CN114978684B - PBFT consensus method based on improved condensation hierarchical clustering - Google Patents

PBFT consensus method based on improved condensation hierarchical clustering Download PDF

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CN114978684B
CN114978684B CN202210555917.2A CN202210555917A CN114978684B CN 114978684 B CN114978684 B CN 114978684B CN 202210555917 A CN202210555917 A CN 202210555917A CN 114978684 B CN114978684 B CN 114978684B
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李正权
覃瑞卿
陆雅雯
谭立容
顾斌
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Abstract

The invention discloses a PBFT consensus method based on improved aggregation hierarchical clustering, and belongs to the technical field of block chains. Firstly, performing target division and clustering on all network consensus nodes by utilizing an improved aggregation hierarchical clustering algorithm, and forming a cluster at the lower layer of a block chain link point consensus hierarchical model; then, the slave nodes in each cluster reach consensus through a three-stage PBFT consensus algorithm, and a consensus master node of each cluster is promoted; finally, all the consensus master nodes form an upper consensus set of the block chain link point consensus hierarchical model, and all the nodes reach message agreement through a three-stage PBFT consensus algorithm. The method effectively overcomes the defects of the traditional clustering algorithm based on the partition, improves the problems of poor scalability, weak adaptability, difficult evaluation of the clustering effect and the like of the aggregation hierarchical clustering method, further improves the clustering accuracy and the system consensus efficiency, and can be flexibly applied to node consensus scenes of various scales.

Description

PBFT consensus method based on improved condensation hierarchical clustering
Technical Field
The invention relates to a PBFT consensus method based on improved aggregation hierarchical clustering, and belongs to the technical field of block chains.
Background
Blockchain technology is a database technology that combines distributed storage, consensus mechanisms, smart Contracts (SC), and cryptography. The block chain technology in the broad sense is a brand new decentralised infrastructure and distributed computing paradigm that uses encryption chain block structures to verify and store data, distributed node consensus algorithms to generate and update data, and automated script code programming and operating data. Essentially, the data information node is a series of data blocks which are arranged in time sequence and are mutually connected by utilizing cryptography, and the whole network block data information node has the characteristics of decentralization, time sequence irreversibility, consensus consistency, programmability and high safety reliability. Nodes in a blockchain are typically referred to as computers or servers in the blockchain network, that is, any computer or server (including cell phones, etc.) connected to the blockchain network is referred to as a node.
The consensus layer in the block chain architecture is one of the cores of the block chain technology, and in a block chain decentralization system with highly dispersed decision weights, a consensus algorithm can enable each node to achieve effective consensus on block data, so that the problem of message consistency of the consensus node is essentially solved, and the authenticity, safety and effectiveness of grain condition data can be ensured.
Blockchains include private chains, public chains, and federated chains. A federated chain is a blockchain that is jointly managed by multiple authorized organizations, and each node corresponds to a presentity organization, it has the following two advantages: first, the very limited number of nodes makes the transaction extremely fast, and consensus is easy to reach; second, data is limited to internal institutions and users only having access. Therefore, based on the characteristics of the alliance chain, the alliance chain technology is deeply fused in the future fields such as grain security, so that data counterfeiting and malicious tampering are prevented, and data security is ensured.
In the alliance chain consensus scene, the small-scale consensus cluster formed by the alliance internal mechanism and the users thereof adopts the PBFT consensus algorithm with higher efficiency, and all consensus nodes participate in the consensus process, thereby ensuring the accuracy and consistency of the information, such as the application of HyperLedger. However, because the view switching frequency of the conventional PBFT consensus algorithm and the communication complexity generated in the consensus process contain a secondary term, when facing to a large-scale network consensus node under the lead of a allied institution, the algorithm can quickly generate huge communication transmission quantity, so that the communication consumption cost is greatly increased, and the PBFT consensus algorithm is not universally applicable. Therefore, the consensus process for how to improve the PBFT consensus algorithm to optimize a large number of network consensus nodes is a future research hotspot.
Patent CN110113388A proposes a scheme of a PBFT consensus mechanism based on an improved K-means algorithm, which firstly adopts the K-means algorithm to divide consensus nodes in a blockchain system into a plurality of clusters to form sub-consensus clusters, and then forms a backbone consensus cluster by cluster center nodes in the clusters, so that the traditional PBFT global decentralization consensus is improved to two decentralization consensus within a small range. Although this scheme improves consensus efficiency to some extent and reduces the number of communications, the following drawbacks still exist: firstly, an initial cluster center selection problem; second, the influence of outlier data; third, K-medoids are not suitable for high-dimensional data set clustering, and these disadvantages result in poor robustness and poor clustering accuracy of the scheme, so that the consensus effect is poor, and in addition, the scheme cannot be applied to a large number of node consensus scenes under the leadership of similar grain alliance chain institutions.
Disclosure of Invention
The method aims to solve the problems that the existing consensus method is low in clustering accuracy, poor in consensus effect, poor in applicability and the like. The invention provides a PBFT consensus method based on improved aggregation hierarchical clustering, which comprises the following steps:
a first object of the present invention is to provide a blockchain system identification method based on improved condensed hierarchical clustering, the method comprising:
step one: performing target division and clustering on all the consensus nodes in the block chain system by using an improved aggregation hierarchical clustering method to form K clustering clusters, wherein each clustering cluster comprises intra-cluster consensus master nodes and intra-cluster slave nodes;
step two: the slave nodes in the clusters reach consensus through a three-stage PBFT consensus algorithm, and the master node in the cluster consensus of each cluster is promoted;
step three: all intra-cluster consensus master nodes form a new consensus set together, and three-stage PBFT consensus of all intra-cluster consensus master nodes among clusters is completed in the set;
step four: the intra-cluster consensus master node respectively sends final consensus messages to the respective intra-cluster slave nodes, and the intra-cluster slave nodes package the messages and perform uplink operation to complete synchronous update of local block data;
the improved aggregation hierarchical clustering method is characterized in that class cluster extraction requirements are set, and class clusters are extracted according to the class cluster extraction requirements while clustering is performed in the execution process; clustering the consensus nodes by using the same similarity measure; and adding a feedback mechanism with class information, and regarding the similarity measurement between the common node pairs as a vector, wherein the vector comprises direction information and distance information.
Optionally, the improved condensation hierarchical clustering method includes:
step 11: taking each consensus node in each block chain system as a single cluster, wherein N clusters are formed in total;
step 12: calculating the similarity measurement between clusters according to the inter-cluster distance measurement criterion
Figure BDA0003654965920000021
Step 13: comparing the inter-cluster similarity measures
Figure BDA0003654965920000022
And the lowest similarity measure between the mergeable class clusters
Figure BDA0003654965920000023
If it meets->
Figure BDA0003654965920000024
And->
Figure BDA0003654965920000025
The class clusters with the highest similarity measure are combined into a new class cluster, otherwise, the lowest similarity among the current combinable class clusters is reduced;
wherein: alpha is constant, 0<α<1；
Figure BDA0003654965920000026
An included angle theta is 45 degrees with the positive coordinate axis;
step 14: and (3) repeating the steps 11 to 13 to reach the number K of the target clusters which are satisfied by the user.
Optionally, the step two adopts a master node election formula to elect the common master node in the cluster, and the master node election formula is as follows:
p＝v mod|R|
wherein: p is the master node number, v is the view number, |r| is the total number of nodes in the cluster, |r|=3f+1, and f is the number of malicious nodes.
Optionally, the step of the slave node in the cluster in the second step achieving consensus through a three-stage PBFT consensus algorithm includes:
step 21: the client c initiates a transaction request, the common master node in each cluster receives a request transaction block message m packaged by the system, the request transaction block message m is assigned with a number n, and a pre-preparation message is broadcast to slave nodes in each cluster, wherein the format of the pre-preparation message is as follows:
<<PRE-PREPARE，v，h，t，n，D(m)>s i ，m>
where i represents the node, h is the height of the current block, t is the unique identification timestamp, D (m) is the digital digest of the request transaction block message m, s i Receiving a digital signature of the message for the i-th node;
step 22: the slave nodes in the cluster verify the received pre-preparation message from the common master node in the cluster, if the verification is passed, the common master node enters a preparation stage, and each cluster slave node broadcasts the preparation message, wherein the format of the preparation message is as follows:
<<PREPARE，v，h，n，i，D(m)>s i >
when each cluster receives 2f from different node 2 +1 prepare messages, marking the verification as passed, then entering the validation phase, where f 2 Representing the number of Byzantine malicious nodes in a cluster from a node set;
step 23: broadcasting a confirmation message from the node in the cluster to all nodes except the node in the cluster, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when the slave node in the cluster receives 2f from different nodes 2 When +1 acknowledgement message, it indicates that verification is passed, and marks that the slave node consensus phase in the cluster is completedThe initial consensus message is received by the consensus master node in each cluster.
Optionally, the third step includes:
step 31: all intra-cluster consensus master nodes form an inter-cluster master node consensus set;
step 32: in the cluster master node consensus set, the master node continuously sends preparation information to other master nodes and verifies the preparation information; if the verification is passed, the consensus enters a preparation stage, and the inter-cluster master node broadcasts a preparation message with the following format:
<<PREPARE，v，h，n，D(m)>s i ，m>
when the master node in the set receives 2f from different nodes 1 +1 prepare messages, marking the verification as passed, then entering the validation phase, where f 1 Representing the number of Bayesian malicious nodes in the cluster master node consensus set;
step 33: each master node in the set broadcasts a confirmation message to all nodes except the master node, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when a master node in the set receives 2f from a different master node 1 And when +1 confirmation message is received, the verification is passed, and meanwhile, the completion of the inter-cluster master node consensus stage is marked, and all master nodes receive the final consensus message.
A second object of the present invention is to provide a blockchain system based on improved condensed hierarchical clustering, including a plurality of consensus nodes, the blockchain system employing an improved condensed hierarchical clustering method to construct a blockchain link point consensus hierarchical model, the model including: a slave node PBFT consensus layer in the cluster and a master node PBFT consensus layer among the clusters;
the block chain system adopts the improved aggregation hierarchical clustering method to cluster all consensus nodes in the system to form K clustering clusters, wherein each clustering cluster comprises intra-cluster consensus master nodes and intra-cluster slave nodes, the intra-cluster slave nodes form an intra-cluster slave node PBFT consensus layer, and the intra-cluster consensus master nodes form inter-cluster master node PBFT consensus layers;
the slave nodes in the cluster in the slave node PBFT consensus layer reach consensus through a three-stage PBFT consensus algorithm, and the master node in the cluster consensus of each cluster is promoted; all intra-cluster consensus master nodes in the inter-cluster master node PBFT consensus layer form a new consensus set together, and three-stage PBFT consensus of all intra-cluster consensus master nodes among clusters is completed in the set;
the improved aggregation hierarchical clustering method is characterized in that class cluster extraction requirements are set, and class clusters are extracted according to extraction requirements preset by a user while clustering is performed in the execution process; clustering the consensus nodes by using the same similarity measure; and adding a feedback mechanism with class information, and regarding the similarity measurement between the common node pairs as a vector, wherein the vector comprises direction information and distance information.
Optionally, the improved condensation hierarchical clustering method includes:
step 11: taking each consensus node in each block chain system as a single cluster, wherein N clusters are formed in total;
step 12: calculating the similarity measurement between clusters according to the inter-cluster distance measurement criterion
Figure BDA0003654965920000041
Step 13: comparing the inter-cluster similarity measures
Figure BDA0003654965920000042
And the lowest similarity measure between the mergeable class clusters
Figure BDA0003654965920000043
If it meets->
Figure BDA0003654965920000044
And->
Figure BDA0003654965920000045
The class clusters with the highest similarity measure are combined into a new class cluster, otherwise, the class clusters are reducedThe lowest similarity among the current combinable class clusters is low;
wherein: alpha is constant, 0<α<1；
Figure BDA0003654965920000046
An included angle theta between the similarity and the positive coordinate axis is 45 degrees;
step 14: and (3) repeating the steps 11 to 13 to reach the number K of the target clusters which are satisfied by the user.
Optionally, in the slave node PBFT consensus layer in the cluster, a master node election formula is adopted to elect the master node in the cluster, and the master node election formula is as follows:
p＝vmod|R|
wherein: p is the master node number, v is the view number, |r| is the total number of nodes in the cluster, |r|=3f+1, and f is the number of malicious nodes.
Optionally, the intra-cluster slave nodes in the intra-cluster slave node PBFT consensus layer reach consensus through a three-stage PBFT consensus algorithm, including:
step 21: the client c initiates a transaction request, the common master node in each cluster receives a request transaction block message m packaged by the system, the request transaction block message m is assigned with a number n, and a pre-preparation message is broadcast to slave nodes in each cluster, wherein the format of the pre-preparation message is as follows:
<<PRE-PREPARE，v，h，t，n，D(m)>s i ，m>
where i represents the node, h is the height of the current block, t is the unique identification timestamp, D (m) is the digital digest of the request transaction block message m, s i Receiving a digital signature of the message for the i-th node;
step 22: the slave nodes in the cluster verify the received pre-preparation message from the common master node in the cluster, if the verification is passed, the common master node enters a preparation stage, and each cluster slave node broadcasts the preparation message, wherein the format of the preparation message is as follows:
<<PREPARE，v，h，n，i，D(m)>s i >
when each cluster receives 2f from different node 2 +1 message ready, flagUpon verification, a validation phase is entered wherein f 2 Representing the number of Byzantine malicious nodes in a cluster from a node set;
step 23: broadcasting a confirmation message from the node in the cluster to all nodes except the node in the cluster, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when the slave node in the cluster receives 2f from different nodes 2 And when +1 confirmation message, the verification is passed, and meanwhile, the completion of the intra-cluster slave node consensus stage is marked, and each intra-cluster consensus master node receives the initial consensus message.
Optionally, all intra-cluster consensus master nodes in the inter-cluster master node PBFT consensus layer form a new consensus set together, and the process of completing three-stage PBFT consensus of all intra-cluster consensus master nodes in the cluster includes:
step 31: all intra-cluster consensus master nodes form an inter-cluster master node consensus set;
step 32: in the cluster master node consensus set, the master node continuously sends preparation information to other master nodes and verifies the preparation information; if the verification is passed, the consensus enters a preparation stage, and the inter-cluster master node broadcasts a preparation message with the following format:
<<PREPARE，v，h，n，D(m)>s i ，m>
when the master node in the set receives 2f from different nodes 1 +1 prepare messages, marking the verification as passed, then entering the validation phase, where f 1 Representing the number of Bayesian malicious nodes in the cluster master node consensus set;
step 33: each master node in the set broadcasts a confirmation message to all nodes except the master node, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when a master node in the set receives 2f from a different master node 1 When +1 confirmation message, the verification is passed, and the completion of the inter-cluster master node consensus stage is marked, and all the master nodes receiveAnd finally, the consensus message is consistent.
The invention has the beneficial effects that:
according to the PBFT consensus method based on the improved aggregation hierarchical clustering, firstly, target division and clustering are carried out on all network consensus nodes by utilizing an aggregation hierarchical clustering algorithm, and K clustering clusters are formed at the lower layer of a block chain link point consensus hierarchical model; then, the slave nodes in each cluster reach consensus through a three-stage PBFT consensus algorithm, and a consensus master node of each cluster is promoted; finally, all the consensus master nodes form an upper consensus set of the block chain link point consensus hierarchical model, and all the nodes reach message agreement through a three-stage PBFT consensus algorithm.
Compared with the existing clustering scheme, the clustering method can well overcome the defects of the partitioning clustering algorithm, not only can identify clusters of various shapes in a data sample, but also can divide the cluster number of different levels according to requirements under the visualization of a clustering structure so as to meet the occasions with different granularity requirements, thereby improving the clustering accuracy in the consensus process, optimizing the consensus effect and being applicable to a large number of node consensus scenes.
Aiming at the problems of poor scalability, weak adaptability, difficult evaluation of clustering effect and the like, the invention improves the aggregation hierarchical clustering method, combines the application scene of the alliance chains of large-scale network consensus nodes, and comprises the following steps:
(1) The user sets the cluster extraction requirement, and clusters are extracted while clustering is performed in the algorithm process, so that the scalability of the algorithm is greatly improved;
(2) The data are clustered by using the same similarity measure, and the clustering effect of the algorithm can be compared and evaluated equally;
(3) A feedback mechanism with class information is added in the algorithm, and the similarity measurement between the data point pairs is regarded as a vector, wherein the vector comprises direction information and distance information.
Simulation results prove that the improved aggregation hierarchical clustering method is utilized to carry out target division and clustering on a large number of network consensus nodes, the robustness is enhanced, the communication times are reduced, the clustering accuracy and consensus efficiency are further improved,
in addition, the invention can effectively prevent data counterfeiting and malicious tampering, ensure data safety, and can be flexibly applied to node consensus scenes of various scales.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a block link point consensus layering model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a three-stage PBFT consensus algorithm according to an embodiment of the invention;
FIG. 3 is a flow chart of an H-PBFT consensus method in accordance with an embodiment of the present invention;
FIG. 4 (a) is a graph of a condensed hierarchical clustering algorithm lineage according to the present invention, and FIG. 4 (b) is a graph of consensus node clustering results according to the present invention;
FIG. 5 is a comparison graph of node consensus time consuming experiments in accordance with an embodiment of the present invention;
FIG. 6 is a graph of single consensus communication count ratio according to an embodiment of the present invention;
FIG. 7 is a graph comparing H-PBFT and PBFT throughput according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
Embodiment one:
the embodiment provides a block chain system consensus method based on improved aggregation hierarchical clustering, which comprises the following steps:
step one: performing target division and clustering on all the consensus nodes in the block chain system by using an improved aggregation hierarchical clustering method to form K clustering clusters, wherein each clustering cluster comprises intra-cluster consensus master nodes and intra-cluster slave nodes;
step two: the slave nodes in the clusters reach consensus through a three-stage PBFT consensus algorithm, and the master node in the cluster consensus of each cluster is promoted;
step three: all intra-cluster consensus master nodes form a new consensus set together, and three-stage PBFT consensus of all intra-cluster consensus master nodes among clusters is completed in the set;
step four: the intra-cluster consensus master node respectively sends final consensus messages to the respective intra-cluster slave nodes, and the intra-cluster slave nodes package the messages and perform uplink operation to complete synchronous update of local block data;
the improved aggregation hierarchical clustering method is characterized in that class cluster extraction requirements are set, and class clusters are extracted according to the class cluster extraction requirements while clustering is performed in the execution process; clustering the consensus nodes by using the same similarity measure; and adding a feedback mechanism with class information, and regarding the similarity measurement between the common node pairs as a vector, wherein the vector comprises direction information and distance information.
Embodiment two:
the present embodiment provides a blockchain system consensus method based on improved aggregation hierarchical clustering, referring to fig. 2, the method includes:
step one: performing target division and clustering on a large number of network consensus nodes by adopting an improved aggregation hierarchical clustering algorithm, identifying clustering clusters of various shapes in a data sample, wherein each effective consensus clustering cluster must contain more than 3 consensus nodes, otherwise, the effective consensus clustering clusters are invalid consensus outliers, and constructing a block chain link point consensus layering model;
the result information obtained by the traditional condensation hierarchical clustering algorithm is rich in information quantity and high in accuracy, but the algorithm is poor in scalability, weak in adaptability and difficult to evaluate in clustering effect, and a low-quality clustering result is easy to cause at the cost of high time complexity. Therefore, in combination with the application scenario of the blockchain technology in the alliance chain of large-scale network consensus nodes, the following improvement method is proposed for the problems in the algorithm.
1) The user sets the cluster extraction requirement, and clusters are extracted while clustering is performed in the algorithm process, so that the scalability of the algorithm is greatly improved.
2) The data are clustered by using the same similarity measure, and the clustering effect of the algorithm can be compared and evaluated equally.
3) A feedback mechanism with class information is added in the algorithm, and the similarity measurement between the data point pairs is regarded as a vector, wherein the vector comprises direction information and distance information. After improvement, the clustering algorithm has strong robust performance, enhanced adaptability and can identify partial noise points.
Figure BDA0003654965920000081
And carrying out target division and clustering on the large-scale network consensus nodes through a condensation hierarchical clustering algorithm, and constructing a block chain link point consensus hierarchical model, as shown in figure 1.
Step two: the first round of slave node three-stage PBFT consensus is needed to be carried out in each cluster, and K obtained cluster centers are promoted;
the lower three-stage PBFT consensus algorithm of the block chain link point consensus hierarchical model is an algorithm for solving the problem of the consensus consistency of the nodes of the main copy when malicious nodes exist in a network under the node distributed license chain environment. Each View has three roles, namely a Client (Client), a master node (Leader) and a slave node (Follower), wherein the master node is a data backup node, the master node is mainly responsible for sequencing and numbering requests from clients and broadcasting the requests to the slave nodes, and the slave nodes are mainly responsible for verifying messages and feeding back the clients. All operations of the main node cluster and the auxiliary node cluster are in a consensus algorithm view of a main node leader, only one main node exists in the current view, so that the main node leader consensus process, if the main node fails, the system automatically triggers a view switching protocol, changes and generates a new next view environment, and then selects a new main node, so that the consensus process is continued.
Step three: and carrying out new three-stage PBFT consensus on K clustering center nodes on the upper layer of the block chain link point consensus hierarchical model, and finally, achieving consensus on all nodes and consistent information.
The PBFT consensus method based on the improved aggregation hierarchical clustering in the embodiment mainly comprises four stages of cluster division, intra-cluster slave node consensus, inter-cluster master node consensus and block data synchronization, and the flow is as follows.
1) Clustering and dividing stage: the blockchain system divides all nodes into K clusters by a modified condensed hierarchical clustering algorithm.
2) A slave node consensus phase within the cluster: the client c initiates a transaction request, a master node selects a common master node by a master node selection formula, other nodes are slave nodes, and small-range three-stage PBFT common recognition is carried out in the cluster.
(1) Each master node receives the request transaction block message m packaged by the system, distributes the request transaction block message m with the number n, broadcasts a pre-preparation message to slave nodes in each cluster, and the format of the pre-preparation message is as follows
<<PRE-PREPARE，v，h，t，n，D(m)>s i ，m>
(2) The slave nodes in the clusters continuously verify that the pre-preparation messages from the master nodes in each cluster are received. If the verification is passed, the consensus enters a preparation stage, each cluster broadcasts a preparation message from the node, and the preparation message is in the format of
<<PREPARE，v，h，n，i，D(m)>s i >
When each cluster receives 2f from a different node 2 +1 prepare messages, marking the pass of the verification, then enter the validation phase.
(3) Each slave node in the cluster broadcasts a confirmation message to all nodes except the slave node, wherein the confirmation message is in the format of
<<COMMIT，v，h，n，i，D(m)>s i >
When each cluster receives from a node2f of node 2 And when +1 confirmation message is received, the verification is passed, meanwhile, the completion of the slave node consensus stage in the cluster is marked, and the master node receives the initial consensus message.
3) An inter-cluster master node consensus phase: the master nodes of all clusters together form a new consensus set, three-stage PBFT consensus of all master nodes among the clusters is completed in the set, and the execution of the consensus message is returned to the slave nodes in all clusters.
(1) The slave nodes in the cluster reach consensus equivalent to the pre-preparation stage of completing the upper three-stage PBFT consensus.
(2) The master nodes in the cluster master node consensus set continuously send preparation messages to other master nodes and verify the preparation messages. If the verification is passed, the consensus enters a preparation stage, and the main nodes among clusters broadcast preparation messages with the format of
<<PREPARE，v，h，n，D(m)>s i ，m>
When the master node in the set receives 2f from different nodes 1 +1 prepare messages, marking the pass of the verification, then enter the validation phase.
(3) Each master node in the set broadcasts a confirmation message to all nodes except the master node, wherein the confirmation message is in the format of
<<COMMIT，v，h，n，i，D(m)>s i >
When a master node in the set receives 2f from a different master node 1 And when +1 confirmation message is received, the verification is passed, and meanwhile, the completion of the inter-cluster master node consensus stage is marked, and all master nodes receive the final consensus message.
4) Block data synchronization stage: and after the previous consensus process, each master node respectively sends a final consensus message to each cluster slave node to which the master node belongs, and the slave nodes package the message in blocks and perform uplink operation to complete synchronous update of the local block data.
Figure BDA0003654965920000101
Figure BDA0003654965920000111
In order to make the purposes, technical schemes and advantages of the invention clearer, the method utilizes a condensation hierarchical clustering algorithm to carry out target division and clustering on all network consensus nodes, shows the advantages of longitudinally appointed satisfactory clustering number and high-dimensional data clustering of users, compares some PBFT consensus methods with the proposed improved method to realize the scheme of block chain link point consensus, and shows the superiority of the method in the aspects of consensus efficiency, communication times and communication throughput performance.
The simulation adopts a node consensus application scene under the guidance of a grain depot organization at a certain place in a simulated grain alliance chain.
Fig. 4 (a) is a graph of a condensed hierarchical clustering algorithm lineage according to the present invention, and fig. 4 (b) is a graph of consensus node clustering results according to the present invention. In the simulation experiment of the aggregation hierarchical clustering algorithm, in each local grain depot mechanism, 30 network consensus nodes are assumed to be generated manually and randomly each time in each bin, each consensus node contains transaction request information needing to be consensus, the transaction request information comprises grain condition data such as the quantity of in-out and in-out storage, the quantity of stored grains, the temperature and the like, experimental results of any hierarchical clustering quantity satisfactory to users can be obtained longitudinally through index standardization and taking Euclidean distance as a similarity measurement criterion, and a pedigree diagram is shown in fig. 4 (a).
When the input index is positive and negative [1, 1] and the number of the designated input clusters is 5, 30 network consensus nodes generated manually and randomly in each bin are clustered into 5 types of clusters, and the clustering result is shown in fig. 4 (b).
Clustering results: number of input clusters 5
Class 1 is: node 24 (total number of nodes less than 4, outliers);
class 2 is: node 21, node 8, node 6, node 5;
class 3 is: node 27, node 4, node 25, node 13, node 11, node 26, node 18, node 3, node 30, node 19, node 10, node 22;
class 4 is: node 7, node 20, node 15, node 1, node 23;
class 5 is: node 14, node 17, node 2, node 16, node 12, node 9, node 29, node 28.
FIG. 5 is a comparison graph of node consensus time-consuming experiments of the present invention. 300 (30/group×10 group) network consensus nodes are generated, and the network delay time represents Euclidean distance as a similarity measurement criterion, and the formula is as follows:
d(v i ,v j )＝delay(v i ,v j )
under the assumption that the experimental conditions are that under the condition that uncontrollable factors such as time of node processing information, CPU running speed, network congestion and the like are not considered, block chain link point consensus experiments are carried out on the traditional PBFT consensus method and the K-PBFT consensus method and H-PBFT consensus methods under different clustering K values, 10 groups of independent repeated experiments are carried out, and experimental comparison results are shown in the figure.
As can be seen from the graph, the average single consensus time consumption of the H-PBFT consensus method under any K value is less than that of the PBFT consensus method and the K-PBFT consensus method, and the larger the clustering K value is, the less the average single consensus time consumption of the H-PBFT consensus method is. Experimental results show that the H-PBFT consensus method obviously reduces the time consumption of the consensus process after carrying out target division and clustering on the nodes, and obviously improves the consensus efficiency.
FIG. 6 is a graph of a single consensus communication count ratio according to the present invention;
the communication complexity can intuitively quantify the communication times, is one of the reference indexes of the block chain link point consensus method performance, and compares the H-PBFT consensus method with the node consensus process of the traditional PBFT consensus method.
1) Communication overhead of traditional PBFT consensus method
Assuming that N (N > 3) nodes are generated by the block chain consensus experiment, the total communication times are as follows after the PBFT three-stage consensus as described above:
P 1 ＝2N 2 -2N
2) The H-PBFT consensus method of the invention has communication overhead
Assuming that N (N is more than 3) nodes are generated by a block chain consensus experiment, setting the clustering number as K, and carrying out PBFT three-stage consensus and slave node block data synchronization on the lower layer respectively, so that the total communication times can be easily calculated:
Figure BDA0003654965920000121
the ratio of single consensus communication times of the two methods can be obtained by the following formula:
Figure BDA0003654965920000122
a curve diagram of the ratio of the number of times of single consensus communication by simulating a traditional PBFT consensus method and using the H-PBFT consensus method of the invention is shown in figure 6.
From the figure, the following conclusions are drawn:
when the total node number N is constant and K=1, the H-PBFT consensus method is the traditional PBFT consensus method, and the communication times of the two methods are the same.
When K is continuously increased, Z is also increased, and Z is more than 1, and the H-PBFT consensus method is illustrated to have fewer communication times than the traditional H-PBFT consensus method through a condensation hierarchical clustering method. When K takes an extreme point, Z reaches the maximum value, and the H-PBFT consensus method obtains the optimal clustering number K, so that the communication frequency is the minimum.
When K continues to increase, Z starts to decrease, indicating that the number of communications is increasing due to the excessive number of clusters.
Fig. 7 is a graph of throughput versus a conventional PBFT consensus method and an H-PBFT consensus method according to the present invention. The H-PBFT consensus method is a PBFT consensus algorithm obtained through hierarchical clustering optimization of a condensed hierarchical clustering algorithm. After the small-range consensus in each cluster at the lower layer is realized, the communication overhead is greatly reduced through the consensus of a relatively small number of main nodes at the upper layer, so that the transaction can be processed faster under the condition of facing a large number of network consensus nodes, and the higher throughput is kept, as shown in fig. 7.
The graph shows that the H-PBFT consensus method has higher throughput under any K value than the conventional H-PBFT consensus method, and the H-PBFT consensus method has higher single consensus throughput when the clustering K value is larger. Experimental results show that the H-PBFT consensus method of the invention can obviously improve throughput after carrying out target division and clustering on nodes, and obviously enhance the capability of processing transaction matters in unit time of the system.
Some steps in the embodiments of the present invention may be implemented by using software, and the corresponding software program may be stored in a readable storage medium, such as an optical disc or a hard disk.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims (8)

1. A blockchain system consensus method based on improved condensed hierarchical clustering, the method comprising:
step one: performing target division and clustering on all the consensus nodes in the block chain system by using an improved aggregation hierarchical clustering method to form K clustering clusters, wherein each clustering cluster comprises intra-cluster consensus master nodes and intra-cluster slave nodes;
step two: the slave nodes in the clusters reach consensus through a three-stage PBFT consensus algorithm, and the master node in the cluster consensus of each cluster is promoted;
step three: all intra-cluster consensus master nodes form a new consensus set together, and three-stage PBFT consensus of all intra-cluster consensus master nodes among clusters is completed in the set;
step four: the intra-cluster consensus master node respectively sends final consensus messages to the respective intra-cluster slave nodes, and the intra-cluster slave nodes package the messages and perform uplink operation to complete synchronous update of local block data;
the improved aggregation hierarchical clustering method is characterized in that class cluster extraction requirements are set, and class clusters are extracted according to the class cluster extraction requirements while clustering is performed in the execution process; clustering the consensus nodes by using the same similarity measure; adding a feedback mechanism with class information, and regarding similarity measurement between the consensus node pairs as a vector, wherein the vector comprises direction information and distance information;
the improved condensation hierarchical clustering method comprises the following steps:
step 11: taking each consensus node in each block chain system as a single cluster, wherein N clusters are formed in total;
step 12: calculating the similarity measurement between clusters according to the inter-cluster distance measurement criterion
Figure FDA0004235716970000011
Step 13: comparing the inter-cluster similarity measures
Figure FDA0004235716970000012
And the lowest similarity measure between the mergeable class clusters
Figure FDA0004235716970000013
If it meets->
Figure FDA0004235716970000014
And->
Figure FDA0004235716970000015
The class clusters with the highest similarity measure are combined into a new class cluster, otherwise, the lowest similarity among the current combinable class clusters is reduced;
wherein: alpha is constant, 0<α<1；
Figure FDA0004235716970000016
An included angle theta is 45 degrees with the positive coordinate axis;
step 14: and (3) repeating the steps 11 to 13 to reach the number K of the target clusters which are satisfied by the user.
2. The consensus method according to claim 1, wherein step two uses a master node election formula to elect the intra-cluster consensus master node, the master node election formula being:
p＝vmod|R|
wherein: p is the master node number, v is the view number, |r| is the total number of nodes in the cluster, |r|=3f+1, and f is the number of malicious nodes.
3. The consensus method according to claim 2, wherein the step of the intra-cluster slave node reaching the consensus by a three-phase PBFT consensus algorithm in step two comprises:
step 21: the client c initiates a transaction request, the common master node in each cluster receives a request transaction block message m packaged by the system, the request transaction block message m is assigned with a number n, and a pre-preparation message is broadcast to slave nodes in each cluster, wherein the format of the pre-preparation message is as follows:
<<PRE-PREPARE，v，h，t，n，D(m)>s i ，m>
where i represents the node, h is the height of the current block, t is the unique identification timestamp, D (m) is the digital digest of the request transaction block message m, s i Receiving a digital signature of the message for the i-th node;
step 22: the slave nodes in the cluster verify the received pre-preparation message from the common master node in the cluster, if the verification is passed, the common master node enters a preparation stage, and each cluster slave node broadcasts the preparation message, wherein the format of the preparation message is as follows:
<<PREPARE，v，h，n，i，D(m)>s i >
when each cluster receives 2f from different node 2 +1 prepare messages, marking the verification as passed, then entering the validation phase, where f 2 Representing the number of Byzantine malicious nodes in a cluster from a node set;
step 23: broadcasting a confirmation message from the node in the cluster to all nodes except the node in the cluster, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when saidIn-cluster slave nodes receive 2f from different nodes 2 And when +1 confirmation message, the verification is passed, and meanwhile, the completion of the intra-cluster slave node consensus stage is marked, and each intra-cluster consensus master node receives the initial consensus message.
4. A consensus method according to claim 3, wherein said step three comprises:
step 31: all intra-cluster consensus master nodes form an inter-cluster master node consensus set;
step 32: in the cluster master node consensus set, the master node continuously sends preparation information to other master nodes and verifies the preparation information; if the verification is passed, the consensus enters a preparation stage, and the inter-cluster master node broadcasts a preparation message with the following format:
<<PREPARE，v，h，n，D(m)>s i ，m>
when the master node in the set receives 2f from different nodes 1 +1 prepare messages, marking the verification as passed, then entering the validation phase, where f 1 Representing the number of Bayesian malicious nodes in the cluster master node consensus set;
step 33: each master node in the set broadcasts a confirmation message to all nodes except the master node, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when a master node in the set receives 2f from a different master node 1 And when +1 confirmation message is received, the verification is passed, and meanwhile, the completion of the inter-cluster master node consensus stage is marked, and all master nodes receive the final consensus message.
5. A blockchain system based on improved condensed hierarchical clustering, comprising a plurality of consensus nodes, characterized in that the blockchain system adopts an improved condensed hierarchical clustering method to construct a blockchain link point consensus hierarchical model, the model comprising: a slave node PBFT consensus layer in the cluster and a master node PBFT consensus layer among the clusters;
the block chain system adopts the improved aggregation hierarchical clustering method to cluster all consensus nodes in the system to form K clustering clusters, wherein each clustering cluster comprises intra-cluster consensus master nodes and intra-cluster slave nodes, the intra-cluster slave nodes form an intra-cluster slave node PBFT consensus layer, and the intra-cluster consensus master nodes form inter-cluster master node PBFT consensus layers;
the slave nodes in the cluster in the slave node PBFT consensus layer reach consensus through a three-stage PBFT consensus algorithm, and the master node in the cluster consensus of each cluster is promoted; all intra-cluster consensus master nodes in the inter-cluster master node PBFT consensus layer form a new consensus set together, and three-stage PBFT consensus of all intra-cluster consensus master nodes among clusters is completed in the set;
the improved aggregation hierarchical clustering method is characterized in that class cluster extraction requirements are set, and class clusters are extracted according to extraction requirements preset by a user while clustering is performed in the execution process; clustering the consensus nodes by using the same similarity measure; adding a feedback mechanism with class information, and regarding similarity measurement between the consensus node pairs as a vector, wherein the vector comprises direction information and distance information;
the improved condensation hierarchical clustering method comprises the following steps:
step 11: taking each consensus node in each block chain system as a single cluster, wherein N clusters are formed in total;
step 12: calculating the similarity measurement between clusters according to the inter-cluster distance measurement criterion
Figure FDA0004235716970000031
Step 13: comparing the inter-cluster similarity measures
Figure FDA0004235716970000032
And the lowest similarity measure between the mergeable class clusters
Figure FDA0004235716970000033
If it meets->
Figure FDA0004235716970000034
And->
Figure FDA0004235716970000035
The class clusters with the highest similarity measure are combined into a new class cluster, otherwise, the lowest similarity among the current combinable class clusters is reduced;
wherein: alpha is constant, 0<α<1；
Figure FDA0004235716970000036
An included angle theta between the similarity and the positive coordinate axis is 45 degrees;
step 14: and (3) repeating the steps 11 to 13 to reach the number K of the target clusters which are satisfied by the user.
6. The blockchain system of claim 5, wherein a master node election formula is adopted to elect a master node in the intra-cluster slave node PBFT consensus layer, the master node election formula being:
p＝vmod|R|
wherein: p is the master node number, v is the view number, |r| is the total number of nodes in the cluster, |r|=3f+1, and f is the number of malicious nodes.
7. The blockchain system of claim 6, wherein the intra-cluster slave nodes in the intra-cluster slave node PBFT consensus layer agree through a three-phase PBFT consensus algorithm comprising:
step 21: the client c initiates a transaction request, the common master node in each cluster receives a request transaction block message m packaged by the system, the request transaction block message m is assigned with a number n, and a pre-preparation message is broadcast to slave nodes in each cluster, wherein the format of the pre-preparation message is as follows:
<<PRE-PREPARE，v，h，t，n，D(m)>s i ，m>
where i represents the node, h is the height of the current block, t is the unique identification timestamp, D (m) is the digital digest of the request transaction block message m, s i For the ith nodeReceiving a digital signature of the message;
step 22: the slave nodes in the cluster verify the received pre-preparation message from the common master node in the cluster, if the verification is passed, the common master node enters a preparation stage, and each cluster slave node broadcasts the preparation message, wherein the format of the preparation message is as follows:
<<PREPARE，v，h，n，i，D(m)>s i >
when each cluster receives 2f from different node 2 +1 prepare messages, marking the verification as passed, then entering the validation phase, where f 2 Representing the number of Byzantine malicious nodes in a cluster from a node set;
step 23: broadcasting a confirmation message from the node in the cluster to all nodes except the node in the cluster, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when the slave node in the cluster receives 2f from different nodes 2 And when +1 confirmation message, the verification is passed, and meanwhile, the completion of the intra-cluster slave node consensus stage is marked, and each intra-cluster consensus master node receives the initial consensus message.
8. The blockchain system of claim 7, wherein all intra-cluster consensus master nodes in the inter-cluster master node PBFT consensus layer together form a new consensus set, and the process of completing three-phase PBFT consensus of all intra-cluster consensus master nodes within a set comprises:
step 31: all intra-cluster consensus master nodes form an inter-cluster master node consensus set;
step 32: in the cluster master node consensus set, the master node continuously sends preparation information to other master nodes and verifies the preparation information; if the verification is passed, the consensus enters a preparation stage, and the inter-cluster master node broadcasts a preparation message with the following format:
<<PREPARE，v，h，n，D(m)>s i ，m>
when the master node in the set receives 2f from different nodes 1 When +1 pieces of the preparation message are prepared,if the verification is passed, the verification stage is entered, wherein f 1 Representing the number of Bayesian malicious nodes in the cluster master node consensus set;
step 33: each master node in the set broadcasts a confirmation message to all nodes except the master node, wherein the confirmation message is in the format of:
<<COMMIT，v，h，n，i，D(m)>s i >
when a master node in the set receives 2f from a different master node 1 And when +1 confirmation message is received, the verification is passed, and meanwhile, the completion of the inter-cluster master node consensus stage is marked, and all master nodes receive the final consensus message.
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