CN110138597A - Based on the block chain DPOS of credit score and node clustering common recognition mechanism improved method - Google Patents
Based on the block chain DPOS of credit score and node clustering common recognition mechanism improved method Download PDFInfo
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- CN110138597A CN110138597A CN201910306559.XA CN201910306559A CN110138597A CN 110138597 A CN110138597 A CN 110138597A CN 201910306559 A CN201910306559 A CN 201910306559A CN 110138597 A CN110138597 A CN 110138597A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/30—Decision processes by autonomous network management units using voting and bidding
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H04L67/1044—Group management mechanisms
- H04L67/1051—Group master selection mechanisms
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- G—PHYSICS
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- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
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Abstract
The invention discloses a kind of based on the block chain DPOS of credit score and node clustering common recognition mechanism improved method.The present invention defines the basic conceptions such as credit score for DPOS common recognition mechanism ballot not positive the problems such as cannot rejecting in time with malicious agent node.The node being added to for the first time for each in block chain network, credit score will be initialized to 100, under credit rewards and punishments, the whole network node becomes the probability of agent node voting and can reduce abnormal nodes by way of statistics credit score again, and utilize the K-Means clustering algorithm in data mining, the whole network node is clustered referring to multiple characteristic values of node, the relatively high class group of degree of belief will have bigger probability to win competition power in next round competition, this will further decrease the generation of invalid block or malice block in block chain network.
Description
Technical field
The present invention relates to a kind of based on the block chain DPOS of credit score and node clustering common recognition mechanism improved method.
Background technique
Block chain is substantially the database of a decentralization, while the Floor layer Technology as bit coin, is to make for a string
It is associated the data block generated with cryptography method, contains the letter of a batch bit coin network trading in each data block
Breath, for verifying the validity of its information and generating next block.Common recognition mechanism is the core of block chain, it passes through special section
How the ballot of point, the in a short period of time verifying and confirmation of complete swap solve in shortages trust, freely open
The problem of reaching common understanding in the network put.
Common recognition refers to the result mutually reached an agreement without associated multiple participants to a certain problem.Being total in block chain
What knowledge was mainly studied is the check problem after book keeping operation power assignment problem and block generation.Currently, in the research of common recognition mechanism
Hold, the existing common recognition algorithm of block catenary system mainly has POW, POS, POL, DPOS etc..POW (Proof-Of-Work) works
Amount proves common recognition mechanism, has been successfully applied in bit coin, it can solve the data one in complete open, free network
Cause property problem, but the mechanism can consume a large amount of calculation power and other resources, and the data compliance time is longer, it is difficult to
Meet universal business demand；The main thought of POS (Proof-Of-Stake) be node obtain block book keeping operation power probability with
The token that node is held is directly proportional, and POS reduces the consumption of mathematical operation bring to a certain extent, but it is in some common recognitions
It is still less applicable in the relatively high service environment of time requirement；POL (Proof-Of-Luck) is a kind of in trusted execution ring
The common recognition mechanism established on border, it can greatly improve block generation efficiency, but this processor also proposed it is higher
Requirement, needing corresponding hardware supported just can be with；DPOS (Delegated-Proof-Of-Stake) is that one kind can be real
The common recognition mechanism of existing block chain second level verification, DPOS are selected agent node by way of ballot, are finally completed by agent node
The generation and verifying of block, but to malicious node without timely responsive measures in DPOS, and there are nodes to vote not
Positive phenomenon, these are likely to cause the safety of system to reduce.
Summary of the invention
The purpose of the present invention is the deficiencies for current block chain common recognition mechanism in practical applications, propose a kind of based on letter
With the block chain DPOS of integral and node clustering common recognition mechanism improved method.
Because there is the problems such as not positive and malicious agent node of voting cannot reject in time in DPOS common recognition mechanism, so this
Invention defines the basic conceptions such as credit score.Under credit rewards and punishments, the whole network node passes through the side for voting and counting credit score
Formula, which can reduce abnormal nodes, becomes the probability of agent node, and utilizes the K-Means clustering algorithm in data mining, reference
Multiple attributes of node cluster the whole network node, and the relatively high class group of degree of belief will have bigger in next round competition
Probability wins competition power, this will further decrease invalid or malice block generation.
It is to implement letter because it is unsupervised clustering algorithm why the present invention, which selects K-Means clustering algorithm,
It is clean to be illustrated, and Clustering Effect is also good.The core concept of K-Means clustering algorithm is: K seed point is randomly selected first,
Then it asks all the points to the distance of seed point, and point is included in apart from nearest seed point group, until all the points are included into
After in group, then seed point is moved to seed group center, finally repeated the above process, until until seed point does not move.
In order to achieve the above objectives, the present invention adopts the following technical solutions:
It is a kind of based on the block chain DPOS of credit score and node clustering know together mechanism improved method, concrete operation step is such as
Under:
1) initialize credit score: credit score is that a kind of credit ginseng assigned when block chain network by system is added in node
Number, is the important behaviour form of node trusting degree, the node being added to for the first time in block chain network for each, credit product
100 will be initialized to by dividing；
2) new transaction occurs in block chain network, the whole network node competition book keeping operation, competes successful node after transaction
This all information traded is recorded in the newly generated block of block chain network；
3) system judges block newly generated in network, judges whether it is effective block, if not effective district
Block then judges whether it is malice block again；
4) the whole network node votes to block newly generated in network, is divided into yeas and nays, if voting for
It then indicates that the node thinks that newly generated block is effective block, indicates that the node thinks newly generated block if voting against
For invalid block even malice block；
5) system counts voting results, and carries out credit rewards and punishments to the whole network node according to voting results, lays equal stress on
New calculate node credit score；
6) credit score thresholding is set, when the credit score of node is lower than 95, system will inhibit it to become agency's section
Point；
7) using credit score, ballot whether actively, node be added the time of network and node hold the quantity of token as
Cluster feature value, and these characteristic values are normalized, then utilize K-Means clustering algorithm by all nodes of the whole network
Gather for four classes；
8) after end of clustering, credit score highest, ballot is most positive, the time longest of network is added and possesses number of tokens
It measures that highest one kind and possesses highest degree of belief, the node that such is organized in the competition of next round will have bigger probability to obtain
Book keeping operation power；Similarly, credit score is minimum, ballot is most passive, it is most short that the time of network is added and it is minimum to possess token quantity
That one kind possesses minimum degree of belief, and the probability that the node in such obtains book keeping operation power in the competition of next round will significantly drop
It is low.
Compared with prior art, the invention has the advantages that
Firstly, under credit rewards and punishments, the whole network node can be reduced by way of ballot invention defines credit score
Abnormal nodes become the probability of agent node；Secondly, using the K-Means clustering algorithm in data mining, referring to the more of node
A attribute clusters the whole network node, and the relatively high class group of degree of belief will have bigger probability to win in next round competition
Book keeping operation power, the generation of invalid block or malice block will be effectively reduced in this；Finally, the same class node after cluster is in next round
Competed in competition book keeping operation successful probability be it is identical, this just effectively prevents degree of belief higher respective nodes monopolization book keeping operation power
A possibility that.
Detailed description of the invention
Fig. 1 is that the present invention is based on the processes of the block chain DPOS of credit score and node clustering common recognition mechanism improved method
Figure.
Fig. 2 is that the present invention uses K-Means to the effect picture after block chain node clustering.
Specific embodiment
In order to facilitate the understanding of those skilled in the art, being carried out below in conjunction with attached drawing and embodiment to the present invention further
Description.
As shown in Figure 1, it is a kind of based on the block chain DPOS of credit score and node clustering common recognition mechanism improved method, specifically
Operating procedure is as follows:
1) credit score is initialized.The node being added to for the first time in block chain network for each, credit score will be by
It is initialized as 100；
2) new transaction occurs in block chain network, the whole network node competition book keeping operation, competes successful node after transaction
This all information traded is recorded in the newly generated block of block chain network；
3) system judges block newly generated in network, judges whether it is effective block, if not effective district
Block then judges whether it is malice block again；
4) the whole network node starts to vote to block newly generated in network, node can vote for the block or
Person votes against, and indicates that the node thinks that newly generated block is effective block if voting for, if voting against indicates
The node thinks that newly generated block is invalid block even malice block；
5) system counts voting results, and carries out credit rewards and punishments to the whole network node according to voting results, lays equal stress on
New calculate node credit score；Assuming that the current credit score of node is Score, then rewards and punishments mode is as shown in table 1 below:
1 credit rewards and punishments mode of table
6) credit score thresholding is set, when the credit score of node is lower than 95, system will inhibit it to become agency's section
Point；
7) using credit score, ballot whether actively, node be added the time of network and node hold the quantity of token as
Cluster feature value, and these characteristic values are normalized, then utilize K-Means clustering algorithm by all nodes of the whole network
Gather for four classes, effect after cluster is as shown in Fig. 2, wherein the degree of belief highest of Cluster 1, central node are gathered around after going normalization
Some credit scores are up to 130, and the degree of belief of Cluster 2 is minimum, and central node goes the trust possessed after normalization product
Divide only 74, in ten wheel competitions, the average credit of every one kind node integrates as shown in table 2 below；
The every class node average credit of table 2 integral
| One wheel | Two wheels | Three-wheel | Four-wheel | Five wheels | Six wheels | Seven wheels | Eight wheels | Nine wheels | Ten wheels | |
| Cluster1 | 101.06 | 102.06 | 103.12 | 104.18 | 105.24 | 106.3 | 107.3 | 108.36 | 109.42 | 110.48 |
| Cluster2 | 100 | 99 | 99 | 98 | 97 | 97 | 97 | 96 | 96 | 95 |
| Cluster3 | 100.98 | 101.04 | 102.02 | 102.02 | 102.02 | 103.02 | 103.08 | 104.08 | 105.06 | 106.04 |
| Cluster4 | 100.98 | 101.98 | 102.96 | 103.96 | 103.96 | 104.96 | 104.96 | 105.96 | 106.94 | 107.94 |
8) after end of clustering, the node in Cluster 1 will have bigger probability to be kept accounts in the competition of next round
Power.Similarly, the node in Cluster 2 possesses minimum degree of belief, and the node in such is remembered in the competition of next round
The probability of account power will be greatly lowered.
Claims (1)
1. a kind of based on the block chain DPOS of credit score and node clustering common recognition mechanism improved method, which is characterized in that specific
Operating procedure is as follows:
1) initialize credit score: credit score is that a kind of credit parameter assigned when block chain network by system is added in node,
It is the important behaviour form of node trusting degree, the node being added to for the first time for each in block chain network, credit score
It will be initialized to 100；
2) new transaction occurs in block chain network, after transaction the whole network node competition book keeping operation, compete successful node by this
The all information of secondary transaction is recorded in the newly generated block of block chain network；
3) system judges block newly generated in network, judges whether it is effective block, if not effective block, then
Judge whether it is malice block again；
4) the whole network node votes to block newly generated in network, is divided into yeas and nays, the table if voting for
Show that the node thinks that newly generated block is effective block, indicates that the node thinks that newly generated block is nothing if voting against
Imitate block even malice block；
5) system counts voting results, and carries out credit rewards and punishments to the whole network node according to voting results, and count again
Operator node credit score；
6) credit score thresholding is set, when the credit score of node is lower than 95, system will inhibit it to become agent node；
7) using credit score, ballot whether actively, node is added the time of network and node holds the quantity of token as clustering
Characteristic value, and these characteristic values are normalized, then all nodes of the whole network are gathered be using K-Means clustering algorithm
Four classes；
8) after end of clustering, credit score highest, ballot is most positive, the time longest of network is added and possesses token quantity most
That high one kind possesses highest degree of belief, such node organized will have bigger probability to be kept accounts in the competition of next round
Power；Similarly, credit score is minimum, ballot is most passive, it is most short that the time of network is added and possesses minimum that of token quantity
Class possesses minimum degree of belief, and the probability that the node in such obtains book keeping operation power in the competition of next round will be greatly lowered.
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| CN111131181A (en) * | 2019-12-05 | 2020-05-08 | 重庆邮电大学 | Reputation mechanism and DPBFT algorithm-based block chain dynamic DPoS consensus method |
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| CN111770103A (en) * | 2020-06-30 | 2020-10-13 | 中国科学技术大学 | Network node security attribute evaluation method based on block chain consensus result feedback |
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Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111131181B (en) * | 2019-12-05 | 2022-02-08 | 重庆邮电大学 | Reputation mechanism and DPBFT algorithm-based block chain dynamic DPoS consensus method |
| CN111131181A (en) * | 2019-12-05 | 2020-05-08 | 重庆邮电大学 | Reputation mechanism and DPBFT algorithm-based block chain dynamic DPoS consensus method |
| CN111182510A (en) * | 2020-01-09 | 2020-05-19 | 重庆邮电大学 | Industrial Internet of things node consensus method based on block chain |
| CN111182510B (en) * | 2020-01-09 | 2022-05-20 | 重庆邮电大学 | Industrial Internet of things node consensus method based on block chain |
| CN111541737A (en) * | 2020-03-25 | 2020-08-14 | 广东工业大学 | AED equipment position sharing method based on block chain |
| CN111541737B (en) * | 2020-03-25 | 2023-10-10 | 广东工业大学 | AED equipment position sharing method based on blockchain |
| CN113472825B (en) * | 2020-03-30 | 2023-01-13 | 中国移动通信集团设计院有限公司 | NB-IoT terminal transaction processing method and device based on block chain |
| CN113472825A (en) * | 2020-03-30 | 2021-10-01 | 中国移动通信集团设计院有限公司 | NB-IoT (NB-IoT) terminal transaction processing method and device based on block chain |
| CN111770103A (en) * | 2020-06-30 | 2020-10-13 | 中国科学技术大学 | Network node security attribute evaluation method based on block chain consensus result feedback |
| CN111770103B (en) * | 2020-06-30 | 2021-12-14 | 中国科学技术大学 | Network node security attribute evaluation method based on block chain consensus result feedback |
| CN112333251A (en) * | 2020-10-26 | 2021-02-05 | 中国电力科学研究院有限公司 | Block chain consensus distributed power transaction agent node selection method and system |
| CN112364388A (en) * | 2020-10-28 | 2021-02-12 | 中车工业研究院有限公司 | Sensor data authentication method and device based on block chain |
| CN112991068B (en) * | 2021-05-20 | 2021-08-20 | 卓尔智联(武汉)研究院有限公司 | Method and device for sharing DPoS (certificate of authority) common identification, electronic equipment and storage medium |
| CN112991068A (en) * | 2021-05-20 | 2021-06-18 | 卓尔智联(武汉)研究院有限公司 | Method and device for sharing DPoS (certificate of authority) common identification, electronic equipment and storage medium |
| CN113438283A (en) * | 2021-06-07 | 2021-09-24 | 北京科技大学 | Improved method of block chain DPOS (distributed data processing System) consensus mechanism based on HK (K-k) clustering |
| CN113438283B (en) * | 2021-06-07 | 2022-09-27 | 北京科技大学 | Improved method of block chain DPOS (distributed data processing System) consensus mechanism based on HK (K-k) clustering |
| CN114465866A (en) * | 2022-01-21 | 2022-05-10 | 北京航空航天大学 | Block chain generation method and device, storage medium and electronic equipment |
| CN114465866B (en) * | 2022-01-21 | 2023-08-15 | 北京航空航天大学 | DPoS method based on trust degree and PBFT |
| CN115081539A (en) * | 2022-07-21 | 2022-09-20 | 交通运输部科学研究院 | Delegation rights and interests certification consensus method and device, electronic equipment and readable storage medium |
| CN115081539B (en) * | 2022-07-21 | 2022-11-15 | 交通运输部科学研究院 | Delegation rights and interests certification consensus method and device, electronic equipment and readable storage medium |
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