Opinion evolution simulation method and system based on improved deffuant model

Abstract

The application provides a viewpoint evolution simulation method and system based on an improved Deffuant model, and relates to the technical field of viewpoint evolution result evaluation. The application firstly constructs an initial scale-free network between individuals, and sets initial parameters and evolution running parameters of the initial scale-free network, including initial directed contact degree; then, after adding a new node to the initial scale-free network, it is judged whether the viewpoint value difference between any two nodes in the scale-free network after adding the new node is greater than an interaction threshold value, and the viewpoint value of any node in the scale-free network after adding the new node is updated by selecting a first improved Deffuant model and a second improved Deffuant model according to the size relationship between the viewpoint value difference and the interaction threshold value; until the scale-free network after adding the new node reaches a preset number of rounds, the viewpoint evolution simulation process is ended, and the scale-free network at the ending moment and the current viewpoint value of each node in the network are output. The viewpoint evolution simulation result of the application is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of opinion evolution result evaluation, and particularly relates to an opinion evolution simulation method and system based on an improved Deffuant model. BACKGROUND

[0002] In real life, each individual does not exist independently, but lives in a social network, and the opinion behavior of each individual will have an impact on other individuals in the network, thereby forming an internally interconnected group, and the interconnected group creates group behavior. The information interaction and behavior selection between individuals will directly affect the group behavior evolution of the group to which the individual belongs. If the group behavior evolution trend can be grasped, and even the opinion evolution result of the individual (or group) can be precisely controlled, it will be possible to effectively intervene and timely guide the group behavior.

[0003] At present, relevant scholars generally use opinion dynamics models to study the interaction between behavior subjects and the spread of opinions in social networks. Opinion dynamics models are divided into continuous and discrete opinion dynamics models. Compared with discrete models, continuous models can more fully reflect people's attitude towards things and people's psychological state. Continuous models include the following several models: Deffuant model, Hegselmann-Krause model, Social Judgment Theory Brehmer model, and the currently more commonly used continuous model is the Deffuant model. The traditional Deffuant model is relatively simple in handling individual opinion interaction, and it believes that nodes with too large opinion difference will not interact.

[0004] However, in real life, individuals may interact in opinion as long as they have a connection, and even if the opinion gap is very large, it may also have an impact on each other's opinions. In addition, the same individual has different degrees of recognition for different opinions received from different individuals, and the interaction of different opinions may lead to changes in the relationship between individuals. As can be seen, when considering the real situation, the opinion evolution evaluation result based on the traditional Deffuant model must be inaccurate. SUMMARY

[0005] (I) Technical problems solved

[0006] In view of the deficiencies of the prior art, the present application provides an opinion evolution simulation method and system based on an improved Deffuant model, which solves the problem of low precision of opinion evolution evaluation result based on the traditional Deffuant model.

[0007] (II) Technical solutions

[0008] To achieve the above object, the present application is implemented by the following technical solutions:

[0009] In the first aspect, the present application first proposes a viewpoint evolution simulation method based on an improved Deffuant model, which comprises:

[0010] S1, constructing an initial scale-free network between individuals with individuals as nodes, and setting initial parameters and evolution running parameters of the initial scale-free network, wherein the initial parameters include initial directed connection degrees between any two nodes in the initial scale-free network;

[0011] S2, adding a node to the initial scale-free network, and judging whether the viewpoint value difference between any two nodes in the scale-free network after adding the node is greater than an interaction threshold value, if yes, executing S3; if not, executing S4;

[0012] S3, updating the directed connection degrees between any two nodes in the scale-free network after adding the node based on a first improved Deffuant model, and updating the viewpoint values of any node in the scale-free network after adding the node based on the updated directed connection degrees;

[0013] S4, updating the viewpoint values of any node in the scale-free network after adding the node based on a second improved Deffuant model;

[0014] S5, judging whether the evolution simulation number of the scale-free network after adding the node reaches a preset round number T, if yes, executing S6; if not, continuously adding a node and executing S2;

[0015] S6, ending the viewpoint evolution simulation process, and outputting the scale-free network at the ending moment and the viewpoint values of each node at this moment.

[0016] Preferably, the interaction rule of the first improved Deffuant model is:

[0017] O i (t+1)=O i (t)+R ij ′μ i (O j (t)-O i (t))

[0018] O j (t+1)=O j (t)+R ji ′μ j (O i (t)-O j (t))

[0019] Wherein, O i (t)、Oj (t) respectively represent the opinion value of node i, j at time t; O i (t+1), O j (t+1) respectively represent the opinion value of node i, j at time t+1; R ij ', R ji ' represent the directed connection degree in the first improved Deffuant model, wherein R ij ' represents the directed connection degree of node i to node j in the first improved Deffuant model; R ji ' represents the directed connection degree of node j to node i in the first improved Deffuant model; μ i , μ j respectively represent the self-trust value of node i, j.

[0020] Preferably, the interaction rule of the second improved Deffuant model is as follows:

[0021] O i (t+1) = O i (t) + R ij μ i (O j (t) - O i (t))

[0022] O j (t+1) = O j (t) + R ji μ j (O i (t) - O j (t))

[0023] wherein R ij and R ji respectively represent the directed connection degree of node i to node j and node j to node i in the second improved Deffuant model.

[0024] Preferably, the calculation formula of the directed connection degree in the first improved Deffuant model is as follows:

[0025]

[0026]

[0027] In a second aspect, the present application further provides a view evolution simulation system based on the improved Deffuant model, which comprises:

[0028] a processing unit, configured to perform the following steps:

[0029] S1, construct an initial scale-free network among individuals with individuals as nodes, and set initial parameters and evolution running parameters of the initial scale-free network, the initial parameters including initial directed contact degrees between any two nodes in the initial scale-free network;

[0030] S2, add a node to the initial scale-free network, judge whether the viewpoint value difference between any two nodes in the scale-free network after adding the node is greater than the interaction threshold, if yes, execute S3; if not, execute S4;

[0031] S3, update the directed contact degrees between any two nodes in the scale-free network after adding the node based on the first improved Deffuant model, and update the viewpoint values of any node in the scale-free network after adding the node based on the updated directed contact degrees;

[0032] S4, update the viewpoint values of any node in the scale-free network after adding the node based on the second improved Deffuant model;

[0033] S5, judge whether the evolution simulation number of the scale-free network after adding the node reaches the preset round number T, if yes, execute S6; if not, continue to add nodes and execute S2;

[0034] S6, end the viewpoint evolution simulation process;

[0035] an output unit for outputting the scale-free network at the end time and the viewpoint values of each node at this time.

[0036] Preferably, the interaction rule of the first improved Deffuant model is:

[0037] O i (t+1)=O i (t)+R ij ′μ i (O j (t)-O i (t))

[0038] O j (t+1)=O j (t)+R ji ′μ j (O i (t)-O j (t))

[0039] wherein, O i (t), O j (t) represent the viewpoint values of i, j nodes at t moment respectively; O i (t+1), O j (t+1) represent the viewpoint values of i, j nodes at t+1 moment respectively; Rij R ji represents the directed connection degree in the first improved Deffuant model, wherein R ij R represents the directed connection degree of node i to node j in the first improved Deffuant model; R ji R represents the directed connection degree of node j to node i in the first improved Deffuant model; μ i μ j respectively represent the self-trust value of i and j nodes.

[0040] Preferably, the interaction rule of the second improved Deffuant model is as follows:

[0041] O i (t+1) = O i (t) + R ij μ i (O j (t) - O i (t))

[0042] O j (t+1) = O j (t) + R ji μ j (O i (t) - O j (t))

[0043] wherein R ij and R ji respectively represent the directed connection degree of node i to node j and node j to node i in the second improved Deffuant model.

[0044] Preferably, the calculation formula of the directed connection degree in the first improved Deffuant model is as follows:

[0045]

[0046]

[0047] (Three) beneficial effects

[0048] The application provides a view evolution simulation method and system based on an improved Deffuant model.

[0049] 1. The application firstly constructs an initial scale-free network between individuals as nodes, and sets the initial parameters of the initial scale-free network including the initial directed contact degree and the evolution running parameters; then after adding a new node in the initial scale-free network, it is judged whether the difference of the opinion value between any two nodes in the scale-free network after adding the new node is greater than the interaction threshold value, and the first improved Deffuant model and the second improved Deffuant model are selected respectively according to the size relationship between the opinion value difference and the interaction threshold value to update the opinion value of any node in the scale-free network after adding the new node; until the scale-free network after adding the new node reaches the preset number of rounds, the opinion evolution simulation process is ended, and the scale-free network at the end time and the opinion value of each node at this time are output. The application considers that the interaction of different opinions may lead to the change of the relationship between individuals, which in turn affects the real situation of individual opinions, and compared with the prior art, the opinion evolution simulation result is more accurate.

[0050] 2. The application considers that the interaction of different opinions may lead to the change of the relationship between individuals, which in turn affects the real situation of individual opinions, introduces the 'directed contact degree' parameter between nodes, improves the traditional Deffuant model, that is, designs two kinds of directed contact degrees between nodes based on the size relationship between the opinion value difference between any two nodes in the scale-free network after adding the new node and the interaction threshold value, and then designs the first improved Deffuant model and the second improved Deffuant model, and updates the opinion value of the scale-free network and the nodes in the network by using the two improved models respectively, so that the opinion evolution simulation result is more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0052] Figure 1 The flow chart of the opinion evolution simulation method based on the improved Deffuant model in the application;

[0053] Figure 2 The initial scale-free network graph in the embodiment of the application;

[0054] Figure 3 The scale-free network graph in the embodiment of the application after adding a new node but not updating the opinion value of the node;

[0055] Figure 4 The scale-free network graph in the embodiment of the application after adding a new node and updating the opinion value of the node. DETAILED DESCRIPTION

[0056] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application are described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0057] The application provides a viewpoint evolution simulation method and system based on an improved Deffuant model, solves the problem of low precision of viewpoint evolution simulation results based on a traditional Deffuant model, and achieves the purpose of better simulating the interaction and propagation evolution of viewpoints of individuals (or groups) in a social network.

[0058] The technical solutions in the embodiments of the present application are as follows to solve the above technical problems:

[0059] Since the interaction of different individual viewpoints may lead to changes in the relationship between individuals, which in turn affects the individual viewpoints, the prior art does not take this real situation into account, resulting in the problem of low precision of viewpoint evolution simulation results based on the traditional Deffuant model. In order to solve this problem, the technical solutions of the present application first use a scale-free network (BA network) model to simulate the scale-free network between nodes in a social network. Then, the “directed degree of contact” parameter between nodes is introduced to improve the traditional Deffuant model, and a first improved Deffuant model and a second improved Deffuant model are designed. Finally, the two improved models are used to update the scale-free network after the addition of new nodes and the viewpoint values of each node, and the obtained viewpoint evolution simulation results are more accurate.

[0060] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the drawings in the specification and specific embodiments.

[0061] The process of simulating the viewpoint evolution process after the addition of new nodes in the traditional Deffuant model is as follows: it is assumed that at a certain time, only two individuals meet randomly (i.e., two connected nodes in the network exchange), and viewpoint interaction can only occur when the viewpoint values of the two individuals are within a certain difference range, which is the interaction threshold ε. The influence degree of one individual on the viewpoint of the other depends on the trust degree μ of the other. If the difference between the viewpoint values of the two individuals is greater than the interaction threshold, no influence on the viewpoint values of each other will occur. Therefore, the viewpoint interaction formula and interaction rules in the traditional Deffuant model are as follows:

[0062] If |O i(t) = O j (t) = O

[0063] O i (t) = O i (t) = O i (t) = O j (t) = O i (t) = O

[0064] O j (t) = O j (t) = O j (t) = O i (t) = O j (t) = O

[0065] if |O i (t) = O j (t) = O

[0066] O i (t) = O i (t) = O

[0067] O j (t) = O j (t) = O

[0068] wherein O i (t), O j (t) represent the view value of i, j nodes at t moment respectively; O i (t+1), O j (t+1) represent the view value of i, j nodes at t+1 moment (namely the moment after adding new nodes), namely the updated view value; μ i , μ j represent the self-trust value of i, j nodes respectively; ε represents the interaction threshold value. However, in real life, as long as two individuals have contact, view interaction may occur, and even if the view gap is large, it may also affect each other's view. Therefore, the view evolution simulation technology based on the traditional Deffuant model cannot obtain accurate simulation results, and in order to solve this problem, the technical scheme of the present application is proposed.

[0069] Embodiment 1:

[0070] In the first aspect, the present application first proposes a view evolution simulation method based on an improved Deffuant model, referring to Figure 1 The method comprises the following steps:

[0071] S1, an initial scale-free network between individuals is constructed by taking individuals as nodes, and initial parameters and evolution running parameters of the initial scale-free network are set, wherein the initial parameters comprise initial directed contact degree between any two nodes in the initial scale-free network;

[0072] S2, adding a new node in the initial scale-free network, judging whether the difference between the viewpoints of any two nodes in the scale-free network after adding the new node is greater than the interaction threshold value, if yes, executing S3; if no, executing S4;

[0073] S3, updating the directed connection degree between any two nodes in the scale-free network after adding the new node based on the first improved Deffuant model, and updating the viewpoint of any node in the scale-free network after adding the new node based on the updated directed connection degree;

[0074] S4, updating the viewpoint of any node in the scale-free network after adding the new node based on the second improved Deffuant model;

[0075] S5, judging whether the evolution simulation times of the scale-free network after adding the new node reach the preset round number T, if yes, executing S6; if no, continuously adding a new node and executing S2;

[0076] S6, ending the viewpoint evolution simulation process, and outputting the scale-free network at the ending moment and the viewpoint of each node at this moment.

[0077] It can be seen that, first, the initial scale-free network between individuals is constructed by taking individuals as nodes, and the initial parameters including the initial directed connection degree of the initial scale-free network and the evolution running parameters are set; then, after adding a new node in the initial scale-free network, whether the difference between the viewpoints of any two nodes in the scale-free network after adding the new node is greater than the interaction threshold value is judged, and the viewpoint of any node in the scale-free network after adding the new node is updated by selecting the first improved Deffuant model and the second improved Deffuant model according to the size relationship between the viewpoint difference and the interaction threshold value; until the scale-free network after adding the new node reaches the preset round number, the viewpoint evolution simulation process is ended, and the scale-free network at the ending moment and the viewpoint of each node at this moment are output. The embodiment considers that the interaction of different viewpoints may lead to the change of the relationship between individuals, which in turn affects the real situation of individual viewpoints, and compared with the prior art, the viewpoint evolution simulation result is more accurate.

[0078] Now we take the green travel viewpoint as an example, combine the attached Figures 1-4 and the explanation of S1-S6 specific steps to explain the implementation process of one embodiment of the application in detail. The viewpoint evolution simulation method based on the improved Deffuant model in the embodiment specifically includes the following steps:

[0079] S1, constructing an initial scale-free network among individuals with individuals as nodes, and setting initial parameters of the initial scale-free network and evolution running parameters, the initial parameters including initial directed connection degree between any two nodes in the initial scale-free network.

[0080] In the process of promoting the formation of green low-carbon production mode and lifestyle, each person is an individual, and each individual has his own recognition degree of green travel, that is, the opinion value. In this embodiment, an initial scale-free network is constructed with each individual in a group as a node, and initial parameters of the initial scale-free network and evolution running parameters are set. The initial parameters of the initial scale-free network include initial node number M, initial opinion value of green travel of each node, directed connection degree R between each two connected nodes, and self-trust degree μ of each node.

[0081] Since in reality, two people with closer relationship have obviously stronger influence on each other's opinion, and two people with general relationship have weaker influence on each other. When the opinions of two individuals are opposite, the connection between them may be reduced, or even the connection of one party to the other is reduced to 0. After the connection is reduced, the influence degree of each other's opinion will be greatly reduced. In order to quantify the mutual connection between nodes, the "connection degree" parameter between nodes is introduced. The connection degree refers to the mutual connection between two nodes. The connection degree affects the influence of two adjacent nodes on each other's opinion value, and the higher the connection degree, the greater the influence on each other's opinion value. When the opinion value difference is large, it will lead to a reduction in the "connection degree". Moreover, in actual situation, the connection degree between two nodes is directional, that is, the connection degree of A to B and the connection degree of B to A are different, so we call it directed connection degree. In this embodiment, the parameter R(i,j) (R ∈ [0, 2]) is introduced to represent the connection degree between two nodes, wherein R = 1 represents general connection, less than 1 represents weak connection, and greater than 1 represents strong connection. ij R ji R ij ≠R ji .

[0082] Referring to Figure 2 , M nodes are numbered in turn as 0, 1,..., M-1; and the initial green travel opinion value of each node is between 0 and 1. In this embodiment, M = 5, wherein A, B, C, D and E represent five nodes (corresponding to five individuals in reality) numbered 0, 1, 2, 3 and 4 in the scale-free network. Each node has its own opinion value, that is, the recognition degree of each person to the green travel opinion is between 0 and 1. In this embodiment, the initial opinion value of each node is set as 0.5. Figure 2In the embodiment, the initial green travel viewpoint values of A, B, C, D and E are 0.8, 0.2, 0.6, 0.7 and 0.3 respectively, wherein 0.8 represents very agree with green travel, 0.2 represents very disagree with green travel, and other values are similar.

[0083] The initial scale-free network evolution operation parameters include the pre-set evolution simulation process round number T and the interaction threshold value ε. In the embodiment, the interaction threshold value ε is set to 0.5. In addition, the trust degrees of both parties are set to 0.5.

[0084] S2, a node is added in the initial scale-free network, and it is judged whether the viewpoint value difference between any two nodes in the scale-free network after the node is added is greater than the interaction threshold value. If yes, S3 is executed; if no, S4 is executed.

[0085] At t time, a new node F (i.e. a new individual is added in the group) is added in the scale-free network, and the number of the node F is i (at this time, i=5). A node is randomly selected for connection, and according to the actual situation, the newly added node is preferentially connected to the largest node with the most adjacent nodes. In the embodiment, the node F is preferentially connected to the largest node B with the most adjacent nodes, as shown in FIG. 2. Figure 3 Figure 3 The scale-free network graph when the new node is added is shown in FIG. 2. Then, the viewpoint value of green travel of the newly added node, the directed connection degree R of the connected node and the trust degree μ of itself are given.

[0086] 1) any two nodes are randomly selected in the scale-free network after the node is added, and the viewpoint value difference of green travel between the two nodes is calculated. Specifically, O represents the current viewpoint value of each node at t time (O∈[0, 1]), wherein O i (t) represents the viewpoint value of the i node at t time, O j (t) represents the viewpoint value of the j node at t time, and |O i (t)-O j (t)| represents the viewpoint value difference of green travel between the i node and the j node.

[0087] 2) it is judged whether the viewpoint value difference is greater than the above-mentioned set interaction threshold value ε, and then different viewpoint value updating steps are executed according to the size relationship between the viewpoint value difference and the interaction threshold value ε.

[0088] ​Since at a certain moment, the difference of the opinions between any two nodes in the scale-free network will affect the directed connection degree between the two nodes, and this directed connection degree will in turn affect the opinion value between the two nodes at this moment, and the opinion influence of node i on node j will be closely related to the connection degree of i on j, the higher the connection degree, the greater the influence. Therefore, in order to obtain the opinion value of any node in the scale-free network after the new node is added, it is necessary to update the directed connection degree between the two nodes after the new node is added, and then update the opinion value of any node in the scale-free network after the new node is added according to the updated directed connection degree. Specifically, the difference of the opinions between any two nodes at time t is compared with the above interaction threshold value ε, if the difference of the opinions is greater than the interaction threshold value, S3 is executed; if the difference of the opinions is not greater than the interaction threshold value, S4 is executed.

[0089] S3, updating the directed connection degree between any two nodes in the scale-free network after the new node is added based on the first improved Deffuant model, and updating the opinion value of any node in the scale-free network after the new node is added based on the updated directed connection degree.

[0090] When the difference of the opinions of the two parties is greater than the interaction threshold value ε, the two parties will still have communication and influence each other's opinions, but this time's communication will affect the connection degree of each other. The degree of influence on the connection degree depends on the difference of the opinions and the degree of opinion identification of the two parties, the smaller the difference of the opinions and the higher the degree of opinion identification of the two parties, the smaller the degree of influence, and vice versa. At this time, we take as the influence coefficient of this time's communication on the directed connection degree, then the directed connection degree of each other changes to:

[0091]

[0092]

[0093] At this time, when |O i (t)-O j (t)|>ε, the opinion value of any node in the scale-free network after the new node is added is updated by using the first improved Deffuant model, wherein the interaction rule of the first improved Deffuant model is:

[0094] O i (t+1)=O i (t)+R ij ′μ i (O j (t)-O i (t))

[0095] O j (t+1)=O j (t)+R jiμ j (O i (t)-O j (t))

[0096] wherein, O i (t) and O j (t) represent the opinion value of node i and j at time t respectively; O i (t+1) and O j (t+1) represent the opinion value of node i and j at time t+1 respectively; R ij ′ and R ji ′ represent the directed connection degree in the first improved Deffuant model, wherein, R ij ′ represents the directed connection degree of node i to node j in the first improved Deffuant model; R ji ′ represents the directed connection degree of node j to node i in the first improved Deffuant model; μ i and μ j represent the self-trust value of node i and j respectively.

[0097] S4, updating the opinion value of any node in the scale-free network after adding the new node based on the second improved Deffuant model.

[0098] When the opinion value difference between two nodes at time t is less than or equal to the interaction threshold ε, that is, |O i (t)-O j (t)|≤ε, the opinion value of any node in the scale-free network after adding the new node is updated by using the second improved Deffuant model, wherein the interaction rule of the second improved Deffuant model is:

[0099] O i (t+1)=O i (t)+R ij μ i (O j (t)-O i (t))

[0100] O j (t+1)=O j (t)+R ji μ j (O i (t)-O j (t))

[0101] wherein, R ij and R jirespectively represent the directed connection degree of node i to node j and node j to node i in the second improved Deffuant model, that is, the directed connection degree when the difference between the view values of any two nodes at time t is not greater than the interaction threshold value ε.

[0102] S5, judge whether the evolution simulation number of the scale-free network after adding a new node reaches the preset number of rounds T, if yes, execute S6, if not, continue to add a new node and execute S2.

[0103] After adding a new node and completing the view value update of any node in the scale-free network in the above manner, it is judged whether the evolution simulation number of the scale-free network at the current time reaches the preset number of rounds T, if the evolution simulation number of the scale-free network at the current time reaches the preset number of rounds T, S6 is executed, if the evolution simulation number of the scale-free network at the current time is less than the preset number of rounds T, the number of added nodes i is set to i+1, and S2 is continued to be executed.

[0104] S6, end the view evolution simulation process, and output the scale-free network at the end time and the view value of each node at this time.

[0105] The above process is repeated until the evolution simulation number of the scale-free network at the current time reaches the preset number of rounds T, the view evolution simulation process is ended, and the current view value of each node in the scale-free network at this time is output. See Figure 4 , Figure 4 The view value of the added node obtained by using the improved Deffuant model is updated.

[0106] At this point, the entire process of the view evolution simulation method based on the improved Deffuant model is completed.

[0107] Example 2:

[0108] In a second aspect, the application further provides a view evolution simulation system based on an improved Deffuant model, which comprises:

[0109] A processing unit is configured to execute the following steps:

[0110] S1, construct an initial scale-free network between individuals as nodes, and set initial parameters and evolution running parameters of the initial scale-free network, wherein the initial parameters include initial directed connection degrees between any two nodes in the initial scale-free network;

[0111] S2, add a new node in the initial scale-free network, judge whether the difference between the view values of any two nodes in the scale-free network after adding a new node is greater than an interaction threshold value, if yes, execute S3, if not, execute S4;

[0112] S3, updating the directed connection degree between any two nodes in the newly added node based scale-free network based on the first improved Deffuant model, and updating the opinion value of any node in the newly added node based scale-free network based on the updated directed connection degree;

[0113] S4, updating the opinion value of any node in the newly added node based scale-free network based on the second improved Deffuant model;

[0114] S5, judging whether the evolution simulation number of the newly added node based scale-free network reaches the preset round number T, if yes, executing S6, if not, continuing to add nodes and executing S2;

[0115] S6, ending the opinion evolution simulation process;

[0116] an output unit for outputting the scale-free network at the ending moment and the opinion value of each node at this moment.

[0117] Optionally, the interaction rule of the first improved Deffuant model is:

[0118] O i (t+1)=O i (t)+R ij ′μ i (O j (t)-O i (t))

[0119] O j (t+1)=O j (t)+R ji ′μ j (O i (t)-O j (t))

[0120] wherein, O i (t), O j (t) represent the opinion value of nodes i and j at t moment respectively; O i (t+1), O j (t+1) represent the opinion value of nodes i and j at t+1 moment respectively; R ij ′, R ji ′ represent the directed connection degree in the first improved Deffuant model, wherein, R ij ′ represents the directed connection degree of node i to node j in the first improved Deffuant model; R ji ′ represents the directed connection degree of node j to node i in the first improved Deffuant model; μ i , μ j represent the self-trust value of nodes i and j respectively.

[0121] Optionally, the interaction rule of the second improved Deffuant model is:

[0122] O i (t+1)=O i (t)+R ij μ i (O j (t)-O i (t))

[0123] O j (t+1)=O j (t)+R ji μ j (O i (t)-O j (t))

[0124] Wherein, R ij and R ji respectively represent the directed connection degree of node i to node j and node j to node i in the second improved Deffuant model.

[0125] Optionally, the calculation formula of the directed connection degree in the first improved Deffuant model is:

[0126]

[0127]

[0128] It can be understood that the opinion evolution simulation system based on the improved Deffuant model provided by the embodiments of the present application corresponds to the opinion evolution simulation method based on the improved Deffuant model, and the related content explanation, examples, beneficial effects and the like of the opinion evolution simulation method based on the improved Deffuant model can be referred to the corresponding content in the opinion evolution simulation method based on the improved Deffuant model, which will not be repeated here.

[0129] In summary, compared with the prior art, the present application has the following beneficial effects:

[0130] 1. The application firstly constructs an initial scale-free network between individuals as nodes, and sets the initial parameters of the initial scale-free network including the initial directed contact degree and the evolution running parameters; then after adding a new node in the initial scale-free network, it is judged whether the opinion value difference between any two nodes in the scale-free network after adding the new node is greater than the interaction threshold value, and the first improved Deffuant model and the second improved Deffuant model are selected respectively according to the size relationship between the opinion value difference and the interaction threshold value to update the opinion value of any node in the scale-free network after adding the new node; until the scale-free network after adding the new node reaches the preset number of rounds, the opinion evolution simulation process is ended, and the scale-free network at the end time and the opinion value of each node at this time are output. The application considers that the interaction of different opinions may lead to the change of the relationship between individuals, which in turn affects the real situation of individual opinions, and the opinion evolution simulation result is more accurate compared with the prior art.

[0131] 2. The application considers that the interaction of different opinions may lead to the change of the relationship between individuals, which in turn affects the real situation of individual opinions, introduces the“directed contact degree”parameter between nodes, improves the traditional Deffuant model, that is, designs two kinds of directed contact degrees between nodes based on the size relationship between the opinion value difference between any two nodes in the scale-free network after adding the new node and the interaction threshold value, and then designs the first improved Deffuant model and the second improved Deffuant model, and updates the opinion value of the scale-free network and the nodes in the network by using the two improved models respectively, so that the opinion evolution simulation result is more accurate.

[0132] It should be noted that in this text, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply that these entities or operations have any such actual relationship or order. Moreover, the terms“include”,“contain” or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement“including a…” does not exclude the presence of other identical elements in the process, method, article or equipment including the element.

[0133] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A viewpoint evolution simulation method based on an improved Deffuant model, characterized in that, Applied to the concept of green travel, the method includes: S1. Construct an initial scale-free network between individuals using individuals as nodes, and set the initial parameters and evolutionary operation parameters of the initial scale-free network. The initial parameters include the initial directed connection degree between any two nodes in the initial scale-free network; where an individual refers to a person. S2. Add a node to the initial scale-free network. Determine whether the difference in viewpoint values ​​between any two nodes in the scale-free network after adding the node is greater than the interaction threshold. If yes, proceed to S3; otherwise, proceed to S4. S3. Update the directed connection degree between any two nodes in the scale-free network after adding nodes based on the first improved Deffuant model, and update the view value of any node in the scale-free network after adding nodes based on the updated directed connection degree. S4. Update the viewpoint value of any node in the scale-free network after adding nodes based on the second improved Deffuant model; S5. Determine whether the number of evolution simulations of the scale-free network after adding new nodes has reached the preset number of rounds T. If yes, proceed to S6; otherwise, continue adding nodes and proceed to S2. S6. End the view evolution simulation process and output the scale-free network at the end time and the view value of each node at this time; The interaction rules of the first improved Deffuant model are as follows: in, , They represent t time i, j The node's viewpoint value; , They represent respectively t +1 moment i , j The node's viewpoint value; , This represents the directed connection degree in the first improved Deffuant model, where, This represents the directed connection degree between node i and node j in the first improved Deffuant model; This represents the directed relationship between node j and node i in the first improved Deffuant model; , They represent i, j The node's own trust level; The interaction rules of the second improved Deffuant model are as follows: in, and Let represent the directed connection degree between node i and node j and between node j and node i in the second improved Deffuant model, respectively. The formula for calculating the directed connection degree in the first improved Deffuant model is as follows: 。 2. A viewpoint evolution simulation system based on an improved Deffuant model, characterized in that, The system, applied to the concept of green travel, includes: Processing unit, used to perform the following steps: S1. Construct an initial scale-free network between individuals using individuals as nodes, and set the initial parameters and evolutionary operation parameters of the initial scale-free network. The initial parameters include the initial directed connection degree between any two nodes in the initial scale-free network; where an individual refers to a person. S2. Add a node to the initial scale-free network. Determine whether the difference in viewpoint values ​​between any two nodes in the scale-free network after adding the node is greater than the interaction threshold. If yes, proceed to S3; otherwise, proceed to S4. S3. Update the directed connection degree between any two nodes in the scale-free network after adding nodes based on the first improved Deffuant model, and update the view value of any node in the scale-free network after adding nodes based on the updated directed connection degree. S4. Update the viewpoint value of any node in the scale-free network after adding nodes based on the second improved Deffuant model; S5. Determine whether the number of evolution simulations of the scale-free network after adding new nodes has reached the preset number of rounds T. If yes, proceed to S6; otherwise, continue adding nodes and proceed to S2. S6. End the viewpoint evolution simulation process; The output unit is used to output the scale-free network at the end time and the view value of each node at this time; The interaction rules of the first improved Deffuant model are as follows: in, , They represent t time i, j The node's viewpoint value; , They represent respectively t +1 moment i , j The node's viewpoint value; , This represents the directed connection degree in the first improved Deffuant model, where, This represents the directed connection degree between node i and node j in the first improved Deffuant model; This represents the directed relationship between node j and node i in the first improved Deffuant model; , These represent the self-trust values ​​of nodes i and j, respectively. The interaction rules of the second improved Deffuant model are as follows: in, and Let represent the directed connection degree between node i and node j and between node j and node i in the second improved Deffuant model, respectively. The formula for calculating the directed connection degree in the first improved Deffuant model is as follows: 。

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