Business consensus method and computer device for supply chain traceability business relevance

Abstract

The application provides a business consensus method for supply chain traceability business correlation and computer equipment, and relates to the technical field of blockchains. The application realizes business consensus by selecting key subjects from traceability subject social network participating subjects through digital signature technology, determines consensus nodes by the key subject set obtained through business consensus, and realizes blockchain consensus of signed transaction data by the consensus nodes. The application realizes consensus through consensus nodes, simplifies the consensus process, and improves the consensus efficiency. Meanwhile, the application determines the consensus nodes in PBFT through the key subject set, reduces the communication times in the commit stage compared with the traditional PBFT, and reduces the communication cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain, in particular to a business consensus method for supply chain traceability business relevance and a computer device. BACKGROUND

[0002] The blockchain technology has the characteristics of openness and transparency, non-tamperability and easy traceability, and has good compatibility with the supply chain traceability business. In the supply chain traceability business, the blockchain technology mainly serves as a distributed information storage system to ensure the traceability and non-tamperability of the supply chain traceability data.

[0003] The existing consensus mechanism scheme in the traceability business mainly regards each supply chain participant as a node in the blockchain to participate in the consensus process of the traceability data on the chain, and each participant jointly records the account.

[0004] However, the combination of the existing traceability business and the blockchain consensus mainly considers the participation of peer nodes in the consensus, and does not consider the influence of non-homogeneous business subjects as consensus nodes on the consensus process. The more nodes participating in the consensus, the lower the consensus efficiency. SUMMARY

[0005] (I) Technical problems to be solved

[0006] In view of the deficiencies in the prior art, the present application provides a business consensus method for supply chain traceability business relevance and a computer device, which solves the technical problem of low consensus efficiency in the prior art.

[0007] (II) Technical solutions

[0008] To achieve the above purpose, the present application is realized by the following technical solutions:

[0009] In a first aspect, the present application provides a business consensus method for supply chain traceability business relevance, comprising:

[0010] S1, obtaining supply chain traceability business relationship data;

[0011] S2, signing the transaction data submitted by the two parties of the transaction in the supply chain traceability business relationship data through a digital signature technology;

[0012] S3, constructing a social network among the subjects based on the supply chain traceability business relationship data; calculating the centrality index of the social network, comprehensively evaluating the centrality index of the nodes in the social network by using the entropy method, calculating the business relevance of the subject corresponding to the node according to the evaluation result, and identifying a key subject set;

[0013] S4, determining the consensus nodes in the PBFT through the key subject set, and performing blockchain consensus on the signed transaction data through the consensus nodes.

[0014] Preferably, the social network between subjects is constructed based on the supply chain traceability business relationship data, comprising:

[0015] An adjacency matrix between subjects is generated according to the supply chain traceability business relationship data;

[0016] The weight of the edge between two subjects is determined according to the latest transaction time interval, the latest transaction frequency and the latest transaction amount between the two subjects in the supply chain traceability business relationship data;

[0017] A weighted adjacency matrix between subjects is generated according to the adjacency matrix between subjects and the weight of the edge between two subjects;

[0018] The social network is constructed according to the weighted adjacency matrix.

[0019] Preferably, the weight of the edge between two subjects is determined according to the latest transaction time interval, the latest transaction frequency and the latest transaction amount between the two subjects in the supply chain traceability business relationship data, comprising:

[0020] According to the relationship strength between any two subjects with transaction records, the weight of the edge between the two subjects is obtained by normalizing and weighting the three centrality indicators of the latest transaction time interval R, the latest transaction frequency F and the latest transaction amount M. r f m i j

[0021]

[0022] Preferably, the centrality indicators include strength centrality, closeness centrality, betweenness centrality and clustering coefficient.

[0023] Preferably, the calculation formulas of the strength centrality, the closeness centrality, the betweenness centrality and the clustering coefficient include:

[0024] Strength centrality of a node

[0025] Closeness centrality of a node

[0026] Betweenness centrality of a node

[0027] Clustering coefficient

[0028] wherein D vi represents the strength of the weighted network node, v i ​​​​​And node v j Connected, then Otherwise N is the total number of nodes; Indicates the shortest path length of node v i To node v j , the path length is the number of edges connecting two nodes; node v i Located on the shortest path between node s and node t Otherwise η st The number of all shortest paths between node s and node t; Indicates the number of connecting edges between node v i And the directly adjacent node.

[0029] Preferably, before performing the comprehensive evaluation of the centrality indicators of the nodes in the social network by using the entropy method, the business consensus method further comprises:

[0030] The centrality indicator data is normalized, specifically:

[0031]

[0032] Wherein, x ij Each centrality indicator data of each subject before normalization; X ij Each centrality indicator data of each subject after normalization.

[0033] Preferably, the comprehensive evaluation of the centrality indicators of the nodes in the social network by using the entropy method, according to the evaluation result, the business relevance of the node corresponding subject is calculated, and the key subject set is identified, including

[0034] After calculating the weight P ij Of each centrality indicator data in all centrality indicator data of this data subject, the information entropy E j Of each centrality indicator is calculated, specifically:

[0035]

[0036]

[0037] When P ij =0, define

[0038] According to the information entropy of each centrality indicator, the weight w j Of each centrality indicator is determined, and the calculation formula is:

[0039]

[0040] Wherein, 0≤wj ≤1,

[0041] According to the service relevance R(vi) of the computing node:

[0042]

[0043] Wherein, R0 is the initial service relevance of the subject node per period, and the initial service relevance of all subject nodes before the first consensus period is 0;

[0044] In a consensus period, the first i subjects are selected to form a key subject set in the order of service relevance from large to small.

[0045] Preferably, the service consensus method further comprises:

[0046] The consensus node is updated through a node state update formula, and the node state update formula is as follows:

[0047]

[0048] Wherein, alpha and beta are limit coefficients.

[0049] In a second aspect, the present application provides a computer device, comprising:

[0050] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs comprise a service consensus method for service relevance of supply chain traceability business.

[0051] (Three) beneficial effects

[0052] The present application provides a service consensus method for service relevance of supply chain traceability business and a computer device. Compared with the prior art, the following beneficial effects are achieved:

[0053] The business consensus method for the supply chain traceability business correlation of the application first acquires supply chain traceability business relationship data; then signs the transaction data submitted by the transaction parties in the supply chain traceability business relationship data through a digital signature technology; constructs a social network between subjects based on the supply chain traceability business relationship data; calculates the centrality index of the social network, comprehensively evaluates the centrality index of the nodes in the social network by using an entropy method, calculates the business correlation of the nodes corresponding to the subjects according to the evaluation result, and identifies a key subject set; finally, the consensus nodes in the PBFT are determined through the key subject set, and the signed transaction data is subjected to a blockchain consensus through the consensus nodes. The application realizes the business consensus by selecting the key subjects from the participating subjects in the traceability subject social network through the digital signature technology, and then determines the consensus nodes through the key subject set obtained through the business consensus, and the signed transaction data is subjected to a blockchain consensus by the consensus nodes. The application realizes the consensus through the consensus nodes, simplifies the consensus process, and improves the consensus efficiency. At the same time, the consensus nodes in the PBFT are determined through the key subject set, which reduces the communication times in the commit stage compared with the traditional PBFT, and reduces the communication cost. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0055] Figure 1 The block diagram of the business consensus method for the supply chain traceability business correlation of the embodiment of the application;

[0056] Figure 2 The specific flowchart of the business consensus method for the supply chain traceability business correlation of the embodiment of the application;

[0057] Figure 3 The social network;

[0058] Figure 4 The social network in the specific case. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the technical scheme in the embodiments of the application is described clearly and completely. Obviously, the described embodiments are some of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0060] The embodiment of the application provides a business consensus method for supply chain traceability business relevance and a computer device, solves the technical problem of low consensus efficiency in the prior art, and realizes the PBFT (Byzantine Fault Tolerance Consensus) algorithm using the proxy consensus method, simplifies the consensus process, and improves the consensus efficiency.

[0061] The technical solution in the embodiment of the application is used to solve the above technical problem, and the general idea is as follows:

[0062] In a large-scale supply chain, the importance of a product traceability system is increasingly prominent. Due to the decentralization and traceability of the blockchain, the blockchain technology provides a solution for improving the supply chain traceability business system, and the consensus mechanism in the blockchain provides a guarantee for the consistency of the traceability data. However, the traditional blockchain consensus mechanism cannot well solve the problems of a large number of participants in the large-scale supply chain traceability business, business impact caused by business data sharing, privacy infringement and interest loss, and low consensus efficiency. Therefore, the embodiment of the application proposes a business consensus system based on multiple subjects, expands the content of the traditional blockchain consensus, and completes the common coordination work of the subjects from the aspects of data authenticity, business relevance, and data consistency. Meanwhile, the embodiment of the application improves the traditional PBFT algorithm based on the business relevance of the subjects, proposes a consensus node screening method referring to the behavior of the subjects, optimizes the consensus process, has lower communication complexity, and has higher consensus efficiency.

[0063] In order to better understand the above technical solution, the above technical solution will be described in detail in combination with the description of the drawings and the specific embodiments.

[0064] It should be noted that the traceability of the supply chain product refers to the ability to determine the location of the supply chain product in circulation, record and track the parts, processes and raw materials in the process. It can also be summarized as the ability to trace the production history and application status of the product according to certain identification. Once the product quality problem occurs, the product information such as the product production enterprise, the place of production, the logistics, and the sales information can be queried through the product traceability code. Through information technology, the data of the automobile parts in each link of the whole process from "production-warehousing-logistics-sales" is accurately recorded and stored, the product is queried and authenticated by using the product traceability code through the traceability design, the accuracy and integrity of the related information of each link of the traceability are ensured, and the product traceability is realized.

[0065] The embodiment of the application provides a business consensus method for supply chain traceability business relevance, as shown in Figure 1 The embodiment of the application provides a business consensus method for supply chain traceability business relevance, as shown in

[0066] S1, acquiring supply chain traceability business relationship data;

[0067] S2, the transaction data submitted by the transaction parties in the supply chain traceability business relationship data is signed through a digital signature technology;

[0068] S3, a social network between subjects is constructed based on the supply chain traceability business relationship data, the centrality indexes of the social network are calculated, the centrality indexes of the nodes in the social network are comprehensively evaluated by using an entropy value method, the business correlation of the nodes corresponding to the subjects is calculated according to the evaluation results, and a key subject set is identified;

[0069] S4, the consensus nodes in the PBFT are determined through the key subject set, and the signed transaction data is subjected to a blockchain consensus through the consensus nodes.

[0070] The embodiment of the application realizes business consensus by selecting key subjects from the subject social network participating subjects through the digital signature technology, realizes business consensus through the key subject set obtained through the business consensus, and subjects the signed transaction data to a blockchain consensus through the consensus nodes. The embodiment of the application realizes consensus through the consensus nodes, simplifies the consensus process, and improves the consensus efficiency. Meanwhile, the consensus nodes in the PBFT are determined through the key subject set, the communication times in the commit stage are reduced compared with the traditional PBFT, and the communication cost is reduced.

[0071] The specific process of the business consensus method for the supply chain traceability business correlation of the embodiment of the application is as shown in Figure 2 .

[0072] The various steps will be described in detail as follows:

[0073] In step S1, the supply chain traceability business relationship data is acquired. The specific implementation process is as follows:

[0074] The supply chain traceability business relationship data mainly includes subject information data and business transaction data. The subject information data includes suppliers, manufacturers, transporters, and distributors, and the business transaction data is the business transaction data between the above-mentioned subjects.

[0075] In step S2, the transaction data submitted by the transaction parties in the supply chain traceability business relationship data is signed through a digital signature technology. The specific implementation process is as follows:

[0076] When the supply chain traceability business generates data on-chain demand, the parties related to the on-chain data use the digital signature technology to sign the submitted transaction data. The digital signature of the directly related or indirectly related subjects of the traceability business relationship data is used to complete the authenticity verification and endorsement of the source of the on-chain data, and all the on-chain data needs to be digitally signed by the corresponding subjects before entering the on-chain transaction pool. In the case where the source of the business data is determined, the cost of fabricating the traceability business data will be greatly increased.

[0077] In step S3, a social network between subjects is constructed based on supply chain traceability business relationship data, a centrality index of the social network is calculated, an entropy method is used to comprehensively evaluate the centrality index of nodes in the social network, a business relevance of a subject corresponding to a node is calculated according to an evaluation result, and a key subject set is identified. The specific implementation process is as follows:

[0078] S301, a social network between subjects is constructed based on supply chain traceability business relationship data, specifically:

[0079] The set of subjects in the supply chain traceability business relationship data is defined as V = {v1, v2, v3, … v n}, for any subject v i ∈V, the historical transaction database of the subject v i is traversed, and if there is a transaction record with other subjects, it is recorded as 1, otherwise it is recorded as 0, and an adjacency matrix between subjects is generated.

[0080]

[0081] Considering the strength of the connection relationship between subjects in the business relationship network, which is reflected as the weight of the edge between nodes in the social network graph, the RFM model is used to calculate the relationship strength between subjects. The relationship strength between any two subjects with transaction records is investigated, and through the three centrality indexes of the latest transaction interval R (the shorter the interval, the higher the relationship strength), the latest transaction frequency F (the more the frequency, the higher the relationship strength), and the latest transaction amount M (the greater the amount, the higher the relationship strength), after normalization, the weights W r , W f , W m are added, and finally the weight of the edge between the two subjects is obtained. The edge weight between subjects v i ,v j is

[0082]

[0083] A weighted adjacency matrix between subjects is constructed from the weight of the transaction relationship between subjects:

[0084]

[0085] A weighted undirected network graph, i.e. a social network, is constructed according to the weighted adjacency matrix, as shown in Figure 3 .

[0086] S302, the centrality index of the social network is calculated, the entropy method is used to comprehensively evaluate the centrality index of the nodes in the social network, the business relevance of the subject corresponding to the node is calculated according to the evaluation result, and the key subject set is identified. Specifically:

[0087] In the social network constituted by the supply chain traceability business relationship data, the higher the centrality of the node is, the higher the business relevance of the node corresponding subject is to a certain extent. There are many centrality indexes for evaluating the centrality of the node, and the four centrality indexes are selected in the embodiment of the application, which reflect the importance of the node in the social network from the association relationship and the position relationship of the node.

[0088] ①Strength centrality of the node

[0089] ②Closeness centrality of the node

[0090] ③Betweenness centrality of the node

[0091] ④Clustering coefficient

[0092] wherein, denotes the strength of the weighted network node, the node v i is connected with the node v j , then otherwise n is the total number of nodes; denotes the shortest path length of the node v i to the node v j , the path length is the number of edges between the two nodes; the node v i is located on the shortest path between the node s and the node t otherwise η st is the number of all shortest paths between the node s and the node t; denotes the number of edges between the node v i and the directly adjacent node.

[0093] The four centrality indexes are comprehensively used, the business relevance of each subject corresponding to the node in the business relationship network is calculated by using the entropy value method, and the key subject is identified.

[0094] ①The centrality index data matrix is obtained after the centrality index data is normalized.

[0095] (positive centrality index)

[0096] (negative centrality index)

[0097]

[0098] ②After the weight P of each data in all centrality index data of the data subject is calculated, ij the information entropy E of each centrality index is calculated.j .

[0099] i = 1…n, j = 1…m

[0100]

[0101] When P ij = 0, define

[0102] wherein x ij is each centrality index data of each subject before normalization; X ij is each centrality index data of each subject after normalization;

[0103] ③Determine the weight w j of each centrality index.

[0104]

[0105] wherein 0≤w j ≤1,

[0106] ④Calculate the node business relevance

[0107]

[0108] R0 is the initial business relevance of each subject node in each period, and the initial business relevance of all subject nodes before the first consensus period is 0.

[0109] In a consensus period T, the first i subjects are selected to form a key subject set according to the business relevance from large to small.

[0110] In step S4, the consensus nodes in the PBFT consensus algorithm are determined by the key subject set, and the signed transaction data is subjected to blockchain consensus by the consensus nodes. The specific implementation process is as follows:

[0111] The nodes corresponding to the key subject set form a consensus node set {N1, N2…Ni}, complete all consensus processes within the consensus period T, and the other nodes are backup nodes. Since the consensus nodes are selected from the subjects with high business relevance, the probability of becoming malicious nodes is low, and the completion of one communication can be considered as completing the consistency protocol. At the same time, in the corresponding consensus period T, the consensus nodes {N1, N2Ni} are non-Bayeville nodes with high probability, so the consensus mechanism can optimize the communication times in the commit phase.

[0112] In the specific implementation process, step S4 further includes updating the consensus nodes. Specifically:

[0113] In the update phase of B-PBFT, B-PBFT needs to complete the update of nodes, the record of malicious nodes, offline nodes and the like. After a certain number of consensus cycles, the node business relevance is iterated with the replacement of the consensus cycle. In the business relationship network, the business relevance of the key node may be significantly higher than that of other nodes. Therefore, when the business relevance of the node reaches a certain threshold, a limiting coefficient needs to be added to limit the growth rate of the business relevance. At the same time, there is a certain probability that the consensus node will be down during the consensus process, so the down node needs to be removed.

[0114] The specific calculation method of the node state update is as follows.

[0115]

[0116] Wherein, α and β are limiting coefficients.

[0117] The following will be described in detail through specific cases:

[0118] Suppose there are three suppliers A, B and C, two manufacturers D and E, and two transporters F and G in a supply chain system. Their subject numbers are v1, v2…v7 respectively. In a consensus cycle, W r , W f , W m are -0.2, 0.9, 0.3 respectively, and the business data records among them are as follows:

[0119] A supplier sells a plurality of parts to D, R=10, F=20, M=30, A and D endorse the transaction to generate transaction information Tx 14 , and

[0120] B supplier sells a plurality of parts to D, R=8, F=16, M=24, B and D endorse the transaction to generate transaction information Tx 24 , and

[0121] C supplier sells a plurality of parts to B, R=2, F=10, M=16, C and B endorse the transaction to generate transaction information Tx 32 , and

[0122] A supplier sells a plurality of parts to E, R=6, F=14, M=26, A and E endorse the transaction to generate transaction information Tx 15 , and

[0123] B supplier sells a plurality of parts to E, R=2, F=16, M=28, B and E endorse the transaction to generate transaction information Tx25 Similarly,

[0124] Supplier C sells multiple batches of parts to supplier D, with R=4, F=14, and M=16. Suppliers C and D endorse the transactions, generating transaction information Tx. 34 Similarly,

[0125] Transporter F transports parts from supplier A. R=4, F=2, M=10. Both F and A endorse the transaction, generating transaction information Tx. 61 Similarly,

[0126] Transporter F delivers cars from manufacturer D to the dealer. R=6, F=6, M=20. F and D endorse the transaction, generating transaction information Tx. 46 Similarly,

[0127] Transporter G delivers E's car to the dealer. R=4, F=4, M=12. G and E endorse the transaction, generating transaction information Tx. 57 Similarly,

[0128] Based on the above data, complete the operation process of the consensus system for putting business data on the blockchain.

[0129] Step 1: Consensus on Authenticity

[0130] By having both parties in a transaction sign the submitted transaction data using digital signature technology, the authenticity of the source of the data uploaded to the blockchain is verified and endorsed. All data uploaded to the blockchain must be digitally signed by the corresponding entity before it can enter the transaction pool to be uploaded to the blockchain.

[0131] Process Two: Business Consensus

[0132] To achieve business consensus, key stakeholders are selected from the participants in the social network used for tracing the source of data. Unlike traditional consensus mechanisms, consensus nodes differ in their approach to non-fungible data, necessitating the selection of key stakeholders within the platform to reach a consensus. This process also aims to reach a consensus on privacy and interests among the parties involved in on-chain exchanges, preventing disputes between stakeholders during the tracing process. The method for selecting key stakeholders is as follows:

[0133] ①Construction of the main node social network

[0134] The weighted adjacency matrix between subjects is:

[0135]

[0136] Intersubjective social networks are Figure 4 As shown.

[0137] 2. Key node related centrality index calculation

[0138] The strength centrality of the node, the proximity centrality of the node, the betweenness centrality of the node and the clustering coefficient are calculated, and the centrality indexes of each node are as shown in the following table:

[0139] Table 1 Numerical values of each centrality index of the subject

[0140]

[0141] 3. Entropy method is used to calculate the business relevance of each subject

[0142] The normalized (and non-negative translation) data of each subject centrality index, the entropy weight and the business relevance of the subject are calculated, as shown in the following table:

[0143] Table 2 Normalized data of each centrality index of the subject

[0144]

[0145] Table 3 Information entropy, utility and weight of each centrality index

[0146]

[0147] Table 4 Business relevance of each subject

[0148] Body [R (vi) ]]> v5 0.7605 v4 0.5789 v2 0.5060 v1 0.4844 v3 0.3577 v7 0.0417 v6 0.0200

[0149] 3. Arranged according to the business relevance, the key subjects are selected as {v5, v4, v2, v1}

[0150] Process three: Consensus of blockchain data consistency

[0151] The nodes corresponding to the selected key subjects are used as consensus nodes to complete the improved PBFT consensus, and the consistency of the on-chain data is ensured through the stages of pre-preparation, preparation, commitment and reply. Since the consensus nodes are selected by the business-related subjects, the probability of becoming malicious nodes is low. A restriction coefficient α and β is added to the business relevance in the update stage, and when the node business relevance reaches the threshold value, the increase speed will be greatly limited, and the down nodes are limited to participate in the next round of consensus. According to the node state update formula, the business relevance of the node is updated.

[0152] The embodiment of the application also provides a computer device, comprising:

[0153] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising a business consensus method for supply chain traceability business correlation as described above.

[0154] In summary, compared with the prior art, the following beneficial effects are achieved:

[0155] 1、The embodiment of the present application realizes business consensus by selecting key subjects from the subject social network of the traceability subject through digital signature technology, determines consensus nodes through the key subject set obtained through business consensus, and performs blockchain consensus on the signed transaction data by the consensus nodes. The embodiment of the present application simplifies the consensus process and improves the consensus efficiency by proxying consensus through the consensus nodes. At the same time, the consensus nodes in PBFT are determined through the key subject set, which reduces the communication times in the commit stage and reduces the communication cost compared with the traditional PBFT.

[0156] 2、The social network constructed through transactions and the definition and calculation of business correlation can effectively select appropriate key subjects that can authenticate the on-chain transaction data, and can authenticate the legality and compliance of the authenticity, interest correlation and privacy correlation of the on-chain transaction data. The feasibility of traceability is improved, and the disputes involving the privacy interests of all parties in the traceability information sharing are solved. It is ensured that the traceability platform can be legally and compliantly operated under the dynamic alliance, and the privacy and interests of all parties are protected.

[0157] 3、The embodiment of the present application adds the subject attribute centrality index of business correlation and the node state updating mechanism to the traditional PBFT algorithm fault tolerance problem, so that the business consensus method has higher fault tolerance. Under the guarantee of high business correlation, the opportunity of Byzantine node participating in consensus is greatly reduced, thereby increasing the fault tolerance of the business consensus method.

[0158] It should be noted that, in this text, relational terms such as first and second are used merely to distinguish one entity or action from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0159] The above examples are only used to illustrate the technical solutions of the present application, but not to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those ordinarily skilled in the art should understand: the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A business consensus method for supply chain traceability business relevance, characterized in that, The method comprises the following steps: S1, obtaining supply chain traceability business relationship data; S2, signing the transaction data submitted by the two parties in the supply chain traceability business relationship data through digital signature technology; S3, constructing a social network between subjects based on the supply chain traceability business relationship data; calculating the centrality index of the social network, using the entropy method to comprehensively evaluate the centrality index of the nodes in the social network, calculating the business relevance of the nodes corresponding to the subjects according to the evaluation results, and identifying the key subject set; S4, determining the consensus nodes in PBFT through the key subject set, and performing blockchain consensus on the signed transaction data through the consensus nodes; The method for constructing a social network between subjects based on supply chain traceability business relationship data comprises the following steps: According to the supply chain traceability business relationship data, an adjacency matrix between subjects is generated; According to the latest transaction time interval, the latest transaction frequency and the latest transaction amount between the two subjects in the supply chain traceability business relationship data, the weight of the edge between the two subjects is determined; According to the adjacency matrix between subjects and the weight of the edge between the two subjects, a weighted adjacency matrix between subjects is generated; According to the weighted adjacency matrix, a social network is constructed.

2. The business consensus method for supply chain traceability business relevance of claim 1, wherein, The method for determining the weight of the edge between the two subjects according to the latest transaction time interval, the latest transaction frequency and the latest transaction amount between the two subjects in the supply chain traceability business relationship data comprises the following steps: According to the relationship strength between any two subjects existing transaction records, through its recent transaction interval R, the number of recent transactions F, the recent transaction amount M three centrality indicators, after normalization, weighted W r , W f , W m , get the weight of the edge between two subjects, the edge weight of subject v i ,v j v 3. The business consensus method for supply chain traceability business relevance of any one of claims 1-2, wherein, The centrality index includes strength centrality, closeness centrality, betweenness centrality and clustering coefficient.

4. The business consensus method for supply chain traceability business relevance of claim 3, wherein, The calculation formula of the strength centrality, closeness centrality, betweenness centrality and clustering coefficient comprises: Strength centrality of a node Proximity centrality of nodes Betweenness centrality of a node Clustering coefficient wherein, denotes the strength of a weighted network node, node v i is connected to node v j , then otherwise n is the total number of nodes; denotes the shortest path length from node v i to node v j , the path length being the number of edges connecting two nodes; node v i is on the shortest path from node s to node t otherwise η st is the number of shortest paths between node s and node t; denotes the number of edges between node v i and its directly adjacent nodes.

5. The business consensus method for supply chain traceability business relevance of any one of claims 1-2, wherein, Before performing the comprehensive evaluation of the centrality index of the nodes in the social network using the entropy method, the business consensus method further comprises: The centrality index data is normalized, specifically: Wherein, x ij is each centrality index data of each subject before normalization; X ij is each centrality index data of each subject after normalization.

6. The business consensus method for supply chain traceability business relevance of claim 5, wherein, The method for comprehensively evaluating the centrality index of the nodes in the social network using the entropy method, calculating the business relevance of the nodes corresponding to the subjects according to the evaluation results, and identifying the key subject set comprises The normalized centrality index data is calculated in the weight P of all centrality index data in the data body ij After that, the information entropy E of each centrality index is calculated j Specifically, When P ij = 0, define According to the information entropy of each centrality indicator, the weight w of each centrality indicator is determined j The calculation formula is: wherein 0≤w j ≤1, According to the business relevance R(vi) of the nodes: Wherein, R0 is the initial business relevance of the subject node in each period, and the initial business relevance of all subject nodes before the first consensus period is 0. In a consensus period, the first i subjects are selected to form a key subject set in descending order of business relevance.

7. The business consensus method for supply chain traceability business relevance of any one of claims 1-2, wherein, The business consensus method further comprises: The consensus nodes are updated through a node state update formula, and the node state update formula is as follows: Wherein, α and β are restriction coefficients.

8. A computer device, comprising: One or more processors, memories, and one or more programs, wherein the one or more programs are stored in the memories and configured to be executed by the one or more processors, the programs comprising a business consensus method for supply chain traceability business relevance as claimed in any one of claims 1-7. ​

Images