CN116233132A - Energy block chain link point consensus method based on improved Raft consensus mechanism - Google Patents
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Abstract
The invention discloses an energy block chain link point consensus method based on an improved Raft consensus mechanism, which comprises the steps of dividing nodes into A, B, C three different categories according to the energy use ownership of the nodes; configuring an improved Raft consensus mechanism corresponding to each category according to specific attributes of different categories and the comprehensive reputation value, and adopting the improved Raft consensus mechanism to respectively select a representative node set of each category from the A, B, C categories; forming a total representative node set by using the A, B, C three types of representative node sets, and participating in PBFT consensus; and updating the node attribution according to the energy use ownership after the energy use transaction. By the method, the fairness is improved, the participation enthusiasm of the energy block chain network nodes is improved, and the problems of high delay, low efficiency and poor expandability caused by the increase of the number of the nodes are solved.
Description
Technical Field
The invention relates to the technical field of energy block chains, in particular to an energy block chain link point consensus method based on an improved Raft consensus mechanism.
Background
The energy usage rights are comprehensive energy consumption rights which are allowed to be used by individuals, families or enterprises distributed or traded by the authorities under the premise of legal, safe and environment-friendly. In order to promote the green development and promote the popularization and sustainable development of renewable energy sources, the right-to-use transaction has important necessity and significance.
The number of the energy block chain nodes is large, the difference of the energy consumption weights owned by individual scattered households to large enterprise organizations is large, and the same consensus mechanism is used to have a certain degree of public compliance, for example, consensus algorithms such as rights and interests Proving (POS), authority Proving (POA) and the like can enable the probability of obtaining rights and interests of the nodes with a large number of rights and interests to be higher, so that the centralisation degree is deepened, the mobility is reduced, and the richer is caused. In addition, the consensus brings a institutional threshold, so that a large number of nodes which do not reach the energy consumption threshold participate in low enthusiasm, and the industry development is not facilitated.
The practical Bayesian fault-tolerant consensus (PBFT) algorithm has the advantages of high transaction confirmation speed, capability of tolerating attacks of malicious nodes, ensuring the reliability and safety of the system, low energy consumption, environmental protection and energy conservation. But the performance of the PBFT algorithm is greatly reduced with the increase of the number of nodes, so that the PBFT algorithm is difficult to be applied to a large-scale energy transaction distributed system.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an energy block chain link point consensus method based on an improved Raft consensus mechanism, which divides the category of the energy block chain link point consensus method according to the energy consumption owned by a node, designs an improved Raft algorithm according to the demand attribute of different categories, selects representative nodes to participate in PBFT consensus, and mainly solves the problems that the existing consensus algorithm has low performance, high delay, gradually centralization and the phenomenon that the node participation enthusiasm is reduced due to the use of the same consensus algorithm in large-scale energy transaction.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the energy block link point consensus method based on the improved Raft consensus mechanism comprises the following steps:
s10, dividing the nodes into A, B, C three different categories according to the energy consumption amount of the nodes, wherein 40% of the nodes after the energy consumption amount value is ranked in the whole network belong to the A category, 40% -80% of the nodes after the energy consumption amount value is ranked in the whole network belong to the B category, and 20% of the nodes before the energy consumption amount value is ranked in the whole network belong to the C category;
s20, configuring an improved Raft consensus mechanism corresponding to each category according to specific attributes of different categories in combination with the comprehensive reputation value, and adopting the improved Raft consensus mechanism to respectively select a representative node set of each category from three categories A, B, C, wherein a category A node adopts a PoEI+Raft consensus mechanism, a category B node adopts a PoEIV+Raft consensus mechanism, and a category C node adopts a PoEIC+Raft consensus mechanism;
s30, forming a total representative node set by the representative node sets of the three categories A, B, C, participating in PBFT consensus, and after consensus is achieved, transmitting the log information to the follower node by the representative nodes;
and S40, updating the node attribution according to the energy use ownership after the energy use transaction.
Specifically, the improved Raft consensus mechanism in step S20 includes:
selecting a candidate node according to the comprehensive reputation value;
then, according to the specific attribute of each category, a probability value is given to each candidate node;
and finally, generating a random number, and selecting the candidate nodes with probability values larger than the random number into the representative node set.
Specifically, the integrated reputation value is determined by a global reputation value and feedback trust value weighted calculation.
The global credit value is determined by the weighted calculation of the transaction success rate, the guarantee rate, the participation enthusiasm, the historical transaction amount rate and the rewarding value of the node.
And the feedback trust value is determined by the weighted calculation of the satisfaction degree evaluation, the transaction frequency and the evaluation similarity of the two transaction parties.
More specifically, the class specific attribute corresponding to the poei+raft consensus mechanism adopted by the class a node is the energy usage ownership, charitable donation amount and comprehensive reputation value of the node.
More specifically, the class specific attribute corresponding to the poeiv+raft consensus mechanism adopted by the class B node is the energy consumption flow property, charitable donation amount and comprehensive reputation value of the node, wherein the energy consumption flow property is positively related to the weight.
More specifically, the class specific attribute corresponding to the poeic+raft consensus mechanism adopted by the class C node is the energy consumption rate, charitable donation amount and comprehensive reputation value of the node, wherein the energy consumption rate and the weight are inversely related.
Furthermore, node transaction attenuation factors are also considered in the improved Raft consensus mechanism, the weight of the latest transaction record is increased, and the weight corresponding to the configuration history transaction amount is greater than the weight corresponding to the long-term holding useful energy weight value.
Specifically, after the consensus is reached in step S30, the log replication information of each representative node is forwarded to the follower node, and the follower node receives and records most log replication commands of representative nodes belonging to the category.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the nodes are divided into three types by using the energy ownership, different improved Raft consensus mechanisms are used for the actual conditions and specific requirements of different types of node users, and the participation enthusiasm and reliability of the nodes are effectively improved. The three improved Raft consensus mechanisms used by the invention are all improved based on the Raft consensus algorithm, and in the energy transaction with more nodes, the consensus process can be divided into a plurality of layers according to the credit value and the energy consumption weight, so that the consensus efficiency is improved, the problems of high delay, low efficiency and poor expandability caused by the number of the nodes are solved, and the reliability and the safety of the system are ensured.
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FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Fig. 2 is a block diagram of an improved Raft consensus mechanism in accordance with an embodiment of the present invention.
FIG. 3 is a schematic diagram of a class node configuration and log replication process according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of node probability values when nodes are selected to represent nodes in an embodiment of the present invention.
Detailed Description
The invention will now be further described with reference to the accompanying drawings and examples, embodiments of which include, but are not limited to, the following examples.
Nodes in the Raft consensus algorithm are divided into a leader node, a candidate node and a follower node. The leader node is responsible for receiving log entries from clients, maintaining replication and submission of the log, and informing the follower of its presence by sending a heartbeat. Each follower is randomly allocated with a timeout period, and when the heartbeat from the leader is not received in the timeout period, the current leader is considered to be down, and the state of the current leader is changed into a candidate state. The candidate becomes the new leader if it receives support from most nodes.
The traditional Raft consensus algorithm has the defects of larger network traffic and lower efficiency because the leader node needs to process the requests from all nodes and communicate with all nodes frequently. When the number of nodes is large, the problem of expansibility such as overlong election time, influence on system performance and the like can occur.
In view of the above, the invention improves the stage of the shift consensus algorithm, combines parameters such as reputation value and the like to select candidate persons and representative nodes, optimizes the election process, slightly reduces the voting process and improves the efficiency.
As shown in fig. 1 to 2, the implementation of the energy block link point consensus method based on the improved Raft consensus mechanism mainly includes the processes of node classification, selecting candidates according to the integrated reputation value, selecting representative nodes according to specific attributes of different categories, log replication, and the like.
The specific contents are as follows:
1. node classification
Network nodes in an energy blockchain are first classified into three different categories according to the amount of ownership (credit) of energy assigned to an enterprise, organization or individual by the governing authority of energy management. The nodes with the energy right having the magnitude rank of 40% after the whole network belong to the A class, the nodes with the energy right having the magnitude rank of 40% -80% of the whole network belong to the B class, and the nodes with the energy right having the magnitude rank of 20% before the whole network belong to the C class.
2. Selecting candidate nodes based on integrated reputation values
The integrated reputation value is determined by a global reputation value and a feedback trust value weighted calculation. The global credit value relates to the transaction success rate, the deposit ratio, the participation enthusiasm, the historical transaction amount ratio and the rewarding value of the node; the feedback trust value relates to the satisfaction evaluation, the transaction frequency and the evaluation similarity of the two transaction parties to each other.
Step 2.1: and initializing node states, wherein each node state is initialized to be a follower state.
Step 2.2: calculating the transaction success rate, the deposit ratio, the participation enthusiasm, the historical transaction amount ratio, the rewarding value, the satisfaction evaluation, the transaction frequency and the evaluation similarity of the nodes.
Success rate of transaction (T) s ）：
For the successful transaction number of node i in time period T, T 0 And (5) the total transaction initiation number of the node i in the T time period.
Ensure the gold ratio%
）：d i Representing the guard gold of node i,Dindicating the total guarantee in the class to which node i belongs.
Participation enthusiasm (TE) i ）：
For the number of outstanding transactions of node i within time period T,T o1 is the total transaction initiation amount for node i during time period T,T o2 is the average historical transaction amount per unit time in the time period T,nindicating the total number of nodes within the category,
is corresponding weight and is adjusted according to actual conditions.
Historical transaction amount ratio%HT）：TA i Representing the amount of transactions at node i,
indicating the total transaction amount of the nodes in the category in the T time period, and n indicating the total node number in the category. Introducing time factor, T cur Represents the current time, T start Representing the transaction time, increasing the latest transaction record weight.
Prize value [ ]Re): if the node finds out the malicious behavior of other nodes, the value can be promoted after the report is successful.
Representing malicious nodesReputation value of j>
An appropriate parameter may be determined based on the whole network reputation value.
Satisfaction evaluation [ ]
): evaluation of each other by each party after the transaction is completed, such as satisfaction evaluation of node j to node i, 0 indicates complete dissatisfaction, and 1 indicates complete satisfaction.
Trade frequency [ ]
): describing transaction frequency between nodes, +.>
Representing the sum of transactions between node i and node j, including the number of successful transactions +.>
And the number of outstanding transactions->
，T i Representing the sum of transactions between node i and other nodes,kindicating that k nodes have transacted with node i.
Evaluation of similarity [ (]
）：/>
The smaller the value is, the more similar the evaluation of the node j to the node i is to the evaluation of other nodes to the node i, and the abnormal evaluation value is calculated by adopting the quartered bit distance. Firstly, sorting all satisfaction evaluation received by the node i, and then calculating the medianQ1Dividing all numbers into two groups greater than and less than the median, and calculating the median of the two groupsQ2AndQ3. Tetrad difference @IQR) Equal toQ3 - Q2According toIQRCalculating an outlier, whereinAV2Indicating serious abnormality, exceeding which is regarded as invalid, is provided with +.>
Zero;AV1indicating a decrease in reliability, therefore, a positive integer parameter +.>
To reduce correlation. />
Step 2.3: calculating node feedback trust value (FR ij ) From satisfaction evaluation, transaction frequency, evaluationAnd (5) similarity calculation.
Indicating that there is no transaction record between nodes i, j.
Step 2.4: and calculating the comprehensive credit value RV of the node i.
The weight is represented by a weight that,
step 2.5: and sequencing the comprehensive reputation values of all the nodes in the category, wherein the node states which are ranked m before and meet the Raft requirement become candidates. The actual candidate node number y is less than or equal to m.
3. Selecting representative nodes based on specific attributes of different categories
Step 3.1: calculating candidate nodes { c } of each category according to the specific data of the category to which the node belongs 1 ，c 2 ，…，c i-1 ，c i ，c i+1 ，…，c y Probability values of }.
In this step, the contribution of large enterprises to society is considered, and the voluntary donations are temporarily stored in a main management department for specifying banks for charitable activities and institutions, and are expressed by charitable Donation account.
Class a: the PoEI+Lift consensus mechanism is a proof consensus algorithm of energy holding capacity, and most of the nodes are scattered households, and personal demands and energy using capacity are small. The probability PBL of node i selecting the representative node is thus calculated from the usage rights ownership GI, the charitable Donation amount destination and the weighted composite reputation value RV. GI (GI) Sta Indicating the energy consumption and GI of the node in long term possession f Representing the amount of energy used by the node in the flow. Weights corresponding to energy values in circulation
Weight +.>
。/>
Class B: the PoEI+Lift consensus mechanism is an energy flow proving consensus algorithm, the nodes have a certain number of energy use rights, the nodes are encouraged to actively trade the redundant energy use rights, and the probability PBL of the node i when the representative node is selected is calculated according to the energy use rights flow Nego, charitable Donation amount destination and a weighted comprehensive credit value RV. GI (GI) total Representing the total energy usage rights possessed by the node. The flow of buyers is set to 0 because the buyer exceeds the set limit and does not reach the aim of energy conservation and emission reduction, and punishment is required. And the weight corresponding to the energy consumption weight which is kept stationary for a long time is reduced in a nonlinear manner with the increase of time, and the weight corresponding to the node with higher flow-through performance is higher.
Class C: the PoEIC+Lift consensus mechanism is an energy consumption amount proving consensus algorithm, and the nodes belong to large enterprise organizations, and have large energy consumption amount. The probability PBL of node i selecting the representative node is thus calculated from the energy consumption ratio W, the charitable Donation amount contribution and the weighted composite reputation value RV. GI (GI) consume Representing the usage rights amount, GI, consumed by node i usage monitored by the smart device rated The value is the amount allocated by the energy authorities. Consumption rate of energy consumption related to energy consumption of the nodeThe difference between the weight initial allocation amount and the consumption amount adopted by the intelligent equipment is positive, which indicates that the emission reduction target is reached on the basis of the allocated energy consumption, and the weight is increased; the difference being negative indicates excessive emissions, and a weight decrease, based on the assigned energy usage. The higher the weight the greater the probability that the node becomes the representative node.
Step 3.2: and generating a random number R, wherein nodes with probability values larger than R are selected as representative nodes, and the nodes belong to the representative node set of the category. As shown in fig. 4, candidate node c 1 ，c 2 ，…，c i-1 ，c i ，c i+1 ，…，c y Is a representative node of the category to which it belongs.
4. Log replication
Step 4.1: and forming a representative node set by the representative nodes selected by each class, and performing PBFT consensus.
Step 4.2: after consensus is reached, the log information is copied by each representative node and forwarded to the follower node, and the follower node receives and records most log copying commands of representative nodes belonging to the category. If the malicious behavior of the representative node is found, the malicious behavior can be reported. As shown in fig. 3.
5. Updating node attribution according to the energy use ownership after the energy use transaction occurs, recalculating according to the latest changed parameters, changing the whole network node ranking, changing node category attribution, and using the corresponding consensus mechanism to prevent rights and interests from being concentrated.
Other less than perfect matters are known in the art.
The above embodiments are only preferred embodiments of the present invention, and not intended to limit the scope of the present invention, but all changes made by adopting the design principle of the present invention and performing non-creative work on the basis thereof shall fall within the scope of the present invention.
Claims (10)
1. The energy block link point consensus method based on the improved Raft consensus mechanism is characterized by comprising the following steps of:
s10, dividing the nodes into A, B, C three different categories according to the energy consumption amount of the nodes, wherein 40% of the nodes after the energy consumption amount value is ranked in the whole network belong to the A category, 40% -80% of the nodes after the energy consumption amount value is ranked in the whole network belong to the B category, and 20% of the nodes before the energy consumption amount value is ranked in the whole network belong to the C category;
s20, configuring an improved Raft consensus mechanism corresponding to each category according to specific attributes of different categories in combination with the comprehensive reputation value, and adopting the improved Raft consensus mechanism to respectively select a representative node set of each category from three categories A, B, C, wherein a category A node adopts a PoEI+Raft consensus mechanism, a category B node adopts a PoEIV+Raft consensus mechanism, and a category C node adopts a PoEIC+Raft consensus mechanism;
s30, forming a total representative node set by the representative node sets of the three categories A, B, C, participating in PBFT consensus, and after consensus is achieved, transmitting the log information to the follower node by the representative nodes;
and S40, updating the node attribution according to the energy use ownership after the energy use transaction.
2. The method of claim 1, wherein the improved Raft consensus mechanism in step S20 comprises:
selecting a candidate node according to the comprehensive reputation value;
then, according to the specific attribute of each category, a probability value is given to each candidate node;
and finally, generating a random number, and selecting the candidate nodes with probability values larger than the random number into the representative node set.
3. The method of claim 2, wherein the integrated reputation value is determined by a weighted calculation of a global reputation value and a feedback reputation value.
4. The method of claim 3, wherein the global reputation value is determined by weighted calculation of transaction success rate, deposit rate, participation aggressiveness, historical transaction amount rate, and rewards value of the node.
5. The method for link point consensus of energy blocks based on an improved Raft consensus mechanism according to claim 3 wherein the feedback confidence value is determined by a weighted calculation of the satisfaction rating, the frequency of the transaction, and the similarity of the ratings of the transaction parties to each other.
6. The energy block link point consensus method based on the improved Raft consensus mechanism according to claim 2, wherein the class specific attribute corresponding to the poei+raft consensus mechanism adopted by the class a node is the energy use ownership, charity donation and comprehensive reputation of the node.
7. The method for link point consensus of energy blocks based on an improved Raft consensus mechanism according to claim 2, wherein the class-B node adopts the poiiv+raft consensus mechanism as the specific attribute of the class corresponding to the node's energy consumption flow, charitable donation and comprehensive reputation, wherein the energy consumption flow is positively correlated with the weight.
8. The method for link point consensus of energy blocks based on an improved Raft consensus mechanism according to claim 2, wherein the class specific attribute corresponding to the poeic+raft consensus mechanism adopted by the class C node is a consumption ratio of energy consumption, a charitable donation amount, and a composite reputation value of the node, wherein the consumption ratio of energy consumption is inversely related to the weight.
9. The method for link point consensus of energy blocks based on an improved Raft consensus mechanism according to claim 2, wherein node transaction attenuation factors are also considered in the improved Raft consensus mechanism, the weight of the latest transaction record is increased, and the weight corresponding to the configured historical transaction amount is greater than the weight corresponding to the long-term holding useful energy weight value.
10. The method according to claim 1, wherein after the consensus is reached in step S30, the log information is forwarded to the follower node by each representative node, and the follower node receives and records most of the log replication commands of the representative nodes belonging to the category.
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| DONGHEE KIM: "Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement", 《2021 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN)》 * |
| MINGZHE LIU: "The Combination of Blockchain and Internet of Things - Challenges and Solutions to Security Issues", 《2022 5TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE (PRAI)》 * |
| 宋琪杰;陈铁明;陈园;马栋捷;翁正秋;: "面向物联网区块链的共识机制优化研究", 电信科学, no. 02 * |
| 段靓;吕鑫;刘凡;: "基于信任委托的区块链分层共识优化", 计算机工程, no. 10 * |
| 陆歌皓;谢莉红;李析禹;: "区块链共识算法对比研究", 计算机科学, no. 1 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117527834A (en) * | 2024-01-04 | 2024-02-06 | 成都理工大学 | Improved PBFT consensus method based on reputation scoring mechanism |
| CN117527834B (en) * | 2024-01-04 | 2024-03-26 | 成都理工大学 | Improved PBFT consensus method based on reputation scoring mechanism |
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| CN116233132B (en) | 2023-07-18 |
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