CN114254508A - Fully-associative voting method based on block link right authorization certification consensus mechanism - Google Patents

Fully-associative voting method based on block link right authorization certification consensus mechanism Download PDF

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CN114254508A
CN114254508A CN202111571605.2A CN202111571605A CN114254508A CN 114254508 A CN114254508 A CN 114254508A CN 202111571605 A CN202111571605 A CN 202111571605A CN 114254508 A CN114254508 A CN 114254508A
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陈艺霞
林铭炜
庄丹
姚志强
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Abstract

The invention relates to a fully-associative voting method based on a block link share right authorization certification consensus mechanism, which comprises the steps that firstly, each node selects the preference information of a certain node from a symmetrical language term set to vote; collecting voting information of all the block chain nodes, calculating probability distribution information of the language terms, and modeling group voting information of each block chain node by using a proportional hesitation fuzzy language term set; in block chain networksnBlock chain link pointMay be expressed asnEach scale is hesitant to obscure the set of language terms; comparing every two link points of all blocks to obtain a secondary probability matrix; then calculating each block chain nodeiCumulative likelihood of (d); will be provided withnThe accumulated possibility degrees of the block chain nodes are sorted in descending order; finally according to the preset number of the agent nodesmSelecting cumulative probability rank aheadmThe individual block chain link points are taken as representatives, and the fairness of the share right authorization certification consensus mechanism is effectively improved by adopting the technical scheme.

Description

Fully-associative voting method based on block link right authorization certification consensus mechanism
Technical Field
The invention relates to the technical field of block chain consensus, in particular to a fully-associative voting method based on a block chain share right certification consensus mechanism.
Background
The blockchain may provide a trusted, secure and efficient environment for different application scenarios. The method is successfully applied to the fields of traffic systems, industrial Internet of things systems, medical information sharing platforms, smart cities and the like.
The block chain is composed of four core technologies of a distributed account book, asymmetric encryption, an intelligent contract and a consensus mechanism. The distributed account book adopts a decentralized design concept, and a block chain platform is constructed to serve as a distributed network. Users can freely join the distributed block chain network and participate in the recording activity of the transaction together. At the same time, as the number of people involved increases, people often do not reach consensus. The consensus mechanism may solve the problem of how to achieve consensus in blockchains in a distributed environment.
The equity grant proof consensus mechanism is an effective and democratic alternative to the consensus problem, requiring block chain nodes to vote, representatives to manage the block chain network, and then core changes are proposed by these representatives. In a conventional equity certificate consensus scheme, each blockchain node votes for the nodes in each round of selection. When n representatives are needed, the first n block chain link points with the largest number of votes received are selected as the representatives.
So far, only the research of the Chongqing Chongshielman team at the Chongqing post and telecommunications university focuses on how to represent group voting information of voted block link points, but still faces a significant challenge, i.e. does not consider the strength of "support" and "opposition" opinions, in which case, information loss problems may result, thereby reducing the accuracy of knowledge expression. Therefore, there is a need to invent an improved method for the equity proof mechanism to solve the information loss problem and ensure the fairness of voting.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a fully-associative voting method which provides fine-grained voting options for voting nodes, optimizes the node voting process through the cumulative probability and the drawing algorithm and effectively selects the best agent node based on a block link right authorization certification consensus mechanism.
The invention relates to a fully-associative voting method based on a block link right authorization certification consensus mechanism, which adopts the following technical scheme: the method comprises the following steps:
s1: introducing a symmetric language term set into a shareholder authority certification consensus mechanism of the blockchain network, and voting by each node in the blockchain network one by one on all nodes in the blockchain network by selecting one language term from the symmetric language term set;
s2: each node in the block chain network of the collection region receives votes of all the nodes, and group voting information of each node is formed respectively;
s3: calculating probability distribution information of language terms in the group voting information of each node, modeling the group voting information of each node by using a proportional hesitation fuzzy language term set, and respectively generating a proportional hesitation fuzzy language term set;
s4: pairwise comparison is carried out on the proportional hesitation fuzzy language term sets of all nodes in the block chain network to obtain a secondary probability matrix, and the accumulated probability of each node is calculated;
s5: and sorting the accumulated possibility degrees of the nodes in a descending order, and selecting a plurality of nodes ranked at the top as representatives.
Further, in the step S1, the set of symmetric language terms S ═ S-θ，...，s-2，s-1，s0，s1，s2，...，sθWhere θ ≧ 1, if the block link point is disclaimed, then the term s is used0Indicating its vote.
Further, in the step S3, the group voting information received by each node is defined by a fuzzy language term set with a certain hesitation ratio
Figure RE-GDA0003492853300000021
εμ-2, -1, 0, 1, 2, theta ≧ 1, where,
Figure RE-GDA0003492853300000022
meaning language term
Figure RE-GDA0003492853300000023
The frequency of occurrence.
Further, in the step S4, let
Figure RE-GDA0003492853300000024
And
Figure RE-GDA0003492853300000025
is a two-scale hesitant fuzzy language term set, wherein
Figure RE-GDA0003492853300000026
Then the two comparison secondary likelihood matrices are:
Figure RE-GDA0003492853300000027
wherein the content of the first and second substances,
Figure RE-GDA0003492853300000028
representing a set of proportional hesitant ambiguous language terms
Figure RE-GDA0003492853300000029
Is greater than or equal to
Figure RE-GDA00034928533000000210
The probability of satisfying:
Figure RE-GDA00034928533000000211
ρij+ρ ji1 is that
Figure RE-GDA00034928533000000212
Figure RE-GDA00034928533000000213
If it is
Figure RE-GDA00034928533000000214
Then
Figure RE-GDA00034928533000000215
Wherein the content of the first and second substances,
Figure RE-GDA00034928533000000216
to represent
Figure RE-GDA00034928533000000217
And
Figure RE-GDA00034928533000000218
the relationship value between:
Figure RE-GDA0003492853300000031
the cumulative probability p of the nodeiIs calculated as
Figure RE-GDA0003492853300000032
Further, in step S5, the cumulative likelihood of the nodes is sorted in descending order
Figure RE-GDA0003492853300000033
Wherein { p }(1)，ρ(2)，...，ρ(n)Is { ρ1，ρ2，...，ρnAccording to rho(i)≥ρ(j)The order is in descending order. If ρ(m)≠ρ(m+1)Then, the block link points with accumulated probability ranked in the top m are selected as representatives.
Further, if
Figure RE-GDA0003492853300000034
Figure RE-GDA0003492853300000035
Then the first m- (m) with the accumulated likelihood ranking is selected1+1) block chain nodes as representatives, using a lottery algorithm
Figure RE-GDA0003492853300000036
Selecting the remaining m1+1 proxy nodes.
Compared with the prior art, the invention has the following beneficial effects:
1. adopting a symmetric language term set to provide fine-grained voting options for voting nodes;
2. the concept of a proportional hesitation fuzzy language term set is adopted to represent the group voting information received by the voting nodes, the information representation is more accurate and more comprehensive, and no information loss condition exists;
3. by adopting the accumulative probability and lottery algorithm, a new block chain link point sequencing and proxy node selection algorithm is provided;
in conclusion, the invention can effectively solve the problem of information loss and ensure the voting fairness of the block link right authorization certification consensus mechanism.
Drawings
The accompanying drawings, which are described herein to provide a further understanding of the application, are included in the following description:
FIG. 1 is a diagram illustrating a node receiving votes from all nodes according to the present invention;
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
Referring to fig. 1 and fig. 2, a fully associative voting method based on a block link right proof of identity mechanism according to an embodiment includes the following steps:
s1: introducing a symmetric language term set into a shareholder authority certification consensus mechanism of the blockchain network, and voting by each node in the blockchain network one by one on all nodes in the blockchain network by selecting one language term from the symmetric language term set;
s2: each node in the block chain network of the collection region receives votes of all the nodes, and group voting information of each node is formed respectively;
s3: calculating probability distribution information of language terms in the group voting information of each node, modeling the group voting information of each node by using a proportional hesitation fuzzy language term set method, and respectively generating proportional hesitation fuzzy language term sets;
s4: pairwise comparison is carried out on the proportional hesitation fuzzy language term sets of all nodes in the block chain network to obtain a secondary probability matrix, and the accumulated probability of each node is calculated;
s5: and sorting the accumulated possibility degrees of the nodes in a descending order, and selecting a plurality of nodes ranked at the top as representatives.
In step S1, assuming 21 voting nodes in this example, each node can vote for all nodes, from the set of symmetric language terms S ═ S-2＝“Very opposed”,s-1＝“Opposed”,s0＝“Neutral”,s1＝“Supported”, s2The language term is selected to express its own preference information in "Very supported".
In step S2, the matrix represents the voting information table received by 21 nodes in this example as follows:
Figure RE-GDA0003492853300000041
Figure RE-GDA0003492853300000051
table 1 voting information table in step S3, the group voting information of 21 nodes can be modeled as a proportional hesitation fuzzy language term set form:
Figure RE-GDA0003492853300000052
Figure RE-GDA0003492853300000053
Figure RE-GDA0003492853300000054
Figure RE-GDA0003492853300000055
Figure RE-GDA0003492853300000056
Figure RE-GDA0003492853300000057
Figure RE-GDA0003492853300000058
Figure RE-GDA0003492853300000059
Figure RE-GDA00034928533000000510
Figure RE-GDA00034928533000000511
Figure RE-GDA00034928533000000512
Figure RE-GDA0003492853300000061
Figure RE-GDA0003492853300000062
Figure RE-GDA0003492853300000063
Figure RE-GDA0003492853300000064
Figure RE-GDA0003492853300000065
Figure RE-GDA0003492853300000066
Figure RE-GDA0003492853300000067
Figure RE-GDA0003492853300000068
Figure RE-GDA0003492853300000069
Figure RE-GDA00034928533000000610
in step S4, two-by-two comparison is performed on all block link points to obtain a secondary likelihood matrix table as follows:
Figure RE-GDA00034928533000000611
TABLE 2 two-level likelihood matrix table
In step S4, the cumulative likelihood of each blockchain node i, the cumulative likelihood ρ of each blockchain node i, is calculatediThe calculation is as follows:
Figure RE-GDA0003492853300000071
Figure RE-GDA0003492853300000072
Figure RE-GDA0003492853300000073
Figure RE-GDA0003492853300000074
Figure RE-GDA0003492853300000075
Figure RE-GDA0003492853300000076
Figure RE-GDA0003492853300000077
Figure RE-GDA0003492853300000078
Figure RE-GDA0003492853300000079
Figure RE-GDA00034928533000000710
Figure RE-GDA00034928533000000711
in step S5, the accumulated likelihood of the tile link points is sorted in descending order as:
N15＞N1＞N16＞N9＞N3＞N13＞N10＞N4＞N2＞N8＞N5＞N18＞N12＞N17＞N7＞N6＞N11＞N14＞N21＞N19＞N20。
the block link points with accumulated likelihood ranking in the top 5 are selected as representatives, i.e., nodes 15, 1, 16, 9, 3 are picked as proxy nodes.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. A fully-associative voting method based on a block link right authorization certification consensus mechanism is characterized by comprising the following steps: the method comprises the following steps:
s1: introducing a symmetric language term set into a shareholder authority certification consensus mechanism of the blockchain network, and voting by each node in the blockchain network one by one on all nodes in the blockchain network by selecting one language term from the symmetric language term set;
s2: each node in the block chain network of the collection region receives votes of all the nodes, and group voting information of each node is formed respectively;
s3: calculating probability distribution information of language terms in the group voting information of each node, modeling the group voting information of each node by using a proportional hesitation fuzzy language term set, and respectively generating a proportional hesitation fuzzy language term set;
s4: pairwise comparison is carried out on the proportional hesitation fuzzy language term sets of all nodes in the block chain network to obtain a secondary probability matrix, and the accumulated probability of each node is calculated;
s5: and sorting the accumulated possibility degrees of the nodes in a descending order, and selecting a plurality of nodes ranked at the top as representatives.
2. The method of claim 1, wherein the method comprises: in step S1, the set of symmetric language terms S ═ S-θ，...，s-2，s-1，s0，s1，s2，...，sθWhere θ ≧ 1, if the block link point is disclaimed, then the term s is used0Indicating its vote.
3. The method of claim 1, wherein the method comprises: in step S3, the group voting information received by each node is defined by a fuzzy language term set with a certain hesitation ratio
Figure FDA0003423935730000011
εμ-2, -1, 0, 1, 2, theta ≧ 1, where,
Figure FDA0003423935730000012
meaning language term
Figure FDA0003423935730000013
The frequency of occurrence.
4. The method of claim 1, wherein the method comprises: in the step S4, let
Figure FDA0003423935730000014
And
Figure FDA0003423935730000015
is a two-scale hesitant fuzzy language term set, wherein
Figure FDA0003423935730000016
Figure FDA0003423935730000017
Then the two comparison secondary likelihood matrices are:
Figure FDA0003423935730000018
wherein the content of the first and second substances,
Figure FDA0003423935730000019
representing a set of proportional hesitant ambiguous language terms
Figure FDA00034239357300000110
Is greater than or equal to
Figure FDA00034239357300000111
The probability of satisfying:
Figure FDA0003423935730000021
ρij+ρji1 is that
Figure FDA0003423935730000022
Figure FDA0003423935730000023
If it is
Figure FDA0003423935730000024
Then
Figure FDA0003423935730000025
Wherein the content of the first and second substances,
Figure FDA0003423935730000026
to represent
Figure FDA0003423935730000027
And
Figure FDA0003423935730000028
the relationship value between:
Figure FDA0003423935730000029
the cumulative probability p of the nodeiIs calculated as
Figure FDA00034239357300000210
5. The method of claim 1, wherein the method comprises: in the step S5, the cumulative probability of the nodes is sorted in descending order
Figure FDA00034239357300000211
Wherein { p }(1)，ρ(2)，...，ρ(n)Is { ρ1，ρ2，...，ρnAccording to rho(i)≥ρ(j)The order is in descending order. If ρ(m)≠ρ(m+1)Then, the block link points with accumulated probability ranked in the top m are selected as representatives.
6. The method of claim 5, wherein the method comprises: if it is
Figure FDA00034239357300000212
Figure FDA00034239357300000213
Then the first m- (m) with the accumulated likelihood ranking is selected1+1) block chain nodes as representatives, using a lottery algorithm
Figure FDA00034239357300000214
Selecting the remaining m1+1 proxy nodes.
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CN109872152A (en) * 2019-01-11 2019-06-11 平安科技（深圳）有限公司 Block chain common recognition method and relevant device based on share authorisation verification mechanism
CN113709222A (en) * 2021-08-16 2021-11-26 重庆邮电大学 Method for selecting proxy nodes in block chain based on improved weighted score function

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US10200196B1 (en) * 2018-04-25 2019-02-05 Blockchain Asics Llc Cryptographic ASIC with autonomous onboard permanent storage
CN109783879A (en) * 2018-12-21 2019-05-21 西安电子科技大学 A kind of radar emitter signal discrimination efficiency appraisal procedure and system
CN109872152A (en) * 2019-01-11 2019-06-11 平安科技（深圳）有限公司 Block chain common recognition method and relevant device based on share authorisation verification mechanism
CN113709222A (en) * 2021-08-16 2021-11-26 重庆邮电大学 Method for selecting proxy nodes in block chain based on improved weighted score function

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