CN113486118A - Consensus node selection method and device - Google Patents
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Abstract
A method and a device for selecting a consensus node are provided, the method comprises the following steps: classifying the consensus nodes in the block chain network selectable consensus node pool to obtain three types of consensus nodes; acquiring log data of a block chain network, and determining a weight index corresponding to each common identification node according to the log data; inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model to obtain node weight values corresponding to the common identification nodes; and according to the node weight value corresponding to each consensus node, selecting the consensus nodes from the three types of consensus nodes according to the obtained node selection rule. The invention realizes reasonable selection of the consensus nodes of the blockchain network by classifying the consensus nodes and setting the weighted values, avoids the influence of excessive number of the nodes on the consensus efficiency and the transaction processing performance of the blockchain network, improves the credit degree responsible for generating the blockchain nodes, can be suitable for different application scene requirements, and realizes intelligent data decision of consensus node selection.
Description
Technical Field
The present invention relates to the field of block chain technologies, and in particular, to a method and an apparatus for selecting a consensus node.
Background
A federation chain is a blockchain of organizations or enterprises that collectively participate in management, each organization or enterprise contributing and managing one or more federation chain nodes. With the increase of the number of the added organizations or organizations, the number of the block chain network nodes of the alliance chain is increased gradually. At present, the number of nodes in the block chain network of the alliance chain affects the transaction processing performance, and the larger the number of nodes is, the lower the transaction processing performance is. In the context of current blockchain technology, high TPS and the degree of decentralization are essentially irreconcilable contradictions, and complete centration and complete decentralization are both desirable. For this reason, the real financial application scenario is more suitable to be implemented in a relatively decentralized or weakly centralized manner, and particularly, for a federation chain with multiple organizations or institutions and multiple nodes, this manner is more suitable because the federation chain is managed by multiple organizations or institutions, is only open to members in the federation, and can be joined or withdrawn only after authorization, and the permission joining mode on the other hand understands that the credibility of joining organizations or institutions is also checked from a non-technical level, rather than solving the trust problem from a technical level.
The block chain network is established on the basis of a decentralized point-to-point network, and realizes the consistency processing of uplink transactions among scattered nodes by means of a consensus algorithm. The design of the consensus algorithm/mechanism affects the efficiency of consensus and thus the transaction processing performance. In order to reduce the cost of synchronous communication, a mainstream consensus algorithm generally realizes internal mutual trust by selecting a trust node list as a sub-network in a blockchain network with numerous nodes to participate in a consensus process, thereby improving the consensus efficiency and the transaction processing performance.
At present, the consensus algorithm of the block chain mainstream is as follows: POW: a workload attestation mechanism; POS: a stock right attestation mechanism; DPOS: an authorized equity attestation mechanism; POI: are all POS variants in nature; POP: the upgrading of all the previous steps; PBFT: byzantine fault-tolerant consistency algorithm; and (3) Raft: the algorithm used by the Fabric current version. The above algorithms each adopt some mechanism to select one (Leader node) or a certain number of node representatives to be responsible for generating the block.
The above consensus algorithm has various problems, and briefly, POW election responsible for generating the block node consumes a lot of resources, and has low transaction performance. The POS, DPOS, POI, POP and Raft are mainly used for improving the transaction amount by reducing the number of nodes responsible for generating the blocks, but the mode of electing the nodes responsible for generating the blocks is not high in credit degree or concerned about the computing power of the nodes. The PBFT solves the Byzantine fault tolerance problem and is more suitable for the situation that the number of nodes is not too much.
Disclosure of Invention
In view of the problems in the prior art, a primary object of embodiments of the present invention is to provide a method and an apparatus for selecting consensus nodes, so as to implement reasonable selection of consensus nodes in a blockchain network and avoid the influence of an excessive number of nodes on the consensus efficiency and transaction processing performance in the blockchain network.
In order to achieve the above object, an embodiment of the present invention provides a method for selecting a consensus node, where the method includes:
classifying the consensus nodes in the block chain network optional consensus node pool to obtain Leader nodes, verification nodes and consensus nodes of three types of executing Byzantine protocol nodes;
acquiring log data of the block chain network, and determining a weight index corresponding to each common identification node according to the log data;
inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model to obtain node weight values corresponding to the common identification nodes;
and according to the node weight value corresponding to each consensus node, selecting the consensus nodes from the three types of consensus nodes according to 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 determining, according to the log data, a weight index corresponding to each common node includes:
performing data analysis on the log data to obtain a weight index corresponding to each common identification node; 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, the inputting the weight index corresponding to each common node into a pre-established weight calculation model to obtain the node weight value corresponding to each common node includes:
inputting the weight indexes corresponding to the consensus nodes into a pre-established weight calculation model, calculating products of the weight indexes and corresponding calculation coefficients, and taking 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.
Optionally, in an embodiment of the present invention, the selecting, according to the node weight value corresponding to each consensus node, a consensus node from three types of consensus nodes according to the obtained node selection rule includes:
acquiring a node selection rule; the node selection rule comprises a node type and a node number;
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 quantity.
The embodiment of the invention also provides a device for selecting the consensus node, which comprises the following components:
the node classification module is used for classifying the consensus nodes in the block chain network selectable consensus node pool to obtain Leader nodes, verification nodes and consensus nodes executing Byzantine protocol nodes;
the weight index module is used for acquiring log data of the block chain network and determining a weight index corresponding to each common identification node according to the log data;
the weight value module is used for inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model to obtain node weight values corresponding to the common identification nodes;
and 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.
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 common identification 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 selecting module includes:
a selection rule unit for obtaining a node selection rule; the node selection rule comprises a node type and a node number;
and the node selecting unit is used for 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 quantity.
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, wherein the processor implements the 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 realizes the reasonable selection of the consensus nodes of the blockchain network by classifying the consensus nodes and setting the weight values, thereby avoiding the influence of excessive number of the nodes on the consensus efficiency and the transaction processing performance of the blockchain network, improving the credit degree responsible for generating the blockchain nodes, being suitable for different application scene requirements, being beneficial to balancing the contradiction between high performance and decentralization degree, complete centralization and complete decentralization and realizing the intelligent data decision of the consensus node selection.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a flowchart of a consensus node selection method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating determining a weight index of a consensus node according to 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 diagram illustrating a classification of consensus 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 present invention;
FIG. 6 is a schematic diagram of node weight values according to an embodiment of the present invention;
FIG. 7 is a system diagram illustrating a method for selecting a consensus node according to an embodiment of the present invention;
FIG. 8 is a diagram illustrating selection of a consensus node in an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a consensus node selecting apparatus according to an embodiment of the present invention;
FIG. 10 is a diagram illustrating a structure of a weight index module according to an embodiment of the present invention;
FIG. 11 is a schematic structural 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 present invention.
Detailed Description
The embodiment of the invention provides a consensus node selection method and device.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart illustrating a method for selecting a consensus node according to an embodiment of the present invention, where an execution subject of the method for selecting a consensus node according to an 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, the common identification nodes in the block chain network optional common identification node pool are classified to obtain the common identification nodes of three types, namely, Leader node, verification node and executing Byzantine protocol node.
The consensus nodes in the block chain network optional consensus node pool are classified, the requirements of different application scenes are met based on different election strategies, and the consensus nodes are divided into 3 types including Leader nodes, verification nodes and nodes executing a Byzantine protocol.
Further, the Leader node: the method is suitable for the condition that the selectable consensus node is mainly contributed by a certain or a few organizations/institutions, the block is generated by the elected Leader node, and the consistency of the candidate transaction set is not required to be coordinated among a plurality of nodes, so that the efficiency of generating the final block is improved. The Leader node mode is not suitable for implementing a Byzantine fault-tolerant scenario.
Further, the verification 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, a few verification nodes belonging to a few organizations or institutions are selected to generate candidate transaction sets, the number of the candidate transaction sets generated by the few verification nodes is controlled within a certain range, the consistency performance of the candidate transaction sets is favorably coordinated subsequently, and the multi-centralization concept of the weak center mode can be realized.
Further, executing a Byzantine protocol node: under the above scenario of applying election verification nodes, the node is composed of a small number of nodes of a small number of organizations or institutions selected from the selectable consensus node pool, and multiple rounds of consensus are performed on multiple candidate transaction sets generated by the small number of executing byzantine protocol nodes on the basis of the byzantine protocol on a small number of verification nodes, so that a final candidate transaction set is generated.
And step S2, acquiring the log data of the block chain network, and determining the weight index corresponding to each common identification node according to the log data.
The method comprises the steps of obtaining log data from a block chain 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 a weight index corresponding to each common identification node. Specifically, the weight index includes a processing capability index and a credit index.
Further, the processing capability index is a relevant index affecting the processing capability, and includes 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 related indexes influencing credit, and comprises industry background conditions of the organization to which the node belongs, cumulative traffic conditions in a period of time, total technical capacity reflected by technical team and infrastructure conditions and the like.
And step S3, inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model to obtain the node weight values corresponding to the common identification nodes.
And inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model, and calculating the node weight values through the weight calculation model. Specifically, the node weight value includes a processing capacity weight value, a credit weight value, and a comprehensive weight value.
Further, the processing capacity index is multiplied by the corresponding calculation coefficient, and the processing capacity weight value of each consensus node is obtained through accumulation. And multiplying the credit index by the corresponding calculation coefficient, and accumulating to obtain the credit weight value of each consensus node. And the comprehensive weight value represents a credit weight value and a processing capacity weight value of the comprehensive consideration consensus node, and the comprehensive weight value of each consensus node is obtained by respectively multiplying the credit weight value and the processing capacity weight value by corresponding calculation coefficients and accumulating. Specifically, each weight index corresponds to a different calculation coefficient, and the calculation coefficient may be preset.
Further, the processing capacity weight value is suitable for selecting a verification node, and the comprehensive weight value is suitable for selecting a Leader node and a Byzantine protocol execution node.
And step S4, 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.
Under different applicable scenes, when the block chain network carries out transaction or needs to reselect a block participating in consensus packaging, consensus node selection is carried out according to a node selection rule. Specifically, the node selection rule may be manually input or preset, and the node selection rule includes the type, number, proportion, and the like of the consensus node to be selected.
Further, different types of participation consensus packing nodes with different numbers or proportions set by the node selection rule are selected from the selectable node list corresponding to the node weight value within the set range according to a preset random algorithm or a mode that the node weight value is from high to low.
As an embodiment of the present invention, the log data includes a system log, an application log, and a service log.
The method comprises the steps of obtaining various log data of a block chain network, including a system log, an application log and a service log, and carrying out data processing and analysis.
As an embodiment of the present invention, as shown in fig. 2, determining a weight index corresponding to each common node according to the log data includes:
step S21, performing data analysis on the log data to obtain a weight index corresponding to each common identification node; wherein the weight index comprises a processing capacity index and a credit index;
and step S22, acquiring a preset calculation coefficient corresponding to the weight index according to the processing capacity index and the credit index.
And performing data processing and analysis on the log data to obtain a weight index corresponding to each common identification node. The weight index includes a processing capability index and a credit index.
Further, each weight index corresponds to a different calculation coefficient, and the calculation coefficient may be preset, so that a preset calculation coefficient corresponding to each weight index may be obtained according to the processing capability 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 the weight indexes corresponding to the consensus nodes into a pre-established weight calculation model, calculating products of the weight indexes and corresponding calculation coefficients, and taking 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.
And inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model, and calculating the node weight values through the weight calculation model. Specifically, the node weight value includes a processing capacity weight value, a credit weight value, and a comprehensive weight value.
Further, the processing capacity index is multiplied by the corresponding calculation coefficient, and the processing capacity weight value of each consensus node is obtained through accumulation. And multiplying the credit index by the corresponding calculation coefficient, and accumulating to obtain the credit weight value of each consensus node. And the comprehensive weight value represents a credit weight value and a processing capacity weight value of the comprehensive consideration consensus node, and the comprehensive weight value of each consensus node is obtained by respectively multiplying the credit weight value and the processing capacity weight value by corresponding calculation coefficients and accumulating.
As an embodiment of the present invention, as shown in fig. 3, the selecting, according to the node weight value corresponding to each consensus node, a consensus node from three types of consensus nodes according to the obtained node selection rule includes:
step S31, acquiring a node selection rule; the node selection rule comprises a node type and a node number;
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 quantity.
Under different applicable scenes, when the block chain network carries out transaction or needs to reselect a block participating in consensus packaging, consensus node selection is carried out according to a node selection rule.
Further, the node selection rule may be manually input or preset, and the node selection rule includes the type, number, proportion, and the like of the consensus node to be selected.
Further, different types of participation consensus packing nodes with different numbers or proportions set by the node selection rule are selected from the selectable node list corresponding to the node weight value within the set range according to a preset random algorithm or a mode that the node weight value is from high to low.
In an embodiment of the present invention, as shown in fig. 4, a schematic diagram of a consensus node classification in an embodiment of the present invention is shown, and a process shown in the diagram is to classify nodes participating in consensus, adapt to the needs of different application scenarios based on different election strategies, and totally classify the nodes into 3 classes:
(1) leader node: the method is suitable for the condition that the selectable consensus node is mainly contributed by a certain or a few organizations/institutions, the block is generated by the elected Leader node, and the consistency of the candidate transaction set is not required to be coordinated among a plurality of nodes, so that the efficiency of generating the final block is improved. The Leader node mode is not suitable for implementing a Byzantine fault-tolerant scenario.
(2) 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, a few verification nodes belonging to a few organizations or institutions are selected to generate candidate transaction sets, the number of the candidate transaction sets generated by the few verification nodes is controlled within a certain range, the consistency performance of the candidate transaction sets is favorably coordinated subsequently, and the multi-centralization concept of the weak center mode can be realized.
(3) Executing Byzantine protocol nodes: under the above scenario of applying election verification nodes, the node is composed of a small number of nodes of a small number of organizations or institutions selected from the selectable consensus node pool, and multiple rounds of consensus are performed on multiple candidate transaction sets generated by the small number of executing byzantine protocol nodes on the basis of the byzantine protocol on a small number of verification nodes, so that a final candidate transaction set is generated.
In this embodiment, weights are set for the selectable consensus nodes, the nodes participating in consensus are selected by the weights, and a node weight calculation model is designed, as shown in fig. 5.
(1) Processing capacity weight value: the comprehensive computing and processing capacity of the selected node is considered from the technical aspect, so that the selected node has better and more reliable processing capacity. The method comprises the steps of selecting relevant indexes (including processing performance conditions in a period of time, computing resource conditions, abnormal occurrence conditions in a period of time and the like) which mainly affect processing capacity, multiplying the indexes by different computing coefficients, and accumulating to obtain the processing capacity weight values of the nodes.
(2) Credit weight value: and considering the comprehensive credit of the selected nodes from the service level, so that the selected consensus nodes can reflect that the organization or the organization to which the consensus nodes belong has better credit endorsements, and the confidence of consensus is improved. The credit weight value of the node is obtained by selecting relevant indexes (including industry background conditions of the mechanism to which the node belongs, accumulated traffic conditions within a period of time, total technical capacity reflected by technical team and infrastructure conditions and the like) which mainly affect 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 multiplied by different calculation coefficients respectively to obtain the comprehensive weight value of the node through accumulation.
(4) As shown in fig. 6, the processing power weight value is adapted to select "verification node", and the composite weight value is adapted to select "Leader node" and "execute byzantine protocol node".
In this embodiment, the selection of the weight index and the corresponding coefficient setting are generated by data analysis, and an intelligent decision mechanism is introduced to realize an intelligent data decision for selecting the consensus node, as shown in fig. 7.
(1) Collecting various log data of the blockchain network by the data platform, wherein the log data comprises a system log, an application log and a service log, processing and analyzing the data by the data analysis platform, selecting a weight index and a corresponding calculation coefficient, and establishing a weight calculation model.
(2) And converting the weight calculation model into a decision rule, deploying the decision rule in an intelligent data decision engine, periodically calculating the weight of the optional consensus node of the blockchain network by the decision engine according to the decision rule, and outputting the weight calculation result to a blockchain service platform (BaaS).
(3) And editing and deploying a 'consensus intelligent contract' to the blockchain network by the blockchain service platform, calling the consensus intelligent contract after the blockchain service platform receives the node weight output by the decision engine, and chaining the node weight data, wherein the node weight data is stored in a state database in the blockchain network node.
In this embodiment, as shown in fig. 8, in different applicable scenarios, the blockchain network selects a set number or ratio of different types of participating consensus packing nodes from the list of selectable nodes with weights within a set range based on a random algorithm or in a manner that weights are from high to low at each transaction or when a participating consensus packing block needs to be reselected.
The invention realizes the control of the number of the sharing and identifying packaging nodes participated in the blockchain network, thereby avoiding the influence of excessive number of the nodes on the sharing and identifying efficiency and the transaction processing performance on the blockchain network; on a credible system realized on the original technical level of the block chain, the credibility responsible for generating the block nodes is improved; the consensus node selection mode can be suitable for different application scene requirements; the method is beneficial to balancing the contradiction between high performance and decentralization degree, complete centralization and complete decentralization; and an intelligent decision mechanism is introduced to realize intelligent data decision of consensus node selection.
Specifically, the consensus nodes are classified and weighted, so that the consensus nodes of the blockchain network are reasonably selected, the influence of excessive number of the nodes on the consensus efficiency and the transaction processing performance of the blockchain network is avoided, the credit degree responsible for generating the blockchain nodes is improved, the block chain network consensus node selection method can be suitable for different application scene requirements, the contradiction between high performance and decentralization degree, complete centralization and complete decentralization is favorably balanced, and the intelligent data decision of the consensus node selection is realized.
Fig. 9 is a schematic structural diagram of a consensus node selecting apparatus according to an embodiment of the present invention, where the apparatus includes:
the node classification module 10 is configured to classify the consensus nodes in the optional consensus node pool of the blockchain network to obtain Leader nodes, verification nodes, and consensus nodes of three types for executing byzantine protocol nodes.
The consensus nodes in the block chain network optional consensus node pool are classified, the requirements of different application scenes are met based on different election strategies, and the consensus nodes are divided into 3 types including Leader nodes, verification nodes and nodes executing a Byzantine protocol.
Furthermore, the Leader node is suitable for the condition that the selectable consensus node is mainly contributed by a certain or a few organizations/institutions, the block is generated by the elected Leader node, and the consistency of the candidate transaction set is not required to be coordinated among a plurality of nodes, so that the efficiency of generating the final block is improved. The Leader node mode is not suitable for implementing a Byzantine fault-tolerant scenario.
Furthermore, the verification node is suitable for the situation that the selectable consensus node is contributed by a plurality of organizations/institutions, and meanwhile, the number of the selectable consensus node pool nodes is large, a few verification nodes belonging to a few organizations or institutions are selected to generate the candidate transaction sets, and the number of the candidate transaction sets generated by the few verification nodes is controlled within a certain range, so that the consistency performance of the candidate transaction sets is favorably coordinated subsequently, and the multi-centralization concept of realizing the weak center mode can be considered.
Further, the nodes executing the byzantine protocol are composed of a few nodes of a few organizations or institutions selected from the selectable common identification node pool under the scene of the applicable election verification nodes, and a plurality of candidate transaction sets generated by the few nodes executing the byzantine protocol on the very few verification nodes based on the byzantine protocol are subjected to multiple rounds of common identification, so that a final candidate transaction set is generated.
And the weight index module 20 is configured to obtain log data of the block chain network, and determine a weight index corresponding to each common identification node according to the log data.
The method comprises the steps of obtaining log data from a block chain 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 a weight index corresponding to each common identification node. Specifically, the weight index includes a processing capability index and a credit index.
Further, the processing capability index is a relevant index affecting the processing capability, and includes 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 related indexes influencing credit, and comprises industry background conditions of the organization to which the node belongs, cumulative traffic conditions in a period of time, total technical capacity reflected by technical team and infrastructure conditions and the like.
And the weight value module 30 is configured to input 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.
And inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model, and calculating the node weight values through the weight calculation model. Specifically, the node weight value includes a processing capacity weight value, a credit weight value, and a comprehensive weight value.
Further, the processing capacity index is multiplied by the corresponding calculation coefficient, and the processing capacity weight value of each consensus node is obtained through accumulation. And multiplying the credit index by the corresponding calculation coefficient, and accumulating to obtain the credit weight value of each consensus node. And the comprehensive weight value represents a credit weight value and a processing capacity weight value of the comprehensive consideration consensus node, and the comprehensive weight value of each consensus node is obtained by respectively multiplying the credit weight value and the processing capacity weight value by corresponding calculation coefficients and accumulating. Specifically, each weight index corresponds to a different calculation coefficient, and the calculation coefficient may be preset.
Further, the processing capacity weight value is suitable for selecting a verification node, and the comprehensive weight value is suitable for selecting a Leader node and a Byzantine protocol execution node.
And the node selecting module 40 is configured to select the consensus node from the three types of consensus nodes according to the node weight value corresponding to each consensus node according to the obtained node selection rule.
Under different applicable scenes, when the block chain network carries out transaction or needs to reselect a block participating in consensus packaging, consensus node selection is carried out according to a node selection rule. Specifically, the node selection rule may be manually input or preset, and the node selection rule includes the type, number, proportion, and the like of the consensus node to be selected.
Further, different types of participation consensus packing nodes with different numbers or proportions set by the node selection rule are selected from the selectable node list corresponding to the node weight value within the set range according to a preset random algorithm or a mode that the node weight value is from high to low.
As an 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:
a weight index unit 21, configured to perform data analysis on the log data to obtain a weight index corresponding to each common 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 the weight index corresponding to each common 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 common node; 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 selecting module 40 includes:
a selection rule unit 41, configured to obtain a node selection rule; the node selection rule comprises a node type and a node number;
and a node selecting unit 42, configured to select corresponding consensus nodes from the three types of consensus nodes according to node weight values corresponding to the consensus nodes and according to the node types and the node numbers.
Based on the same application concept as the consensus node selection method, the invention also provides the consensus node selection device. Because the principle of solving the problem of the consensus node selection device is similar to that of the consensus node selection method, the implementation of the consensus node selection device can refer to the implementation of the consensus node selection method, and repeated details are omitted.
The invention realizes the reasonable selection of the consensus nodes of the blockchain network by classifying the consensus nodes and setting the weight values, thereby avoiding the influence of excessive number of the nodes on the consensus efficiency and the transaction processing performance of the blockchain network, improving the credit degree responsible for generating the blockchain nodes, being suitable for different application scene requirements, being beneficial to balancing the contradiction between high performance and decentralization degree, complete centralization and complete decentralization and realizing the intelligent data decision of the consensus node selection.
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, wherein the processor implements the 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: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in fig. 12; furthermore, the electronic device 600 may also comprise components not shown in fig. 12, which may be referred to in the prior art.
As shown in fig. 12, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling 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 relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 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 to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 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 portion 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 application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The 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, 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 receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (12)
1. A method for selecting a consensus node is characterized by comprising the following steps:
classifying the consensus nodes in the block chain network optional consensus node pool to obtain Leader nodes, verification nodes and consensus nodes of three types of executing Byzantine protocol nodes;
acquiring log data of the block chain network, and determining a weight index corresponding to each common identification node according to the log data;
inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model to obtain node weight values corresponding to the common identification nodes;
and according to the node weight value corresponding to each consensus node, selecting the consensus nodes from the three types of consensus nodes according to the obtained node selection rule.
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 the determining a weight index corresponding to each common identification node according to the log data comprises:
performing data analysis on the log data to obtain a weight index corresponding to each common identification node; 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 according to claim 3, wherein the inputting the weight index corresponding to each common node into a pre-established weight calculation model to obtain the node weight value corresponding to each common node comprises:
inputting the weight indexes corresponding to the consensus nodes into a pre-established weight calculation model, calculating products of the weight indexes and corresponding calculation coefficients, and taking 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.
5. The method according to claim 1, wherein the selecting the consensus node from the three types of consensus nodes according to the node weight value corresponding to each consensus node according to the obtained node selection rule comprises:
acquiring a node selection rule; the node selection rule comprises a node type and a node number;
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 quantity.
6. An apparatus for selecting a consensus node, the apparatus comprising:
the node classification module is used for classifying the consensus nodes in the block chain network selectable consensus node pool to obtain Leader nodes, verification nodes and consensus nodes executing Byzantine protocol nodes;
the weight index module is used for acquiring log data of the block chain network and determining a weight index corresponding to each common identification node according to the log data;
the weight value module is used for inputting the weight indexes corresponding to the common identification nodes into a pre-established weight calculation model to obtain node weight values corresponding to the common identification nodes;
and 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.
7. The apparatus of claim 6, wherein the log data comprises a system log, an application log, and a traffic log.
8. The apparatus of claim 6 or 7, 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 common identification 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.
9. The apparatus according to claim 8, wherein 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.
10. The apparatus of claim 6, wherein the node selection module comprises:
a selection rule unit for obtaining a node selection rule; the node selection rule comprises a node type and a node number;
and the node selecting unit is used for 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 quantity.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
12. 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 5.
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