Local random consensus method

Abstract

The invention relates to the technical field of industrial networks, in particular to a local random consensus method. Comprising the following steps: inputting a Boolean value of a node in an industrial network system, excluding a collapsed node, and selecting a decision value; obtaining a probability lower bound of the node; a consensus is achieved based on a probability lower bound; by using public coins, when a Boolean value is input into an industrial network system, nodes can reach a consensus within polynomial time and output a decision value; and a part of nodes are excluded to participate in calculation, so that the nodes in the system can reach a consensus more quickly in polynomial time, and the requirements of low delay and high reliability of the industrial network system are met.

Description

Technical Field

[0001] The present invention relates to the field of industrial network technology, and in particular to a local random consensus method. Background Art

[0002] With the rapid development of the Industrial Internet, data interaction and decision-making in industrial network systems have become increasingly complex. Traditional centralized decision-making systems are unable to meet the requirements of low latency and high reliability.

[0003] Therefore, it is necessary to propose a local random consensus method suitable for industrial network systems. Summary of the invention

[0004] The purpose of the present invention is to provide a local random consensus method, aiming to solve the technical problem that traditional centralized decision-making systems are difficult to meet the requirements of low latency and high reliability.

[0005] To achieve the above purpose, the present invention adopts a local random consensus method, which includes the following steps:

[0006] Enter the Boolean values ​​of nodes in the industrial network system, exclude crashed nodes, and select decision values;

[0007] Get the probability lower bound of the node;

[0008] Reach consensus based on the probability lower bound.

[0009] Among them, in the steps of inputting the Boolean value of the node in the industrial network system, excluding the crashed node, and selecting the decision value:

[0010] The number of nodes in the preset industrial network system is n, and the Boolean value of the input node is {0, 1};

[0011] Randomly exclude the crashed nodes and obtain the decision values ​​of the remaining nodes; the decision values ​​include 0 and 1.

[0012] Among them, in the step of randomly excluding crashed nodes:

[0013] The default number of crash nodes is f, where , randomly exclude nodes.

[0014] Among them, in the step of obtaining the lower bound of the probability of the node:

[0015] Get the remaining The probability lower bound of the decision value of a node is 0 and 1.

[0016] Among them, in the step of obtaining the lower bound of the probability of the node:

[0017] Get the remaining The probability lower bound of the decision value of a node is 0 and 1.

[0018] Among them, in the step of obtaining the lower bound of the probability of the node:

[0019] Based on the coin probability method, let the coin set of any node be C i , v i is the decision value;

[0020] Remaining The lower bound of the probability that the decision value of a node is 1 is:

[0021]

[0022] Remaining The lower bound of the probability that the decision value of a node is 0 is:

[0023]

[0024] A local random consensus method of the present invention inputs the Boolean value of a node in an industrial network system, excludes crashed nodes, and selects a decision value; obtains the probability lower bound of the node; and reaches a consensus based on the probability lower bound. By using public coins, when a Boolean value is input in an industrial network system, the nodes can reach a consensus and output a decision value in polynomial time; excluding some nodes from participating in the calculation can make the nodes in the system reach a consensus more quickly in polynomial time, meeting the requirements of low latency and high reliability of the industrial network system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 It is a schematic diagram of the steps of the local random consensus method of the present invention.

[0027] Figure 2 It is a program diagram of Algorithm 1 of the present invention.

[0028] Figure 3 It is a program diagram of Algorithm 2 of the present invention.

[0029] Figure 4 It is a decision flow chart of the local random consensus method of the present invention. DETAILED DESCRIPTION

[0030] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0031] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0032] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0033] See also Figure 1 to Figure 4 ,in Figure 1 It is a schematic diagram of the steps of the local random consensus method. Figure 2 is the program diagram of Algorithm 1. Figure 3 This is the program diagram of Algorithm 2. Figure 4 It is a decision flow chart of the local random consensus method.

[0034] The present invention provides a local random consensus method, comprising the following steps:

[0035] S100: Inputting Boolean values ​​of nodes in the industrial network system, excluding crashed nodes, and selecting a decision value.

[0036] In this embodiment, the number of nodes in the industrial network system is preset to n, and the Boolean value of the node is input; wherein the Boolean value is {0, 1}; the crashed nodes are randomly excluded, and the decision values ​​for selecting the remaining nodes are obtained; wherein the decision values ​​include 0 and 1.

[0037] The default number of crash nodes is f, where , randomly exclude nodes.

[0038] S200: Obtain the probability lower bound of the node.

[0039] In this embodiment, randomly exclude After nodes, in the remaining Execute on each node as follows Figure 2 Algorithm 1 shown, where Algorithm 1 is local random consensus-public coin.

[0040] Algorithm 1 considers the worst case, that is, all the nodes excluded from the operation are good nodes, so the remaining nodes participating in the operation need to wait for at least Coins arrive. Similarly, you need to wait for at least Coin sets arrived.

[0041] The public coin is transferred by coin probability, so that node i will transfer v i The probability of setting it to 0 or 1 is close. From Algorithm 1, it is easy to see that if the coin set of any node is C i , v i is the decision value; if C i If there is 0 in C, then after the node broadcasts, all other good nodes can see C i In this case, all good nodes will propose 0.

[0042] At the same time C i Each coin in The coins are concentrated, so it can be considered that Therefore, it can be concluded that:

[0043] Remaining The lower bound of the probability that the decision value of a node is 1 is:

[0044]

[0045] Remaining The lower bound of the probability that the decision value of a node is 0 is:

[0046]

[0047] S300: Reach consensus based on probability lower bound.

[0048] In this embodiment, based on the probability lower bound of the node decision value being 1, the probability lower bound of the node decision value being 0 and Figure 3 The algorithm 2 shown in the figure reaches a consensus, where algorithm 2 is a local random consensus; the overall decision-making flow chart is as follows Figure 4 shown.

[0049] When all nodes participating in the calculation are good nodes and the input values ​​are the same, the industrial network system can obtain a decision value within two rounds.

[0050] At line 23 of Algorithm 2, the general coin-tossing method may use each node to use The probability of taking 0 and taking 1 is the method, but in the present invention, the average time complexity can reach O(2 n ).

[0051] In Algorithm 1, considering the worst case, that is, the excluded nodes are all good nodes, all nodes that may crash participate in the calculation, and the industrial network system can also obtain a decision value in polynomial time.

[0052] At the same time, because the system excludes nodes, the system shortens the waiting time by at least Unit information transmission time.

[0053] In the present invention, the Boolean value of the node is input in the industrial network system, the crashed node is excluded, and the decision value is selected; the probability lower bound of the node is obtained; and consensus is reached based on the probability lower bound. By using public coins, when the Boolean value is input in the industrial network system, the nodes can reach a consensus and output a decision value in polynomial time; excluding some nodes from participating in the calculation can make the nodes in the system reach a consensus more quickly in polynomial time, meeting the requirements of low latency and high reliability of the industrial network system.

[0054] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0055] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A local random consensus method, characterized in that: The steps include: Enter the Boolean values ​​of nodes in the industrial network system, exclude crashed nodes, and select decision values; Get the probability lower bound of the node; Reach consensus based on the probability lower bound.

2. The local random consensus method according to claim 1, characterized in that: In the steps of inputting Boolean values ​​of nodes, excluding crashed nodes, and selecting decision values ​​in an industrial network system: The number of nodes in the preset industrial network system is n, and the Boolean value of the input node is {0, 1}; Randomly exclude the crashed nodes and obtain the decision values ​​selected by the remaining nodes; The decision values ​​include 0 and 1.

3. The local random consensus method according to claim 2, characterized in that: In the step of randomly excluding crashed nodes: The default number of crash nodes is f, where , randomly exclude nodes.

4. The local random consensus method according to claim 3, characterized in that: In the step of obtaining the lower bound of the probability of a node: Get the remaining The probability lower bound of the decision value of a node is 0 and 1.

5. The local random consensus method according to claim 4, characterized in that: In the step of obtaining the lower bound of the probability of a node: Get the remaining The probability lower bound of the decision value of a node is 0 and 1.

6. The local random consensus method according to claim 5, characterized in that: In the step of obtaining the lower bound of the probability of a node: Based on the coin probability method, let the coin set of any node be C i , v i is the decision value; Remaining The lower bound of the probability that the decision value of a node is 1 is: Remaining The lower bound of the probability that the decision value of a node is 0 is:

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