[go: up one dir, main page]

CN114254508A - A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism - Google Patents

A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism Download PDF

Info

Publication number
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
Authority
CN
China
Prior art keywords
node
nodes
block chain
language term
probability
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111571605.2A
Other languages
Chinese (zh)
Inventor
陈艺霞
林铭炜
庄丹
姚志强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fujian Normal University
Original Assignee
Fujian Normal University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fujian Normal University filed Critical Fujian Normal University
Priority to CN202111571605.2A priority Critical patent/CN114254508A/en
Publication of CN114254508A publication Critical patent/CN114254508A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Mathematical Optimization (AREA)
  • Signal Processing (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Analysis (AREA)
  • Finance (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Technology Law (AREA)
  • Software Systems (AREA)
  • Algebra (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

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

一种基于区块链股权授权证明共识机制的全关联投票方法A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism

技术领域technical field

本发明涉及区块链共识技术领域，具体涉及了一种基于区块链股权授权证明共识机制的全关联投票方法。The invention relates to the technical field of blockchain consensus, in particular to a fully associated voting method based on a blockchain equity authorization proof consensus mechanism.

背景技术Background technique

区块链可以为不同的应用场景提供可信任的、安全的和高效的环境。它在交通系统、工业物联网系统、医疗信息共享平台和智慧城市等领域都取得了成功的应用。Blockchain can provide a trusted, secure and efficient environment for different application scenarios. It has been successfully applied in transportation systems, industrial IoT systems, medical information sharing platforms, and smart cities.

区块链分别由分布式账本、非对称加密、智能契约和共识机制四种核心技术组成。分布式账本采用分散化的设计理念，构建区块链平台作为分布式网络。用户可以自由加入分布式区块链网络，共同参与交易的记录活动。与此同时，当涉及的人员不断增加时，人们通常无法达成共识。共识机制可以解决如何在分布式环境下的区块链中实现共识的问题。The blockchain consists of four core technologies: distributed ledgers, asymmetric encryption, smart contracts and consensus mechanisms. The distributed ledger adopts a decentralized design concept to build a blockchain platform as a distributed network. Users can freely join the distributed blockchain network and jointly participate in the recording activities of transactions. At the same time, consensus is often not reached when the number of people involved is increasing. The consensus mechanism can solve the problem of how to achieve consensus in the blockchain in a distributed environment.

股权授权证明共识机制是解决共识问题的一种有效和民主的替代方案，要求区块链节点进行投票，选择代表来管理区块链网络，然后由这些代表提出核心变更。在传统的股权授权证明共识方案中，每个区块链节点为每一轮选择中的节点进行投票。当需要n个代表时，选择收到投票次数最多的前n个区块链节点作为代表。The Proof-of-Stake consensus mechanism is an efficient and democratic alternative to the consensus problem, requiring blockchain nodes to vote, selecting representatives to govern the blockchain network, and those representatives then propose core changes. In the traditional proof-of-stake consensus scheme, each blockchain node votes for the nodes in each round of selection. When n representatives are required, the top n blockchain nodes that have received the most votes are selected as representatives.

目前为止，只有重庆邮电大学徐光侠团队的研究专注于如何表示被投票的区块链节点的群体投票信息，但仍面临着重大挑战，即没有考虑到“支持”和“反对”意见的强度，在这种情况下，可能导致信息丢失问题，从而降低知识表达的准确性。因此，需要发明一种解决信息丢失问题和确保投票公平性的股权授权证明机制改进方法。So far, only the research of Xu Guangxia's team at Chongqing University of Posts and Telecommunications has focused on how to represent the group voting information of the voted blockchain nodes, but still faces a major challenge, that is, without considering the strength of "support" and "opposition" opinions, in In this case, it may lead to the problem of information loss, thereby reducing the accuracy of knowledge expression. Therefore, it is necessary to invent an improved method for the proof-of-stake authorization mechanism to solve the problem of information loss and ensure voting fairness.

发明内容SUMMARY OF THE INVENTION

针对现有技术的不足，本发明提供一种为投票节点提供细粒度的投票选项，通过累积可能度和抽签算法优化节点投票过程，以有效地选择最佳代理节点的基于区块链股权授权证明共识机制的全关联投票方法。In view of the deficiencies of the prior art, the present invention provides a fine-grained voting option for voting nodes, and optimizes the node voting process by accumulating probability and a lottery algorithm, so as to effectively select the best proxy node based on blockchain equity authorization certificate A fully associated voting method for the consensus mechanism.

本发明的一种基于区块链股权授权证明共识机制的全关联投票方法，采用以下技术方案：其包括步骤如下：A fully associated voting method based on the blockchain equity authorization proof consensus mechanism of the present invention adopts the following technical solutions: it includes the following steps:

S1：在区块链网络的股权授权证明共识机制中引入对称语言术语集，区块链网络中的每个节点逐个对区块链网络中的所有节点从对称语言术语集中选择一个语言术语进行投票；S1: A symmetric language term set is introduced into the consensus mechanism of the proof-of-stake authorization of the blockchain network, and each node in the blockchain network votes one by one for all nodes in the blockchain network to select a language term from the symmetric language term set ;

S2：收集区块链网络中的每个节点收到所有节点的投票，分别形成每个节点的群体投票信息；S2: Collect the votes of all nodes received by each node in the blockchain network, and form the group voting information of each node;

S3：计算每个节点的群体投票信息中语言术语的概率分布信息，利用比例犹豫模糊语言术语集对每个节点的群体投票信息进行建模，各自生成比例犹豫模糊语言术语集；S3: Calculate the probability distribution information of language terms in the group voting information of each node, use the proportional hesitant fuzzy language term set to model the group voting information of each node, and generate a proportional hesitant fuzzy language term set;

S4：对区块链网络中所有节点的比例犹豫模糊语言术语集进行两两比较，得到二级可能度矩阵，并计算每个节点的累积可能度；S4: Make a pairwise comparison of the proportional hesitant fuzzy language term sets of all nodes in the blockchain network to obtain a secondary probability matrix, and calculate the cumulative probability of each node;

S5：将节点的累积可能度按降序排序，选择排位在前的若干个节点作为代表。S5: Sort the cumulative probability of the nodes in descending order, and select several nodes ranked first as representatives.

进一步，所述步骤S1中，对称语言术语集S＝{s-θ，...，s-2，s-1，s0，s1，s2，...，sθ}，其中θ≥1,如果区块链节点弃权，则使用语言术语s0表示其投票。Further, in the step S1, the symmetric language term set S={s -θ ,..., s -2 , s -1 , s 0 , s 1 , s 2 ,..., s θ }, where θ ≥ 1, if the blockchain node abstains, the linguistic term s 0 is used to denote its vote.

进一步，所述步骤S3中，将每个节点接收到的群体投票信息用比例犹豫模糊语言术语集

Figure RE-GDA0003492853300000021
εμ＝-θ，...，-2，-1，0，1，2，...，θ,θ≥1表示，其中，
Figure RE-GDA0003492853300000022
表示语言术语
Figure RE-GDA0003492853300000023
出现的频率。Further, in the step S3, the group voting information received by each node is used as a proportional hesitant fuzzy language term set
Figure RE-GDA0003492853300000021
ε μ = -θ, ..., -2, -1, 0, 1, 2, ..., θ, θ≥1, where,
Figure RE-GDA0003492853300000022
express language term
Figure RE-GDA0003492853300000023
frequency of occurrence.

进一步，所述步骤S4中，令

Figure RE-GDA0003492853300000024
和
Figure RE-GDA0003492853300000025
为两个比例犹豫模糊语言术语集，其中
Figure RE-GDA0003492853300000026
则二者比较二级可能度矩阵为：Further, in the step S4, let
Figure RE-GDA0003492853300000024
and
Figure RE-GDA0003492853300000025
Hesitant fuzzy language term sets for two scales, where
Figure RE-GDA0003492853300000026
Then the two-level possibility matrix is compared as:

Figure RE-GDA0003492853300000027
Figure RE-GDA0003492853300000027

其中，

Figure RE-GDA0003492853300000028
表示比例犹豫模糊语言术语集
Figure RE-GDA0003492853300000029
大于等于
Figure RE-GDA00034928533000000210
的可能度，满足：in,
Figure RE-GDA0003492853300000028
A set of fuzzy language terms representing proportional hesitation
Figure RE-GDA0003492853300000029
greater or equal to
Figure RE-GDA00034928533000000210
degree of possibility, satisfying:

Figure RE-GDA00034928533000000211
ρij+ρji＝1即
Figure RE-GDA00034928533000000212
Figure RE-GDA00034928533000000213
若
Figure RE-GDA00034928533000000214
则
Figure RE-GDA00034928533000000215
Figure RE-GDA00034928533000000211
ρ ij +ρ ji =1 that is
Figure RE-GDA00034928533000000212
Figure RE-GDA00034928533000000213
like
Figure RE-GDA00034928533000000214
but
Figure RE-GDA00034928533000000215

其中，

Figure RE-GDA00034928533000000216
表示
Figure RE-GDA00034928533000000217
和
Figure RE-GDA00034928533000000218
之间的关系值：in,
Figure RE-GDA00034928533000000216
express
Figure RE-GDA00034928533000000217
and
Figure RE-GDA00034928533000000218
The relationship value between:

Figure RE-GDA0003492853300000031
Figure RE-GDA0003492853300000031

则节点的累积可能度ρi计算为Then the cumulative probability ρ i of the node is calculated as

Figure RE-GDA0003492853300000032
Figure RE-GDA0003492853300000032

进一步，所述的步骤S5中,节点的累积可能度按降序排序为Further, in the described step S5, the cumulative probability of the nodes is sorted in descending order as

Figure RE-GDA0003492853300000033
Figure RE-GDA0003492853300000033

其中，{ρ(1)，ρ(2)，...，ρ(n)}是{ρ1，ρ2，...，ρn}按照ρ(i)≥ρ(j)顺序的降序排列。若ρ(m)≠ρ(m+1)，则选择累积可能度排在前m个的区块链节点作为代表。where { ρ (1) , ρ (2) ,...,ρ (n) } is the descending order of {ρ1,ρ2,..., ρn } in the order of ρ (i) ≥ρ (j) arrangement. If ρ (m) ≠ ρ (m+1) , select the top m blockchain nodes with cumulative likelihood as the representative.

进一步，若

Figure RE-GDA0003492853300000034
Figure RE-GDA0003492853300000035
Further, if
Figure RE-GDA0003492853300000034
Figure RE-GDA0003492853300000035

则先选择累积可能度排在前m-(m1+1)个的区块链节点作为代表，使用彩票算法从

Figure RE-GDA0003492853300000036
选择剩余的m1+1个代理节点。Then first select the blockchain nodes with the top m-(m 1 +1) cumulative likelihood as the representative, and use the lottery algorithm from
Figure RE-GDA0003492853300000036
Select the remaining m 1 +1 proxy nodes.

与现有技术相比，本发明的有益效果如下：Compared with the prior art, the beneficial effects of the present invention are as follows:

1、采用对称语言术语集用于为投票节点提供细粒度的投票选项；1. A symmetric language term set is used to provide fine-grained voting options for voting nodes;

2、采用比例犹豫模糊语言术语集的概念来表示被投票节点接收到的群体投票信息，信息表示更准确，更全面，无信息丢失情况；2. The concept of proportional hesitant fuzzy language term set is used to represent the group voting information received by the voting nodes, the information representation is more accurate and comprehensive, and there is no information loss;

3、采用累积可能度和彩票算法，提出了一种新的区块链节点排序和代理节点选择算法；3. Using the cumulative probability and lottery algorithm, a new blockchain node sorting and proxy node selection algorithm is proposed;

综上所述，使用本发明可以有效解决信息丢失问题，确保区块链股权授权证明共识机制的投票公平性。To sum up, the use of the present invention can effectively solve the problem of information loss and ensure the fairness of voting in the consensus mechanism of blockchain equity authorization proof.

附图说明Description of drawings

此处所说明的附图用来提供对本申请的进一步理解，在附图中：The accompanying drawings described herein are used to provide a further understanding of the present application, in which:

图1为本发明的某个节点收到所有节点的投票示意图；Fig. 1 is a schematic diagram of a node of the present invention receiving votes from all nodes;

图2为本发明的方法流程示意图。FIG. 2 is a schematic flow chart of the method of the present invention.

具体实施方式Detailed ways

参见图1和图2所示，实施例一种基于区块链股权授权证明共识机制的全关联投票方法，其包括步骤如下：Referring to FIG. 1 and FIG. 2, an embodiment of a fully associated voting method based on a blockchain equity authorization proof consensus mechanism includes the following steps:

S1：在区块链网络的股权授权证明共识机制中引入对称语言术语集，区块链网络中的每个节点逐个对区块链网络中的所有节点从对称语言术语集中选择一个语言术语进行投票；S1: A symmetric language term set is introduced into the consensus mechanism of the proof-of-stake authorization of the blockchain network, and each node in the blockchain network votes one by one for all nodes in the blockchain network to select a language term from the symmetric language term set ;

S2：收集区块链网络中的每个节点收到所有节点的投票，分别形成每个节点的群体投票信息；S2: Collect the votes of all nodes received by each node in the blockchain network, and form the group voting information of each node;

S3：计算每个节点的群体投票信息中语言术语的概率分布信息，利用比例犹豫模糊语言术语集方法对每个节点的群体投票信息进行建模，各自生成比例犹豫模糊语言术语集；S3: Calculate the probability distribution information of language terms in the group voting information of each node, use the proportional hesitant fuzzy language term set method to model the group voting information of each node, and generate proportional hesitant fuzzy language term sets respectively;

S4：对区块链网络中所有节点的比例犹豫模糊语言术语集进行两两比较，得到二级可能度矩阵，并计算每个节点的累积可能度；S4: Make a pairwise comparison of the proportional hesitant fuzzy language term sets of all nodes in the blockchain network to obtain a secondary probability matrix, and calculate the cumulative probability of each node;

S5：将节点的累积可能度按降序排序，选择排位在前的若干个节点作为代表。S5: Sort the cumulative probability of the nodes in descending order, and select several nodes ranked first as representatives.

在步骤S1中，本实例中假设有21个投票节点，每个节点可以对所有节点进行投票，从对称语言术语集S＝{s-2＝“Very opposed”,s-1＝“Opposed”,s0＝“Neutral”,s1＝“Supported”, s2＝“Very supported”}中选择语言术语表达自己的偏好信息。In step S1, it is assumed that there are 21 voting nodes in this example, and each node can vote on all nodes. From the symmetric language term set S = {s -2 = "Very opposed", s -1 = "Opposed", s 0 = "Neutral", s 1 = "Supported", s 2 = "Very supported"}, choose language terms to express your preference information.

在步骤S2中，本实例中该矩阵表示21个节点收到的投票信息表如下：In step S2, in this example, the matrix represents the voting information table received by 21 nodes as follows:

Figure RE-GDA0003492853300000041
Figure RE-GDA0003492853300000041

Figure RE-GDA0003492853300000051
Figure RE-GDA0003492853300000051

表1投票信息表在步骤S3中，21个节点的群体投票信息可以建模为比例犹豫模糊语言术语集形式：Table 1 Voting information table In step S3, the group voting information of 21 nodes can be modeled as a proportional hesitant fuzzy language term set form:

Figure RE-GDA0003492853300000052
Figure RE-GDA0003492853300000052

Figure RE-GDA0003492853300000053
Figure RE-GDA0003492853300000053

Figure RE-GDA0003492853300000054
Figure RE-GDA0003492853300000054

Figure RE-GDA0003492853300000055
Figure RE-GDA0003492853300000055

Figure RE-GDA0003492853300000056
Figure RE-GDA0003492853300000056

Figure RE-GDA0003492853300000057
Figure RE-GDA0003492853300000057

Figure RE-GDA0003492853300000058
Figure RE-GDA0003492853300000058

Figure RE-GDA0003492853300000059
Figure RE-GDA0003492853300000059

Figure RE-GDA00034928533000000510
Figure RE-GDA00034928533000000510

Figure RE-GDA00034928533000000511
Figure RE-GDA00034928533000000511

Figure RE-GDA00034928533000000512
Figure RE-GDA00034928533000000512

Figure RE-GDA0003492853300000061
Figure RE-GDA0003492853300000061

Figure RE-GDA0003492853300000062
Figure RE-GDA0003492853300000062

Figure RE-GDA0003492853300000063
Figure RE-GDA0003492853300000063

Figure RE-GDA0003492853300000064
Figure RE-GDA0003492853300000064

Figure RE-GDA0003492853300000065
Figure RE-GDA0003492853300000065

Figure RE-GDA0003492853300000066
Figure RE-GDA0003492853300000066

Figure RE-GDA0003492853300000067
Figure RE-GDA0003492853300000067

Figure RE-GDA0003492853300000068
Figure RE-GDA0003492853300000068

Figure RE-GDA0003492853300000069
Figure RE-GDA0003492853300000069

Figure RE-GDA00034928533000000610
Figure RE-GDA00034928533000000610

在步骤S4中，对所有区块链节点进行两两比较，得到二级可能度矩阵表如下：In step S4, all blockchain nodes are compared in pairs, and the second-level possibility matrix table is obtained as follows:

Figure RE-GDA00034928533000000611
Figure RE-GDA00034928533000000611

表2二级可能度矩阵表Table 2 Level 2 Possibilities Matrix

在步骤S4中，计算每个区块链节点i的累积可能度，每个区块链节点i的累积可能度ρi计算为：In step S4, the cumulative probability of each blockchain node i is calculated, and the cumulative probability ρi of each blockchain node i is calculated as:

Figure RE-GDA0003492853300000071
Figure RE-GDA0003492853300000071

Figure RE-GDA0003492853300000072
Figure RE-GDA0003492853300000072

Figure RE-GDA0003492853300000073
Figure RE-GDA0003492853300000073

Figure RE-GDA0003492853300000074
Figure RE-GDA0003492853300000074

Figure RE-GDA0003492853300000075
Figure RE-GDA0003492853300000075

Figure RE-GDA0003492853300000076
Figure RE-GDA0003492853300000076

Figure RE-GDA0003492853300000077
Figure RE-GDA0003492853300000077

Figure RE-GDA0003492853300000078
Figure RE-GDA0003492853300000078

Figure RE-GDA0003492853300000079
Figure RE-GDA0003492853300000079

Figure RE-GDA00034928533000000710
Figure RE-GDA00034928533000000710

Figure RE-GDA00034928533000000711
Figure RE-GDA00034928533000000711

在步骤S5中，区块链节点的累积可能度按降序排序为：In step S5, the cumulative likelihood of blockchain nodes 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。 N15 >N1> N16 > N9 > N3 > N13 > N10 > N4 > N2 > N8 > N5 > N18 > N12 > N17 > N7 > N6 > N11 >N 14 >N 21 >N 19 >N 20 .

选择累积可能度排在前5个的区块链节点作为代表，即节点15、1、16、9、3被挑选为代理节点。The blockchain nodes with the top 5 cumulative likelihoods are selected as representatives, that is, nodes 15, 1, 16, 9, and 3 are selected as proxy nodes.

尽管已经示出和描述了本发明的实施例，对于本领域的普通技术人员而言，可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由所附权利要求及其等同物限定。Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by 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,
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,
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.
CN202111571605.2A 2021-12-21 2021-12-21 A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism Pending CN114254508A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111571605.2A CN114254508A (en) 2021-12-21 2021-12-21 A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111571605.2A CN114254508A (en) 2021-12-21 2021-12-21 A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism

Publications (1)

Publication Number Publication Date
CN114254508A true CN114254508A (en) 2022-03-29

Family

ID=80796267

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111571605.2A Pending CN114254508A (en) 2021-12-21 2021-12-21 A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism

Country Status (1)

Country Link
CN (1) CN114254508A (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
韩二东: "犹豫模糊语言多准则决策方法及项目群管理应用研究", 30 April 2021 *

Similar Documents

Publication Publication Date Title
Bornmann et al. BRICS countries and scientific excellence: A bibliometric analysis of most frequently cited papers
Kalyanam et al. Leveraging social context for modeling topic evolution
CN111475724B (en) A random walk social network event recommendation method based on user similarity
Wong et al. On the efficiency of social recommender networks
Zhang et al. Mechanism design for finding experts using locally constructed social referral web
Liu et al. Spatial association and explanation of China’s digital financial inclusion development based on the network analysis method
Molchan et al. The'digitalisation trap'of Russian regions
Jiang et al. Message clustering based matrix factorization model for retweeting behavior prediction
CN115022326A (en) Blockchain Byzantine Fault Tolerant Consensus Method Based on Collaborative Filtering Recommendation
Zuo et al. Trust computing for social networking
CN114254508A (en) A Fully Associated Voting Method Based on Blockchain Equity Authorization Proof Consensus Mechanism
Jia et al. Multilayer social network overlapping community detection algorithm based on trust relationship
CN106611339B (en) Seed user screening method, product user influence evaluation method and device
Lin et al. Reducing Uncertainty of Probabilistic Top-$ k $ Ranking via Pairwise Crowdsourcing
Xu et al. How does persuasion happen? a novel bounded confidence opinion dynamics model based on social judgment theory
CN107358533A (en) A kind of user of Web Community recommends method and system
CN116155683A (en) Construction method of HotStuff consensus mechanism based on three-layer tree communication structure
Alirezaee et al. New analytical hierarchical process/data envelopment analysis methodology for ranking decision‐making units
CN109410078A (en) A kind of information propagation prediction method for the mobile social networking shared suitable for object oriented file
Chiu et al. Endogenous preferential treatment in centralized admissions
Candellone et al. Negative ties highlight hidden extremes in social media polarization
Buccafurri et al. Interest assortativity in Twitter
CN113590826A (en) Big data-based engineering project bid inviting management system and method
Yao et al. Multi-objective optimization model and evolutional solution of network node matching problem
Perez-Rivera et al. Engaging for Puerto Rico:# RickyRenuncia (and# RickySeQueda) during El Verano del 19 and digital identities

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20220329

RJ01 Rejection of invention patent application after publication