CN111935207A - Block chain system consensus method based on improved C4.5 algorithm - Google Patents
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Abstract
The invention discloses a block chain system consensus method based on an improved C4.5 algorithm, which comprises a first-level consensus layer consensus: the method comprises the following steps that a slave node in a group sends a request to a master node, the master node packs a plurality of requests into a block after receiving the request for a period of time, and then broadcasts the block to a group to which the block belongs to carry out PBFT consensus for one time; and (3) second-level consensus layer consensus: after the block passes the consensus verification process of the first-level consensus layer, the second-level consensus layer is subjected to the PBFT consensus confirmation. K nodes in the secondary consensus layer elect a main node through an integral voting mechanism, and the node packs the collected requests passing through the consensus of the primary consensus layer in the period of time into a block and broadcasts the block to all nodes of the secondary consensus layer for PBFT consensus.
Description
Technical Field
The invention belongs to the technical field of a block chain system, and particularly relates to a block chain system consensus method based on an improved C4.5 algorithm.
Background
The block chain is a decentralized distributed system and has the characteristics of openness, traceability, decentralization, non-tampering and the like. The consensus algorithm is the core of the blockchain technology, and directly influences the expandability of the blockchain system. The block chain can be divided into a public chain, a private chain and a alliance chain, a common consensus algorithm in the alliance chain is a practical Byzantine fault-tolerant algorithm (PBFT), and the PBFT is an algorithm for solving the problem of Byzantine general and can guarantee the consistency among all nodes under the condition that malicious nodes exist in a network. As shown in fig. 1, which is a flow diagram of a conventional PBFT algorithm, the PBFT consensus algorithm mainly includes a consistency protocol, a view change protocol, and a checkpoint protocol. The consistency protocol is used for ensuring the consistency of data stored by all nodes in the whole network and is realized by mutual communication among the nodes in three stages; the view replacement protocol is used for replacing the fault node so as to ensure the normal operation of the system; the checkpoint protocol is used for regularly clearing out-of-date interactive data to reduce the storage pressure of the nodes, regularly checking whether the system is uniform or not and synchronizing inconsistent nodes. However, the existing byzantine fault-tolerant algorithm has poor expandability and low fault-tolerant rate, or the random selection of the master node can cause the change process of the view to influence the whole consensus process, and the algorithm has larger communication traffic when the number of the nodes is increased, thereby causing network congestion. How to improve the existing PBFT algorithm is a prerequisite for large-scale blockchain applications.
The present invention has been made in view of this situation.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art, and provide a block chain system consensus method based on an improved C4.5 algorithm, in order to solve the technical problem, the basic concept of the technical scheme adopted by the invention is as follows:
the block chain system consensus method based on the improved C4.5 algorithm comprises a first-level consensus layer consensus: the method comprises the following steps that a slave node in a group sends a request to a master node, the master node packs a plurality of requests into a block after receiving the request for a period of time, and then broadcasts the block to a group to which the block belongs to carry out PBFT consensus for one time; and (3) second-level consensus layer consensus: after the block passes the consensus verification process of the first-level consensus layer, the second-level consensus layer is subjected to the PBFT consensus confirmation. K nodes in the secondary consensus layer elect a main node through an integral voting mechanism, and the node packs the collected requests passing through the consensus of the primary consensus layer in the period of time into a block and broadcasts the block to all nodes of the secondary consensus layer for PBFT consensus; if the primary common-identification layer has no new request in the period of time, the node packs a vacant block and sends the vacant block to the secondary common-identification layer common-identification uplink, and then the next election is carried out; a submission stage: after the block is subjected to the consensus of the secondary consensus layer, all the main nodes carry out digital signature on the block and collect digital signatures from other main nodes to represent the authenticity and the effectiveness of the block; then, the block attached to the digital signature set of the secondary common identification layer is packaged into a submit message which is broadcast to all the slave nodes in the primary common identification layer to which the submit message belongs, so that the block can be subjected to uplink operation. The node of any level of the consensus layer receives the submission message of the block; an execution stage: after receiving the submitted message from the master node, the slave node verifies the digital signature set attached to the block, and can judge whether the block is subjected to consensus verification of the secondary consensus layer. If the verification fails, the master node to which the slave node belongs can be considered to have malicious behaviors, and the illegal operation can be reported, so that the effect of upward supervision of the slave node is achieved; if the verification is successful, the requested content for the block can be executed and the block is recorded for uplink.
After the technical scheme is adopted, compared with the prior art, the invention has the following beneficial effects.
The method ensures the reliability of the main node to a certain extent, reduces the view switching times, further obtains higher consensus efficiency, improves the global decentralized consensus into hierarchical multicentric consensus through node grouping, effectively solves the problem of overlarge communication traffic caused by the increase of the number of nodes due to the pure use of a PBFT algorithm, improves the honesty probability of the main node, reduces the consensus time among the nodes, and improves the consensus efficiency.
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention to its proper form. It is obvious that the drawings in the following description are only some embodiments, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a flow chart of a prior art PBFT algorithm;
FIG. 2 is a diagram of a decision tree model;
FIG. 3 is a two-layer blockchain network diagram after nodes are grouped;
FIG. 4 is a flow chart of a modified PBFT.
It should be noted that the drawings and the description are not intended to limit the scope of the inventive concept in any way, but to illustrate it by a person skilled in the art with reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and the following embodiments are used for illustrating the present invention and are not intended to limit the scope of the present invention.
Examples
The C4.5 decision tree is a classical decision tree algorithm, and the model is shown in fig. 2, and determines the features to be classified by calculating and comparing the information gain rates of the features, and the basic flow is as follows:
(1) let the sample set S have S training samples, divide the sample set into m classes, the number of the i class instances is Si，SiThe/s is the probability PiInfo (S) is the entropy of the category information, which is calculated by the formula:
(2) if feature A is selected as a split feature, then the sampleThis set S is divided into k subsets S1,S2,...,SkLet feature A have k uncorrelated values { alpha }1,α2,...,αkIs then SjThe number of training individuals in the i-th class is SijInfo (S) is aiThe calculation formula of the conditional information entropy of (1) is as follows:
wherein the content of the first and second substances,
is SjSample probability of class i.
(3) Calculating the information gain of the condition attribute A:
Gain(A，S)＝Info(S)-InfoA(S) (3)
(4) the conditional attribute A is used as a dividing standard to divide the training set S into k subsets, aiThen corresponds to the training set SjThe entropy of the attribute a in the sample S is:
(5) the information gain ratio of attribute a is:
in order to make the C4.5 algorithm more suitable for the blockchain model, the embodiment improves the method, and the conventional C4.5 algorithm easily generates the problems of redundant rules, too large scale of the decision tree, too slow classification speed, and the like when generating the decision tree. In combination with the practical application scenario of the block chain consensus mechanism, in the PBFT consensus mechanism, data information carried by attribute values of some existing nodes is similar, and this embodiment proposes an improved C4.5 decision tree algorithm based on cosine similarity, as shown in formula (6). If the difference between the information entropies of any two attribute values is within a small range, as shown in equation (7). And calculating cosine similarity of the two attribute values, and combining the attribute values with the similarity within a threshold value range, as shown in formula (8). The algorithm can effectively reduce the scale of the decision tree, reduce redundant rules and improve the classification speed.
Where, the smaller η is, the better, generally not more than 0.1, where η is set to 0.1
The improved C4.5 basic flow is as follows:
(1) calculating the information entropy of each attribute value in the node attribute and the information gain rate of the attribute;
(2) comparing whether an attribute value pair with the information entropy within the threshold range exists in the attribute values of each attribute according to the formula (7), if so, calculating (3), and if not, turning to (6);
(3) calculating cosine similarity values of the two attribute value pairs according to the formula (6), if the cosine similarity values are larger than a threshold value of 0.9, indicating that the two vectors have high similarity, turning to the formula (4), and otherwise, turning to the formula (6);
(4) the two attribute value vectors are merged into a new attribute value vector according to equation (8), and the new vector represents the new subset, the new attribute value. Deleting the attribute values which originally participate in comparison in the attributes, and adding new attribute values to form new attributes;
(5) recalculating the information entropy and the information gain rate of the attribute according to the modified attribute;
(6) and selecting the attribute with the maximum information gain rate from the attribute set as a splitting attribute.
And combining the C4.5 algorithm with a PBFT consensus mechanism, performing trust evaluation classification on consensus nodes participating in consensus by using a decision tree algorithm, and sequentially numbering as {0,1, …, M-1} according to the trust level. Based on the trust level evaluation classification, sampling and grouping are carried out according to the proportion of K, the first K nodes of the trust level evaluation are main nodes of each group, the model is shown in figure 3, each node in the group keeps a list, an address of a node in the group appears on the list, and the address marks that the nodes are in the same group. There is an upper limit M/K on the number of nodes in the group. When a new node a is added, a self request is issued to the network, a public key P is attached, then the network enters a waiting state, after a main node b in the network receives request information from the new node, the main node b determines that the number of nodes in the group of the main node does not reach the upper limit, the received adding information is attached with a timestamp and the public key S of the node, the public key S is encrypted by the P and is transmitted to a secondary common layer in the form of a message, other nodes of the secondary common layer determine to add the group after receiving the determined message (possibly simultaneously receiving a plurality of messages, and the earliest timestamp is taken as the main node), and a receipt message is sent and encrypted by the S. b, after receiving the message, broadcasting the message to the nodes in the whole group, updating the node list, synchronizing the latest node list to the node a, and a immediately synchronizing all the data of the blocks in the whole network. When a node c exits, the node c needs to submit an exiting application with a public key S to a main node d in the group, the main node d receives the exiting information with a timestamp and the public key S of the node, uses P encryption and submits the exiting application to a secondary consensus layer in the form of a message, and after other main nodes agree, sends a receipt message and uses S encryption. And the master node d sends the information of the node c to the group to update the node connection information in the group, and when the 2f +1 node in the group is confirmed to complete the update, the node c really exits the block chain network.
In the secondary consensus layer, in order to further reduce the probability that the malicious node becomes the master node, the master node is selected by adopting an integral voting mechanism. The voting result is calculated in the following way:
t ═ α + integrated value ═ β (α + β ═ 1) (9)
Where T is the final fraction, and α, β are correction parameters that are set differently according to the change in the node integral value. Alpha is more than or equal to 0, beta is less than or equal to 1.
1. Calculation of the integral value: at system initialization, each node is assigned an integration value of 50 points. If a node successfully produces a block and is verified to be valid in one period, the system awards 10 points of credit; if the node does not complete the block within the specified time, the system deducts 10 integral values. The integral value is gradually accumulated along with the behavior of the node, at this time, in order to avoid the integral value from being too rich and lean, the integral threshold value is set to be 200 minutes, and after 200 minutes, the system resets the integral value to be 50.
2. Regarding maintenance of the bonus table, selecting a highest node of the trust level in an alliance chain as an authoritative node to maintain the bonus table, binding the hash value of successfully executed consensus content as evidence to be bonus after the nodes are successfully identified, verifying the hash value by the authoritative node, accumulating the bonus on the node if the verification is passed, deducting the bonus on the node according to the node address if the verification is not passed, and finally completing the operation of adding 1 point to all the bonus points in the bonus table before the start of the next stage of consensus to complete the establishment of a bonus system in the maintenance process of the bonus table, so that on one hand, the influence of a rogue node on the consensus of a block chain system is relieved from the perspective of a game theory; on the other hand, the optimality of the master node selection is ensured, and the possibility of the master node doing malicious work is further reduced.
The improved PBFT consensus algorithm flow chart is shown in FIG. 4, and the main flow stages are as follows:
(1) first-level consensus layer consensus: the slave nodes in the group send requests to the master node. After receiving a period of requests, the main node packs a plurality of requests into a block, and then broadcasts the block to the group to which the block belongs to carry out PBFT consensus once.
(2) And (3) second-level consensus layer consensus: after the block passes the consensus verification process of the first-level consensus layer, the second-level consensus layer is subjected to the PBFT consensus confirmation. K nodes in the secondary consensus layer elect a main node through an integral voting mechanism, and the node packs the collected requests passing through the consensus of the primary consensus layer in the period of time into a block and broadcasts the block to all nodes of the secondary consensus layer for PBFT consensus; and if the primary common identification layer has no new request in the period of time, the node packs a vacant block and sends the vacant block to the secondary common identification uplink, and then the next election is carried out.
(3) A submission stage: after the block is subjected to the consensus of the secondary consensus layer, all the main nodes carry out digital signature on the block and collect digital signatures from other main nodes to represent the authenticity and the effectiveness of the block; then, the block attached to the digital signature set of the secondary common identification layer is packaged into a submit message which is broadcast to all the slave nodes in the primary common identification layer to which the submit message belongs, so that the block can be subjected to uplink operation. The node of any level of the consensus layer receives the commit message of the block.
(4) An execution stage: after receiving the submitted message from the master node, the slave node verifies the digital signature set attached to the block, and can judge whether the block is subjected to consensus verification of the secondary consensus layer. If the verification fails, the master node to which the slave node belongs can be considered to have malicious behaviors, and the illegal operation can be reported, so that the effect of upward supervision of the slave node is achieved; if the verification is successful, the requested content for the block can be executed and the block is recorded for uplink.
Although the present invention has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. The block chain system consensus method based on the improved C4.5 algorithm is characterized by comprising a first-level consensus layer consensus: the method comprises the following steps that a slave node in a group sends a request to a master node, the master node packs a plurality of requests into a block after receiving the request for a period of time, and then broadcasts the block to a group to which the block belongs to carry out PBFT consensus for one time; and (3) second-level consensus layer consensus: after the block passes the consensus verification process of the first-level consensus layer, the second-level consensus layer is subjected to the PBFT consensus confirmation. K nodes in the secondary consensus layer elect a main node through an integral voting mechanism, and the node packs the collected requests passing through the consensus of the primary consensus layer in the period of time into a block and broadcasts the block to all nodes of the secondary consensus layer for PBFT consensus; if the primary common-identification layer has no new request in the period of time, the node packs a vacant block and sends the vacant block to the secondary common-identification layer common-identification uplink, and then the next election is carried out; a submission stage: after the block is subjected to the consensus of the secondary consensus layer, all the main nodes carry out digital signature on the block and collect digital signatures from other main nodes to represent the authenticity and the effectiveness of the block; then, the block attached to the digital signature set of the secondary common identification layer is packaged into a submit message which is broadcast to all the slave nodes in the primary common identification layer to which the submit message belongs, so that the block can be subjected to uplink operation. The node of any level of the consensus layer receives the submission message of the block; an execution stage: after receiving the submitted message from the master node, the slave node verifies the digital signature set attached to the block, and can judge whether the block is subjected to consensus verification of the secondary consensus layer. If the verification fails, the master node to which the slave node belongs can be considered to have malicious behaviors, and the illegal operation can be reported, so that the effect of upward supervision of the slave node is achieved; if the verification is successful, the requested content for the block can be executed and the block is recorded for uplink.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112866399A (en) * | 2021-01-28 | 2021-05-28 | 深圳大学 | Improved PBFT consensus method, device, intelligent terminal and storage medium |
| CN112860482A (en) * | 2021-01-27 | 2021-05-28 | 西南林业大学 | Block chain consensus performance optimization method |
| CN113645074A (en) * | 2021-08-11 | 2021-11-12 | 永旗(北京)科技有限公司 | Consensus method based on block chain |
| CN113849556A (en) * | 2021-08-11 | 2021-12-28 | 国网甘肃省电力公司信息通信公司 | Sharing mechanism based on block chain distributed database |
| CN113922864A (en) * | 2021-10-09 | 2022-01-11 | 郑州大学 | Multi-layer satellite network security guarantee method based on Byzantine consensus |
| CN114301598A (en) * | 2021-12-14 | 2022-04-08 | 中国人民解放军国防科技大学 | Block chain consensus algorithm, system and storage medium based on hierarchical authority |
| CN114363354A (en) * | 2021-12-30 | 2022-04-15 | 海南大学 | Block chain consensus method based on DIKWP model |
| CN114449000A (en) * | 2021-12-28 | 2022-05-06 | 北京邮电大学 | Vehicle network data consensus optimization storage method and storage system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112860482A (en) * | 2021-01-27 | 2021-05-28 | 西南林业大学 | Block chain consensus performance optimization method |
| CN112866399A (en) * | 2021-01-28 | 2021-05-28 | 深圳大学 | Improved PBFT consensus method, device, intelligent terminal and storage medium |
| CN112866399B (en) * | 2021-01-28 | 2023-01-31 | 深圳大学 | Improved PBFT consensus method, device, intelligent terminal and storage medium |
| CN113645074A (en) * | 2021-08-11 | 2021-11-12 | 永旗(北京)科技有限公司 | Consensus method based on block chain |
| CN113849556A (en) * | 2021-08-11 | 2021-12-28 | 国网甘肃省电力公司信息通信公司 | Sharing mechanism based on block chain distributed database |
| CN113922864A (en) * | 2021-10-09 | 2022-01-11 | 郑州大学 | Multi-layer satellite network security guarantee method based on Byzantine consensus |
| CN114301598A (en) * | 2021-12-14 | 2022-04-08 | 中国人民解放军国防科技大学 | Block chain consensus algorithm, system and storage medium based on hierarchical authority |
| CN114449000A (en) * | 2021-12-28 | 2022-05-06 | 北京邮电大学 | Vehicle network data consensus optimization storage method and storage system |
| CN114449000B (en) * | 2021-12-28 | 2022-10-11 | 北京邮电大学 | Internet of vehicles data consensus optimization storage method and storage system |
| CN114363354A (en) * | 2021-12-30 | 2022-04-15 | 海南大学 | Block chain consensus method based on DIKWP model |
| CN114363354B (en) * | 2021-12-30 | 2022-09-30 | 海南大学 | Block chain consensus method based on DIKWP model |
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