CN111556149A - Node selection method and device based on Raft consensus algorithm - Google Patents
Node selection method and device based on Raft consensus algorithm Download PDFInfo
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- CN111556149A CN111556149A CN202010344275.2A CN202010344275A CN111556149A CN 111556149 A CN111556149 A CN 111556149A CN 202010344275 A CN202010344275 A CN 202010344275A CN 111556149 A CN111556149 A CN 111556149A
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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
- H04L67/104—Peer-to-peer [P2P] networks
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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
- H04L67/104—Peer-to-peer [P2P] networks
- H04L67/1061—Peer-to-peer [P2P] networks using node-based peer discovery mechanisms
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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
- H04L67/104—Peer-to-peer [P2P] networks
- H04L67/1074—Peer-to-peer [P2P] networks for supporting data block transmission mechanisms
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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
Abstract
The embodiment of the application discloses a node selection method and a device based on a Raft consensus algorithm, and the method comprises the following steps: determining the right-to-gain ratio corresponding to each candidate node according to a preset distribution principle; distributing marks to each candidate node according to the right-to-gain ratio corresponding to each candidate node; and selecting a target mark from the marks, and taking a candidate node corresponding to the target mark as a target node. Therefore, by carrying out rights and interests distribution on each node, carrying out mark distribution according to the rights and interests ratio corresponding to each node, selecting the target mark, taking the corresponding candidate node as the target node, and selecting the node through the determination of the rights and interests ratio and the selection of the target mark, the target node can be determined quickly, and the selection of the node is completed.
Description
Technical Field
The application relates to the technical field of internet, in particular to a node selection method and device based on a Raft consensus algorithm.
Background
At present, the block chain technology has a wider application prospect as a distributed shared database based on point-to-point network propagation. The blockchain technology has a decentralized feature, and when information transmission and data storage are performed, consistency and correctness of all accounting nodes in the blockchain need to be guaranteed through a consensus algorithm.
The method comprises the steps of performing distributed consensus in two stages, selecting a leader node in the first stage, and performing logic processing state updating, block accounting and the like in the second stage. In the Raft consensus algorithm, the problem that the efficiency of selecting a leader node is low exists in the first stage.
Disclosure of Invention
In view of this, embodiments of the present application provide a node selection method and apparatus based on a Raft consensus algorithm, which can implement node selection with higher efficiency.
In order to solve the above problem, the technical solution provided by the embodiment of the present application is as follows:
in a first aspect, the present application provides a node selection method based on a Raft consensus algorithm, the method including:
determining the right-to-gain ratio corresponding to each candidate node according to a preset distribution principle;
distributing marks to each candidate node according to the right-to-gain ratio corresponding to each candidate node;
and selecting a target mark from the marks, and taking a candidate node corresponding to the target mark as a target node.
Optionally, when the label is a digital label, the allocating a label to each candidate node according to the right-to-gain ratio corresponding to each candidate node respectively includes:
acquiring a digital mark sequence; the digital mark sequence is a digital mark sequence consisting of digital marks with different numerical values;
and according to the right-gain ratio corresponding to each candidate node, respectively acquiring the number marks corresponding to the right-gain ratio from the number mark sequence, and distributing the number marks to the corresponding candidate nodes.
Optionally, the selecting a target mark from the marks includes:
randomly selecting one of the numerical marks allocated to the candidate nodes as a target mark.
Optionally, the allocation principle is to allocate the corresponding rights and interests based on the hardware performance and/or the resource proportion corresponding to the candidate node.
Optionally, the method further includes:
and when the target node does not receive the feedback information of other candidate nodes within the preset time, resetting the target node as a candidate node and reselecting the node.
In a second aspect, the present application provides a node selection apparatus based on a Raft consensus algorithm, the apparatus comprising:
the right-interest determining unit is used for determining the right-interest ratio corresponding to each candidate node according to a preset distribution principle;
the mark distribution unit is used for distributing marks to the candidate nodes according to the right-to-gain ratios corresponding to the candidate nodes;
a selection unit configured to select a target marker from the markers;
and the marking unit is used for taking the candidate node corresponding to the target mark as the target node.
Optionally, when the mark is a digital mark, the mark allocating unit is configured to acquire a digital mark sequence; the digital mark sequence is a digital mark sequence consisting of digital marks with different numerical values;
and according to the right-gain ratio corresponding to each candidate node, respectively acquiring the number marks corresponding to the right-gain ratio from the number mark sequence, and distributing the number marks to the corresponding candidate nodes.
Optionally, the selecting unit is specifically configured to randomly select one of the numerical labels assigned to the candidate nodes as the target label.
Optionally, the allocation principle is to allocate the corresponding rights and interests based on the hardware performance and/or the resource proportion corresponding to the candidate node.
Optionally, the apparatus further comprises:
and the reselection device is used for resetting the target node as the candidate node and reselecting the node when the target node does not receive the feedback information of other candidate nodes within the preset time.
Therefore, the embodiment of the application has the following beneficial effects:
according to the node selection method and device based on the Raft consensus algorithm, the right-to-gain ratio corresponding to each candidate node is determined according to the preset distribution principle; distributing marks to each candidate node according to the right-to-gain ratio corresponding to each candidate node; and selecting a target mark from the marks, and taking a candidate node corresponding to the target mark as a target node. Therefore, by carrying out rights and interests distribution on each node, carrying out distribution of marks according to the rights and interests ratio corresponding to each node, selecting the target mark, taking the corresponding node as the target node, and selecting the node through determination of the rights and interests ratio and selection of the target mark, the target node can be determined quickly, and the selection of the node is completed.
Drawings
Fig. 1 is a flowchart of a node selection method based on a Raft consensus algorithm according to an embodiment of the present application;
fig. 2 is an exemplary diagram of a node selection method based on a Raft consensus algorithm according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a node selection device based on a Raft consensus algorithm according to an embodiment of the present application.
Detailed Description
In order to facilitate understanding and explaining the technical solutions provided by the embodiments of the present application, the following description will first describe the background art of the present application.
The traditional Raft consensus algorithm selects a leader node from the nodes of the blockchain network, the leader node interacts with the client in one period, and has the broadcasting right and the accounting right of the blockchain network in one period, and after the period is finished, the next leader node is selected. And initializing the states of all nodes in the block chain network in the selected initial state, setting all the nodes as the crowd nodes, and initiating election by the node with the preset waiting time when the crowd nodes do not receive heartbeat from the leader node in a period, namely selecting the leader node. The preset waiting time of each node is randomly set, and may be different. Correspondingly, the crowd nodes are changed into candidate nodes, votes for the crowd nodes, sends a vote application to other nodes, and the other nodes perform corresponding votes. If the number of votes of the node is the largest, the node can become a leader node; if other nodes become leader nodes, the nodes are converted into crowd nodes; if the leader node is not generated within a period of time, the candidate state is kept, and election is initiated again.
After researching the traditional consensus algorithm, particularly the Raft consensus algorithm, the inventor finds that in the process of leader node selection of the Raft consensus algorithm, a plurality of node states and a plurality of stages of waiting period, election period and voting period exist, so that the leader node selection efficiency is low.
Based on this, the embodiment of the application provides a node selection method based on a Raft consensus algorithm, and firstly, according to a preset allocation principle, the right-to-gain ratio corresponding to each candidate node is determined; secondly, distributing marks to each candidate node according to the right-to-gain ratio corresponding to each candidate node; and finally, selecting a target mark from the marks, and taking a candidate node corresponding to the target mark as a target node.
In order to facilitate understanding of the technical solutions provided by the embodiments of the present application, a node selection method based on a Raft consensus algorithm provided by the embodiments of the present application is described below with reference to the accompanying drawings.
Referring to fig. 1, the figure is a flowchart of a node selection method based on a Raft consensus algorithm according to an embodiment of the present application, where the method includes steps S101 to S103.
S101: and determining the right-to-gain ratio corresponding to each candidate node according to a preset distribution principle.
Before the selection of the node is carried out, the node meeting the node candidate requirement in the block chain network can be used as a candidate node so as to carry out the determination of the right-to-gain ratio and the selection of the node. In the embodiment of the present application, the node candidate requirement to be met by the node updated as the candidate node is not limited, and in a possible implementation manner, all nodes in the blockchain network may be updated as the candidate node to participate in the selection of the node.
And determining the right-to-gain ratio corresponding to each candidate node according to a preset distribution principle. The allocation principle may be a method for allocating rights and interests to the nodes.
Since the conventional Raft consensus algorithm does not consider the properties of the nodes in the selection process of the leader node, it can be understood that the performance of each node in the block chain network may be different, and when the node with better performance is selected as the leader node, the overall efficiency of the block chain network is improved. In this embodiment of the present application, the allocation principle may be to allocate the corresponding rights and interests based on the hardware performance and/or resource proportion corresponding to the candidate node, specifically, more rights and interests may be allocated to the nodes with better hardware performance and/or more resource proportion, the node rights and interests may be calculated according to the hardware performance and/or resource proportion of the nodes, and the rights and interests may be determined according to the obtained calculation result. The hardware performance may be performance of hardware equipment corresponding to the node, and the resource occupation may be a proportion of various resources possessed by the node in resources occupied by the entire node, where the resource may be capital investment possessed by the node or network resources possessed by the node. And corresponding rights and interests are allocated according to the hardware performance and/or resource proportion corresponding to the nodes, so that the nodes with better hardware performance and/or more resources are more likely to be selected, and the efficiency of the whole blockchain network is improved.
S102: and respectively allocating marks to each candidate node according to the right-to-gain ratio corresponding to each candidate node.
A label is assigned to each candidate node corresponding to the equity to share ratio each candidate node has. It will be appreciated that the labels assigned to the various candidate nodes are different in order to make the selection of the target node. The equity ratios corresponding to the candidate nodes may be assigned tokens according to the method of selecting the target token from the tokens. For example, a corresponding number of labels may be assigned according to the equity proportion of each candidate node, and a candidate node with a higher equity proportion may have a larger number of labeled candidate nodes, and when selecting a target label, the probability of selecting the node may be higher.
In this embodiment, a specific manner of assigning a label to a candidate node is not limited, and in a possible implementation manner, a label may be randomly assigned to the candidate node according to a right-to-gain ratio through a random algorithm.
In a possible implementation manner, when the label is a digital label, the label is respectively allocated to each candidate node according to the right-to-gain ratio corresponding to each candidate node, and specifically the method may include the following two steps:
a1: acquiring a digital mark sequence; wherein, the digital mark sequence is a digital mark sequence consisting of digital marks with different numerical values.
When marked as a numeric token, a sequence of numeric tokens may be composed of numeric tokens of different numeric values. In the embodiment of the present application, the numerical value of the numerical mark is not limited, and the numerical value obtained by random calculation through a random algorithm may be used as the numerical value of the numerical mark. The sequence of the digital mark sequence is not limited in this embodiment. The digital marks can be arranged in the order from big to small or from small to big, or can be randomly arranged.
A2: and according to the right-gain ratio corresponding to each candidate node, respectively acquiring the number marks corresponding to the right-gain ratio from the number mark sequence, and distributing the number marks to the corresponding candidate nodes.
And acquiring a certain number of digital marks from the digital mark sequence according to the right-to-gain ratio corresponding to each candidate node, wherein the number corresponds to the right-to-gain ratio corresponding to the candidate node. The acquired digital mark can be randomly acquired from the digital mark sequence or acquired from the digital mark sequence according to a certain sequence.
And distributing the acquired digital mark to a corresponding candidate node, wherein the candidate node corresponds to the distributed digital mark.
S103: and selecting a target mark from the marks, and taking a candidate node corresponding to the target mark as a target node.
The target marker is selected from the markers, and it should be noted that the markers are markers assigned to the candidate nodes, that is, the target marker is selected from the markers assigned to the candidate nodes.
The selection method of the target marker is not limited in the embodiments of the present application, and in a possible implementation manner, the target marker may be selected from the markers by a random algorithm. The number of the selected target marks is consistent with the number of the target nodes to be selected, and when the number of the target nodes to be selected is one, the number of the target marks is also one.
And taking the node corresponding to the target mark as a target node to finish the selection of the node. Specifically, in the Raft consensus algorithm, the obtained target node may be a leader node.
It can be understood that in the node selection method of the conventional Raft consensus algorithm, the condition that the votes obtained by different candidate nodes are consistent may occur, which may cause the failure of the selection of the leader node at this time, and the leader node needs to be selected again, thereby reducing the node selection efficiency. In the embodiment of the application, one marker can be selected as the target marker, so that the target node can be effectively selected.
Correspondingly, when the tag is a digital tag, selecting the target tag from the tags may specifically include: randomly selecting one of the numerical marks allocated to the candidate nodes as a target mark. In one possible implementation, a numerical label may be chosen as the target label by a random algorithm.
In a possible implementation manner, after the selection of the node is completed, when the target node does not receive the feedback information of the candidate node within a preset time, the target node is reset to the candidate node, and the node selection is performed again.
It can be understood that, when the selected target node does not receive the feedback information of other nodes within the preset time, the target node may be considered to be failed, and a new target node needs to be determined again. And resetting the target node as a candidate node, and re-performing the step of node selection.
In this embodiment of the present application, a specific time length of the preset time is not limited, and in a possible implementation manner, the preset time may be a period of time in which the target node serves as the leader node.
In the embodiment of the application, the right-to-gain ratio corresponding to each candidate node is determined according to a preset allocation principle; distributing marks to each candidate node according to the right-to-gain ratio corresponding to each candidate node; and finally, selecting a target mark from the marks, and taking a candidate node corresponding to the target mark as a target node. The target node can be selected by adopting fewer steps, the selection time of the target node is saved, the corresponding target node can be effectively selected in each node selection by selecting the target mark, the problem of selection failure can be avoided, and the node selection efficiency in the Raft consensus algorithm is improved.
The following description is made with reference to specific scenarios.
Referring to fig. 2, the figure is an exemplary diagram of a node selection method based on a Raft consensus algorithm according to an embodiment of the present application.
In the embodiment of the present application, there are A, B, C and D four candidate nodes, and the preset allocation principle is to allocate the corresponding rights and interests based on the hardware performance and the resource ratio corresponding to the candidate nodes. According to the hardware performance and the resource ratio corresponding to each candidate node, determining that the right-to-gain ratios corresponding to A, B, C and D are 20%, 30%, 10% and 40%, respectively. The obtained digital mark sequence is 10 digital marks in 1-10, and the number of the digital marks corresponding to the four candidate nodes is determined to be 2, 3, 1 and 4 according to the right-to-gain ratios corresponding to the four candidate nodes. And randomly selecting a corresponding number of digital marks from the digital mark sequence, and correspondingly distributing the digital marks to the candidate nodes. The numerical labels of the corresponding a candidate nodes are the 1 and 3 numerical labels, the numerical labels of the corresponding B candidate nodes are the 2, 4 and 5 numerical labels, the numerical label of the corresponding C candidate node is the 7 numerical label, and the numerical labels of the corresponding D candidate nodes are the 6, 8, 9 and 10 numerical labels. Randomly selecting one digital mark from 10 digital marks in 1-10, wherein the 9 digital mark is a D candidate node, and the D candidate node is taken as a target node. And when the D target node does not receive the feedback of other nodes within a certain time, resetting the D target node as a candidate node, and selecting the node again.
Referring to fig. 3, the figure is a schematic structural diagram of a node selection device based on a Raft consensus algorithm according to an embodiment of the present application. As shown in fig. 3, the node selection apparatus includes:
a right-to-gain determining unit 301, configured to determine a right-to-gain ratio corresponding to each candidate node according to a preset allocation principle;
a label allocation unit 302, configured to allocate labels to the candidate nodes according to the right-to-gain ratios corresponding to the candidate nodes;
a selecting unit 303 for selecting a target marker from the markers;
a marking unit 304, configured to take the candidate node corresponding to the target mark as a target node.
Optionally, when the mark is a digital mark, the mark allocating unit 302 is configured to acquire a digital mark sequence; the digital mark sequence is a digital mark sequence consisting of digital marks with different numerical values;
and according to the right-gain ratio corresponding to each candidate node, respectively acquiring the number marks corresponding to the right-gain ratio from the number mark sequence, and distributing the number marks to the corresponding candidate nodes.
Optionally, the selecting unit 303 is specifically configured to randomly select one of the numerical labels assigned to the candidate nodes as the target label.
Optionally, the allocation principle is to allocate the corresponding rights and interests based on the hardware performance and/or the resource proportion corresponding to the candidate node.
Optionally, the apparatus further comprises:
and the reselection device is used for resetting the target node as the candidate node and reselecting the node when the target node does not receive the feedback information of other candidate nodes within the preset time.
It should be noted that, in the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the system or the device disclosed by the embodiment, the description is simple because the system or the device corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A node selection method based on a Raft consensus algorithm is characterized by comprising the following steps:
determining the right-to-gain ratio corresponding to each candidate node according to a preset distribution principle;
distributing marks to each candidate node according to the right-to-gain ratio corresponding to each candidate node;
and selecting a target mark from the marks, and taking a candidate node corresponding to the target mark as a target node.
2. The method according to claim 1, wherein when the label is a digital label, the assigning labels to the candidate nodes according to the right-to-gain ratios corresponding to the candidate nodes respectively comprises:
acquiring a digital mark sequence; the digital mark sequence is a digital mark sequence consisting of digital marks with different numerical values;
and according to the right-gain ratio corresponding to each candidate node, respectively acquiring the number marks corresponding to the right-gain ratio from the number mark sequence, and distributing the number marks to the corresponding candidate nodes.
3. The method of claim 2, wherein said selecting a target marker from said markers comprises:
randomly selecting one of the numerical marks allocated to the candidate nodes as a target mark.
4. The method according to claim 1, wherein the allocation principle is to allocate the corresponding rights and interests based on hardware performance and/or resource ratio corresponding to the candidate nodes.
5. The method of claim 1, further comprising:
and when the target node does not receive the feedback information of other candidate nodes within the preset time, resetting the target node as a candidate node and reselecting the node.
6. A node selection apparatus based on a Raft consensus algorithm, the apparatus comprising:
the right-interest determining unit is used for determining the right-interest ratio corresponding to each candidate node according to a preset distribution principle;
the mark distribution unit is used for distributing marks to the candidate nodes according to the right-to-gain ratios corresponding to the candidate nodes;
a selection unit configured to select a target marker from the markers;
and the marking unit is used for taking the candidate node corresponding to the target mark as the target node.
7. The apparatus of claim 6, wherein when the tag is a digital tag, the tag assigning unit is configured to obtain a sequence of digital tags; the digital mark sequence is a digital mark sequence consisting of digital marks with different numerical values;
and according to the right-gain ratio corresponding to each candidate node, respectively acquiring the number marks corresponding to the right-gain ratio from the number mark sequence, and distributing the number marks to the corresponding candidate nodes.
8. The apparatus according to claim 7, wherein the selection unit is specifically configured to randomly select one of the numerical labels assigned to the candidate nodes as the target label.
9. The apparatus of claim 6, wherein the allocation principle is to allocate the corresponding rights based on hardware performance and/or resource ratio corresponding to the candidate node.
10. The apparatus of claim 6, further comprising:
and the reselection device is used for resetting the target node as the candidate node and reselecting the node when the target node does not receive the feedback information of other candidate nodes within the preset time.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109481936A (en) * | 2018-10-26 | 2019-03-19 | 咪咕文化科技有限公司 | A kind of block chain accounting nodes selection method, device and computer readable storage medium |
| CN109547527A (en) * | 2018-10-12 | 2019-03-29 | 广西师范大学 | Subregion in block chain based on credit mechanism is quickly known together method |
| CN110049029A (en) * | 2019-04-04 | 2019-07-23 | 矩阵元技术（深圳）有限公司 | Common recognition node determines method, apparatus, computer equipment and storage medium |
| WO2019192062A1 (en) * | 2018-04-04 | 2019-10-10 | 上海金丘信息科技股份有限公司 | Dynamic stake consensus method based on trusted members |
| US20190327084A1 (en) * | 2018-04-19 | 2019-10-24 | Electronics And Telecommunications Research Institute | Method for selecting consensus node using nonce and method and apparatus for generating blockchain using the same |
| CN110956542A (en) * | 2019-11-07 | 2020-04-03 | 支付宝(杭州)信息技术有限公司 | Block chain system and operation method, device and equipment thereof |
-
2020
- 2020-04-27 CN CN202010344275.2A patent/CN111556149B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019192062A1 (en) * | 2018-04-04 | 2019-10-10 | 上海金丘信息科技股份有限公司 | Dynamic stake consensus method based on trusted members |
| US20190327084A1 (en) * | 2018-04-19 | 2019-10-24 | Electronics And Telecommunications Research Institute | Method for selecting consensus node using nonce and method and apparatus for generating blockchain using the same |
| CN109547527A (en) * | 2018-10-12 | 2019-03-29 | 广西师范大学 | Subregion in block chain based on credit mechanism is quickly known together method |
| CN109481936A (en) * | 2018-10-26 | 2019-03-19 | 咪咕文化科技有限公司 | A kind of block chain accounting nodes selection method, device and computer readable storage medium |
| CN110049029A (en) * | 2019-04-04 | 2019-07-23 | 矩阵元技术（深圳）有限公司 | Common recognition node determines method, apparatus, computer equipment and storage medium |
| CN110956542A (en) * | 2019-11-07 | 2020-04-03 | 支付宝(杭州)信息技术有限公司 | Block chain system and operation method, device and equipment thereof |
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