CN113486118B - Consensus node selection method and device - Google Patents
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Abstract
A consensus node selection method and device, the method includes: classifying the consensus nodes in the selectable consensus node pool of the block chain network to obtain three types of consensus nodes; acquiring log data of a block chain network, and determining weight indexes corresponding to all consensus nodes according to the log data; inputting weight indexes corresponding to the consensus nodes into a pre-established weight calculation model to obtain node weight values corresponding to the consensus nodes; and selecting the consensus nodes from the three types of consensus nodes according to the node weight values corresponding to the consensus nodes and the obtained node selection rules. The invention classifies the consensus nodes and sets the weight value, so as to realize reasonable selection of the consensus nodes of the block chain network, avoid the influence of excessive number of nodes on the consensus efficiency and transaction processing performance on the block chain network, improve the credit degree responsible for generating the block nodes, be applicable to different application scene requirements and realize intelligent data decision of the selection of the consensus nodes.
Description
Technical Field
The present invention relates to the field of blockchain technologies, and in particular, to a method and an apparatus for selecting a consensus node.
Background
A federated chain is a blockchain that is jointly managed by multiple organizations or organizations, each contributing and managing one or more federated chain nodes. As the number of joined organizations or organizations increases, the number of alliance chain blockchain network nodes increases. Currently, the number of nodes in the alliance chain blockchain network affects the transaction processing performance, and the more the number of nodes is, the transaction processing performance is reduced. In the current blockchain technical background, the high TPS and the degree of decentralization are basically irreconcilable contradictions, and both complete decentralization and complete decentralization are ideal conditions. The method is more suitable for realizing a relatively decentralised or weakly centralized mode, and is particularly suitable for a coalition chain with more organizations or institutions and more nodes, because the coalition chain is jointly participated and managed by a plurality of organizations or institutions, members in the coalition are only opened, the members in the coalition can be added or withdrawn after authorization, and the permission adding mode is understood from the non-technical aspect to audit the credibility of the added organizations or institutions on the other hand, and the trust problem is not solved purely from the technical aspect.
The blockchain network is based on a decentralised point-to-point network, and the consistency processing of the uplink transaction among the decentralized nodes is realized by means of a consensus algorithm. The design of the consensus algorithm/mechanism affects the efficiency of the consensus and thus the transaction processing performance. In order to reduce the cost of synchronous communication, the mainstream consensus algorithm generally selects a trust node list in a blockchain network with a plurality of nodes as a sub-network to realize internal mutual trust and participate in a consensus achievement process, thereby improving the consensus efficiency and the transaction processing performance.
Currently, the predominant consensus algorithm of blockchain is: POW: a workload certification mechanism; POS: a stock right proving mechanism; DPOS: an authorized equity proof mechanism; POI: essentially all variations of POS; POP: is the upgrade of all the previous ones; PBFT: a Bayesian fault tolerance consistency algorithm; raft: algorithms used by current versions of Fabric. The above algorithms each employ some mechanism to select one (Leader node) or a certain number of node representatives to be responsible for generating the block.
The consensus algorithm has various problems, and in short, the POW election is responsible for generating block nodes, which requires a lot of resources and has low transaction performance. POS, DPOS, POI, POP, raft is mainly to increase the transaction amount by reducing the number of responsible block nodes, but the way of electing responsible block nodes is not high in reliability, or is not concerned with the computational power problem of the nodes. The PBFT solves the problem of the bayer fault tolerance, and is more suitable for the situation that the number of nodes is not too large.
Disclosure of Invention
Aiming at the problems existing in the prior art, the main purpose of the embodiment of the invention is to provide a method and a device for selecting consensus nodes, which realize reasonable selection of the consensus nodes of a blockchain network and avoid the influence of excessive number of nodes on the consensus efficiency and transaction processing performance on the blockchain network.
In order to achieve the above object, an embodiment of the present invention provides a method for selecting a consensus node, the method including:
classifying the consensus nodes in the selectable consensus node pool of the block chain network to obtain a Leader node, a verification node and three types of consensus nodes for executing Bayesian protocol nodes;
acquiring log data of the blockchain network, and determining weight indexes corresponding to all consensus nodes according to the log data;
inputting weight indexes corresponding to the consensus nodes into a pre-established weight calculation model to obtain node weight values corresponding to the consensus nodes;
and selecting the consensus nodes from the three types of consensus nodes according to the node weight values corresponding to the consensus nodes and the obtained node selection rules.
Optionally, in an embodiment of the present invention, the log data includes a system log, an application log, and a service log.
Optionally, in an embodiment of the present invention, the determining, according to the log data, a weight index corresponding to each consensus node includes:
carrying out data analysis on the log data to obtain weight indexes corresponding to all the consensus nodes; wherein the weight index comprises a processing capacity index and a credit index;
and acquiring a preset calculation coefficient corresponding to the weight index according to the processing capacity index and the credit index.
Optionally, in an embodiment of the present invention, inputting the weight index corresponding to each consensus node into a pre-established weight calculation model, and obtaining the node weight value corresponding to each consensus node includes:
inputting weight indexes corresponding to all the consensus nodes into a pre-established weight calculation model, calculating the product of all the weight indexes and corresponding calculation coefficients, and taking the product as a node weight value corresponding to all the consensus nodes; the node weight value comprises a processing capacity weight value, a credit weight value and a comprehensive weight value.
Optionally, in an embodiment of the present invention, the selecting the consensus node according to the obtained node selection rule from the three types of consensus nodes according to the node weight value corresponding to each consensus node includes:
acquiring a node selection rule; the node selection rule comprises node types and node numbers;
and selecting corresponding consensus nodes from the three types of consensus nodes according to the node weight values corresponding to the consensus nodes and the node types and the node numbers.
The embodiment of the invention also provides a consensus node selection device, which comprises:
the node classification module is used for classifying the consensus nodes in the block chain network selectable consensus node pool to obtain a Leader node, a verification node and a consensus node executing Bayesian protocol node;
the weight index module is used for acquiring log data of the blockchain network and determining weight indexes corresponding to all consensus nodes according to the log data;
the weight value module is used for inputting weight indexes corresponding to the consensus nodes into a pre-established weight calculation model to obtain node weight values corresponding to the consensus nodes;
the node selection module is used for selecting the common node from the three types of common node according to the node weight value corresponding to each common node and the obtained node selection rule.
Optionally, in an embodiment of the present invention, the log data includes a system log, an application log, and a service log.
Optionally, in an embodiment of the present invention, the weight index module includes:
the weight index unit is used for carrying out data analysis on the log data to obtain weight indexes corresponding to the consensus nodes; wherein the weight index comprises a processing capacity index and a credit index;
and the calculation coefficient unit is used for acquiring a preset calculation coefficient corresponding to the weight index according to the processing capacity index and the credit index.
Optionally, in an embodiment of the present invention, the weight value module is further configured to input a weight index corresponding to each consensus node into a pre-established weight calculation model, calculate a product of each weight index and a corresponding calculation coefficient, and use the product as a node weight value corresponding to each consensus node; the node weight value comprises a processing capacity weight value, a credit weight value and a comprehensive weight value.
Optionally, in an embodiment of the present invention, the node selection module includes:
the selecting rule unit is used for acquiring node selecting rules; the node selection rule comprises node types and node numbers;
the node selection unit is used for selecting the corresponding consensus node from the three types of consensus nodes according to the node weight value corresponding to each consensus node and the node type and the node number.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method when executing the program.
The present invention also provides a computer readable storage medium storing a computer program for executing the above method.
The invention classifies the consensus nodes and sets the weight value to realize reasonable selection of the consensus nodes of the block chain network, thereby avoiding the influence of excessive number of nodes on the consensus efficiency and transaction processing performance on the block chain network, improving the credit degree responsible for generating the block nodes, being applicable to different application scene requirements, being beneficial to balancing the contradiction of high performance and decentralization degree, complete decentralization and realizing the intelligent data decision of the selection of the consensus nodes.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for selecting a consensus node according to an embodiment of the present invention;
FIG. 2 is a flowchart of determining a consensus node weight indicator in an embodiment of the present invention;
FIG. 3 is a flow chart of selecting a consensus node in an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating classification of common nodes in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a node weight calculation model according to an embodiment of the invention;
FIG. 6 is a schematic diagram of node weight values according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a system architecture using a method for selecting common node according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of selecting common nodes according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a device for selecting a common node according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of a weight index module according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a node selection module according to an embodiment of the present invention;
fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The embodiment of the invention provides a method and a device for selecting a consensus node.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart of a method for selecting a common node according to an embodiment of the present invention, where an execution body of the method for selecting a common node provided by the embodiment of the present invention includes, but is not limited to, a computer. The method shown in the figure comprises the following steps:
step S1, classifying the consensus nodes in the selectable consensus node pool of the block chain network to obtain three types of consensus nodes, namely a Leader node, a verification node and a node executing a Bayesian protocol.
The method comprises the steps of classifying consensus nodes in a selectable consensus node pool of a blockchain network, adapting to the requirements of different application scenes based on different election strategies, and classifying the consensus nodes into 3 types, wherein the 3 types comprise a Leader node, a verification node and a node for executing a Bayesian protocol.
Further, the Leader node: the method is suitable for the situation that the selectable consensus nodes are mainly contributed by a certain or very few organizations/institutions, the blocks are generated by the selected Leader nodes, and the consistency of candidate transaction sets is not required to be coordinated among a plurality of nodes, so that the efficiency of generating the final blocks is improved. The Leader node mode is not suitable for implementing a bayer fault-tolerant scenario.
Further, the authentication node: the method is suitable for the situation that the selectable consensus nodes are contributed by a plurality of organizations/institutions and the number of the selectable consensus node pool nodes is large, the few verification nodes belonging to a few organizations or institutions are selected to generate candidate transaction sets, the number of the plurality of candidate transaction sets generated by the few verification nodes is controlled within a certain range, the subsequent coordination of the consistency performance of the candidate transaction sets is facilitated, and the multi-centering concept of a weak center mode can be realized.
Further, the bayer protocol node is executed: in the scenario of the above-mentioned applicable election verification node, the node is composed of a few nodes of a few organizations or institutions selected from the optional consensus node pool, and the multiple candidate transaction sets generated by the few execution Bayesian protocol nodes on the few verification nodes based on the Bayesian protocol are subjected to multiple rounds of consensus, so that the final candidate transaction set is generated.
And S2, acquiring log data of the block chain network, and determining weight indexes corresponding to all consensus nodes according to the log data.
The method comprises the steps of obtaining log data from a blockchain network, wherein the log data comprises a system log, an application log and a service log, and then carrying out data processing and analysis on the log data to obtain weight indexes corresponding to all the consensus nodes. Specifically, the weight index includes a processing capability index and a credit index.
Further, the processing capability index is a related index affecting the processing capability, including a processing performance condition within a period of time, a computing resource condition, an abnormal occurrence condition within a period of time, and the like. The credit index is a relevant index affecting the credit, and comprises an industry background condition of an organization to which the node belongs, an accumulated traffic condition in a period of time, an overall technical capability reflected by technical team and infrastructure conditions, and the like.
And S3, inputting the weight index corresponding to each consensus node into a pre-established weight calculation model to obtain a node weight value corresponding to each consensus node.
The weight index corresponding to each consensus node is input into a pre-established weight calculation model, and the node weight value is calculated through the weight calculation model. Specifically, the node weight value includes a processing capability weight value, a credit weight value, and a comprehensive weight value.
Further, the processing capability index is multiplied by a corresponding calculation coefficient, and the processing capability weight value of each consensus node is obtained through accumulation. And multiplying the credit index by a corresponding calculation coefficient, and accumulating to obtain the credit weight value of each consensus node. The comprehensive weight value represents the credit weight value and the processing capacity weight value of the comprehensive consideration consensus node, and the comprehensive weight value of each consensus node is obtained through accumulation by multiplying the credit weight value and the processing capacity weight value by corresponding calculation coefficients respectively. Specifically, each weight index corresponds to a different calculation coefficient, and the calculation coefficient can be preset.
Further, the processing capability weight value is suitable for selecting "verification node", and the comprehensive weight value is suitable for selecting "Leader node" and "executing Bayesian protocol node".
And S4, selecting the common node from the three types of common nodes according to the node weight value corresponding to each common node and the obtained node selection rule.
Under different applicable scenes, when the blockchain network is involved in the consensus packaging block in each transaction or needs to be selected again, the consensus node selection is performed according to the node selection rules. Specifically, the node selection rule may be manually input or preset, where the node selection rule includes types, numbers, proportions, and the like of the consensus nodes to be selected.
Further, according to a preset random algorithm or in a mode that node weight values are from high to low, selecting different types of participation consensus packaging nodes with the number or proportion set by the node selection rule from a selectable node list with the corresponding node weight values within a set range.
As one embodiment of the present invention, the log data includes a system log, an application log, and a service log.
And acquiring various log data of the blockchain network, including a system log, an application log and a service log, and performing data processing and analysis.
As an embodiment of the present invention, as shown in fig. 2, determining, according to the log data, a weight index corresponding to each consensus node includes:
step S21, data analysis is carried out on the log data to obtain weight indexes corresponding to all the consensus nodes; wherein the weight index comprises a processing capacity index and a credit index;
step S22, according to the processing capacity index and the credit index, a preset calculation coefficient corresponding to the weight index is obtained.
And carrying out data processing and analysis on each log data to obtain the weight index corresponding to each consensus node. The weight index includes a processing power index and a credit index.
Further, each weight index corresponds to a different calculation coefficient, and the calculation coefficient can be preset, so that the preset calculation coefficient corresponding to each weight index can be obtained according to the processing capacity index and the credit index.
In this embodiment, inputting the weight index corresponding to each consensus node into a pre-established weight calculation model, and obtaining the node weight value corresponding to each consensus node includes: inputting weight indexes corresponding to all the consensus nodes into a pre-established weight calculation model, calculating the product of all the weight indexes and corresponding calculation coefficients, and taking the product as a node weight value corresponding to all the consensus nodes; the node weight value comprises a processing capacity weight value, a credit weight value and a comprehensive weight value.
The weight index corresponding to each consensus node is input into a pre-established weight calculation model, and the node weight value is calculated through the weight calculation model. Specifically, the node weight value includes a processing capability weight value, a credit weight value, and a comprehensive weight value.
Further, the processing capability index is multiplied by a corresponding calculation coefficient, and the processing capability weight value of each consensus node is obtained through accumulation. And multiplying the credit index by a corresponding calculation coefficient, and accumulating to obtain the credit weight value of each consensus node. The comprehensive weight value represents the credit weight value and the processing capacity weight value of the comprehensive consideration consensus node, and the comprehensive weight value of each consensus node is obtained through accumulation by multiplying the credit weight value and the processing capacity weight value by corresponding calculation coefficients respectively.
As an embodiment of the present invention, as shown in fig. 3, according to the node weight value corresponding to each consensus node, performing consensus node selection according to the obtained node selection rule from three types of consensus nodes includes:
step S31, acquiring a node selection rule; the node selection rule comprises node types and node numbers;
and step S32, selecting corresponding consensus nodes from the three types of consensus nodes according to the node weight values corresponding to the consensus nodes and the node types and the node numbers.
Under different applicable scenes, when the blockchain network is involved in the consensus packaging block in each transaction or needs to be selected again, the consensus node selection is performed according to the node selection rules.
Further, the node selection rule may be manually input or preset, where the node selection rule includes a type, number, proportion, etc. of the consensus nodes to be selected.
Further, according to a preset random algorithm or in a mode that node weight values are from high to low, selecting different types of participation consensus packaging nodes with the number or proportion set by the node selection rule from a selectable node list with the corresponding node weight values within a set range.
In an embodiment of the present invention, as shown in fig. 4, which is a schematic diagram illustrating classification of common nodes in an embodiment of the present invention, the process shown in the figure classifies the nodes participating in the common node, and is classified into 3 types based on different election strategies to adapt to the needs of different application scenarios:
(1) Leader node: the method is suitable for the situation that the selectable consensus nodes are mainly contributed by a certain or very few organizations/institutions, the blocks are generated by the selected Leader nodes, and the consistency of candidate transaction sets is not required to be coordinated among a plurality of nodes, so that the efficiency of generating the final blocks is improved. The Leader node mode is not suitable for implementing a bayer fault-tolerant scenario.
(2) And (3) verifying the node: the method is suitable for the situation that the selectable consensus nodes are contributed by a plurality of organizations/institutions and the number of the selectable consensus node pool nodes is large, the few verification nodes belonging to a few organizations or institutions are selected to generate candidate transaction sets, the number of the plurality of candidate transaction sets generated by the few verification nodes is controlled within a certain range, the subsequent coordination of the consistency performance of the candidate transaction sets is facilitated, and the multi-centering concept of a weak center mode can be realized.
(3) Executing a Bayesian protocol node: in the scenario of the above-mentioned applicable election verification node, the node is composed of a few nodes of a few organizations or institutions selected from the optional consensus node pool, and the multiple candidate transaction sets generated by the few execution Bayesian protocol nodes on the few verification nodes based on the Bayesian protocol are subjected to multiple rounds of consensus, so that the final candidate transaction set is generated.
In this embodiment, weights are set for the selectable consensus nodes, the participating consensus nodes are selected by the weights, and a node weight calculation model is designed, as shown in fig. 5.
(1) Processing capability weight value: the comprehensive computing and processing power of the selected node is considered from the technical aspect, so that the selected node has better and more reliable processing power. The processing capability weight value of the node is obtained by selecting related indexes (including processing performance conditions in a period of time, computing resource conditions, abnormal occurrence conditions in a period of time and the like) mainly influencing the processing capability, multiplying the indexes by different computing coefficients, and accumulating.
(2) Credit weight value: the comprehensive credit of the selected node is considered from the service level, so that the selected consensus node can reflect that the organization or institution to which the node belongs has better credit endorsement, thereby improving the credit degree of the consensus. The credit weight value of the node is obtained by selecting relevant indexes (including the industry background condition of the organization to which the node belongs, the accumulated traffic condition in a period of time, the total technical capability reflected by technical team and infrastructure conditions and the like) mainly influencing the credit, multiplying the indexes by different calculation coefficients, and accumulating.
(3) Comprehensive weight value: the credit weight value and the processing capacity weight value of the node are comprehensively considered, and the credit weight value and the processing capacity weight value are respectively multiplied by different calculation coefficients, so that the comprehensive weight value of the node is obtained through accumulation.
(4) As shown in fig. 6, the processing capability weight value is suitable for selecting "authentication node", and the comprehensive weight value is suitable for selecting "Leader node" and "execute bayer protocol node".
In this embodiment, the selection of the weight index and the corresponding coefficient setting are generated by data analysis, and by introducing an intelligent decision mechanism, the intelligent data decision of the consensus node selection is realized, as shown in fig. 7.
(1) Various log data of the blockchain network, including a system log, an application log and a service log, are collected by a data platform, the data analysis platform performs data processing and analysis, weight indexes and corresponding calculation coefficients are selected, and a weight calculation model is established.
(2) And converting the weight calculation model into decision rules, deploying the decision rules in an intelligent data decision engine, periodically calculating the weights of the selectable consensus nodes of the blockchain network by the decision engine according to the decision rules, and outputting the weight calculation results to a blockchain service platform (BaaS).
(3) The block chain service platform edits and deploys a 'common intelligent contract' to the block chain network, and after receiving the node weight output by the decision engine, the block chain service platform invokes the common intelligent contract to uplink the node weight data, and the node weight data is stored in a state database in the block chain network node.
In this embodiment, as shown in fig. 8, in different applicable scenarios, the blockchain network selects a set number or proportion of different types of participating consensus packaging nodes from the list of selectable nodes with weights within a set range based on a certain random algorithm or in a manner of from high to low according to weights when each transaction or the participation consensus packaging block needs to be reselected.
The invention realizes the control of the number of the block chain network participating in the consensus packaging nodes, thereby avoiding the influence of excessive number of nodes on the consensus efficiency and transaction processing performance on the block chain network; on the basis of a trusted system realized in the prior art of the blockchain, the credit for generating the blocknodes is improved; the selection mode of the consensus nodes can be suitable for different application scene requirements; the contradiction of high performance and decentration degree, complete decentration and complete decentration is facilitated to be balanced; by introducing an intelligent decision mechanism, intelligent data decision of the consensus node selection is realized.
Specifically, the invention classifies the consensus nodes and sets the weight value to realize reasonable selection of the consensus nodes of the block chain network, thereby avoiding the influence of excessive number of nodes on the consensus efficiency and transaction processing performance on the block chain network, improving the credit degree responsible for generating the block nodes, being applicable to different application scene requirements, being beneficial to balancing the contradiction of high performance and decentralization degree, complete decentralization and realizing intelligent data decision of the selection of the consensus nodes.
Fig. 9 is a schematic structural diagram of a consensus node selection device according to an embodiment of the present invention, where the device includes:
the node classification module 10 is configured to classify the consensus nodes in the selectable consensus node pool of the blockchain network to obtain three types of consensus nodes, i.e. a Leader node, a verification node and a node executing a bayer pattern protocol.
The method comprises the steps of classifying consensus nodes in a selectable consensus node pool of a blockchain network, adapting to the requirements of different application scenes based on different election strategies, and classifying the consensus nodes into 3 types, wherein the 3 types comprise a Leader node, a verification node and a node for executing a Bayesian protocol.
Furthermore, the Leader node is suitable for the situation that the optional consensus node is mainly contributed by a certain or very few organizations/institutions, and the block is generated by the selected Leader node, so that the consistency of the candidate transaction sets is not required to be coordinated among a plurality of nodes, and the efficiency of generating the final block is improved. The Leader node mode is not suitable for implementing a bayer fault-tolerant scenario.
Furthermore, the verification node is suitable for the situation that the selectable consensus nodes are contributed by a plurality of organizations/institutions, and meanwhile, the number of the selectable consensus node pool nodes is large, few verification nodes belonging to a few organizations or institutions are selected to generate candidate transaction sets, the number of the plurality of candidate transaction sets generated by the few verification nodes is controlled within a certain range, the subsequent coordination of the consistency performance of the candidate transaction sets is facilitated, and the multi-centering concept of a weak center mode can be realized.
Further, under the scenario of the above applicable election verification node, the node is composed of a few nodes of a few organizations or institutions selected from the optional consensus node pool, and the multiple candidate transaction sets generated by the few nodes of the Bayesian protocol are subjected to multiple rounds of consensus based on the Bayesian protocol, so that the final candidate transaction set is generated.
The weight index module 20 is configured to obtain log data of the blockchain network, and determine a weight index corresponding to each consensus node according to the log data.
The method comprises the steps of obtaining log data from a blockchain network, wherein the log data comprises a system log, an application log and a service log, and then carrying out data processing and analysis on the log data to obtain weight indexes corresponding to all the consensus nodes. Specifically, the weight index includes a processing capability index and a credit index.
Further, the processing capability index is a related index affecting the processing capability, including a processing performance condition within a period of time, a computing resource condition, an abnormal occurrence condition within a period of time, and the like. The credit index is a relevant index affecting the credit, and comprises an industry background condition of an organization to which the node belongs, an accumulated traffic condition in a period of time, an overall technical capability reflected by technical team and infrastructure conditions, and the like.
The weight value module 30 is configured to input weight indexes corresponding to the consensus nodes into a weight calculation model established in advance, so as to obtain node weight values corresponding to the consensus nodes.
The weight index corresponding to each consensus node is input into a pre-established weight calculation model, and the node weight value is calculated through the weight calculation model. Specifically, the node weight value includes a processing capability weight value, a credit weight value, and a comprehensive weight value.
Further, the processing capability index is multiplied by a corresponding calculation coefficient, and the processing capability weight value of each consensus node is obtained through accumulation. And multiplying the credit index by a corresponding calculation coefficient, and accumulating to obtain the credit weight value of each consensus node. The comprehensive weight value represents the credit weight value and the processing capacity weight value of the comprehensive consideration consensus node, and the comprehensive weight value of each consensus node is obtained through accumulation by multiplying the credit weight value and the processing capacity weight value by corresponding calculation coefficients respectively. Specifically, each weight index corresponds to a different calculation coefficient, and the calculation coefficient can be preset.
Further, the processing capability weight value is suitable for selecting "verification node", and the comprehensive weight value is suitable for selecting "Leader node" and "executing Bayesian protocol node".
The node selection module 40 is configured to perform common node selection from the three types of common nodes according to the obtained node selection rule according to the node weight value corresponding to each common node.
Under different applicable scenes, when the blockchain network is involved in the consensus packaging block in each transaction or needs to be selected again, the consensus node selection is performed according to the node selection rules. Specifically, the node selection rule may be manually input or preset, where the node selection rule includes types, numbers, proportions, and the like of the consensus nodes to be selected.
Further, according to a preset random algorithm or in a mode that node weight values are from high to low, selecting different types of participation consensus packaging nodes with the number or proportion set by the node selection rule from a selectable node list with the corresponding node weight values within a set range.
As one embodiment of the present invention, the log data includes a system log, an application log, and a service log.
As an embodiment of the present invention, as shown in fig. 10, the weight index module 20 includes:
the weight index unit 21 is configured to perform data analysis on the log data to obtain a weight index corresponding to each consensus node; wherein the weight index comprises a processing capacity index and a credit index;
and a calculation coefficient unit 22, configured to obtain a preset calculation coefficient corresponding to the weight index according to the processing capability index and the credit index.
In this embodiment, the weight value module is further configured to input weight indexes corresponding to the consensus nodes into a pre-established weight calculation model, calculate products of the weight indexes and corresponding calculation coefficients, and use the products as node weight values corresponding to the consensus nodes; the node weight value comprises a processing capacity weight value, a credit weight value and a comprehensive weight value.
As an embodiment of the present invention, as shown in fig. 11, the node selection module 40 includes:
a selection rule unit 41, configured to obtain a node selection rule; the node selection rule comprises node types and node numbers;
the node selecting unit 42 is configured to select a corresponding consensus node from the three types of consensus nodes according to the node weight value corresponding to each consensus node and the node type and the node number.
Based on the same application conception as the consensus node selection method, the invention also provides a consensus node selection device. Because the principle of the common node selection device for solving the problem is similar to that of a common node selection method, the implementation of the common node selection device can refer to the implementation of a common node selection method, and the repetition is omitted.
The invention classifies the consensus nodes and sets the weight value to realize reasonable selection of the consensus nodes of the block chain network, thereby avoiding the influence of excessive number of nodes on the consensus efficiency and transaction processing performance on the block chain network, improving the credit degree responsible for generating the block nodes, being applicable to different application scene requirements, being beneficial to balancing the contradiction of high performance and decentralization degree, complete decentralization and realizing the intelligent data decision of the selection of the consensus nodes.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method when executing the program.
The present invention also provides a computer readable storage medium storing a computer program for executing the above method.
As shown in fig. 12, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, a power supply 170. It is noted that the electronic device 600 need not include all of the components shown in fig. 12; in addition, the electronic device 600 may further include components not shown in fig. 12, to which reference is made to the related art.
As shown in fig. 12, the central processor 100, also sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 100 receives inputs and controls the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information about failure may be stored, and a program for executing the information may be stored. And the central processor 100 can execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides an input to the central processor 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, or the like. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. Memory 140 may also be some other type of device. Memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage 142, the application/function storage 142 for storing application programs and function programs or a flow for executing operations of the electronic device 600 by the central processor 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, address book applications, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. A communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, etc., may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and to receive audio input from the microphone 132 to implement usual telecommunication functions. The audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 130 is also coupled to the central processor 100 so that sound can be recorded locally through the microphone 132 and so that sound stored locally can be played through the speaker 131.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. A method for consensus node selection, the method comprising:
classifying the consensus nodes in the selectable consensus node pool of the block chain network to obtain a Leader node, a verification node and three types of consensus nodes for executing Bayesian protocol nodes;
acquiring log data of the blockchain network, and determining weight indexes corresponding to all consensus nodes according to the log data;
inputting weight indexes corresponding to the consensus nodes into a pre-established weight calculation model to obtain node weight values corresponding to the consensus nodes;
selecting the consensus nodes from the three types of consensus nodes according to the node weight values corresponding to the consensus nodes and the obtained node selection rules;
wherein, the selecting the consensus nodes according to the obtained node selection rules from the three types of consensus nodes according to the node weight values corresponding to the consensus nodes comprises:
acquiring a node selection rule; the node selection rules are manually input or preset and comprise node types, node quantity and node proportion;
and selecting the node quantity or the node proportion of different node types of the participation consensus packaging nodes set by the node selection rule from the selectable node list with the corresponding node weight value within the set range according to a preset random algorithm or in a mode that the node weight value corresponding to each consensus node is from high to low.
2. The method of claim 1, wherein the log data comprises a system log, an application log, and a traffic log.
3. The method according to claim 1 or 2, wherein determining the weight index corresponding to each consensus node according to the log data comprises:
carrying out data analysis on the log data to obtain weight indexes corresponding to all the consensus nodes; wherein the weight index comprises a processing capacity index and a credit index;
and acquiring a preset calculation coefficient corresponding to the weight index according to the processing capacity index and the credit index.
4. The method of claim 3, wherein inputting the weight index corresponding to each consensus node into a pre-established weight calculation model, obtaining the node weight value corresponding to each consensus node comprises:
inputting weight indexes corresponding to all the consensus nodes into a pre-established weight calculation model, calculating the product of all the weight indexes and corresponding calculation coefficients, and taking the product as a node weight value corresponding to all the consensus nodes; the node weight value comprises a processing capacity weight value, a credit weight value and a comprehensive weight value.
5. A consensus node selection device, the device comprising:
the node classification module is used for classifying the consensus nodes in the block chain network selectable consensus node pool to obtain a Leader node, a verification node and a consensus node executing Bayesian protocol node;
the weight index module is used for acquiring log data of the blockchain network and determining weight indexes corresponding to all consensus nodes according to the log data;
the weight value module is used for inputting weight indexes corresponding to the consensus nodes into a pre-established weight calculation model to obtain node weight values corresponding to the consensus nodes;
the node selection module is used for selecting the consensus nodes from the three types of consensus nodes according to the node weight values corresponding to the consensus nodes and the obtained node selection rules;
wherein, the node selection module includes:
the selecting rule unit is used for acquiring node selecting rules; the node selection rules are manually input or preset and comprise node types, node quantity and node proportion;
the node selection unit is used for selecting the node quantity or the node proportion of the different node types of the participation consensus packaging nodes set by the node selection rule from the selectable node list with the corresponding node weight value within the set range according to a preset random algorithm or in a mode that the node weight value corresponding to each consensus node is from high to low.
6. The apparatus of claim 5, wherein the log data comprises a system log, an application log, and a traffic log.
7. The apparatus according to claim 5 or 6, wherein the weight index module comprises:
the weight index unit is used for carrying out data analysis on the log data to obtain weight indexes corresponding to the consensus nodes; wherein the weight index comprises a processing capacity index and a credit index;
and the calculation coefficient unit is used for acquiring a preset calculation coefficient corresponding to the weight index according to the processing capacity index and the credit index.
8. The apparatus of claim 7, wherein the weight module is further configured to input weight indexes corresponding to the consensus nodes into a pre-established weight calculation model, calculate products of the weight indexes and corresponding calculation coefficients, and use the products as node weight values corresponding to the consensus nodes; the node weight value comprises a processing capacity weight value, a credit weight value and a comprehensive weight value.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 4 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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