Block chain social network information dissemination optimization method and related equipment

Abstract

A block chain social network information dissemination optimization method and related equipment relate to the field of network information dissemination, by adopting the above technical scheme, a server can comprehensively analyze behavior evolution laws of agents, opinion leaders and voters in a block chain social network information dissemination process, find out an optimal evolution stability strategy, and improve the efficiency of the block chain social network information dissemination optimization. Compared with a traditional credit evaluation mechanism, the method has the advantages that the problems of user collusion, poor-quality information diffusion and the like are better solved, and the quality and the efficiency of information spreading are improved.

Description

Technical Field

[0001] The present application relates to the field of network information dissemination, and in particular to a blockchain social network information dissemination optimization method and related equipment. Background Art

[0002] With the rapid development of blockchain technology and social networks, the deep integration of the two has given rise to a new social form, blockchain social networks. Blockchain social networks have the characteristics of decentralization, information cannot be tampered with, and user data is autonomous and controllable, providing users with a safer and more transparent social environment. In blockchain social networks, the quality and efficiency of information dissemination have an important impact on the healthy development of the entire network.

[0003] At present, the dissemination of information in blockchain social networks is mainly managed by setting information release permissions and establishing a credit evaluation system. That is, by assessing the credit points of users, different information release permissions are granted according to the level of credit points, and smart contracts are used to record the information dissemination process to achieve traceability of information dissemination behavior.

[0004] However, as the scale of blockchain social networks expands and the user base grows, the credit evaluation mechanism has shown limitations in the face of a complex and ever-changing social environment. Due to the interest relationship between users, some users may collude to improve their credit ratings, resulting in the spread of low-quality information in the network, reducing the efficiency of consensus dissemination, causing security issues, and making it difficult to highlight high-quality content. Summary of the invention

[0005] The present application provides a blockchain social network information dissemination optimization method and related equipment, which are used to solve problems such as user collusion and the spread of inferior information, and improve the quality of information dissemination.

[0006] In a first aspect, the present application provides a blockchain social network information dissemination optimization method, which is applied to a server. The method includes: establishing a game subject in a blockchain social network information dissemination system, wherein the game subject includes an agent, an opinion leader, and a voter, wherein the agent is responsible for information dissemination, the opinion leader is responsible for information guidance, and the voter is responsible for information judgment; according to the game strategies corresponding to each of the game subjects, a strategy combination set is determined, wherein the game strategy includes an agent strategy, an opinion leader strategy, and a voter strategy, wherein the strategy combination set includes multiple strategy combinations, wherein the agent strategy includes disseminating high-quality information or disseminating low-quality information, the opinion leader strategy includes guiding or not guiding, and the voter strategy includes guiding or not guiding. The voter strategy includes support or opposition; based on the preset profit calculation rules and income cost parameters, the profit value of the game subject under each strategy combination in the strategy combination set is calculated to form a game subject strategy combination profit matrix, which includes the agent profit, opinion leader profit and voter profit corresponding to each strategy combination; according to the game subject strategy combination profit matrix, the agent evolutionary game replication dynamic equation, the opinion leader evolutionary game replication dynamic equation and the voter evolutionary game replication dynamic equation are determined, the variable in the agent evolutionary game replication dynamic equation is the probability of the agent spreading high-quality information, and the opinion leader evolutionary game replication dynamic equation is the probability of the agent spreading high-quality information. The variable in is the probability of opinion leader guidance, and the variable in the voter evolutionary game replication dynamic equation is the voter support probability; respectively solve the agent evolutionary game replication dynamic equation, the opinion leader evolutionary game replication dynamic equation and the voter evolutionary game replication dynamic equation to obtain the evolutionary stable point of the agent's probability of spreading high-quality information, the evolutionary stable point of the opinion leader's guidance probability and the evolutionary stable point of the voter's support probability; combine the evolutionary stable point of the agent's probability of spreading high-quality information, the evolutionary stable point of the opinion leader's guidance probability and the evolutionary stable point of the voter's support probability to obtain multiple equilibrium points; combine the evolutionary stable point of the agent's probability of spreading high-quality information, the evolutionary stable point of the opinion leader's guidance probability and the evolutionary stable point of the voter's support probability to obtain multiple equilibrium points; The leader evolutionary game replication dynamic equation and the voter evolutionary game replication dynamic equation are used to respectively calculate the partial derivatives of the probability of the agent spreading high-quality information, the probability of the opinion leader guiding, and the probability of the voter supporting, and obtain the Jacobian matrix; the multiple equilibrium points are respectively substituted into the Jacobian matrix to obtain the first eigenvalue, the second eigenvalue, and the third eigenvalue corresponding to each equilibrium point; based on the first eigenvalue, the second eigenvalue, the third eigenvalue and the Lyapunov stability judgment rule, the stability judgment result of each equilibrium point is determined; the strategy combination corresponding to the equilibrium point with a stable stability judgment result is determined as an evolutionary stable strategy, and the income cost parameter is optimized.

[0007] By adopting the above technical solution, the server can comprehensively analyze the behavioral evolution laws of agents, opinion leaders and voters in the process of information dissemination in blockchain social networks, find the optimal evolutionary stability strategy, and promote rapid convergence to the ideal state by optimizing the income cost parameters. Compared with the traditional credit evaluation mechanism, this method better solves problems such as user collusion and the spread of inferior information, and improves the quality and efficiency of information dissemination.

[0008] In combination with some embodiments of the first aspect, in some embodiments, determining the agent evolutionary game replication dynamic equation specifically includes: determining the agent high-quality benefit expectation function and the agent low-quality benefit expectation function respectively according to the benefits of all agents in the game subject strategy combination benefit matrix, the opinion leader guidance probability and the voter support probability, the agent high-quality benefit expectation function is used to represent the benefit of the agent disseminating high-quality information, and the agent low-quality benefit expectation function is used to represent the benefit of the agent disseminating low-quality information; based on the probability of the agent disseminating high-quality information, the agent high-quality benefit expectation function and the agent low-quality benefit expectation function, obtaining the agent average benefit function; according to the agent high-quality benefit expectation function, the agent low-quality benefit expectation function and the agent average benefit function, obtaining the agent evolutionary game replication dynamic equation.

[0009] By adopting the above technical solution, the solution process of the agent evolutionary game replication dynamic equation was refined, and the expected function of the agent's high-quality income and the expected function of the agent's low-quality income were established respectively. Then, the agent's average income function was obtained by combining the probability of the agent spreading high-quality information, and finally the agent evolutionary game replication dynamic equation was derived. This method takes into account the changes in the agent's income under different strategy choices, can accurately characterize the evolution process of the agent's strategy choice, and takes into account the influencing factors of the opinion leader's guidance probability and the voter's support probability, making the model more in line with the actual situation and helping to find a more reasonable evolutionary stable strategy.

[0010] In combination with some embodiments of the first aspect, in some embodiments, solving the voter evolutionary game replication dynamic equation to obtain the evolutionary stable point of the voter support probability specifically includes: setting the voter evolutionary game replication dynamic equation to zero to obtain a candidate stable value of the voter support probability; taking partial derivatives of the voter evolutionary game replication dynamic equation to obtain a voter evolutionary game derivative equation, the voter evolutionary game derivative equation is used to represent the changing trend of the voter support probability; dividing the parameter space according to the symbolic characteristics of the voter evolutionary game derivative equation to obtain a core parameter threshold, the core parameter threshold is used to divide the parameter space into an increasing function interval and a decreasing function interval, the increasing function interval is used to represent that the voter support probability increases over time, and the decreasing function interval is used to represent that the voter support probability decreases over time; based on the stability theory, judging the first stability of the candidate stable value in the increasing function interval and the second stability in the decreasing function interval respectively; if the first stability and the second stability are both stable, then determining the candidate stable value as the evolutionary stable point of the voter support probability.

[0011] By adopting the above technical solution, the server can not only find the evolutionary stable point of the voter's support probability, but also analyze the changing trend of the voter's support probability over time, which helps to understand the evolution law of voters' behavior.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the stability judgment result of each equilibrium point is determined based on the first eigenvalue, the second eigenvalue, the third eigenvalue and the Lyapunov stability judgment rule, specifically including: when the first eigenvalue, the second eigenvalue and the third eigenvalue are all negative real parts, the equilibrium point is determined to be a stable point; when there is a positive real part in the first eigenvalue, the second eigenvalue and the third eigenvalue, the equilibrium point is determined to be an unstable point; when there is a target eigenvalue in the first eigenvalue, the second eigenvalue and the third eigenvalue whose real part is zero and the other eigenvalues ​​are all negative real parts, the stability judgment result of the equilibrium point needs to be discussed separately.

[0013] By adopting the above technical solution, based on Lyapunov's stability judgment rule, the server analyzes the three eigenvalues ​​corresponding to the equilibrium point to determine its stability. This judgment method has a strict mathematical theoretical basis and can accurately judge the stability properties of the equilibrium point. Compared with simple numerical analysis methods, it can obtain more reliable judgment results and avoid erroneous judgments caused by numerical errors.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the strategy combination corresponding to the equilibrium point where the stability judgment result is stable is determined as an evolutionary stable strategy, and the income cost parameter is optimized, specifically including: identifying the equilibrium point where the stability judgment result is stable, and obtaining a candidate strategy combination set, the candidate strategy combination set including multiple candidate strategy combinations, the candidate strategy combinations including candidate agent strategies, candidate opinion leader strategies and candidate voter strategies; calculating the total system revenue corresponding to each candidate strategy combination in the candidate strategy combination set, the system total revenue being the weighted sum of the candidate agent revenue, the candidate opinion leader revenue and the candidate voter revenue; based on the system total revenue, sorting the candidate strategy combination set, and selecting the candidate strategy combination with the largest system total revenue as the evolutionary stable strategy.

[0015] By adopting the above technical solution, stable equilibrium points are identified to construct a set of candidate strategy combinations, and the candidate strategy combination with the largest total system benefit is selected as the evolutionary stable strategy. This not only takes into account the stability of the strategy combination, but also focuses on maximizing the overall benefit, balancing the interests of different game players, and promoting the evolution of blockchain social network information dissemination in a direction that is more conducive to overall development. It overcomes the limitation of only considering the benefits of a single game player, making the final evolutionary stable strategy more valuable for practical application.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the income cost parameters are optimized, specifically including: determining the important income cost parameters formed by the evolutionary stable strategy from the income cost parameters, the important income cost parameters including the agent's basic income, the opinion leader's guidance income and the voter's additional income; constructing constraints for the important income cost parameters: based on the constraints, using a numerical optimization method to optimize the important income cost parameters, so that the blockchain social network information dissemination system quickly converges to the evolutionary stable strategy.

[0017] By adopting the above technical solutions, the server optimizes the income and cost parameters, focusing on the important income and cost parameters that affect the formation of evolutionary stable strategies, including agent basic income, opinion leader guidance income and voter additional income. The server constructs reasonable constraints and uses numerical optimization methods to find the optimal parameter configuration and accelerate the convergence of the blockchain social network information dissemination system to the evolutionary stable strategy, thereby effectively adjusting the income distribution of each game subject and encouraging each game subject to choose a game strategy that is conducive to the development of the blockchain social network information dissemination system.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the strategy combination corresponding to the equilibrium point where the stability judgment result is stable as an evolutionary stable strategy and optimizing the income cost parameters, the method also includes: when the probability of the agent spreading high-quality information, the probability of the opinion leader guiding, and the probability of the voter supporting simultaneously reach the evolutionary stable strategy, triggering the smart contract to execute automatically.

[0019] By adopting the above technical solutions, the automatic execution mechanism based on smart contracts has the characteristics of decentralization, transparency and immutability, which can effectively avoid human intervention and cheating, realize the automation and trustworthiness of evolutionary stable strategy execution, and provide technical guarantee for the healthy development of blockchain social networks.

[0020] In a second aspect, an embodiment of the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a server, enable the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the server provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By adopting the above technical solution, the server can comprehensively analyze the behavioral evolution of agents, opinion leaders and voters in the process of information dissemination in blockchain social networks, find the optimal evolutionary stability strategy, and promote rapid convergence to the ideal state by optimizing the income cost parameters. Compared with the traditional credit evaluation mechanism, this method better solves the problems of user collusion and the spread of inferior information, and improves the quality and efficiency of information dissemination.

[0025] 2. By adopting the above technical solution, the solution process of the agent evolutionary game replication dynamic equation is refined, and the expected function of the agent's high-quality income and the expected function of the agent's low-quality income are established respectively. Then, the agent's average income function is obtained by combining the probability of the agent spreading high-quality information, and finally the agent evolutionary game replication dynamic equation is derived. This method takes into account the changes in the agent's income under different strategy choices, can accurately characterize the evolution process of the agent's strategy selection, and takes into account the influencing factors of the opinion leader's guidance probability and the voter's support probability, making the model more in line with the actual situation and helping to find a more reasonable evolutionary stable strategy.

[0026] 3. By adopting the above technical solution, based on Lyapunov's stability judgment rule, the server analyzes the three eigenvalues ​​corresponding to the equilibrium point to determine its stability. This judgment method has a strict mathematical theoretical basis and can accurately judge the stability properties of the equilibrium point. Compared with simple numerical analysis methods, it can obtain more reliable judgment results and avoid erroneous judgments caused by numerical errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a game tree hierarchy diagram of the blockchain social network information dissemination optimization method in the embodiment of the present application; Figure 2 This is a flowchart of a method for optimizing information dissemination in a blockchain social network according to an embodiment of the present application; Figure 3 This is another flowchart of the method for optimizing information dissemination in a blockchain social network according to an embodiment of the present application; Figure 4 It is a schematic diagram of a physical device structure of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] The users involved in information dissemination on blockchain social networks can be roughly divided into four categories: content creators, disseminators, regulators, and audiences. The participants in the optimization process of information dissemination on blockchain social networks are mainly disseminators and regulators. This application subdivides disseminators and regulators into three types of game subjects: agents, opinion leaders, and voters, in order to analyze their strategies and roles in the optimization process of information dissemination on blockchain social networks.

[0031] In blockchain social networks, agents, opinion leaders, and voters play different roles, influencing the process of information dissemination and decision-making in blockchain social networks.

[0032] (1) Delegate: Delegates are individuals or entities that are authorized to perform certain actions or make decisions on behalf of other users in a blockchain social network. They may be elected or delegated to represent a group of users, perform specific tasks, vote on decisions, or manage certain affairs. Delegates usually receive certain powers or rewards to participate in decision-making or perform tasks on behalf of other users.

[0033] (2) Influencer: Influencers are individuals or entities with high influence and attention in blockchain social networks. They attract the attention of other users through their opinions, behaviors or content, and have a certain influence on users. The opinions and behaviors of opinion leaders can trigger discussions in the network and influence the decisions or behaviors of other users.

[0034] (3) Voters: Voters are individuals or entities that participate in the voting or decision-making process in blockchain social networks. They may directly participate in voting on certain proposals, candidates, or decisions, influencing the decision-making results in the network. Voters influence network governance, content screening, or other social network functions by expressing their choices.

[0035] These roles interact with each other in blockchain social networks, jointly influencing information dissemination, community governance, and decision-making processes. Agents may participate in voting on behalf of a group of users, opinion leaders shape user attitudes and behaviors through their influence, and voters are individuals who directly participate in the decision-making process. The three together help the operation of the network, the screening of content, the governance of the community, and help spread high-quality information.

[0036] The three-party game subjects in the process of information dissemination in blockchain social networks have their game strategy analysis as follows: (1) When faced with information dissemination, agents can choose to disseminate high-quality information or low-quality information. Disseminating high-quality information may be for the purpose of improving one's own reputation, increasing attention, or building trust. High-quality information helps to attract more attention and support, bringing long-term benefits to agents. Disseminating low-quality information may bring short-term attention and traffic, but in the long run it may damage the agent's reputation and credibility. Therefore, when making strategic choices, agents need to weigh short-term benefits and long-term impacts.

[0037] (2) When faced with information dissemination, opinion leaders can choose to guide or not guide voters. Guiding voters may be for the purpose of expanding their own influence or promoting more support for a certain view or position, which means that opinion leaders influence voters' decisions by expressing their views or positions. The non-guiding strategy may be for the purpose of maintaining neutrality or avoiding being seen as biased. Therefore, when making strategic choices, opinion leaders need to weigh the relationship between their own interests and fairness and objectivity.

[0038] (3) When faced with information dissemination, voters can choose to vote for or against. Voting for may mean that the voter agrees with the agent's views or positions, or hopes to support those agents who can meet their interests and needs. Voting against may mean that the voter disagrees with the agent's views or positions, or hopes to express his or her dissatisfaction or opposition to certain behaviors. Therefore, when making strategic choices, voters need to weigh the relationship between their own interests and the authenticity and credibility of information.

[0039] To sum up, in the dynamic evolutionary game model, agents have two strategies, and their strategy set is {spread high-quality information, spread low-quality information}; opinion leaders have two strategies, and their strategy set is {guide, do not guide}; voters have two strategies, and their strategy set is {vote for, vote against}.

[0040] This application adopts a dynamic evolutionary game model, and the three game themes have a sequence in the selection of game strategies. The technical problem to be solved by this application is how to spread high-quality information more rationally, that is, to expand the spread of high-quality information and identify the spread of low-quality information. In the blockchain social network, first, the agent chooses to spread high-quality information or low-quality information, then the opinion leader chooses whether to guide, and finally the voter chooses to vote for or against. This is a three-stage dynamic evolutionary game model, and the three-party game process is represented by a hierarchical structure diagram, please refer to Figure 1 , which is a game tree hierarchy diagram of the blockchain social network information dissemination optimization method in the embodiment of the present application.

[0041] The following is a description of the process of the method provided by this implementation. Figure 2, which is a flow chart of the blockchain social network information dissemination optimization method in an embodiment of the present application.

[0042] S201. Establishing the game subject in the blockchain social network information dissemination system, the game subject includes agents, opinion leaders and voters. The agent is responsible for information dissemination, the opinion leader is responsible for information guidance, and the voter is responsible for information judgment; Among them, the game subject refers to the various role entities that participate in the information dissemination process in the process of optimizing information dissemination in blockchain social networks. Information dissemination refers to the process of information diffusion in the blockchain social network information dissemination system. Information guidance refers to the control and guidance of the direction of information dissemination. Information evaluation refers to the process of judging the quality and value of information.

[0043] Specifically, the server divides the three types of game subjects in the blockchain social network information dissemination system into agents, opinion leaders and voters. The server clearly defines the roles and responsibilities of these three types of game subjects, laying the foundation for subsequent game analysis.

[0044] S202, determining a strategy combination set according to the game strategies corresponding to the respective game subjects, wherein the game strategies include agent strategies, opinion leader strategies, and voter strategies, and the strategy combination set includes multiple strategy combinations, wherein the agent strategy includes disseminating high-quality information or disseminating low-quality information, the opinion leader strategy includes guiding or not guiding, and the voter strategy includes supporting or opposing; Among them, the game strategy refers to the action options that each game subject can take. The strategy combination refers to the combination of the game strategy choices. The agent strategy is {spread high-quality information, spread low-quality information}. The opinion leader strategy is {guide, do not guide}. The voter strategy is {vote for, vote against}. High-quality information refers to true and valuable information content. Low-quality information refers to false or low-value information content.

[0045] Specifically, the server analyzes the possible action choices that each game subject may take during the information dissemination process of the blockchain social network, and combines the agent strategy, opinion leader strategy and voter strategy to form a complete strategy combination set. The strategy combination set contains all possible strategy selection combinations, as shown below: Strategy combination set Strategy combination 1 (spreading high-quality information, guiding, and voting in support) Strategy combination 2 (spreading high-quality information, guiding, and voting against) Strategy combination 3 (spread high-quality information, do not guide, and vote in support) Strategy combination 4 (spread high-quality information, do not guide, and vote against) Strategy combination 5 (spreading inferior information, guiding, and voting in favor) Strategy combination 6 (spreading inferior information, guiding, and voting against) Strategy combination 7 (spread inferior information, do not guide, and vote in favor) Strategy combination 8 (spread inferior information, do not guide, and vote against) S203, based on the preset profit calculation rules and the income cost parameters, calculating the profit value of the game subject under each strategy combination in the strategy combination set to form a game subject strategy combination profit matrix, wherein the game subject strategy combination profit matrix includes the agent profit, opinion leader profit and voter profit corresponding to each strategy combination; Among them, the preset profit calculation rules refer to the mathematical models and calculation methods used to calculate the profits obtained by each game subject by choosing different game strategies. The income cost parameters refer to the parameters that affect the profits of each game subject, including basic income, additional rewards, communication costs, etc. The profit value refers to the size of the benefits obtained by the game subject under a specific strategy combination. The game subject strategy combination profit matrix refers to a data structure that records all possible strategy combinations and their corresponding profits. Agent profit refers to the economic or social benefits obtained by the agent in the process of disseminating information. Opinion leader profit refers to the influence enhancement and additional benefits obtained by opinion leaders through guiding behavior. Voter profit refers to the rewards and social recognition obtained by voters after participating in the judgment.

[0046] Specifically, first, the server determines the parameters that affect the benefits of each game subject according to the preset benefit calculation rules, such as the agent's basic communication benefits, information quality rewards and penalties, communication costs, opinion leaders' guidance benefits, changes in reputation values, and voters' voting rewards. Then, for each strategy combination in the strategy combination set, the server calculates the specific benefit values ​​that agents, opinion leaders, and voters can obtain under this strategy combination. These benefit values ​​comprehensively consider the direct benefits and indirect effects of the game subject's behavior. Finally, the server organizes all strategy combinations and their corresponding benefit values ​​into the form of a game subject strategy combination benefit matrix, providing a data basis for subsequent game analysis.

[0047] Assumption 1: In blockchain social networks, it is assumed that the strategic choices of game agents, opinion leaders, and voters are all boundedly rational.

[0048] Hypothesis 2: In the dynamic evolutionary game model, agents have two strategies, and their strategy set is {spread high-quality information, spread low-quality information}; opinion leaders have two strategies, and their strategy set is {guide, do not guide}; voters have two strategies, and their strategy set is {vote for, vote against}.

[0049] Assumption 3: Assume that the probability that the agent adopts the strategy of “spreading high-quality information” is , then the probability of adopting the strategy of "spreading inferior information" is ; Assume that the probability that the opinion leader adopts the “guidance” strategy is , then the probability of adopting the “no guidance” strategy is ; Assume that the probability that a voter adopts the strategy of "voting in favor" is , then the probability of adopting the "vote against" strategy is .

[0050] Assumption 4: When the agent chooses the strategy of “spreading high-quality information”, the cost of spreading information is , the benefit of spreading information is , and can obtain corresponding additional benefits On the contrary, when the agent chooses the strategy of "spreading inferior information", there will be corresponding losses Here, the benefit of the agent spreading information minus the cost is greater than 0. If the agent spreads inferior information, the value is negative after deducting the corresponding loss, that is, , .

[0051] Hypothesis 5: The cost of opinion leaders actively guiding is The corresponding income is , obviously The opportunity cost of opinion leaders not actively guiding is .

[0052] Assumption 6: When voters participate in voting, the cost they pay is When voters vote in favor and the social network information is high-quality information, voters will receive corresponding additional benefits. , when voters vote in favor and the social network information is of poor quality, voters will suffer corresponding losses ; When the voter casts a negative vote and the social network information is of poor quality, the additional benefit the voter obtains is , when a voter casts a negative vote and the social network information is high-quality information, the voter will suffer a corresponding loss Here, the extra benefits of voting correctly are equal, and the losses of voting incorrectly are also equal, and the extra benefits are less than the losses caused by voting incorrectly. At the same time, it is necessary to ensure that the benefits of voting correctly are positive and the benefits of voting incorrectly are negative, that is, .

[0053] Hypothesis 7: Under the guidance of opinion leaders, the overall penalty for voters casting the wrong vote is If the opinion leader does not participate in the guidance, the overall penalty for voters casting the wrong vote is , obviously .

[0054] In order to make the meaning of each variable more intuitive, the symbolic parameter description is as follows:

[0055] Based on the above assumptions about variables, the profit matrix of the three-party game entities under each strategy combination is as follows:

[0056] S204, according to the game subject strategy combination payoff matrix, determine the agent evolutionary game replication dynamic equation, the opinion leader evolutionary game replication dynamic equation and the voter evolutionary game replication dynamic equation, the variable in the agent evolutionary game replication dynamic equation is the probability of the agent spreading high-quality information, the variable in the opinion leader evolutionary game replication dynamic equation is the opinion leader guidance probability, and the variable in the voter evolutionary game replication dynamic equation is the voter support probability; Among them, the evolutionary game replication dynamic equation is a differential equation used to describe the change in the proportion of game strategy selection over time. The agent evolutionary game replication dynamic equation refers to the mathematical equation that describes the change in the proportion of agents who choose to spread high-quality information strategies. The opinion leader evolutionary game replication dynamic equation refers to the mathematical equation that describes the change in the proportion of opinion leaders who choose to guide strategies. The voter evolutionary game replication dynamic equation refers to the mathematical equation that describes the change in the proportion of voters who choose to support strategies.

[0057] Specifically, based on the replicator dynamics theory, the server establishes evolutionary game replication dynamic equations for the three types of game subjects. For the agent group, the agent evolutionary game replication dynamic equation describes how the proportion of individuals that the agent chooses to spread high-quality information changes over time, and the rate of change is related to the current proportion and the relative benefits of high-quality information dissemination; for the opinion leader group, the opinion leader evolutionary game replication dynamic equation describes the evolution of the proportion of individuals that the opinion leader chooses to guide, and the rate of change depends on the current guidance proportion and the difference in benefits brought by the guidance behavior; for the voter group, the voter evolutionary game replication dynamic equation expresses the changing law of the proportion of individuals that voters choose to support attitudes, and the rate of change is related to the current support proportion and the benefit advantage of the support behavior.

[0058] (1) Agent’s expected payoff function and agent evolutionary game replication dynamic equation Assume that the expected return and average return of the agent when adopting the strategies of "spreading high-quality information" and "spreading low-quality information" are , as well as , the calculation formula is as follows: According to the above formula, the agent evolutionary game replication dynamic equation can be obtained as: ; (2) The expected benefit function of opinion leaders and the dynamic equation of opinion leader evolutionary game replication Assuming that the opinion leaders adopt the “guiding” and “non-guiding” strategies, the expected returns and average returns are , as well as , the calculation formula is as follows: According to the above formula, the dynamic equation of opinion leader evolutionary game replication can be obtained as follows: ; (3) Voter’s expected benefit function and voter leader evolution game replication dynamic equation Assuming that voters adopt the strategies of "voting for" and "voting against", the expected returns and average returns are , as well as , the calculation formula is as follows: According to the above formula, the dynamic equation of voter leader evolutionary game replication can be obtained as: ; The dynamic equation of the evolutionary game replication consisting of three game entities is as follows:

[0059] S205, respectively solving the agent's evolutionary game replication dynamic equation, the opinion leader's evolutionary game replication dynamic equation, and the voter's evolutionary game replication dynamic equation to obtain the evolutionary stable point of the agent's probability of spreading high-quality information, the evolutionary stable point of the opinion leader's guidance probability, and the evolutionary stable point of the voter's support probability; Among them, solving the evolutionary game replication dynamic equation means finding a stable solution through mathematical methods. The evolutionary stable point refers to the stable solution of the evolutionary game replication dynamic equation, which indicates the corresponding strategy selection ratio when the blockchain social network information dissemination system reaches a stable state. The evolutionary stable point of the probability of agents spreading high-quality information refers to the final stable value of the proportion of agents who choose to spread high-quality information in the agent group. The evolutionary stable point of the probability of opinion leaders guiding refers to the final stable value of the proportion of opinion leaders who choose to guide in the opinion leader group. The evolutionary stable point of the probability of voter support refers to the final stable value of the proportion of voters who choose to support attitudes in the voter group. A stable solution refers to a special solution that can still return to its original state after a small disturbance.

[0060] Specifically, first, the server solves each evolutionary game replication dynamic equation separately, and the method includes: setting the evolutionary game replication dynamic equation equal to zero, obtaining candidate stable points, analyzing the monotonicity of the evolutionary game replication dynamic equation, determining the existence of the solution, studying the stability of the solution, and verifying whether it is an evolutionary stable point. For the agent evolutionary game replication dynamic equation, the solution is obtained to obtain the stable proportion of agents choosing to spread high-quality information; for the opinion leader evolutionary game replication dynamic equation, the solution is obtained to obtain the stable proportion of opinion leaders choosing to guide; for the voter evolutionary game replication dynamic equation, the solution is obtained to obtain the stable proportion of voters choosing to support attitudes. The server analyzes the properties of these evolutionary stable points to ensure that they can automatically return to a stable state after being slightly disturbed.

[0061] (1) Analysis of agent strategy stability The agent evolutionary game replication dynamic equation is: ; make , the solution is ; right Find the first-order partial derivative with respect to x, and we get: ; make According to the stability theorem of differential equations, the probability that an agent chooses to spread high-quality information must satisfy the following in a stable state: and ; , so About z is an increasing function; when hour, , indicating that all levels are stable under this condition, that is, whether the agent disseminates high-quality information or low-quality information, the agent's strategy will not change over time; when hour, ,Right now It is the only globally stable evolutionary point; when hour, , that is, x=0 is the only globally stable evolutionary point.

[0062] (2) Analysis of the Stability of Opinion Leader Strategies The evolutionary game replication dynamic equation of opinion leaders is: ; make , the solution is ; right Finding the first-order partial derivative with respect to y gives: ; make According to the stability theorem of differential equations, the probability of opinion leaders choosing a guiding strategy must satisfy the following in a stable state: and ; ,make 0, can be obtained ; when , 0, that is In this interval it is an increasing function; when , 0, that is In this interval, it is a decreasing function, and we discuss it in different cases: (a) when , 0, that is It is an increasing function in this interval, so hour, , , indicating that all levels are stable under this condition, that is, whether the opinion leader chooses to guide or not, the opinion leader’s strategy will not change over time. season ,but or There are two stable states, discussed separately: ① hour, ,at this time , so It is an evolutionary stable point; ② hour, ,at this time , so It is an evolutionary stable point; (b) When , 0, that is It is a decreasing function in this interval, so hour, , , indicating that all levels are stable under this condition, that is, whether the opinion leader chooses to guide or not, the opinion leader’s strategy will not change over time. season ,but or There are two stable states, discussed separately: ① hour, ,at this time , so It is an evolutionary stable point; ② hour, ,at this time , so It is an evolutionary stable point.

[0063] (3) Analysis of voter strategy stability The dynamic equation of voter evolutionary game replication is: ; make , the solution is ; right Finding the first-order partial derivative with respect to z gives: ; make According to the stability theorem of differential equations, the probability of opinion leaders choosing a guiding strategy must satisfy the following in a stable state: and ; ,make 0, can be obtained ; when , 0, that is In this interval it is an increasing function; when , 0, that is In this interval, it is a decreasing function, and we discuss it in different cases: (a) when , 0 It is an increasing function in this interval, so hour, , , indicating that all levels are stable under this condition, that is, whether the opinion leader chooses to guide or not, the opinion leader’s strategy will not change over time. season ,but or There are two stable states, discussed separately: ① hour, ,at this time , so It is an evolutionary stable point; ② hour, ,at this time , so It is an evolutionary stable point; (b) When , 0 It is a decreasing function in this interval, so hour, , , indicating that all levels are stable under this condition, that is, whether the opinion leader chooses to guide or not, the opinion leader’s strategy will not change over time. season ,but or There are two stable states, discussed separately: ① hour, ,at this time , so It is an evolutionary stable point; ② hour, ,at this time , so It is an evolutionary stable point.

[0064] S206, combining the evolutionary stable point of the probability of the agent spreading high-quality information, the evolutionary stable point of the probability of the opinion leader guiding, and the evolutionary stable point of the probability of the voter supporting, to obtain multiple equilibrium points; Among them, the combination of evolutionary stable points refers to the permutation and combination of the evolutionary stable point values ​​of the probability of agents spreading high-quality information, the probability of opinion leaders guiding, and the probability of voters supporting. The equilibrium point refers to the stable state that the blockchain social network information dissemination system may reach, which is jointly determined by the game strategies of the three types of game subjects: agents, opinion leaders, and voters. The stable state refers to the state in which the parameters in the blockchain social network information dissemination system no longer change.

[0065] Specifically, first, the server collects the evolutionary stable points of the probability of agents spreading high-quality information, the evolutionary stable points of the probability of opinion leaders guiding, and the evolutionary stable points of the probability of voters supporting. Then, the server combines these evolutionary stable points to obtain the following equilibrium point:

[0066] S207, for the agent's evolutionary game replication dynamic equation, the opinion leader's evolutionary game replication dynamic equation, and the voter's evolutionary game replication dynamic equation, respectively calculate the partial derivatives of the agent's probability of spreading high-quality information, the opinion leader's guidance probability, and the voter's support probability to obtain a Jacobian matrix; Among them, partial derivative refers to the rate of change of the evolutionary game replication dynamic equation to a certain variable. The Jacobian matrix refers to the matrix composed of the partial derivatives of each evolutionary game replication dynamic equation to all variables, which is used to analyze the local stability of the blockchain social network information dissemination system.

[0067] Specifically, first, the server calculates partial derivatives of the three evolutionary game replication dynamic equations: for the agent evolutionary game replication dynamic equation, the partial derivatives of the probability of the agent spreading high-quality information, the probability of opinion leaders guiding, and the probability of voter support are calculated; for the opinion leader evolutionary game replication dynamic equation, the partial derivatives of the probability of the agent spreading high-quality information, the probability of opinion leaders guiding, and the probability of voter support are calculated; for the voter evolutionary game replication dynamic equation, the partial derivatives of the probability of the agent spreading high-quality information, the probability of opinion leaders guiding, and the probability of voter support are also calculated. Then, the server arranges these nine partial derivatives in a specific order and constructs a 3×3 Jacobian matrix. The Jacobian matrix describes the local dynamic characteristics of the blockchain social network information dissemination system near the equilibrium point, and its elements reflect the mutual influence relationship between different variables.

[0068] The Jacobian matrix looks like this: ; ]; ; ; ; ; ; ; ; .

[0069] S208, substituting the multiple equilibrium points into the Jacobian matrix respectively to obtain the first eigenvalue, the second eigenvalue and the third eigenvalue corresponding to each of the equilibrium points; Among them, equilibrium point substitution means substituting the coordinates of the equilibrium point into the Jacobian matrix for calculation. Eigenvalue refers to the root of the characteristic equation of the Jacobian matrix, which is used to judge the stability of the blockchain social network information dissemination system. The first eigenvalue represents the first eigenvalue of the Jacobian matrix, reflecting the stability of the blockchain social network information dissemination system in the first main direction. The second eigenvalue refers to the second eigenvalue of the Jacobian matrix, reflecting the stability of the blockchain social network information dissemination system in the second main direction. The third eigenvalue represents the third eigenvalue of the Jacobian matrix, reflecting the stability of the blockchain social network information dissemination system in the third main direction.

[0070] Specifically, after obtaining the Jacobian matrix of step S207, the server substitutes the equilibrium point in step S206 into the Jacobian matrix to obtain the first eigenvalue, the second eigenvalue, and the third eigenvalue corresponding to each of the equilibrium points, as shown below:

[0071] S209, determining a stability determination result of each equilibrium point based on the first eigenvalue, the second eigenvalue, the third eigenvalue and Lyapunov's stability determination rule; According to Lyapunov's stability judgment rule, the equilibrium point can be determined: (1) If all eigenvalues ​​in the Jacobian matrix have negative real parts, then the equilibrium point is an asymptotically stable point; (2) If at least one of the eigenvalues ​​in the Jacobian matrix has a positive real part, then the equilibrium point is an unstable point; (3) If all eigenvalues ​​in the Jacobian matrix have negative real parts except for the eigenvalue with zero real part, the equilibrium point is in a critical state and the stability cannot be determined by the sign of the eigenvalue.

[0072] The asymptotic stability analysis of the eigenvalues ​​of each equilibrium point is as follows:

[0073] According to the above table, we can know that the equilibrium point , , , The eigenvalues ​​of do not meet the requirements of Lyapunov's stability criterion for the sign of the evolutionary stable point. , it can be seen , Not applicable either. The eigenvalues ​​of are all less than 0, which meets the requirements of Lyapunov's stability judgment rule for the symbol of the evolutionary stable point. The specific discussion of the remaining equilibrium points is as follows: (1) If , equilibrium point As a stable point, the blockchain social network information dissemination system has two stable points and , namely {spread inferior information, guide, and vote against} and {spread high-quality information, guide, and vote in favor};

[0074] (2) If , equilibrium point It is not a stable point. At this time, the blockchain social network information dissemination system has a stable point , that is, {spread high-quality information, guide, and vote in support}.

[0075] S210: Determine the strategy combination corresponding to the equilibrium point whose stability determination result is stable as an evolutionary stable strategy, and optimize the income cost parameters.

[0076] Among them, the evolutionary stable strategy refers to the ultimate stable strategy combination of the blockchain social network information dissemination system, which is a stable state that will not be invaded by other strategy combinations. The stability judgment result refers to the equilibrium point stability conclusion obtained through eigenvalue analysis. A stable equilibrium point refers to an equilibrium point that can return to its original state after a small disturbance. The strategy combination refers to a triple consisting of agent dissemination strategy, opinion leader guidance strategy and voter support strategy. Revenue cost parameter optimization refers to adjusting the parameters related to revenue and cost in the blockchain social network information dissemination system.

[0077] Specifically, first, the server selects the equilibrium points with stable stability judgment results from all equilibrium points. The strategy combinations corresponding to these equilibrium points are evolutionary stable strategies. Then, the server evaluates the performance indicators such as the total system revenue and convergence speed for these evolutionary stable strategies. The server adjusts the income cost parameters through numerical optimization methods, including the basic income of agents, dissemination costs, guidance income of opinion leaders, and support rewards of voters. The optimization goal is to increase the speed at which the blockchain social network information dissemination system converges to the evolutionary stable strategy while maintaining sufficient incentive intensity. The server also needs to perform parameter sensitivity analysis to study the impact of parameter changes on system performance and ensure that the optimized parameter configuration has good robustness.

[0078] By adopting the above technical solution, the server can comprehensively analyze the behavioral evolution laws of agents, opinion leaders and voters in the process of information dissemination in blockchain social networks, find the optimal evolutionary stability strategy, and promote rapid convergence to the ideal state by optimizing the income cost parameters. Compared with the traditional credit evaluation mechanism, this method better solves problems such as user collusion and the spread of inferior information, and improves the quality and efficiency of information dissemination.

[0079] The following is a more detailed description of the process of the method provided by this implementation. Figure 3 , which is another flow chart of the blockchain social network information dissemination optimization method in an embodiment of the present application.

[0080] Step S210 and steps after step S210 may also be implemented by the following steps, which are not limited here: S301, identifying that the stability determination result is a stable equilibrium point, and obtaining a candidate strategy combination set, wherein the candidate strategy combination set includes multiple candidate strategy combinations, and the candidate strategy combination includes a candidate agent strategy, a candidate opinion leader strategy, and a candidate voter strategy; Specifically, according to step S209, (1) if , equilibrium point is a stable point. At this time, the blockchain social network information dissemination system has two stable points and , that is, {spread inferior information, guide, vote against} and {spread high-quality information, guide, vote in favor}, the candidate strategy combination set is {spread inferior information, guide, vote against} and {spread high-quality information, guide, vote in favor}. (2) If , equilibrium point It is not a stable point. At this time, the blockchain social network information dissemination system has a stable point , that is, {spread high-quality information, guide, and vote in favor}, the candidate strategy combination set is {spread high-quality information, guide, and vote in favor}.

[0081] S302, calculating the total system revenue corresponding to each candidate strategy combination in the candidate strategy combination set, where the total system revenue is the weighted sum of the candidate agent revenue, the candidate opinion leader revenue, and the candidate voter revenue; Specifically, following the candidate strategy combination set determined in step S301, according to the game subject strategy combination benefit matrix of step S203, the server can determine the candidate agent benefit, candidate opinion leader benefit and candidate voter benefit under each candidate strategy combination in the candidate strategy combination set, and perform weighted summation of the candidate agent benefit, candidate opinion leader benefit and candidate voter benefit to obtain the total system benefit.

[0082] S303, based on the total revenue of the system, sorting the candidate strategy combination set, and selecting the candidate strategy combination with the largest total revenue of the system as the evolutionary stable strategy; Specifically, following the candidate strategy combination set determined in step S301, the server determines the total system benefits corresponding to each candidate strategy combination in the candidate strategy combination set, and determines the candidate strategy combination with the maximum total system benefit as the evolutionary stable strategy.

[0083] S304, determining the important income cost parameters formed by the evolutionary stable strategy from the income cost parameters, the important income cost parameters including the agent's basic income, the opinion leader's guidance income and the voter's additional income; Among them, the income cost parameter refers to a set of various economic parameters that affect the operation of the blockchain social network information dissemination system, as shown in step S203. Important income cost parameters refer to key economic parameters that have a significant impact on the performance of the blockchain social network information dissemination system. Agent basic income refers to the basic remuneration obtained by the agent for information dissemination. Opinion leader guidance income refers to the basic reward obtained by opinion leaders for implementing guidance behavior. Voter additional income refers to the additional incentives obtained by voters after participating in the judgment.

[0084] Specifically, first, the server conducts a sensitivity analysis on all revenue and cost parameters and calculates the impact of each parameter change on system performance. Through the analysis results, the server determines the three most influential parameters: agent basic income, opinion leader guidance income, and voter additional income. These parameters directly determine the enthusiasm of each game subject to participate and are the key factors to ensure the stable operation of the blockchain social network information dissemination system.

[0085] S305. Construct the constraint conditions of the important income cost parameters: Among them, constraints refer to the restriction rules on the values ​​of important revenue and cost parameters. Parameter upper limit constraints refer to the maximum value that a parameter cannot exceed. Parameter lower limit constraints refer to the minimum value that a parameter cannot fall below. Parameter proportion constraints refer to the proportional relationship that needs to be maintained between different parameters. System cost constraints refer to the restriction conditions on overall economic expenditure. Parameter correlation constraints refer to the dependency relationship between parameters.

[0086] Specifically, the server needs to build constraints at multiple levels: first, set the value range of each parameter, including the upper and lower limits of the parameters, to ensure that the incentive intensity is neither too large to cause excessive system costs nor too small to affect participation enthusiasm; then establish proportional constraints between parameters to ensure a reasonable and balanced distribution of benefits between agents, opinion leaders, and voters; then set system cost constraints to keep economic incentives within an acceptable range; finally, consider the correlation constraints between parameters to reflect the connection between the benefits of different entities. These constraints together constitute the boundary conditions for parameter optimization.

[0087] S306. Based on the constraint condition, a numerical optimization method is used to optimize the important income cost parameter, so that the blockchain social network information dissemination system quickly converges to the evolutionary stable strategy; Among them, numerical optimization method refers to the algorithm that finds the optimal parameter value through computer iteration. Parameter optimization refers to adjusting the parameter value under constraints to achieve the optimization goal. The optimization goal refers to the expected effect to be achieved by adjusting the parameters. Convergence refers to the process in which the state of the blockchain social network information dissemination system gradually approaches the stable state. The convergence speed refers to the length of time required to reach the stable state.

[0088] Specifically, first, the server selects an appropriate numerical optimization algorithm, such as gradient descent, genetic algorithm, or simulated annealing algorithm. Then, the server sets the optimization objective function, including indicators such as system convergence speed and stability strength. During the optimization process, the server repeatedly adjusts the parameter values, evaluates the system performance through numerical simulation, and updates the parameters based on the evaluation results. This process continues until the optimal parameter configuration is found.

[0089] S307: When the probability of the agent spreading high-quality information, the probability of the opinion leader guiding, and the probability of the voter supporting simultaneously reach the evolutionary stable strategy, the smart contract is triggered to execute automatically.

[0090] When the game evolution of the three-party game subjects reaches a stable state, the smart contract is triggered and executed immediately. On the one hand, the instant execution of the smart contract can effectively prevent the collusion nodes from obtaining improper benefits by colluding in advance or guiding voting, thereby preventing malicious behavior from damaging the reputation system. On the other hand, the automation and mandatory execution characteristics of the smart contract significantly improve the efficiency of consensus propagation, making the public opinion propagation process more transparent, fair and efficient.

[0091] In addition, the uniqueness of smart contracts in public opinion dissemination is particularly prominent: its coded rules not only reduce the possibility of human intervention, but also ensure that the governance power in the process of public opinion dissemination is highly decentralized, avoiding the abuse problems that may be caused by centralized management. Through the role of smart contracts, user behavior is precisely constrained, high-quality content can be displayed in a timely manner, and the spread of false information is effectively curbed, ultimately laying the foundation for building a healthy blockchain social network public opinion dissemination environment.

[0092] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 4 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0093] It should be noted that Figure 4 The structure of the server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0094] like Figure 4 As shown, the server includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0095] The following components are connected to the I / O interface 405: an input section 406 including an audio input device, a button switch, etc.; an output section 407 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0096] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the present invention are performed.

[0097] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0099] Specifically, the server of this embodiment includes a processor and a memory, and a computer program is stored in the memory. When the computer program is executed by the processor, the blockchain social network information dissemination optimization method provided in the above embodiment is implemented.

[0100] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiment; or may exist independently without being assembled into the server. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the server, the server implements the blockchain social network information dissemination optimization method provided in the above embodiment.

[0101] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0102] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0103] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A blockchain social network information dissemination optimization method, characterized in that: Applied to a server, the method comprises: Establishing the game subjects in the blockchain social network information dissemination system, the game subjects include agents, opinion leaders and voters. The agents are responsible for information dissemination, the opinion leaders are responsible for information guidance, and the voters are responsible for information judgment; Determine a strategy combination set according to the game strategies corresponding to the respective game subjects, wherein the game strategies include agent strategies, opinion leader strategies and voter strategies, and the strategy combination set includes multiple strategy combinations, wherein the agent strategy includes disseminating high-quality information or disseminating low-quality information, the opinion leader strategy includes guiding or not guiding, and the voter strategy includes supporting or opposing; Based on the preset profit calculation rules and the income cost parameters, the profit value of the game subject under each strategy combination in the strategy combination set is calculated to form a game subject strategy combination profit matrix, wherein the game subject strategy combination profit matrix includes the agent profit, opinion leader profit and voter profit corresponding to each strategy combination respectively; According to the game subject strategy combination benefit matrix, determine the agent evolutionary game replication dynamic equation, the opinion leader evolutionary game replication dynamic equation and the voter evolutionary game replication dynamic equation, the variable in the agent evolutionary game replication dynamic equation is the probability of the agent spreading high-quality information, the variable in the opinion leader evolutionary game replication dynamic equation is the opinion leader guidance probability, and the variable in the voter evolutionary game replication dynamic equation is the voter support probability; Solve the agent evolutionary game replication dynamic equation, the opinion leader evolutionary game replication dynamic equation and the voter evolutionary game replication dynamic equation respectively to obtain the evolutionary stable point of the probability of the agent spreading high-quality information, the evolutionary stable point of the probability of the opinion leader guiding and the evolutionary stable point of the probability of the voter supporting; Combining the evolutionary stable point of the probability of the agent spreading high-quality information, the evolutionary stable point of the probability of the opinion leader guiding, and the evolutionary stable point of the probability of the voter supporting, to obtain multiple equilibrium points; For the agent evolutionary game replication dynamic equation, the opinion leader evolutionary game replication dynamic equation, and the voter evolutionary game replication dynamic equation, the partial derivatives of the probability of the agent spreading high-quality information, the opinion leader's guidance probability, and the voter's support probability are calculated to obtain a Jacobian matrix; Substituting the multiple equilibrium points into the Jacobian matrix respectively, to obtain the first eigenvalue, the second eigenvalue and the third eigenvalue corresponding to each of the equilibrium points; Determine a stability determination result of each equilibrium point based on the first eigenvalue, the second eigenvalue, the third eigenvalue and the Lyapunov stability determination rule; The strategy combination corresponding to the equilibrium point whose stability determination result is stable is determined as an evolutionary stable strategy, and the income cost parameters are optimized.

2. The method according to claim 1, characterized in that: The determination of the agent evolutionary game replication dynamic equation specifically includes: According to the income of all agents in the game subject strategy combination income matrix, the opinion leader guidance probability and the voter support probability, respectively determine the agent high-quality income expectation function and the agent low-quality income expectation function, the agent high-quality income expectation function is used to represent the income of the agent for disseminating high-quality information, and the agent low-quality income expectation function is used to represent the income of the agent for disseminating low-quality information; Based on the probability of the agent spreading high-quality information, the expected function of the agent's high-quality income and the expected function of the agent's low-quality income, an average income function of the agent is obtained; According to the expected function of the agent's high-quality benefits, the expected function of the agent's low-quality benefits and the agent's average benefit function, the agent's evolutionary game replication dynamic equation is obtained.

3. The method according to claim 1, characterized in that Solving the voter evolutionary game replication dynamic equation to obtain the evolutionary stable point of the voter support probability specifically includes: Setting the voter evolutionary game replication dynamic equation to zero, and obtaining a candidate stable value of the voter support probability; Taking partial derivatives of the voter evolutionary game replication dynamic equation to obtain the voter evolutionary game derivative equation, wherein the voter evolutionary game derivative equation is used to represent the changing trend of the voter support probability; Dividing the parameter space according to the symbolic characteristics of the derivative equation of the voter evolution game to obtain a core parameter threshold, wherein the core parameter threshold is used to divide the parameter space into an increasing function interval and a decreasing function interval, wherein the increasing function interval is used to indicate that the probability of support of the voter increases over time, and the decreasing function interval is used to indicate that the probability of support of the voter decreases over time; Based on the stability theory, respectively judging the first stability of the candidate stable value in the increasing function interval and the second stability in the decreasing function interval; If the first stability and the second stability are both stable, the candidate stable value is determined as the evolutionary stable point of the voter support probability.

4. The method according to claim 1, characterized in that: The determining of the stability determination result of each equilibrium point based on the first eigenvalue, the second eigenvalue, the third eigenvalue and the Lyapunov stability determination rule specifically includes: When the first eigenvalue, the second eigenvalue, and the third eigenvalue all have negative real parts, determining that the equilibrium point is a stable point; When there is a positive real part in the first eigenvalue, the second eigenvalue, and the third eigenvalue, determining that the equilibrium point is an unstable point; When the real part of the target eigenvalue among the first eigenvalue, the second eigenvalue and the third eigenvalue is zero and the other eigenvalues ​​are all negative real parts, the stability determination result of the equilibrium point needs to be discussed on a case-by-case basis.

5. The method according to claim 4, characterized in that The step of determining the strategy combination corresponding to the equilibrium point whose stability determination result is stable as an evolutionary stable strategy and optimizing the income cost parameters specifically includes: Identify the equilibrium point where the stability determination result is stable, and obtain a candidate strategy combination set, wherein the candidate strategy combination set includes multiple candidate strategy combinations, and the candidate strategy combinations include candidate agent strategies, candidate opinion leader strategies, and candidate voter strategies; Calculate the total system revenue corresponding to each candidate strategy combination in the candidate strategy combination set, where the total system revenue is the weighted sum of the candidate agent revenue, the candidate opinion leader revenue and the candidate voter revenue; Based on the total system benefit, the candidate strategy combination set is sorted, and the candidate strategy combination with the largest total system benefit is selected as the evolutionary stable strategy.

6. The method according to claim 5, characterized in that The optimization of the revenue cost parameters specifically includes: Determining the important income cost parameters formed by the evolutionary stable strategy from the income cost parameters, wherein the important income cost parameters include agent basic income, opinion leader guidance income and voter additional income; Construct the constraints of the important revenue and cost parameters: Based on the constraints, a numerical optimization method is used to optimize the important revenue cost parameters so that the blockchain social network information dissemination system quickly converges to the evolutionary stable strategy.

7. The method according to claim 1, characterized in that After the step of determining the strategy combination corresponding to the equilibrium point whose stability determination result is stable as an evolutionary stable strategy and optimizing the revenue cost parameters, the method further includes: When the probability of the agent spreading high-quality information, the probability of the opinion leader guiding, and the probability of the voter supporting simultaneously reach the evolutionary stable strategy, the smart contract is triggered to execute automatically.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to execute the method according to any one of claims 1 to 7.

Images