Decision-making Method for Large-group Satellite Emergency Plan in Social Network Environment

Abstract

The present invention provides a decision-making method, system, storage medium and electronic device for large-group satellite emergency plans in a social network environment, which relates to the technical field of satellite emergency plan decision-making. In the present invention, a decision preference matrix of multiple satellite emergency decision-makers and a social network relationship graph among the decision-makers are obtained, and the decision preference matrix uses hesitant fuzzy binary semantics as the decision opinion expression form; according to the decision preference matrix and the social network relationship graph, all decision-makers are clustered into several clusters, and the decision preference matrix of each initial cluster is obtained; according to the decision preference matrix of each initial cluster, a group decision preference matrix under the basis of reaching a consensus is obtained; finally, according to the group decision preference matrix, the finally selected large-group satellite emergency plan is obtained. The problem of trust degree among decision-makers is considered in the clustering process, avoiding defaulting the weighted factor between nodes to 1 for a directed graph, which plays a great role in the accuracy of the clustering result of the large satellite emergency plan.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite emergency plan decision-making, and particularly to a large-group satellite emergency plan decision-making method, system, storage medium and electronic device in a social network environment. Background Art

[0002] In recent years, emergency events such as earthquakes, floods, fires and local wars have occurred frequently around the world, often causing catastrophic consequences. With the continuous development of satellite technology and the continuous increase in users' demand for data, imaging satellites have become an important means to obtain ground information in emergency events. In the actual observation process, high-dynamic and high-timeliness emergency tasks will be generated, and these tasks are often accompanied by uncertainties in occurrence time and quantity. Satellite emergency mission planning is a process of quickly generating multiple sets of emergency plans according to the emergency observation requirements proposed by users. Then, based on emergency decision-making, the best observation plan is quickly selected to obtain the maximum emergency observation benefit. Due to the complexity and uncertainty of emergency decision-making, the participation of multiple decision-makers (DMs) is often required, resulting in a large-group decision-making problem (LGDM).

[0003] Currently, the LGDM problem has received increasing attention in the field of decision science. On the other hand, since satellite emergency plan decision-making involves a wide range of aspects, the LGDM applicable to satellite emergency plan decision-making usually involves a large number of DMs from different professional backgrounds participating in the decision-making process. Such large-group satellite emergency plan decision-making (LGSESDM) has complex large-group characteristics: (1) the group size is relatively large, and generally multiple DMs from different positions need to participate in the decision-making process; (2) a decision must be made within a short time; (3) it is often difficult to reach a consensus agreement among DMs; and (4) a wrong decision or too slow decision may lead to catastrophic losses in emergency tasks.

[0004] As the LGDM problem has received more and more attention from scholars, some new decision-making models and methods have been proposed one after another. These models and methods all include three main steps: large-group clustering, consensus reaching process (CRP) and plan selection process. Clustering is an effective method for organizing and managing large teams because it can divide large teams into small-scale clusters. Many clustering methods have been proposed, such as the K-means algorithm, density-based clustering algorithm and vector space-based clustering method. Traditional consensus is called "hard consensus", and the scalar of the consensus measure has only two indicators, 0 (no consensus reached) or 1 (consensus achieved). It is very difficult and unrealistic to adjust the consensus from 0 to 1 in actual decision-making.

[0005] Most of the above clustering methods use the degree of consistency of decision-makers' decision-making preferences as the clustering criterion, ignoring the internal connections between decision-makers, thus affecting the rationality of the finally selected large-group satellite emergency plan. Summary of the Invention

[0006] (1) Technical Problem to be Solved

[0007] Aiming at the deficiencies of the prior art, the present invention provides a decision-making method, system, storage medium and electronic device for large-group satellite emergency plans in a social network environment, which solves the technical problem that the existing clustering method takes the consistency degree of decision-makers' decision-making preferences as the clustering criterion and ignores the internal connection between decision-makers.

[0008] (2) Technical Solution

[0009] To achieve the above object, the present invention is realized through the following technical solutions:

[0010] A decision-making method for large-group satellite emergency plans in a social network environment includes:

[0011] S1. Obtain the decision-making preference matrix of multiple satellite emergency decision-makers and the social network relationship graph between each decision-maker. The decision-making preference matrix uses the hesitant fuzzy binary semantic set as the decision-making opinion expression form;

[0012] S2. According to the decision-making preference matrix and the social network relationship graph, cluster all decision-makers into several clusters, and obtain the decision-making preference matrix of each initial cluster;

[0013] S3. According to the decision-making preference matrix of each initial cluster, obtain the group decision-making preference matrix based on reaching a consensus;

[0014] S4. According to the group decision-making preference matrix, obtain the finally selected large-group satellite emergency plan.

[0015] Preferably, in S2, the Louvain clustering algorithm based on modularity is used for clustering; specifically including:

[0016] S21. Regard each node of the social network relationship graph as a cluster, and then merge the neighbor nodes of the cluster into the same cluster to obtain multiple clusters;

[0017] S22. According to the trust matrix of the social network relationship graph and the similarity between any two decision-making preference matrices, obtain the weighting factor between different decision-makers in the social network relationship graph; according to the weighting factor between different decision-makers, obtain the modularity of each cluster and the corresponding modularity change amount;

[0018] S23. Repeat the above steps S21-S22 until the overall modularity no longer changes, and finally obtain several clusters;

[0019] S24. Obtain the initial cluster decision preference matrix for each cluster according to the final clustering result.

[0020] Preferably, the S22 specifically includes:

[0021] S221. Represent the trust function value by a tuple of type λ=(t, d), where t, d ∈ [0, 1]. The first component t is a degree of trust, and the second component d is a degree of distrust; define the trust matrix as indicating the decision maker the degree of trust between indicating the decision maker the degree of distrust between, then the trust score between any two decision makers is expressed as

[0022] S222. Use the following formula to obtain the weighting factor between different decision makers in the social network relationship graph

[0023]

[0024] where, represents the similarity between any two of the decision preference matrices;

[0025] are the decision preference matrices of any two decision makers respectively, that is, the decision information of m large groups of satellite emergency plans for n attribute indicators;

[0026] where, are respectively the granularities of the corresponding hesitant fuzzy binary semantic sets;

[0027]

[0028]

[0029] represents and the Hausdorff distance between the hesitant fuzzy binary semantic sets;

[0030] and are composed of several hesitant fuzzy binary semantic sets, and the quantity is determined by the scheduling opinion; is a linguistic label, and the value range is S = {s0, s1, …, s g}; represents the symbol conversion value and the range is [-0.5, 0.5), and g is the potential of S;

[0031] S223. Obtain the modularity Q0 of each cluster according to the weighting factors among the different decision-makers;

[0032]

[0033] Among them, M represents the sum of the weights of all edges in the social network relationship graph; Represents the decision-maker The weighting factor of the edge between, when the network is not a weighted graph, the weight of each edge is 1; Respectively represent the sum of the weighting factors of all edges connected to node l1 or l2; Respectively represent The clusters to which they belong. If Belong to the same cluster C, then Otherwise

[0034] S224. And obtain the corresponding modularity change amount △Q0;

[0035]

[0036] Among them, ∑in represents the sum of the weighting factors of all edges in cluster C, and ∑tot represents the sum of the weighting factors of the edges connected to the points in cluster C. Represents the sum of the weighting factors of the edges connecting node l1 and the points in cluster C.

[0037] Preferably, the S24 specifically includes:

[0038] Based on Calculate the initial decision preference matrix of each cluster; among them, assume that the decision-makers Are clustered into K clusters, and the kth cluster is denoted as C k , N k Represents the number of members in the cluster.

[0039] Preferably, S3 specifically includes:

[0040] S31. Calculate the consensus degree GCL of each current cluster decision preference matrix t ;

[0041]

[0042] S32. Compare the current consensus degree GCL t With the preset threshold :

[0043] If Then obtain the group decision preference matrix Among them Indicates the cluster weight,

[0044]

[0045] wherein, σ represents a permutation such that TS σ(γ) is the γ-th maximum value in the set and Q is the basic unit interval singleton membership function of the fuzzy linguistic quantifier, implemented in the aggregation process, Q: [0, 1] → [0, 1], such that Q(0) = 0, Q(1) = 1, and if a > b then Q(a) > Q(b);

[0046]

[0047] wherein, is e γ (γ = 1,..., N e ) and is the number of direct trust relationships between;

[0048] If transfer to S33;

[0049] S33. Identify the position of the cluster decision preference matrix G a that needs to be adjusted;

[0050]

[0051] S34. Obtain the adjusted cluster decision preference matrix (G a ′) k according to the weights of the K clusters and the expectations of the other decision preference matrices that do not need to be adjusted, and transfer to S31; m×n wherein, δ is the matrix adjustment parameter, and its value range is [0, 1];

[0052]

[0053] The weights of the K clusters are calculated using the formula

[0054] wherein represents the weight of the decision maker, and

[0055] Preferably, in S4, MULTIMOORA is used to obtain the final selected large - group satellite emergency plan.

[0056] Preferably, S4 specifically includes:

[0057] S41. Standardize the group decision preference matrix R c =(x ij ) )m×n , and obtain the dimensionless matrix

[0058]

[0059] S42. Calculate the utility value based on the RS model of the following arithmetic integral and use to obtain the first ranking of the large - group satellite emergency plan in descending order;

[0060]

[0061] S43. Calculate the utility value based on the following RP model and use to obtain the second ranking of the large - group satellite emergency plan in ascending order;

[0062] Determine the maximum value r of each attribute index j and the maximum distance between the relevant alternative options;

[0063]

[0064] where

[0065] S44. Calculate the utility value based on the geometric integral formula based on the following FMF model and use to obtain the third ranking of the large - group satellite emergency plan in descending order;

[0066]

[0067] S45. Adopt the dominance theory method to fuse the above three ranking results to obtain the finally selected large - group satellite emergency plan.

[0068] A decision - making system for large - group satellite emergency plans in a social network environment, comprising:

[0069] An acquisition module, configured to acquire the decision - making preference matrix of multiple satellite emergency decision - makers and the social network relationship graph between each decision - maker, and the decision - making preference matrix uses the hesitant fuzzy binary semantic set as the decision - making opinion expression form;

[0070] A clustering module, configured to cluster all decision - makers into several clusters according to the decision - making preference matrix and the social network relationship graph, and obtain the decision - making preference matrix of each initial cluster;

[0071] A consensus module, configured to obtain the group decision - making preference matrix under the condition of reaching a consensus according to each of the initial cluster decision - making preference matrices;

[0072] A selection module for obtaining the finally selected large-group satellite emergency plan according to the group decision-making preference matrix.

[0073] A storage medium stores a computer program for decision-making of large-group satellite emergency plans in a social network environment, wherein the computer program enables a computer to execute the decision-making method of large-group satellite emergency plans in a social network environment as described above.

[0074] An electronic device includes:

[0075] One or more processors;

[0076] A memory; and

[0077] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs include those for executing the decision-making method of large-group satellite emergency plans in a social network environment as described above.

[0078] (III) Advantageous Effects

[0079] The present invention provides a decision-making method, system, storage medium and electronic device for large-group satellite emergency plans in a social network environment. Compared with the prior art, the following advantageous effects are achieved:

[0080] In the present invention, first, the decision-making preference matrices of multiple satellite emergency decision-makers and the social network relationship graph among the decision-makers are obtained, and the decision-making preference matrix uses the hesitant fuzzy binary semantic set as the decision opinion expression form; then, according to the decision-making preference matrix and the social network relationship graph, all decision-makers are clustered into several clusters to obtain the initial cluster decision-making preference matrices of each cluster; according to each of the initial cluster decision-making preference matrices, the group decision-making preference matrix under the consensus is obtained; finally, according to the group decision-making preference matrix, the finally selected large-group satellite emergency plan is obtained. In the clustering process, the trust degree problem among decision-makers (i.e., considering the trust degree between nodes) is considered, avoiding defaulting the weighted factor between nodes to 1 for a directed graph, which plays a great role in the accuracy of the clustering result of the large satellite emergency plan. Description of the Drawings

[0081] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0082] Figure 1A flowchart of a decision-making method for large-group satellite emergency plans in a social network environment is provided in an embodiment of the present invention;

[0083] Figure 2 An embodiment of the present invention provides different representation schemes in social network analysis. Detailed implementation manners

[0084] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0085] By providing a decision-making method, system, storage medium, and electronic device for large-group satellite emergency plans in a social network environment in an embodiment of the present application, the technical problem that existing clustering methods use the consistency degree of decision-making preferences of decision-makers as the clustering criterion and ignore the internal connections between decision-makers is solved.

[0086] The general idea of the technical solutions in the embodiments of the present application to solve the above technical problems is as follows:

[0087] In an embodiment of the present invention, first, a decision-making preference matrix of multiple satellite emergency decision-makers and a social network relationship graph between each decision-maker are obtained, and the decision-making preference matrix uses a hesitant fuzzy binary semantic set as the decision-making opinion expression form; then, according to the decision-making preference matrix and the social network relationship graph, all decision-makers are clustered into several clusters, and an initial cluster decision-making preference matrix of each cluster is obtained; according to each initial cluster decision-making preference matrix, a group decision-making preference matrix under the basis of reaching a consensus is obtained; finally, according to the group decision-making preference matrix, a large-group satellite emergency plan finally selected is obtained. The trust degree problem between decision-makers (i.e., considering the trust degree between nodes) is considered in the clustering process, avoiding defaulting the weighting factor between nodes to 1 for a directed graph, which plays a great role in the accuracy of the clustering result of the large satellite emergency plan.

[0088] To better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0089] Embodiment:

[0090] As Figure 1 shown, an embodiment of the present invention provides a decision-making method for large-group satellite emergency plans in a social network environment, including:

[0091] S1. Obtain the decision preference matrices of multiple satellite emergency decision-makers and the social network relationship graph among decision-makers. The decision preference matrix uses hesitant fuzzy binary semantic sets as the form of expressing decision opinions.

[0092] S2. According to the decision preference matrix and the social network relationship graph, cluster all decision-makers into several clusters, and obtain the decision preference matrices of each initial cluster.

[0093] S3. According to the decision preference matrices of each initial cluster, obtain the group decision preference matrix under the basis of reaching a consensus.

[0094] S4. According to the group decision preference matrix, obtain the final selected large-group satellite emergency plan.

[0095] In the clustering process of the embodiment of the present invention, the trust degree problem among decision-makers (i.e., considering the trust degree between nodes) is considered, avoiding defaulting the weighted factor between nodes to 1 for a directed graph, which plays a great role in the accuracy of the clustering result of the large satellite emergency plan.

[0096] The following will introduce each step of the above solution in detail:

[0097] In step S1, obtain the decision preference matrices of multiple satellite emergency decision-makers and the social network relationship graph among decision-makers. The decision preference matrix uses hesitant fuzzy binary semantic sets as the form of expressing decision opinions.

[0098] Each expert decision-maker DM provides his / her decision opinion according to the attributes of the satellite emergency decision plan, and forms a decision preference matrix with hesitant fuzzy binary semantic sets (HF2TLSs).

[0099] The selected attributes can be selected according to the actual situation. For example, such as the task completion benefit, including the completion rate and completion benefit of the emergency task; the plan performance, focusing on the disturbance to the original observation plan; the resource utilization rate, such as the conflict degree of the observation time window and the satellite utilization rate.

[0100] In addition, since the trust relationship has always been regarded as a reliable source for evaluating the importance of experts, it is usually studied by social network analysis (SNA). There are three representations in SNA analysis: actor group, relationship itself, and actor criteria (as Figure 2 shown).

[0101] The embodiment of the present invention only takes one type of social network, i.e., the trust network, as an example. In this network, decision-makers clearly express their views as trust and distrust statements.

[0102] Using SNA to analyze the social network relationships of decision-makers, the constructed social network relationship graph is a social structure composed of a set of nodes E and a set of edges L, where the node li represents the decision-maker The directed edge represents a trust relationship. For example, the edges pointing from e5 to e3 and e6 indicate that e5 trusts e3 and e6.

[0103] In step S2, according to the decision preference matrix and the social network relationship graph, all decision-makers are clustered into several clusters, and the decision preference matrix of each initial cluster is obtained.

[0104] In this step, the Louvain clustering algorithm based on modularity is used for clustering, and a new clustering method is formed by combining the above SNA analysis method. In the context of the large-group satellite emergency plan of the embodiment of the present invention, combining the SNA method with the Louvain algorithm with fast clustering speed and high efficiency, the inherent connections among multiple decision-makers (DMs) are embedded into the clustering process; on the one hand, the accuracy and timeliness of the clustering results are increased, and on the other hand, the clustering complexity is reduced.

[0105] The specific content of S2 includes:

[0106] S21. Combining the above SNA, convert the directed graph containing multiple decision-makers into an undirected graph, regard each node of the social network relationship graph as a cluster, and then merge the neighbor nodes of the cluster into the same cluster to obtain multiple clusters;

[0107] S22. According to the trust matrix of the social network relationship graph and the similarity between any two decision preference matrices, obtain the weighted factors between different decision-makers in the social network relationship graph; according to the weighted factors between different decision-makers, obtain the modularity of each cluster and the corresponding change in modularity; specifically include:

[0108] S221. Use a tuple of type λ=(t,d) to represent the trust function value, where t,d∈[0,1], the first component t is a trust degree, and the second component d is a distrust degree.

[0109] The set of trust function values (TFs), or the trust function, will be represented by Λ={λ=(t,d)|t,d∈[0,1]}≡[0,1] 2 (indicating that it satisfies the properties of the triangular norm) is represented.

[0110] Define the trust matrix as indicating the decision-maker The trust degree between indicating the decision-maker The distrust degree between, then the trust score between any two decision-makers is expressed as

[0111] S222. Obtain the weighting factors between different decision-makers in the social network relationship graph by using the following formula (a method for calculating the weights between nodes designed based on the harmonic mean of similarity and trust).

[0112]

[0113] Among them, represents the similarity between any two of the decision preference matrices;

[0114] are the decision preference matrices of any two decision-makers respectively, that is, the decision information of m large-group satellite emergency plans for n attribute indicators;

[0115] Among them, are respectively the granularities of the corresponding hesitant fuzzy binary semantic sets;

[0116]

[0117]

[0118] represents and the Hausdorff distance between the hesitant fuzzy binary semantic sets;

[0119] and are composed of several hesitant fuzzy binary semantic sets, and the quantity is determined by the scheduling opinions; is a linguistic label, and the value range is S = {s0, s1,..., s g}(for example, S = {s0 = extremely poor, s1 = very poor, s2 = poor, s3 = average, s4 = good, s5 = very good, s6 = extremely good}); represents the symbol conversion value and the range is [-0.5, 0.5), and g is the cardinality of S;

[0120] S223. Obtain the modularity Q0 of each cluster according to the weighting factors between different decision-makers;

[0121]

[0122] Among them, M represents the sum of the weights of all edges in the social network relationship graph; represents the weighting factor of the edge between decision-makers When the network is not a weighted graph, the weights of all edges are 1; respectively represent the sum of the weighted factors of all edges connected to node l1 or l2; respectively represent the cluster to which it belongs, if belong to the same cluster C, then otherwise

[0123] S224, and obtain the corresponding modularity change ΔQ0;

[0124]

[0125] where, ∑in represents the sum of the weighted factors of all edges in cluster C, ∑tot represents the sum of the weighted factors of the edges connected to the nodes in cluster C, represents the sum of the weighted factors of the edges connected between node l1 and the nodes in cluster C.

[0126] S23. Repeatedly execute the above steps S21 - S22 until the overall modularity no longer changes, and finally obtain several clusters;

[0127] This step will finally cluster the decision - makers into K clusters; the k - th cluster is denoted as C k ,N k represents the number of members in the cluster.

[0128] S24. According to the final clustering result, based on obtain the initial cluster decision - making preference matrix for each cluster.

[0129] S3. According to each of the above - mentioned initial cluster decision - making preference matrices, obtain the group decision - making preference matrix under the consensus; specifically including:

[0130] S31. Calculate the consensus degree GCL of each current cluster decision - making preference matrix t ;

[0131]

[0132] S32. Compare the current consensus degree GCL t with the preset threshold :

[0133] If then obtain the group decision - making preference matrix where represents the cluster weight,

[0134]

[0135] where, σ represents a permutation such that TSσ(γ) is the γ-th maximum value in the set where Q is the basic unit interval monopsony (BUM) membership function of the fuzzy linguistic quantifier, implemented during the aggregation process, Q: [0, 1] → [0, 1], such that Q(0) = 0, Q(1) = 1, and if a > b then Q(a) > Q(b);

[0136]

[0137] where is the number of direct trust relationships between and

[0138] If transfer to S33;

[0139] S33. Identify the cluster decision preference matrix G a to be adjusted;

[0140]

[0141] S34. Obtain the adjusted cluster decision preference matrix (G a ′) k of G based on the weights of the K clusters and the expectations of the other decision preference matrices that do not need to be adjusted, and transfer to S31; m×n where δ is the matrix adjustment parameter with a value range of [0, 1];

[0142]

[0143] The weights of the K clusters are calculated using the formula

[0144] where represents the weight of the decision maker and and

[0145] S4. Obtain the finally selected large-group satellite emergency plan according to the group decision preference matrix

[0146] In this step, MULTIMOORA is used to obtain the finally selected large-group satellite emergency plan. The MOORA (Multiobjective Optimizaation By Ratio Analysis) method was initially introduced by Brauers and Zavadskas and further enhanced by adding the Full Multiplicative Form (FMF) to generate the multiplicative MOORA (MULTIMOORA) method

[0147] MULTIMOORA is an emerging multi-attribute decision-making method. Compared with other methods, this method is simple, effective, and easy to rank and optimize solutions. MULTIMOORA is based on three subordinate methods, namely the ratio system (RS), the reference point (RP), and the full multiplicative method (FMF). It also uses the dominance theory to calculate the final ranking. Therefore, the MULTIMOORA method can improve the result accuracy when used in the final selection process of satellite emergency plans.

[0148] The specific steps of S4 include:

[0149] S41. Standardize the group decision-making preference matrix R c =(x ij ) m×n to obtain the dimensionless matrix

[0150]

[0151] S42. Calculate the utility value based on the RS model of the following arithmetic integral and obtain the first ranking of the large-group satellite emergency plans in descending order of ;

[0152]

[0153] S43. Calculate the utility value based on the following RP model and obtain the second ranking of the large-group satellite emergency plans in ascending order of ;

[0154] Determine the maximum value r j of each attribute index and the maximum distance between the relevant alternatives;

[0155] [[ID=--42]]

[0156] where

[0157] S44. Calculate the utility value based on the geometric integral formula based on the following FMF model and obtain the third ranking of the large-group satellite emergency plans in descending order of ;

[0158]

[0159] S45. Use the dominance theory method to fuse the above three ranking results to obtain the finally selected large-group satellite emergency plan.

[0160] The embodiment of the present invention provides a large-group satellite emergency plan decision-making system in a social network environment, including:

[0161] An acquisition module, configured to acquire the decision preference matrices of multiple satellite emergency decision-makers and the social network relationship graph among decision-makers, where the decision preference matrix uses hesitant fuzzy binary semantics as the form of expressing decision opinions;

[0162] A clustering module, configured to cluster all decision-makers into several clusters according to the decision preference matrix and the social network relationship graph, and obtain the initial cluster decision preference matrices of each cluster;

[0163] A consensus module, configured to obtain the group decision preference matrix under the condition of reaching a consensus according to the initial cluster decision preference matrices of each cluster;

[0164] A selection module, configured to obtain the final selected large-group satellite emergency plan according to the group decision preference matrix.

[0165] An embodiment of the present invention provides a storage medium, which stores a computer program for making decisions on large-group satellite emergency plans in a social network environment. Among them, the computer program enables a computer to execute the decision-making method for large-group satellite emergency plans in a social network environment as described above.

[0166] An embodiment of the present invention further provides an electronic device, including:

[0167] One or more processors;

[0168] A memory; and

[0169] One or more programs, where the one or more programs are stored in the memory and are configured to be executed by the one or more processors. The programs include the decision-making method for large-group satellite emergency plans in a social network environment as described above.

[0170] It can be understood that the decision-making system, storage medium, and electronic device for large-group satellite emergency plans in a social network environment provided by the embodiments of the present invention correspond to the decision-making method for large-group satellite emergency plans in a social network environment provided by the embodiments of the present invention. For the explanation, examples, beneficial effects, etc. of the relevant content, reference can be made to the corresponding parts in the decision-making method for large-group satellite emergency plans in a social network environment based on blockchain, which will not be elaborated here.

[0171] In summary, compared with the prior art, the following beneficial effects are achieved:

[0172] 1. In the clustering process of the embodiment of the present invention, the trust degree problem among decision-makers (i.e., considering the trust degree between nodes) is considered, avoiding defaulting the weighted factor between nodes to 1 for a directed graph, which plays a great role in the accuracy of the clustering result of the large satellite emergency plan.

[0173] 2. In the context of the large - scale satellite emergency plan of the embodiments of the present invention, by combining the SNA method with the Louvain algorithm which has a fast clustering speed and high efficiency, the inherent connections among multiple decision - makers (DMs) are embedded into the clustering process. On the one hand, the accuracy and timeliness of the clustering results are increased, and on the other hand, the clustering complexity is reduced.

[0174] 3. The embodiments of the present invention utilize the MULITMOORA method to improve the accuracy and robustness of the solution selection results.

[0175] It should be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0176] The above - mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A decision-making method for large-group satellite emergency plans in a social network environment, characterized in that, Including: S1. Obtain the decision preference matrices of multiple satellite emergency decision-makers and the social network relationship graph among decision-makers; The process of obtaining the decision preference matrix includes: Obtain the decision opinions provided by each decision-maker for the attributes of the satellite emergency decision-making plan; the attributes are task completion benefits, plan performance, or resource utilization rate, where the task completion benefits include the completion rate or completion benefits of the emergency task, the plan performance focuses on the perturbation of the original observation plan, and the resource utilization rate is the conflict degree of the observation time window or the satellite utilization rate; Use the hesitant fuzzy binary semantic set as the decision opinion expression form to obtain the decision preference matrix; S2. According to the decision preference matrix and the social network relationship graph, cluster all decision-makers into several clusters and obtain the initial cluster decision preference matrices of each cluster; S3. According to the initial cluster decision preference matrices of each cluster, obtain the group decision preference matrix under the consensus; S4. According to the group decision preference matrix, obtain the finally selected large-group satellite emergency plan; In S2, the Louvain clustering algorithm based on modularity is used for clustering; specifically including: S21. Regard each node of the social network relationship graph as a cluster, and then merge the neighbor nodes of the cluster into the same cluster to obtain multiple clusters; S22. According to the trust matrix of the social network relationship graph and the similarity between any two decision preference matrices, obtain the weighted factors between different decision-makers in the social network relationship graph; according to the weighted factors between different decision-makers, obtain the modularity of each cluster and the corresponding modularity change amount; S23. Repeat the above steps S21 - S22 until the overall modularity no longer changes, and finally obtain several clusters; S24. According to the final clustering result, obtain the initial cluster decision preference matrix of each cluster; S22 specifically includes: S221. Represent the trust function value using a tuple of type λ = (t, d), where t, d ∈ [0, 1]. The first component t is a degree of trust, and the second component d is a degree of distrust. Define the trust matrix as representing the decision maker the degree of trust between representing the decision maker the degree of distrust between, then the trust score between any two decision makers is expressed as S222. Obtain the weighting factors between different decision-makers in the social network relationship graph by using the following formula Among them, represents the similarity between any two of the decision preference matrices; They are the decision preference matrices of any two decision-makers respectively, that is, the decision-making information of m large-group satellite emergency plans for n attribute indicators; Among them, are respectively the granularities of the corresponding hesitant fuzzy binary semantic sets; representation and the hesitant fuzzy binary semantic set Hausdorff distance between; and It consists of several hesitant fuzzy binary semantic sets, and the quantity is determined by the scheduling opinion; is a linguistic label, and its value range is S = {s0, s1, …, s g}; represents the symbol conversion value and its range is [-0.5, 0.5), and g is the cardinality of S; S223. According to the weighted factors between different decision-makers, obtain the modularity Q0 of each cluster; where M represents the sum of the weights of all edges in the social network relationship diagram; represents the decision maker is the weighting factor of the edge between, and when the network is not a weighted graph, the weight of each edge is 1; respectively represent the sum of the weighting factors of all edges connected to node l1 or l2; respectively represent the clusters to which they belong. If belong to the same cluster C, then otherwise S224. And obtain the corresponding modularity change amount △Q0; Among them, ∑in represents the sum of the weighted factors of all edges in cluster C, and ∑tot represents the sum of the weighted factors of the edges connected to the points in cluster C. It represents the sum of the weighted factors of the edges where node l1 is connected to the points in cluster C.

2. The decision-making method for large-group satellite emergency plans in a social network environment according to claim 1, wherein, S24 specifically includes: Based on calculate the initial decision preference matrix for each cluster; where, assume that the decision-makers are clustered into K clusters, and the k-th cluster is denoted as C k ,N k represents the number of members in the cluster.

3. The decision-making method for large-group satellite emergency plans in a social network environment according to claim 2, wherein, S3 specifically includes: S31. Calculate the consensus degree GCL of each current cluster decision preference matrix t ; S32. Compare the current consensus level GCL t with a preset threshold as follows: If then obtain the group decision-making preference matrix where represents the cluster weight Among them, σ represents a permutation such that TS σ(γ) is the γ-th maximum value in the set and Q is the basic unit interval singleton membership function of the fuzzy linguistic quantifier, implemented during the aggregation process, Q: [0, 1] → [0, 1], such that Q(0) = 0, Q(1) = 1, and if a > b then Q(a) > Q(b); Among them, is e γ (γ = 1,..., N e ) and the number of direct trust relationships with ; If Go to S33; S33. Identify the position of the cluster decision preference matrix G a that needs to be adjusted; S34. Obtain G based on the weights of the K clusters and the expectations of the decision preference matrix that do not need to be adjusted a The adjusted cluster decision preference matrix (G k ′) m×n , and transfer to S31; Where δ is the matrix adjustment parameter, and the value range is [0, 1]; Use the formula to calculate the weights of K clusters, where represents the weights of the decision-makers, and 4. The method for making a decision on a large-group satellite emergency plan in a social network environment according to any one of claims 1 to 3, characterized in that In S4, MULTIMOORA is used to obtain the finally selected large-group satellite emergency plan.

5. The decision-making method for large-group satellite emergency plans in a social network environment according to claim 3, characterized in that S4 specifically includes: S41. The group decision-making preference matrix \(R\) described by the standard c =(x ij ) m×n , and obtain the dimensionless matrix S42. Calculate the utility value based on the RS model of the following arithmetic integral i = 1, 2, …, m, and take to obtain the first ranking of the large-group satellite emergency plan in descending order; S43. Calculate the utility value based on the following RP model i = 1, 2, …, m, and take to obtain the second ranking of the large-group satellite emergency plan in ascending order; Determine the maximum value r of each attribute index j The maximum distance between relevant alternative options; Among them S44. Calculate the utility value based on the geometric integral formula on the following FMF model i = 1, 2, …, m, and take to obtain the third ranking of the large-group satellite emergency plan in descending order; S45. Use the dominance theory method to fuse the above three sorting results to obtain the finally selected large-group satellite emergency plan.

6. A decision-making system for large-group satellite emergency plans in a social network environment, characterized in that, For executing the large-group satellite emergency plan decision method in the social network environment as described in claim 1, including: An acquisition module for obtaining the decision preference matrices of multiple satellite emergency decision-makers and the social network relationship graph among decision-makers, and the decision preference matrix uses the hesitant fuzzy binary semantic set as the decision opinion expression form; A clustering module for clustering all decision-makers into several clusters according to the decision preference matrix and the social network relationship graph, and obtaining the initial cluster decision preference matrices of each cluster; A consensus module, configured to obtain a group decision preference matrix based on the consensus, according to each of the initial cluster decision preference matrices; A selection module, configured to obtain a final selected large-group satellite emergency plan according to the group decision preference matrix.

7. A storage medium, characterized in that, It stores a computer program for making decisions on large-group satellite emergency plans in a social network environment. Among them, the computer program enables a computer to execute the method for making decisions on large-group satellite emergency plans in a social network environment according to any one of claims 1 to 5.

8. An electronic device, characterized in that, It includes: One or more processors; A memory; And one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors. The programs include those for executing the method for making decisions on large-group satellite emergency plans in a social network environment according to any one of claims 1 to 5.

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