Emergency plan decision-making method and system for large-scale satellite swarms considering non-cooperative behavior

Abstract

The present invention provides a large-group satellite emergency plan decision-making method, system, storage medium and electronic device considering non-cooperative behavior, and relates to the technical field of satellite emergency plan decision-making. In the present invention, the decision preference matrix of multiple satellite emergency decision makers is obtained, and the decision preference matrix adopts a hesitant fuzzy binary semantic set as the expression form of decision opinions; according to the decision preference matrix, all decision makers are clustered into several clusters, and each initial cluster decision preference matrix and the initial group decision preference matrix are obtained; based on the preset embedded feedback adjustment mechanism, the cluster consensus level and the group consensus level are updated to obtain the group decision preference matrix based on the consensus; according to the group decision preference matrix, the final selected large-group satellite emergency plan is obtained. An embedded feedback adjustment mechanism is designed to serve the management of non-cooperative behavior, so that the opinions of individual decision makers and clusters are closer to the group, thereby improving the decision-making level.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite emergency plan decision-making, and in particular to a large group of satellite emergency plan decision-making method, system, storage medium and electronic equipment taking into account non-cooperative behavior. Background Art

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

[0003] At present, the LGDM problem has attracted more and more 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 decision makers (DMs) from different professional backgrounds to participate 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, generally requiring multiple DMs from different positions to participate in the decision-making process, (2) it must be guaranteed that the decision is made in a short time, (3) it is often difficult to reach a consensus among DMs, and (4) a wrong decision or too slow may lead to catastrophic losses in emergency missions. As the LGDM problem has attracted more and more attention from scholars, some new decision-making models and methods have been proposed one after another. These models and methods all contain three main steps: large group clustering, consensus reaching process (CRP) and plan selection process. Traditional consensus is called "hard consensus". The scalar of consensus measurement has only two indicators: 0 (no consensus) or 1 (consensus is achieved). In actual decision-making, it is very difficult and impractical to adjust the consensus from 0 to 1.

[0004] However, most traditional consensus models focus on traditional decision-making problems involving a small number of DMs (such as 3-5 people) without time constraints, which may not be applicable to large group decision-making in emergency situations. Actual large group decision-making problems cannot only consider mathematical models. The psychological behavior of decision makers and their actual performance when making decisions are also worthy of attention and consideration. This is because there are many non-cooperative behaviors in the actual decision-making process. Considering the possible non-cooperative behaviors in the decision-making process is conducive to ensuring the quality of decision-making. In the LGSESDM problem, since decision makers usually come from different satellite scheduling positions and have different focuses, they are more likely to adopt non-cooperative behaviors to promote their personal interests, so that consensus cannot be reached. When some DMs refuse to make adjustments, the CRP process will stagnate. In addition, LGSESDM must achieve relatively satisfactory results in a short period of time. Therefore, managing non-cooperative behaviors is crucial to ensuring the timeliness and quality of the CRP process. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a large-scale satellite emergency plan decision-making method, system, storage medium and electronic device that take non-cooperative behavior into consideration, which solves the technical problem that the decision-making process ignores non-cooperative behavior, resulting in low accuracy in the consensus reaching process.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A decision-making method for emergency plans of large-scale satellite groups considering non-cooperative behaviors, including:

[0010] S1. Obtaining a decision preference matrix of multiple satellite emergency decision makers, wherein the decision preference matrix uses a hesitant fuzzy binary semantic set as a decision opinion expression form;

[0011] S2. According to the decision preference matrix, cluster all decision makers into several clusters, and obtain each initial cluster decision preference matrix and an initial group decision preference matrix;

[0012] S3. According to each of the initial cluster decision preference matrices and the initial group decision preference matrix, the cluster consensus level and the group consensus level are updated based on a preset embedded feedback adjustment mechanism to obtain a group decision preference matrix based on consensus;

[0013] S4. Obtaining a large-group satellite emergency plan that is finally selected according to the group decision preference matrix.

[0014] Preferably, in S2:

[0015] based on Calculate the initial decision preference matrix for each cluster; where it is assumed that the decision maker Cluster into K clusters, the kth cluster is denoted as C k , N k Indicates the number of members in the cluster; is the decision preference matrix of any decision maker, i.e., the decision information of m large-group satellite emergency plans for n attribute indicators; It consists of several hesitant fuzzy binary semantic sets, the number of which is determined by the scheduling opinions; is a language tag, whose value is S = {s0, s1, ..., S g}; represents the sign conversion value and has a range of [-0.5, 0.5), g is the potential of S;

[0016] based on Calculate the cluster weights, where represents the weight of the decision maker,

[0017] based on The initial group decision preference matrix is ​​calculated.

[0018] Preferably, S3 specifically includes:

[0019] S31. Let t = 0, given the group consensus threshold

[0020] S32. Set t=t+1 and calculate the current cluster consensus level

[0021]

[0022]

[0023] Among them, SIM(G k(t) , G f(t) )=1-D e (G k(t) , G f(t) ) is G k(t) and G f(t) They correspond to the hesitant fuzzy binary semantic set Hausfdorff distance between clusters;

[0024] Calculate the current group consensus level GCL t :

[0025]

[0026] judge and Then go to S35; otherwise, go to S33;

[0027] S33. Based on the current cluster consensus level and the previous cluster consensus level Get the cooperation coefficient used to measure the degree of change in the consensus level of a single cluster

[0028]

[0029] in, Assign values ​​according to actual conditions;

[0030] Determine the cooperation coefficient threshold

[0031]

[0032] Among them, ρ t Is the penalty coefficient, used to determine the cooperation coefficient How much smaller will it fall into the penalty area, satisfying 0≤ρ t ≤1;

[0033] S34, General and as well as GCL t and The size comparison result is obtained, and the embedded feedback adjustment mechanism is introduced to adjust the cluster weight of the non-cooperative cluster and / or the decision preference matrix of its internal decision makers, update the current cluster decision preference matrix, and proceed to S32;

[0034] S35, let t * = t, output the final cluster decision preference matrix Get the final group decision matrix R c , and serves as the group decision preference matrix based on the consensus.

[0035] Preferably, the embedded feedback adjustment mechanism in S3 includes opinion modification, which specifically includes two situations:

[0036] The first one, if The clusters that indicate the need for modification cooperate with the modification decision suggestions, and only the subjective coefficient adjustment is adopted;

[0037]

[0038] Among them, G k(t) Indicates the cluster C that needs to be modified after t iterations k The decision preference matrix, is the subjective correction factor, determined by the cluster;

[0039] The second type, if It indicates that the cluster that needs to be modified does not cooperate with the modification decision suggestion, that is, there is an uncooperative cluster. The objective coefficient and subjective coefficient are used to modify the opinion, including:

[0040] First, by solving the minimum similarity between the opinions of each decision maker in the cluster to be modified and all other decision makers, the location of the decision preference matrix with the largest opinion difference in the cluster is determined.

[0041]

[0042] in, l1≠l2 represents any two decision makers The similarity between the corresponding decision preference matrices;

[0043] Secondly, objectively adjust the decision preference matrix determined above.

[0044]

[0045] in, is the objective adjustment factor, and get;

[0046] Through objective modification, the cluster

[0047] Finally, if subjective adjustments are required,

[0048]

[0049] Preferably, the embedded feedback adjustment mechanism in S3 further includes weight penalty, specifically including:

[0050]

[0051] in, Represents cluster C k The cluster consensus level in the tth iteration, GCL t Represents cluster C k The group consensus level in the tth iteration of Represents cluster C k The cluster weights at iteration t.

[0052] Preferably, in S34, adjusting the cluster weight of the non-cooperative cluster and / or the decision preference matrix of its internal decision makers specifically includes:

[0053] Area 1 is the first cooperation area: if No adjustments are made;

[0054] Area 2 is the subjective adjustment area: if Only subjective coefficient adjustment is used;

[0055] Region 3 is the semi-complete penalty region: if Only subjective coefficient adjustment is used;

[0056] Area 4 is the full penalty area: if Fully penalize non-cooperative clusters, reduce their cluster weights, and use objective coefficient adjustments;

[0057] Area 5 is the objective adjustment area: if Only objective coefficient adjustments are used;

[0058] Area 6 is the second cooperation area: if No adjustments are made.

[0059] Preferably, the S4 uses MULTIMOORA to obtain the final selected large group satellite emergency plan, which specifically includes:

[0060] S41, standard group decision preference matrix R c =(x ij ) m×n , and obtain the dimensionless matrix

[0061]

[0062] S42. Calculate the utility value based on the RS model of the following arithmetic integral i=1,2,...,m,with Get the first ranking of the large group satellite emergency plan in descending order;

[0063]

[0064] S43. Calculate the utility value based on the following RP model i=1,2,...,m,with The second sorting of the emergency plans of large groups of satellites is obtained in ascending order;

[0065] Determine the maximum value r of each attribute index j the maximum distance to the relevant alternatives;

[0066]

[0067] in

[0068] S44. Calculate the utility value based on the geometric integral formula based on the following FMF model i=1,2,...,m,with The third ranking of the large group satellite emergency plan is obtained in descending order;

[0069]

[0070] S45. Use the advantage theory method to integrate the above three sorting results to obtain the final selected large-group satellite emergency plan.

[0071] A decision-making method for emergency plans of large-scale satellite groups considering non-cooperative behaviors, including:

[0072] An acquisition module is used to acquire a decision preference matrix of multiple satellite emergency decision makers, wherein the decision preference matrix uses a hesitant fuzzy binary semantic set as a decision opinion expression form;

[0073] A clustering module, used to cluster all decision makers into a number of clusters according to the decision preference matrix, and obtain each initial cluster decision preference matrix and an initial group decision preference matrix;

[0074] A consensus module, used to update the cluster consensus level and the group consensus level based on the preset embedded feedback adjustment mechanism according to each of the initial cluster decision preference matrices and the initial group decision preference matrix, and obtain the group decision preference matrix based on the consensus;

[0075] The selection module is used to obtain the final selected large-group satellite emergency plan according to the group decision preference matrix.

[0076] A storage medium stores a computer program for emergency plan decision-making for a large group of satellites, wherein the computer program enables a computer to execute the emergency plan decision-making method for a large group of satellites as described above.

[0077] An electronic device, comprising:

[0078] one or more processors;

[0079] storage; and

[0080] 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 including instructions for executing the large group satellite emergency plan decision method as described above.

[0081] (III) Beneficial effects

[0082] The present invention provides a large group of satellite emergency plan decision-making method, system, storage medium and electronic device considering non-cooperative behavior. Compared with the prior art, it has the following beneficial effects:

[0083] In the present invention, the decision preference matrix of multiple satellite emergency decision makers is obtained, and the decision preference matrix adopts hesitant fuzzy binary semantic set as the expression form of decision opinions; according to the decision preference matrix, all decision makers are clustered into several clusters to obtain each initial cluster decision preference matrix and the initial group decision preference matrix; according to each initial cluster decision preference matrix and the initial group decision preference matrix, the cluster consensus level and the group consensus level are updated based on the preset embedded feedback adjustment mechanism to obtain the group decision preference matrix on the basis of consensus; according to the group decision preference matrix, the final selected large group satellite emergency plan is obtained. The embedded feedback adjustment mechanism is designed to serve the management of non-cooperative behavior, so that the opinions of individual decision makers and clusters are close to the group, and the decision level is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 A flowchart of a large group of satellite emergency plan decision-making method considering non-cooperative behavior provided by an embodiment of the present invention;

[0086] Figure 2 A schematic diagram of cluster non-cooperative behavior area division provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] The embodiments of the present application provide a large-group satellite emergency plan decision-making method, system, storage medium and electronic device that take non-cooperative behavior into consideration, thereby solving the technical problem that the decision-making process ignores non-cooperative behavior, resulting in low accuracy in the consensus-reaching process.

[0089] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0090] In an embodiment of the present invention, a decision preference matrix of multiple satellite emergency decision makers is obtained, and the decision preference matrix adopts a hesitant fuzzy binary semantic set as a decision opinion expression form; according to the decision preference matrix, all decision makers are clustered into a number of clusters to obtain each initial cluster decision preference matrix and an initial group decision preference matrix; according to each initial cluster decision preference matrix and the initial group decision preference matrix, the cluster consensus level and the group consensus level are updated based on a preset embedded feedback adjustment mechanism to obtain a group decision preference matrix based on consensus; according to the group decision preference matrix, the final selected large group satellite emergency plan is obtained. An embedded feedback adjustment mechanism is designed to serve the management of non-cooperative behavior, so that the opinions of individual decision makers and clusters are closer to the group, thereby improving the decision-making level.

[0091] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0092] Example:

[0093] In a first aspect, an embodiment of the present invention provides a large group of satellite emergency plan decision-making method considering non-cooperative behavior, including:

[0094] S1. Obtaining a decision preference matrix of multiple satellite emergency decision makers, wherein the decision preference matrix uses a hesitant fuzzy binary semantic set as a decision opinion expression form;

[0095] S2. According to the decision preference matrix, cluster all decision makers into several clusters, and obtain each initial cluster decision preference matrix and an initial group decision preference matrix;

[0096] S3. According to each of the initial cluster decision preference matrices and the initial group decision preference matrix, the cluster consensus level and the group consensus level are updated based on a preset embedded feedback adjustment mechanism to obtain a group decision preference matrix based on consensus;

[0097] S4. Obtaining a large-group satellite emergency plan that is finally selected according to the group decision preference matrix.

[0098] The embodiment of the present invention designs an embedded feedback adjustment mechanism to serve the management of non-cooperative behavior, so that the opinions of individual decision makers and clusters are closer to the group, thereby improving the decision-making level.

[0099] The following will introduce each step of the above solution in detail with specific content:

[0100] In step S1, a decision preference matrix of multiple satellite emergency decision makers is obtained, wherein the decision preference matrix uses a hesitant fuzzy binary semantic set as a decision opinion expression form.

[0101] Each expert decision maker DM provides his / her decision opinion according to the attributes of the satellite emergency decision plan, and constructs a decision preference matrix with hesitant fuzzy binary semantic sets (HF2TLSs).

[0102] The selected attributes can be selected according to actual conditions, such as task completion efficiency, including the completion rate and completion efficiency of emergency tasks; scheme performance, focusing on the disturbance to the original observation scheme; resource utilization, such as the conflict degree of the observation time window, satellite utilization, etc.

[0103] In step S2, all decision makers are clustered into several clusters according to the decision preference matrix, and each initial cluster decision preference matrix and the initial group decision preference matrix are obtained.

[0104] It should be noted that the embodiment of the present invention does not impose any restriction on the type of clustering method used. It can be any one or a combination of several of the partitioning clustering method, density-based square clustering method or hierarchical clustering method in the prior art. Those skilled in the art can make a specific selection according to actual needs.

[0105] Specifically, in S2:

[0106] based on Calculate the initial decision preference matrix for each cluster; where it is assumed that the decision maker Cluster into K clusters, the kth cluster is denoted as C k , N k Indicates the number of members in the cluster; is the decision preference matrix of any decision maker, i.e., the decision information of m large-group satellite emergency plans for n attribute indicators; It consists of several hesitant fuzzy binary semantic sets, the number of which is determined by the scheduling opinions; is a language tag, whose value is S = {s0, s1, ..., s g}; represents the sign conversion value and has a range of [-0.5, 0.5), g is the potential of S;

[0107] based on Calculate the cluster weights, where represents the weight of the decision maker, and

[0108] based on The initial group decision preference matrix is ​​calculated.

[0109] In step S3, according to each of the initial cluster decision preference matrices and the initial group decision preference matrix, the cluster consensus level and the group consensus level are updated based on a preset embedded feedback adjustment mechanism to obtain a group decision preference matrix based on consensus; specifically, the following steps are included:

[0110] S31. Let t = 0, given the group consensus threshold

[0111] S32. Set t=t+1 and calculate the current cluster consensus level

[0112]

[0113]

[0114] Among them, SIM(G k(t) , G f(t) )=1-D e (G k(t) , G f(t) ) is G k(t) and G f(t) They correspond to the hesitant fuzzy binary semantic set Hausfdorff distance between clusters;

[0115] Calculate the current group consensus level GCL t :

[0116]

[0117] judge and Then go to S35; otherwise, go to S33;

[0118] S33. Based on the current cluster consensus level and the previous cluster consensus level Get the cooperation coefficient used to measure the degree of change in the consensus level of a single cluster

[0119]

[0120] in, Assign values ​​according to actual conditions;

[0121] Determine the cooperation coefficient threshold

[0122]

[0123] Among them, ρ t Is the penalty coefficient, used to determine the cooperation coefficient How much smaller will it fall into the penalty area, satisfying 0≤ρ t ≤1;

[0124] S34, General and as well as GCL t and The size comparison result is obtained, and the embedded feedback adjustment mechanism is introduced to adjust the cluster weight of the non-cooperative cluster and / or the decision preference matrix of its internal decision makers, update the current cluster decision preference matrix, and proceed to S32;

[0125] In particular, the embedded feedback regulation mechanism in S34 includes:

[0126] (1) Opinion modification: There are two specific situations:

[0127] The first one, if The clusters that indicate the need for modification cooperate with the modification decision suggestions, and only the subjective coefficient adjustment is adopted;

[0128]

[0129] Among them, G k(t) Indicates the cluster C that needs to be modified after t iterations k The decision preference matrix, is the subjective correction factor, determined by the cluster;

[0130] The second type, if It indicates that the cluster that needs to be modified does not cooperate with the modification decision suggestion, that is, there is an uncooperative cluster. The objective coefficient and subjective coefficient are used to modify the opinion, including:

[0131] First, by solving the minimum similarity between the opinions of each decision maker in the cluster to be modified and all other decision makers, the location of the decision preference matrix with the largest opinion difference in the cluster is determined.

[0132]

[0133] in, l1≠l2 represents any two decision makers The similarity between the corresponding decision preference matrices;

[0134] Secondly, objectively adjust the decision preference matrix determined above.

[0135]

[0136] in, is the objective adjustment factor, and get;

[0137] Through objective modification, the cluster

[0138] It is not difficult to see that the first two steps represent a two-stage objective opinion modification process; the strategy is modified objectively, and the cluster

[0139] Finally, if subjective adjustments are required,

[0140]

[0141] It is not difficult to understand that subjective adjustment tends to cluster decision preference matrix, while objective adjustment focuses on decision preference matrix of individual decision makers. In this adjustment mode, with the three-layer structure of "decision maker-cluster-group" as the criterion, first make the individual decision matrix close to the cluster decision matrix, then make the cluster decision matrix close to the group decision matrix, and finally make the opinions of individual decision makers and clusters close to the group, so as to improve the decision-making level.

[0142] (2) Weight penalty,

[0143]

[0144] in, Represents cluster C k The cluster consensus level in the tth iteration, GCL t Represents cluster C k The group consensus level in the tth iteration of Represents cluster C k The cluster weights at iteration t.

[0145] In the S34, if Figure 2 As shown, the cluster weights of the non-cooperative clusters and / or the decision preference matrix of their internal decision makers are adjusted, including:

[0146] Area 1 is the first cooperation area: if Not only is the level of individual decision maker consensus higher than the group consensus, but the cluster cooperation coefficient is also above the critical point. This indicates that the cluster has made adjustments even though it does not need to do so. Therefore, no further adjustments are needed to respect the cluster's own opinions.

[0147] Area 2 is the subjective adjustment area: if The individual consensus level of the decision maker is higher than the group consensus level but does not reach the consensus threshold, and the cluster cooperation coefficient is also higher than the critical point. This shows that although the cluster has not reached the threshold, it is willing to adjust its opinions. Therefore, it is only necessary to use the subjective coefficient to adjust the opinions to make them closer to the group opinions.

[0148] Region 3 is the semi-complete penalty region: if The cluster consensus level is lower than the group consensus level, while the cluster cooperation coefficient is higher than the critical point. This shows that although the cluster is willing to adjust its opinions, its opinions are too different from those of the group, resulting in the failure to meet the consensus threshold condition. Therefore, its expression level in the group, i.e., its weight, is reduced. At the same time, only the subjective coefficient needs to be used to adjust opinions, which is conducive to consensus.

[0149] Area 4 is the full penalty area: if The cluster consensus level is lower than the group consensus level, and the cluster cooperation coefficient is also lower than the critical point. This indicates that the cluster does not meet the consensus threshold conditions, but is unwilling to adjust its opinions too much, and makes uncooperative behaviors that are not conducive to consensus. It should be completely punished, its weight should be reduced, and its opinions should be objectively modified in two stages using the above objective coefficients.

[0150] Area 5 is the objective adjustment area: if The cluster consensus level is higher than the group consensus level but lower than the threshold, while the cluster cooperation coefficient is lower than the critical point. However, its weight is not reduced because after the consensus iteration, the cluster consensus level still exceeds the group consensus level. Therefore, the cluster has made a small change, which does not mean that it is not cooperative enough. This shows that its opinions and the given adjustment coefficients still have a positive effect on consensus. In other words, there is no need to punish its weight. It is only necessary to use the above objective coefficients to make two-stage objective modifications to its opinions to make them closer to the group decision-making opinions.

[0151] Area 6 is the second cooperation area: if No adjustments are made. The cluster consensus level is higher than the group consensus threshold, while the cluster cooperation coefficient is lower than the critical point. Similar to region 5, this is because the consensus threshold has been exceeded and no excessive changes are needed. It is not judged that its cooperation is low because it has exceeded the threshold. On the contrary, it is considered that it has maintained a high degree of cooperation and respected the opinions of the group. In return, the treatment of the cluster is the same as region 1, respecting the cluster itself.

[0152] In most previous studies, there are few studies on non-cooperative behaviors, and the management methods of non-cooperative behaviors are single, especially for large-group emergency decision-making. No scholar has studied this so far. More importantly, almost all studies are based on the premise that decision-making experts are independent of each other and do not influence each other. However, with the development of social networks, this premise has become difficult to meet. The embodiment of the present invention analyzes the degree of non-cooperation of the cluster in detail, divides the cluster non-cooperative behavior into six categories according to the cooperation coefficient and the cluster consensus level, and proposes different preference adjustment mechanisms to construct a new consensus model, so as to better manage the non-cooperative behavior of one or more clusters in LGSESDM, so as to optimize the consensus-reaching process of large-group satellite emergency decision-making.

[0153] S35, let t * = t, output the final cluster decision preference matrix Get the final group decision matrix R c , and serves as the group decision preference matrix based on the consensus.

[0154] In step S4, the large-group satellite emergency plan that is finally selected is obtained according to the group decision preference matrix.

[0155] In this step, MULTIMOORA is used to obtain the final selected large group satellite emergency solution. The MOORA (Multiobjective Optimization By Ratio Analysis) method was originally introduced by Brauers and Zavadskas and further enhanced by adding the full multiplication method (FMF) to generate the multiplication MOORA (MULTIMOORA) method.

[0156] MULTIMOORA is an emerging multi-attribute decision-making method. Compared with other methods, this method is simple, effective, and easy to rank and optimize the options. MULTIMOORA is based on three subordinate methods, namely the ratio system (RS), reference point (RP), and full multiplication method (FMF). It also uses advantage theory to calculate the final ranking, so the MULTIMOORA method is used in the final selection process of satellite emergency options to improve the accuracy of the results.

[0157] The S4 specifically includes:

[0158] S41, standard group decision preference matrix R c =(x ij ) m×n , and obtain the dimensionless matrix

[0159]

[0160] S42. Calculate the utility value based on the RS model of the following arithmetic integral i=1,2,...,m,with Get the first ranking of the large group satellite emergency plan in descending order;

[0161]

[0162] S43. Calculate the utility value based on the following RP model i=1,2,...,m,with The second sorting of the emergency plans of large groups of satellites is obtained in ascending order;

[0163] Determine the maximum value r of each attribute index j the maximum distance to the relevant alternatives;

[0164]

[0165] in

[0166] S44. Calculate the utility value based on the geometric integral formula based on the following FMF model i=1,2,...,m,with The third ranking of the large group satellite emergency plan is obtained in descending order;

[0167]

[0168] S45. Use the advantage theory method to integrate the above three sorting results to obtain the final selected large-group satellite emergency plan.

[0169] An embodiment of the present invention provides a large group of satellite emergency plan decision-making method considering non-cooperative behavior, including:

[0170] An acquisition module is used to acquire a decision preference matrix of multiple satellite emergency decision makers, wherein the decision preference matrix uses a hesitant fuzzy binary semantic set as a decision opinion expression form;

[0171] A clustering module, used to cluster all decision makers into a number of clusters according to the decision preference matrix, and obtain each initial cluster decision preference matrix and an initial group decision preference matrix;

[0172] A consensus module, used to update the cluster consensus level and the group consensus level based on the preset embedded feedback adjustment mechanism according to each of the initial cluster decision preference matrices and the initial group decision preference matrix, and obtain the group decision preference matrix based on the consensus;

[0173] The selection module is used to obtain the final selected large-group satellite emergency plan according to the group decision preference matrix.

[0174] An embodiment of the present invention provides a storage medium storing a computer program for making emergency plans for large-group satellites, wherein the computer program enables a computer to execute the emergency plan decision method for large-group satellites as described above.

[0175] An embodiment of the present invention further provides an electronic device, including:

[0176] one or more processors;

[0177] storage; and

[0178] 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 including instructions for executing the large group satellite emergency plan decision method as described above.

[0179] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0180] 1. The embodiment of the present invention designs an embedded feedback adjustment mechanism to serve the management of non-cooperative behavior, so that the opinions of individual decision makers and clusters are closer to the group, thereby improving the decision-making level.

[0181] 2. The embodiment of the present invention analyzes the degree of non-cooperation of the cluster in detail, divides the non-cooperative behavior of the cluster into six categories according to the cooperation coefficient and the cluster consensus level, and proposes different preference adjustment mechanisms to construct a new consensus model to better manage the non-cooperative behavior of one or more clusters in LGSESDM, so as to optimize the consensus-reaching process of large-group satellite emergency decision-making.

[0182] 3. The embodiment of the present invention uses the MULITMOORA method to improve the accuracy and robustness of the solution selection result.

[0183] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0184] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A large-scale satellite emergency plan decision-making method considering non-cooperative behavior, characterized in that: include: S1. Obtaining a decision preference matrix of multiple satellite emergency decision makers, wherein the decision preference matrix uses a hesitant fuzzy binary semantic set as a decision opinion expression form; S2. According to the decision preference matrix, cluster all decision makers into several clusters, and obtain each initial cluster decision preference matrix and an initial group decision preference matrix; S3. According to each of the initial cluster decision preference matrices and the initial group decision preference matrix, the cluster consensus level and the group consensus level are updated based on a preset embedded feedback adjustment mechanism to obtain a group decision preference matrix based on consensus; S4. Obtaining a finally selected large group satellite emergency plan according to the group decision preference matrix; In S2: based on Calculate the initial decision preference matrix for each cluster; where it is assumed that the decision maker Cluster into K clusters, the kth cluster is denoted as C k ,N k Indicates the number of members in the cluster; is the decision preference matrix of any decision maker, i.e., the decision information of m large-group satellite emergency plans for n attribute indicators; It consists of several hesitant fuzzy binary semantic sets, the number of which is determined by the scheduling opinions; is a language tag, whose value is S = {s0,s1,…,s g }; represents the sign conversion value and the range is [-0.5, 0.5), g is the potential of S; based on Calculate the cluster weights, where represents the weight of the decision maker, and based on Calculating the initial cluster decision preference matrix; S3 specifically includes: S31. Let t = 0, given the group consensus threshold S32. Set t=t+1 and calculate the current cluster consensus level Among them, SIM(G k(t) ,G f(t) )=1-D e (G k(t) ,G f(t) ) is G k(t) and G f(t) They correspond to the hesitant fuzzy binary semantic set Hausfdorff distance between clusters; Calculate the current group consensus level GCL t : judge and Then go to S35; otherwise, go to S33; S33. Based on the current cluster consensus level and the previous cluster consensus level Get the cooperation coefficient used to measure the degree of change in the consensus level of a single cluster in, Assign values ​​according to actual conditions; Determine the cooperation coefficient threshold Among them, ρ t Is the penalty coefficient, used to determine the cooperation coefficient How much smaller will it fall into the penalty area, satisfying 0≤ρ t ≤1; S34, General and as well as GCL t and The size comparison result is obtained, and the embedded feedback adjustment mechanism is introduced to adjust the cluster weight of the non-cooperative cluster and / or the decision preference matrix of its internal decision makers, update the current cluster decision preference matrix, and proceed to S32; S35, let t * = t, output the final cluster decision preference matrix Get the final group decision matrix R c , and serves as the group decision preference matrix based on the consensus; The embedded feedback adjustment mechanism in S3 includes opinion modification, which specifically includes two cases: The first one, if The clusters that indicate the need for modification cooperate with the modification decision suggestions, and only the subjective coefficient adjustment is adopted; Among them, G k(t) Indicates the cluster C that needs to be modified after t iterations k The decision preference matrix, is the subjective correction factor, determined by the cluster; The second type, if It indicates that the cluster that needs to be modified does not cooperate with the modification decision suggestion, that is, there is an uncooperative cluster. The objective coefficient and subjective coefficient are used to modify the opinion, including: First, by solving the minimum similarity between the opinions of each decision maker in the cluster to be modified and all other decision makers, the location of the decision preference matrix with the largest opinion difference in the cluster is determined. in, l1≠l2 represents any two decision makers The similarity between the corresponding decision preference matrices; Secondly, objectively adjust the decision preference matrix determined above. in, is the objective adjustment factor, and get; Through objective modification, the cluster Finally, if subjective adjustments are required, In S4, MULTIMOORA is used to obtain the final selected large group satellite emergency plan, which specifically includes: S41, standard group decision preference matrix R 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 And Get the first ranking of the large group satellite emergency plan in descending order; S43. Calculate the utility value based on the following RP model And The second sorting of the emergency plans of large groups of satellites is obtained in ascending order; Determine the maximum value r of each attribute index j the maximum distance to the relevant alternatives; in S44. Calculate the utility value based on the geometric integral formula based on the following FMF model And The third ranking of the large group satellite emergency plan is obtained in descending order; S45. Use the advantage theory method to integrate the above three sorting results to obtain the final selected large-group satellite emergency plan.

2. The large-group satellite emergency plan decision-making method as claimed in claim 1, characterized in that: The embedded feedback regulation mechanism in S3 also includes weight penalties, specifically including: in, Represents cluster C k The cluster consensus level in the tth iteration, GCL t Represents cluster C k The group consensus level in the tth iteration of Represents cluster C k The cluster weights at iteration t.

3. The large-group satellite emergency plan decision-making method as claimed in claim 2, characterized in that: In S34, adjusting the cluster weight of the non-cooperative cluster and / or the decision preference matrix of its internal decision makers specifically includes: Area 1 is the first cooperation area: if No adjustments are made; Area 2 is the subjective adjustment area: if Only subjective coefficient adjustment is used; Region 3 is the semi-complete penalty region: if Only subjective coefficient adjustment is used; Area 4 is the full penalty area: if Fully penalize non-cooperative clusters, reduce their cluster weights, and use objective coefficient adjustments; Area 5 is the objective adjustment area: if Only objective coefficient adjustments are used; Area 6 is the second cooperation area: if No adjustments are made.

4. A large-scale satellite emergency plan decision-making system considering non-cooperative behavior, characterized in that: The method for executing the large-group satellite emergency plan decision-making method as claimed in claim 1 comprises: An acquisition module is used to acquire a decision preference matrix of multiple satellite emergency decision makers, wherein the decision preference matrix uses a hesitant fuzzy binary semantic set as a decision opinion expression form; A clustering module, used to cluster all decision makers into a number of clusters according to the decision preference matrix, and obtain each initial cluster decision preference matrix and an initial group decision preference matrix; A consensus module, used to update the cluster consensus level and the group consensus level based on the preset embedded feedback adjustment mechanism according to each of the initial cluster decision preference matrices and the initial group decision preference matrix, and obtain the group decision preference matrix based on the consensus; The selection module is used to obtain the final selected large-group satellite emergency plan according to the group decision preference matrix.

5. A storage medium, characterized in that: The computer program for emergency plan decision-making of a large group of satellites is stored therein, wherein the computer program enables the computer to execute the emergency plan decision-making method for a large group of satellites as claimed in any one of claims 1 to 3.

6. An electronic device, characterized in that: include: one or more processors; Memory; as well as 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 including instructions for executing the large-group satellite emergency plan decision method according to any one of claims 1 to 3.

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