the j index; w (W) j And the weight of the j-th index is represented.
5. The method for matching a blockchain-based service manufacturing resource of claim 4,
the calculation formula of the node liveness integral is as follows:
wherein ,hi 、H i Representing actual traffic and expected traffic of the ith enterprise node, respectively; alpha 1 、α 2 For adjusting the magnitude of the increase in the integral and setting alpha 1 ＜α 2 ；α 3 、α 4 For controlling punishment degree of enterprise node and setting alpha 3 ＞α 4 ；
The calculation formula of the node contribution degree integral is as follows:
wherein ,NF The number of actions to make error consensus; n (N) T The number of acts to make the correct consensus; i a (N F) and Ib (N F ) As a function of the readiness, and when N F When not equal to 0, I a (N T )＝0，I b (N F ) =1; when N is F When=0, I a (N T )＝1，I b (N F )＝0；α 5 Is a weight coefficient and alpha 5 ∈[0,1]；α 6 、α 7 A bonus coefficient and a penalty coefficient, respectively.
6. The method for matching a blockchain-based service manufacturing resource of claim 4,
after each round of consensus is finished, punishment is carried out on Bayesian and busy nodes, and part of credit points are deducted; if the round of consensus is successful, rewarding the non-Bayesian-family nodes, and increasing credit points;
and marking enterprise nodes which are in suspicious node clusters for a long time or become Bayesian nodes for many times as malicious nodes, and removing the malicious nodes from the blockchain after each round of consensus is finished.
7. The blockchain-based service manufacturing resource matching method of claim 6, wherein in each round of consensus, the following punishment mechanism is performed:
if the main node does not respond, E credit points of the main node are deducted;
deducting 0.5E credit points from the Bayesian node participating in the consensus;
deducting 0.2E credit points from enterprise nodes and Bayesian nodes without backup consensus results in the suspicious node cluster;
after each round of consensus is successfully executed, F reputation points are rewarded to the main node, and 0.4F reputation points, 0.2F reputation points and 0.05F reputation points are rewarded to non-Bayesian nodes in the trusted node cluster, the common node cluster and the suspicious node cluster respectively;
wherein ,α 8 punishment coefficients for the master node; alpha 9 Coefficients are awarded to the master node.
8. A blockchain-based service-type manufacturing resource matching system, comprising:
a pre-consumer interaction module for interacting with a consumer, determining a manufacturing service task;
a service-type manufacturing resource matching decomposition module for decomposing the manufacturing service task into a number of sub-tasks, each sub-task being completable by a single manufacturing service provider; screening out a part of manufacturing service providers and resource suppliers according to the credit points, and respectively putting the manufacturing service providers and the resource suppliers into an alternative manufacturing service pool and an alternative resource pool;
a supply and demand matching platform based on a blockchain, comprising:
a supply-demand matching chain for selecting a manufacturing service provider from the alternative manufacturing service pool to perform task matching with a subtask, and selecting a resource provider from the alternative resource pool to perform resource matching with a manufacturing service provider with successful task matching;
a blockchain which adopts a Bayesian fault tolerance consensus mechanism based on quality of service points to endow credit points to all manufacturing service providers and resource suppliers in the system;
and the post enterprise interaction module is used for broadcasting the information of the enterprise users requesting to join the system into the blockchain for consensus, and broadcasting the resource information or manufacturing service information of the enterprise users with successful consensus into the supply and demand matching chain.
9. The blockchain-based service manufacturing resource matching system of claim 8, wherein,
when the pre-consumer interaction module interacts with a consumer, firstly, the product of a successful case is displayed to the consumer for the consumer to order; if the product of the successful case does not meet the requirement of the consumer, receiving the product requirement submitted by the consumer, and determining a manufacturing service task;
the blockchain comprises a research and development chain, a supply chain, a manufacturing chain, a logistics chain, a sales chain and an after-sales chain, and the post-enterprise interaction module broadcasts information of enterprise users requesting to join the system to the blockchain to which the information belongs for consensus.
10. The blockchain-based service type manufacturing resource matching system of claim 8 or 9, further comprising:
the product service module is used for providing transportation, sales and after-sales services for users after the product is manufactured;
and the supervision and authority management module is used for carrying out full-flow supervision on the products from research and development design to sales and after-sales, and checking and authority management on the products aiming at enterprise users.
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