Alliance block chain consensus method based on differential evolution algorithm and credit grouping mechanism

Abstract

The invention relates to the field of block chain technology and supply chain management, in particular to an alliance block chain consensus method based on a differential evolution algorithm and credit grouping mechanism optimization. The invention provides an improved consensus method, and aims to solve the problems of low consensus efficiency and high communication overhead of a traditional PBFT (Practical Byzantine Fault Toolness) algorithm in a large-scale network. According to the method, firstly, a plurality of nodes in a supply chain network are pre-selected through a differential evolution algorithm, a batch of reliable node sets are selected, and the consensus scale is reduced; and then, dynamically grouping the pre-selected nodes by adopting a credit grouping mechanism to ensure that the high-reliability nodes participate in the consensus process, thereby improving the consensus efficiency. And finally, the optimized PBFT algorithm reduces the communication overhead in the consensus process by simplifying a submission stage into a feedback stage. Experimental results show that the consensus efficiency of the alliance block chain can be effectively improved, the communication complexity is reduced, and the method is suitable for data sharing application in a large-scale supply chain network.

Description

Technical Field

[0001] The present invention relates to the fields of blockchain technology and supply chain management, and in particular to a consortium blockchain consensus method based on a differential evolution (DE) algorithm and a credit grouping mechanism. Background Art

[0002] With the rapid advancement of data mining and machine learning technologies, the importance of data in supply chain management has become increasingly prominent. However, the global supply chain has led to an increase in nodes, complex networks, and wide geographical distribution, which has made supply chain management and supervision challenging, resulting in data silos and hindering effective information sharing and full-chain data traceability. Traditional data sharing methods rely on centralized intermediaries, are susceptible to single point failures and security risks, and are difficult to meet the supply chain's needs for data security and transparency.

[0003] As a decentralized data management method, blockchain technology provides a new solution to the data sharing problem in the supply chain with its characteristics of non-tamperability and multi-party trust. However, current blockchain consensus algorithms, such as the Practical Byzantine Fault Tolerance (PBFT) algorithm, still face the problems of low efficiency and high communication overhead in large-scale networks. Although some studies have improved the PBFT algorithm, such as by introducing a reputation mechanism and hierarchical communication to optimize the consensus process, these improvements often bring about problems of system performance degradation and insufficient scalability while improving security. Therefore, there is an urgent need for an improved algorithm that can improve consensus efficiency and reduce communication costs to meet the actual needs of the supply chain alliance blockchain. Summary of the invention

[0004] The purpose of this invention is to provide an improved consensus algorithm based on differential evolution (DE) algorithm and credit grouping mechanism, which is used to optimize the application of alliance blockchain in supply chain data management, improve the consensus efficiency of the system, reduce communication overhead, and enhance the scalability and security of the system.

[0005] To achieve the above objectives, the present invention proposes a new consensus method, comprising the following steps:

[0006] (1) Node pre-selection: The nodes in the supply chain network are screened through the differential evolution algorithm, and reliable nodes are selected for the consensus process, thereby reducing the consensus scale and improving the consensus efficiency.

[0007] (2) Credit grouping mechanism: The pre-selected node set is grouped according to its credit score, and the master node and consensus node are dynamically selected to ensure that the nodes participating in the consensus have high reliability and prevent attacks from malicious nodes.

[0008] (3) Optimize the consensus process: Based on the traditional PBFT algorithm, simplify the submission phase process, use the feedback phase to replace the submission phase, reduce the number of communications between nodes, and thus improve the overall consensus efficiency.

[0009] The beneficial effect of the present invention is that, through the combination of the differential evolution algorithm and the credit grouping mechanism, the performance of the traditional PBFT consensus algorithm is effectively optimized, making the application of blockchain in large-scale supply chain networks more feasible and efficient, while ensuring the security and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 :System architecture diagram of DE-CGPBFT consensus algorithm

[0011] Figure 2 :Node pre-selection flow chart based on differential evolution algorithm

[0012] Figure 3 :Dynamic grouping flow chart of credit grouping mechanism

[0013] Figure 4 : Optimized PBFT consensus process flow chart DETAILED DESCRIPTION

[0014] The present invention specifically includes the following 8 implementation steps.

[0015] (1) System initialization Setup:

[0016] (a) Initialize the parameters of the differential evolution algorithm, including:

[0017] Population size: Set the initial population size to 50 nodes;

[0018] Maximum number of iterations: set to 200 iterations to ensure the convergence of the algorithm;

[0019] Crossover rate: set to 0.9 to ensure the diversity of new individuals;

[0020] Mutation rate: set to 0.2 to enhance the global search capability of the algorithm;

[0021] (b) Initialize node credit score: Set the initial credit score of all nodes to 100.

[0022] (2) Node-Preselection:

[0023] (a) Set the objective function: ;

[0024] Among them, da represents delivery accuracy, ofr represents order fulfillment rate, tc represents transportation cost, and pcu represents production capacity utilization rate;

[0025] (b) Differential mutation: According to the differential evolution algorithm, differential mutation operations are performed on individuals in the population in each iteration to generate new candidate solutions;

[0026] (c) Crossover operation: cross the mutated candidate solutions with individuals in the original population to form a new population of individuals;

[0027] (d) Selection operation: Through the selection operation, the individuals with the best performance are retained to enter the next generation of population, and finally the optimal node set is determined.

[0028] (3) Credit-Calculation:

[0029] (a) Calculate the credit score of each node according to the credit score formula: ;

[0030] Among them, Credit i Represents the credit score of node i, Score i Indicates the performance score of node i during the formula process. The initial performance score of all nodes is 100. If the node is offline and does not participate in the consensus, the node score is reduced by one to reflect the score decay caused by the passage of time. If successful, some nodes participating in the consensus will receive points. Indicates the response delay time of node i during the consensus process; in order to severely punish malicious nodes, the indicator level 2 is used to set the scoring coefficient, Score i The calculation formula is:

[0031] Among them, Score i is the performance score of node i, W i Represents the successful consensus of node i, F i represents failure; if consensus succeeds, W i The value is 1, F i The value is 0; if unsuccessful, W i The value is 0, F i The value is 1;

[0032] (b) After each round of consensus, the node’s credit score is updated and the node’s behavior during the consensus process (such as response speed and accuracy) is recorded to adjust the credit score for the next round.

[0033] (4) Node-Grouping:

[0034] Based on the most recently calculated credit score, nodes are divided into consensus node set and candidate node set:

[0035] (a) The 20% nodes with the highest credit scores are assigned to the consensus node set, and these nodes will participate in the next consensus process;

[0036] (b) The remaining 80% of the nodes enter the candidate node set as backup nodes.

[0037] (5) Primary-Node-Selection:

[0038] (a) At the beginning of each round of consensus, the node with the highest credit score is selected from the consensus node set as the master node, which is responsible for initiating the consensus process and coordinating the work of other nodes;

[0039] (b) The master node election strategy is: ρ = v mod cn, where ρ is the master node number, v is the view number, and cn is the number of consensus nodes.

[0040] (6) Consensus-Execution:

[0041] (a) The master node broadcasts a consensus request to the consensus node set, including the transaction information to be verified and the relevant parameters of the consensus process;

[0042] (b) After receiving the request, each node in the consensus node set verifies the transaction information and generates a feedback message;

[0043] (c) After receiving no less than 2F+1 feedback messages, the master node considers the consensus process complete and generates a new block.

[0044] (7) Result-Confirmation:

[0045] (a) The master node broadcasts the consensus result in the new block to all nodes, updating the blockchain;

[0046] (b) After consensus is achieved, all nodes synchronize the blockchain and are ready to participate in the next round of consensus.

[0047] (8) Credit-Update:

[0048] (a) After each round of consensus, the credit score is updated based on the node’s performance (such as response speed, participation, etc.);

[0049] (b) Candidate nodes with excellent performance will be promoted to the consensus node set, while consensus nodes with poor performance may be downgraded to the candidate node set.

Claims

1. A consortium blockchain consensus method based on differential evolution algorithm and credit grouping mechanism, characterized by: (1) Pre-select multiple nodes in the supply chain network through the differential evolution algorithm (DE), screen out a group of reliable nodes for the consensus process, reduce the consensus scale, and improve the consensus efficiency; (2) Calculate the credit score of each node based on its historical performance and response speed, and dynamically group the nodes into consensus node sets and candidate node sets based on the score; (3) Based on the traditional PBFT (Practical Byzantine Fault Tolerance) algorithm, the submission phase is simplified and the feedback phase is used instead of the submission phase to reduce communication overhead; (4) This method can effectively improve the application efficiency of blockchain in supply chain management, reduce consensus delays, and enhance the scalability and security of the system.

2. The alliance blockchain consensus method based on differential evolution algorithm and credit grouping mechanism according to claim 1 comprises the following steps: (1) System initialization Setup: Set system parameters, including the initial population, crossover rate, mutation rate, and fitness function of the differential evolution algorithm, and initialize the node credit score; (2) Node Preselection: Based on the differential evolution algorithm, select the nodes with the best performance, calculate the objective function value, and iterate the optimization to finally determine the set of nodes participating in the consensus. (3) Credit-Calculation: Based on the historical performance and response speed of each node, the formula Calculate the node's credit score; (4) Node-Grouping: Based on the credit score, nodes are divided into a consensus node set and a candidate node set. High-scoring nodes are used as consensus nodes, and low-scoring nodes are used as candidate nodes. (5) Primary-Node-Selection: The node with the highest credit score is selected from the consensus node set as the primary node. The primary node is responsible for initiating the consensus process and managing data synchronization and verification; (6) Consensus-Execution: The master node broadcasts the consensus request to the consensus node set, the nodes verify each other, and complete the consensus in the feedback phase; (7) Result-Confirmation: After receiving a sufficient number of feedback messages, the master node generates the final consensus result and records it in the blockchain; (8) Credit-Update: After each round of consensus is completed, the credit score is updated based on the performance of the node, and the node grouping status is dynamically adjusted.

3. The alliance blockchain consensus method based on differential evolution algorithm and credit grouping mechanism according to claim 2 is characterized in that: The specific algorithm implementation steps are as follows: (1) System initialization Setup: (a) Initialize the parameters of the differential evolution algorithm, including: Population size: Set the initial population size to 50 nodes; Maximum number of iterations: set to 200 iterations to ensure the convergence of the algorithm; Crossover rate: set to 0.9 to ensure the diversity of new individuals; Mutation rate: set to 0.2 to enhance the global search capability of the algorithm; (b) Initialize node credit score: set the initial credit score of all nodes to 100 points; (2) Node-Preselection: (a) Set the objective function: ; Among them, da represents delivery accuracy, ofr represents order fulfillment rate, tc represents transportation cost, and pcu represents production capacity utilization rate; (b) Differential mutation: According to the differential evolution algorithm, differential mutation operations are performed on individuals in the population in each iteration to generate new candidate solutions; (c) Crossover operation: cross the mutated candidate solutions with individuals in the original population to form a new population of individuals; (d) Selection operation: Through the selection operation, the best performing individuals are retained to enter the next generation population, and the optimal node set is finally determined; (3) Credit-Calculation: (a) Calculate the credit score of each node according to the credit score formula: ; Among them, Crediti represents the credit score of node i, Scorei represents the performance score of node i in the formula process, and the initial performance score of all nodes is 100. If the node is offline and does not participate in the consensus, the node score is reduced by one to reflect the score decay caused by the passage of time; if successful, some nodes participating in the consensus will receive points; It represents the response delay time of node i in the consensus process. In order to severely punish malicious nodes, the index level 2 is used to set the scoring coefficient. The calculation formula of Scorei is: ; Among them, Scorei is the performance score of node i, Wi represents the successful consensus of node i, and Fi represents failure; if the consensus is successful, the Wi value is 1 and the Fi value is 0; if it is unsuccessful, the Wi value is 0 and the Fi value is 1; (b) After each round of consensus, the node’s credit score is updated and the node’s behavior during the consensus process (such as response speed and accuracy) is recorded to adjust the credit score for the next round; (4) Node-Grouping: Based on the most recently calculated credit score, nodes are divided into consensus node set and candidate node set: (a) The 20% nodes with the highest credit scores are assigned to the consensus node set, and these nodes will participate in the next consensus process; (b) The remaining 80% of nodes enter the candidate node set as backup nodes; (5) Primary-Node-Selection: (a) At the beginning of each round of consensus, the node with the highest credit score is selected from the consensus node set as the master node, which is responsible for initiating the consensus process and coordinating the work of other nodes; (b) The master node election strategy is: ρ = v mod cn, where ρ is the master node number, v is the view number, and cn is the number of consensus nodes; (6) Consensus-Execution: (a) The master node broadcasts a consensus request to the consensus node set, including the transaction information to be verified and the relevant parameters of the consensus process; (b) After receiving the request, each node in the consensus node set verifies the transaction information and generates a feedback message; (c) After receiving no less than 2F+1 feedback messages, the master node considers the consensus process complete and generates a new block; (7) Result-Confirmation: (a) The master node broadcasts the consensus result in the new block to all nodes, updating the blockchain; (b) After consensus is completed, all nodes synchronize the blockchain and prepare to participate in the next round of consensus; (8) Credit-Update: (a) After each round of consensus, the credit score is updated based on the node’s performance (such as response speed, participation, etc.); (b) Candidate nodes with excellent performance will be promoted to the consensus node set, while consensus nodes with poor performance may be downgraded to the candidate node set.

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