A method for analyzing near-Earth space game situation based on improved intelligent pig game theory

Abstract

The present invention relates to the field of operations research technology, and specifically to a near-Earth space game situation analysis method based on an improved smart pig game theory, which is used to solve the problem that the existing technology is not intuitive and does not fully consider the evaluation of the subject collaboration, resulting in inaccurate evaluation. The present invention includes: establishing a list of game subjects; establishing a sample set of near-Earth space historical events; classifying the strength of game subjects; classifying the degree of collaboration of game subjects; classifying the degree of collaboration of medium-dominant subjects; calculating the game risk of the target subject; and making game behavior decisions. The present invention determines the strong dominant subject, the medium-dominant subject, and the weak dominant subject by statistically analyzing the sample set; quickly determines the possible team-style confrontation or cooperation; evaluates the confrontation possibility of different types of subjects in the form of a matrix, introduces the concept of medium subjects, and identifies extensive subjects, that is, extensive subjects are more inclined to non-confrontation, which provides the target subject (itself) with the possibility of communication and cooperation.

Description

Technical Field

[0001] The present invention relates to the technical field of operations research, and in particular to a near-Earth space game situation analysis method based on an improved smart pig game theory. Background Art

[0002] Near-Earth space refers to the space region within the range of 1.015 to 6.6 Earth radii from the center of the Earth, that is, the spherical shell region of about 100 to 36,000 km (about 10 Earth radii) from the Earth's sea level. The environment in which a spacecraft flies in this range is called the near-Earth space flight environment, which is divided into the spacecraft body environment and the spacecraft near-Earth space environment. Nearspace refers to the airspace 20 to 100 kilometers from the ground, which has attracted widespread attention internationally due to its important development and application value.

[0003] Near-space technology is a high-tech intensive field that integrates multiple disciplines, involving cutting-edge technologies in many disciplines such as power, energy, materials, pneumatics, and control. With the advancement of science and technology, national defense boundaries and interest boundaries have expanded to any corner that human activities can reach. Near-space spacecraft have unique advantages such as high navigation speed, long airborne time, large payload capacity, and strong survivability, which determine that they have great value in multi-dimensional applications such as payload delivery, information transmission, and logistical support.

[0004] The prior art discloses the following related technologies for conducting operations research on spatial games:

[0005] The Chinese patent with publication number CN115759754A discloses a target threat assessment method for beyond visual range air combat simulation based on dynamic game variable weights; specifically disclosed: including: according to the threat assessment model of beyond visual range air combat and the situation information at the current moment, determining the threat value of each target for each indicator; the indicators include air combat situation, air combat intention, air combat event and air combat capability; the air combat situation includes multiple situation attributes; using the threat values ​​of the situation attributes of all targets to construct a multi-target situation threat matrix, and obtaining the situation attribute basic weight vector based on the entropy weight method; obtaining the situation attribute variable weight matrix; obtaining the comprehensive situation threat value of each target to construct a multi-target multi-indicator threat matrix, and solving the objective indicator weight vector by solving the grey correlation depth and the maximum entropy model; combining the subjective indicator weight vector, establishing a game benefit model and solving the optimal indicator weight vector, and evaluating the threat size of each target based on the GRA-TOPSIS method to obtain the threat size ranking result of each target.

[0006] The Chinese patent with the publication number CN113868932A discloses a task allocation method based on bidding game under complete information; specifically discloses: including data collection, obtaining the strategy data and capability data of users in real application scenarios; preprocessing the acquired data, using the PSO algorithm to obtain the optimal ranking of users under each task; conducting dynamic games, establishing a continuously evolving dynamic game mechanism; conducting attack and defense evolutionary games, taking into account the incomplete information sharing conditions in practical applications, and further constructing an attack and defense evolutionary game model. Through the present invention, an optimization algorithm can be designed for the task allocation process, tasks can be parsed and self-supervised learning can be performed based on the worker's capability function. On this basis, an improved evolutionary attack and defense game is used. Through comparative experiments with different crowd-testing scales and with or without disturbances, the traditional algorithm model converges better, and the crowd-testing cost can be well saved under external disturbances. It has strong practicality and is suitable for all crowdsourcing systems.

[0007] The Chinese patent with the authorization announcement number CN113095481B discloses an air combat maneuvering method based on parallel self-game; specifically disclosed: firstly, a UAV maneuvering model is constructed, then a red and blue motion situation acquisition model is constructed to describe the relative combat situation of red and blue; then, the state space of both red and blue, the action space of both red and blue, and the reward and punishment function are constructed according to the Markov process, and then a maneuvering decision model structure based on the SAC algorithm is constructed, and the parallel self-game training SAC algorithm is realized through air combat confrontation, and finally the trained network is tested, the combat trajectory is displayed, and the combat success rate is counted. The present invention can effectively improve the level of enemy confrontation and increase the combat success rate of the decision model.

[0008] However, the above prior art still has the following problems:

[0009] (1) Existing technologies for evaluating gaming behavior mostly use “descriptive evaluation”, which lacks the intuitiveness of numerical evaluation.

[0010] (2) Existing technologies for numerical evaluation of gaming behavior are mainly based on individual evaluations of a single subject, which fails to fully consider the collaboration between subjects and the consistency of adversarial behavior. There is a risk of triggering widespread confrontation and the evaluation is not accurate enough. Summary of the invention

[0011] To achieve the purpose of the present invention, the present invention is implemented by the following technical solutions:

[0012] A near-Earth space game situation analysis method based on an improved intelligent pig game theory comprises the following steps:

[0013] Step S1: Establish a list of game players based on the predetermined research target group of the game situation analysis. N};

[0014] Step S2: Collect known near-Earth space events, organize the near-Earth space events into a set form, and obtain a near-Earth space historical event sample set { E};

[0015] Step S3: According to the game subject list { N} in the game subject in the near-Earth space historical event sample set { E The number of times the dominant subject appears in the} is divided into strong dominant subject, medium dominant subject and weak dominant subject through cluster analysis;

[0016] Step S4: according to whether there is a cooperative relationship between the strong dominant subjects, the strong dominant subjects are divided into single strong dominant subjects and cooperative strong dominant subjects;

[0017] Step S5: according to the degree of cooperation between the medium dominant subject and the strong dominant subject, the medium dominant subject is divided into an autonomous medium dominant subject, a follower medium dominant subject and an extensive medium dominant subject;

[0018] Step S6: According to the strength and cooperation degree of the target subject, the risk value matrix of the game behavior planned by the target subject is evaluated. Risk ];

[0019] Step S7: Based on the risk value matrix [ Risk ], assess the risk of the near-Earth space gaming behavior that the target subject plans to take, and decide whether to execute the planned near-Earth space gaming behavior.

[0020] Furthermore, in step S1, the game subject list { N}satisfy:

[0021] , ;

[0022] in, Indicates i A game player, I is the total number of game entities, Indicates i A custom organization code for each gaming entity.

[0023] Furthermore, in step S2, the known near-Earth space event is:

[0024] , ;

[0025] in, Indicates j Near-Earth space events, J is the total number of near-Earth space events, Indicates j The dominant subject set of near-Earth space events satisfies ; Existing in the form of a collection, it means that there are multiple subjects co-leading the same near-Earth space event; Indicates j The near-Earth space event starts at and end time The duration interval of the composition.

[0026] Furthermore, step S3 specifically includes:

[0027] Step S31: for all single near-Earth space events The dominant subject set Integrate into a composite set to obtain the leading subject collaboration set for near-Earth space historical events { N domin},satisfy:

[0028] ;

[0029] in, Indicates j The dominant entity set of near-Earth space events, ;

[0030] Step S32: In the leading subject collaboration set { N domin}, for any dominant subject that has appeared, count its dominant times and cooperative subject sequence to satisfy:

[0031] ;

[0032] in, Indicates that in the leading subject collaboration set { N domin} i A leading subject, Indicates that i The number of times the dominant subject appears; Indicates that i A sequence of collaborative entities of a leading entity, Representation and Appearing in the same near-Earth space historical event, that is, the leading subject who cooperates in executing the same near-Earth space historical event the most times, is the number of collaborations between the two. Representation and Appearing in the same near-Earth space historical event, that is, the second most dominant subject in the same near-Earth space historical event that the two cooperated to execute, is the number of cooperation between the two, and so on, it decreases in this way, satisfying:

[0033] ;

[0034] in, and k The dominant subjects have cooperated to execute historical events in near-Earth space. The dominant subject with the least cooperation is represented by ;

[0035] Step S33: Determine a strong dominant subject, a medium dominant subject, and a weak dominant subject according to the number of times the dominant subject appears in the dominant subject collaboration set.

[0036] Further, step S33 specifically includes:

[0037] Step S331: Based on the number of times the dominant subject appears in the dominant subject collaboration set, a dominant subject strength degree series is established { S domin},satisfy:

[0038] ;

[0039] in, Respectively express Take the maximum value, the second largest value, and finally the minimum value, and arrange the middle elements of the sequence in descending order;

[0040] Step S332: the dominant subject strength degree series { S domin} cluster analysis, and classify the dominant subjects according to the number of times they dominate the near-Earth space The values ​​are divided into three groups from large to small. The first group is the strong dominant subject set. , the second group is a medium dominant subject set , the third group is the weak dominant subject .

[0041] Furthermore, step S4 specifically includes:

[0042] Step S41: For a strong dominant subject set , determine the dominant subject Whether the collaborative subject is also a strong subject, the judgment formula is:

[0043] ;

[0044] If the above formula is satisfied, then Be a strong and leading subject of collaboration;

[0045] If the above is not met, then the A single strong dominant entity;

[0046] Step S42: The leading subjects with different cooperation degrees are grouped together to obtain a strong cooperation leading subject group. and a single strong dominant entity set .

[0047] Furthermore, step S5 specifically includes:

[0048] Step S51: For any medium dominant subject , to determine whether its collaborative leading subject is a strong leading subject, the determination formula is:

[0049] ;

[0050] If the above formula is not satisfied, the medium-dominant subject is judged to be an autonomous medium-dominant subject;

[0051] If the above formula is satisfied, and its collaborative dominant subject includes only one strong dominant subject, or includes multiple strong dominant subjects but all strong dominant subjects are mutually collaborative strong dominant subjects, then the medium dominant subject is determined to be a follower medium dominant subject;

[0052] If the above formula is satisfied and it includes multiple strong dominant subjects, but all the strong dominant subjects are not mutually cooperative strong dominant subjects, then the medium dominant subject is judged to be a broad-type medium dominant subject.

[0053] Furthermore, in step S6, the risk value matrix [ Risk ]for:

[0054] [ Risk ]=[ A ]∙[ N s ]+[ B ]∙[ N m1 ]+[ C ]∙[ N m2 ]+[ D ]∙[ N m3 ]+[ E ]∙[ N w ];

[0055] in,[ N s ] is the confrontation risk coefficient matrix of the dominant subject, [ N m1 ] is the confrontation risk coefficient matrix of the follower medium dominant subject, [ Nm2 ] is the confrontation risk coefficient matrix of the autonomous medium-dominant subject, [ N m3 ] is the confrontation risk coefficient matrix of the extensive medium-dominant subject, [ N w ] is the confrontation risk coefficient matrix of the weak dominant subject.

[0056] Furthermore, the risk resistance coefficient is expressed as:

[0057] The percentage of times other agents have made counter-movements or expressions in response to the target agent's past near-Earth space behavior;

[0058] [ A ]、[ B ]、[ C ]、[ D ]and[ E ] are respectively the weighted coefficient matrices of the confrontation risk estimation of the current game behavior of the target subject, namely the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject and the weak dominant subject.

[0059] Further, the weighted coefficient matrix of the risk estimation is determined through expert discussion and evaluation, and satisfies the matrix form:

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] in, , , , , They represent the weighted coefficients of the confrontation risk estimation of the current game behavior of the target subject by the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject, and the weak dominant subject respectively; , , , , They respectively represent the confrontation risk coefficients of the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject, and the weak dominant subject against the current game behavior of the target subject.

[0071] The beneficial effects of the present invention are:

[0072] (1) The present invention establishes a sample set based on historical events that have occurred in near-Earth space. By statistically analyzing the sample set, strong dominant subjects, medium dominant subjects, and weak dominant subjects can be determined. In this way, strong subjects that may cause high-intensity conflicts or cooperation with the target subject (itself), medium subjects that may cause medium-intensity conflicts or cooperation, and weak subjects that may cause low-intensity conflicts or cooperation with the target subject (itself) can be quickly determined.

[0073] (2) The present invention can quickly determine the collaborative subject team by measuring the degree of collaboration among the subjects that simultaneously lead near-Earth space events, and can quickly determine the team-based confrontation or cooperation that the external collaborative group may have on the target subject (itself).

[0074] (3) The present invention evaluates the historical confrontation possibility of different types of subjects against the near-Earth space game behavior generated by the target subject (themselves) and the confrontation possibility against the game behavior that is expected to be taken in the form of a matrix, and provides a comprehensive and numerical confrontation risk assessment algorithm to facilitate accurate decision-making on whether to continue to implement the planned near-Earth space game behavior or to abandon its implementation.

[0075] (4) The present invention improves on the traditional intelligent pig game theory of "big pigs (strong subjects) act autonomously and small pigs (weak subjects) follow habitually", introduces the concept of medium subjects, and identifies broad subjects. That is, broad subjects are more inclined to non-confrontation, which provides the target subject (itself) with the possibility of communication and cooperation. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0077] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0078] Embodiment 1

[0079] according to Figure 1 As shown, this embodiment provides a near-Earth space game situation analysis method based on an improved smart pig game theory, characterized in that it includes the following steps:

[0080] S1. Establish a list of gaming entities:

[0081] Based on the predetermined research target group of the game situation analysis, establish a list of game subjects { N},satisfy: , ;in, Indicates i A game player, I is the total number of game entities, Indicates i A custom organization code for each gaming entity.

[0082] S2. Establish a sample set of historical events in near-Earth space:

[0083] Collection of known near-Earth space events , , the near-Earth space events are grouped into a set, and the near-Earth space historical event sample set { E};

[0084] in, Indicates j Near-Earth space events, J is the total number of near-Earth space events, Indicates j The dominant subject set of near-Earth space events satisfies ; Existing in the form of a collection, it means that there are multiple subjects co-leading the same near-Earth space event; Indicates j The near-Earth space event starts at and end time The duration interval of the composition.

[0085] S3. Classification of the strength of game players:

[0086] According to the number of times the game subject appears as the dominant subject in near-Earth space events, the game dominant subjects are divided into strong dominant subjects, medium dominant subjects and weak dominant subjects through cluster analysis.

[0087] Specifically include:

[0088] Step S31: Establish a collaborative set of leading entities for near-Earth space historical events:

[0089] For all single near-Earth space events The dominant subject set Integrate into a composite set to obtain the leading subject collaboration set for near-Earth space historical events { N domin},satisfy:

[0090] ;

[0091] in, Indicates j The dominant entity set of near-Earth space events, .

[0092] Step S32: Determine the sequence of the leading subject and the cooperating subject:

[0093] In the leading subject collaboration set { N domin}, for any dominant subject that has appeared, count its dominant times and cooperative subject sequence to satisfy:

[0094] ;

[0095] in, Indicates that in the leading subject collaboration set { N domin} i A leading subject, Indicates that i The number of times the dominant subject appears; Indicates that i A sequence of collaborative entities of a leading entity, Representation and Appearing in the same near-Earth space historical event, that is, the leading subject who cooperates in executing the same near-Earth space historical event the most times, is the number of collaborations between the two. Representation and Appearing in the same near-Earth space historical event, that is, the second most dominant subject in the same near-Earth space historical event that the two cooperated to execute, is the number of cooperation between the two, and so on, it decreases in this way, satisfying:

[0096] ;

[0097] in, and k The dominant subjects have cooperated to execute historical events in near-Earth space. The dominant subject with the least cooperation is represented by .

[0098] Step S33: Clustering of dominant subject strength:

[0099] According to the number of times the dominant subject appears in the dominant subject collaboration set, the strong dominant subject, the medium dominant subject and the weak dominant subject are determined.

[0100] Specifically include:

[0101] Step S331: Based on the number of times the dominant subject appears in the dominant subject collaboration set, a dominant subject strength degree series is established { S domin},satisfy:

[0102] ;

[0103] in, Respectively express Take the maximum value, the second largest value, and finally the minimum value, and arrange the middle elements of the sequence in descending order.

[0104] Step S332: the dominant subject strength degree series { S domin} cluster analysis, and classify the dominant subjects according to the number of times they dominate the near-Earth space The values ​​are divided into three groups from large to small. The first group is the strong dominant subject set. , the second group is a medium dominant subject set , the third group is the weak dominant subject .

[0105] Step S4: Classification of the degree of cooperation among game players:

[0106] According to whether there is a collaborative relationship between strong dominant subjects, the strong dominant subjects are divided into single strong dominant subjects and collaborative strong dominant subjects.

[0107] Specifically include:

[0108] Step S41: For a strong dominant subject set , determine the dominant subject Whether the collaborative subject is also a strong subject, the judgment formula is:

[0109] ;

[0110] If the above formula is satisfied, then Be a strong and leading subject of collaboration;

[0111] If the above is not met, then the judgment It is a single strong dominant entity.

[0112] Step S42: The leading subjects with different cooperation degrees are grouped together to obtain a strong cooperation leading subject group. and a single strong dominant entity set .

[0113] Step S5: Classification of the cooperation degree of the medium-dominant subject:

[0114] According to the degree of cooperation between the medium-dominant subject and the strong-dominant subject, the medium-dominant subject is divided into autonomous medium-dominant subject, follower medium-dominant subject and extensive medium-dominant subject.

[0115] Specifically include:

[0116] Step S51: For any medium dominant subject , to determine whether its collaborative leading subject is a strong leading subject, the determination formula is:

[0117] ;

[0118] If the above formula is not satisfied, the medium-dominant subject is judged to be an autonomous medium-dominant subject;

[0119] If the above formula is satisfied, and its collaborative dominant subject includes only one strong dominant subject, or includes multiple strong dominant subjects but all strong dominant subjects are mutually collaborative strong dominant subjects, then the medium dominant subject is determined to be a follower medium dominant subject;

[0120] If the above formula is satisfied and it includes multiple strong dominant subjects, but all the strong dominant subjects are not mutually cooperative strong dominant subjects, then the medium dominant subject is judged to be a broad-type medium dominant subject.

[0121] Step S6: Calculate the target entity's game risk:

[0122] According to the strength and cooperation degree of the target subject, the risk value matrix of the game behavior planned by the target subject is evaluated [ Risk ].

[0123] Risk value matrix[ Risk ]for:

[0124] [ Risk ]=[ A ]∙[ N s ]+[ B ]∙[ N m1 ]+[ C ]∙[ N m2 ]+[ D ]∙[ N m3 ]+[ E ]∙[ N w ];

[0125] in,[ N s ] is the confrontation risk coefficient matrix of the dominant subject, [ N m1 ] is the confrontation risk coefficient matrix of the follower medium dominant subject, [ N m2 ] is the confrontation risk coefficient matrix of the autonomous medium-dominant subject, [ N m3 ] is the confrontation risk coefficient matrix of the extensive medium-dominant subject, [ N w ] is the confrontation risk coefficient matrix of the weak dominant subject.

[0126] The risk factor is expressed as:

[0127] The percentage of times other agents have made counter-movements or expressions in response to the target agent's past near-Earth space behavior;

[0128] [ A ]、[ B ]、[ C ]、[ D ]and[ E ] are respectively the weighted coefficient matrices of the confrontation risk estimation of the current game behavior of the target subject, namely the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject and the weak dominant subject.

[0129] The weighted coefficient matrix for risk estimation is determined through expert discussion and evaluation, and satisfies the matrix form:

[0130] ;

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] in, , , , , They represent the weighted coefficients of the confrontation risk estimation of the current game behavior of the target subject by the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject, and the weak dominant subject respectively; , , , , They respectively represent the confrontation risk coefficients of the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject, and the weak dominant subject against the current game behavior of the target subject.

[0141] Step S7, game behavior decision: based on the risk value matrix [ Risk ], assess the risk of the near-Earth space gaming behavior that the target subject plans to take, and decide whether to execute the planned near-Earth space gaming behavior.

[0142] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A near-Earth space game situation analysis method based on improved intelligent pig game theory, characterized in that: The following steps are involved: Step S1: Establish a list of game players based on the predetermined research target group of the game situation analysis. N }; Step S2: Collect known near-Earth space events, organize the near-Earth space events into a set form, and obtain a near-Earth space historical event sample set { E }; Step S3: According to the game subject list { N } in the game subject in the near-Earth space historical event sample set { E The number of times the dominant subject appears in the} is divided into strong dominant subject, medium dominant subject and weak dominant subject through cluster analysis; Step S4: according to whether there is a cooperative relationship between the strong dominant subjects, the strong dominant subjects are divided into single strong dominant subjects and cooperative strong dominant subjects; Step S5: according to the degree of cooperation between the medium dominant subject and the strong dominant subject, the medium dominant subject is divided into an autonomous medium dominant subject, a follower medium dominant subject and an extensive medium dominant subject; Step S6: According to the strength and cooperation degree of the target subject, the risk value matrix of the game behavior planned by the target subject is evaluated. Risk ]; Step S7: Based on the risk value matrix [ Risk ], assess the risk of the near-Earth space gaming behavior that the target subject plans to take, and decide whether to execute the planned near-Earth space gaming behavior. Wherein, in step S6, the risk value matrix [ Risk ]for: ； in,[ N s ] is the confrontation risk coefficient matrix of the dominant subject, [ N m1 ] is the confrontation risk coefficient matrix of the follower medium dominant subject, [ N m2 ] is the confrontation risk coefficient matrix of the autonomous medium-dominant subject, [ N m3 ] is the confrontation risk coefficient matrix of the extensive medium-dominant subject, [ N w ] is the confrontation risk coefficient matrix of the weak dominant subject, Wherein, the risk resistance coefficient is expressed as: The percentage of times other agents have made counter-movements or expressions in response to the target agent's past near-Earth space behavior; [ A ]、[ B ]、[ C ]、[ D ]and[ E ] are respectively the weighted coefficient matrices of the confrontation risk estimation of the current game behavior of the target subject, namely the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject and the weak dominant subject.

2. According to claim 1, a near-Earth space game situation analysis method based on improved intelligent pig game theory is characterized in that: In step S1, the game subject list { N }satisfy: ， ； in, Indicates i A game player, I is the total number of game entities, Indicates i A custom organization code for each gaming entity.

3. The method for analyzing near-Earth space game situation based on improved intelligent pig game theory according to claim 2 is characterized in that: In step S2, the known near-Earth space event is: ， ； in, Indicates j Near-Earth space events, J is the total number of near-Earth space events, Indicates j The dominant subject set of near-Earth space events satisfies ; Existing in the form of a collection, it means that there are multiple subjects co-leading the same near-Earth space event; Indicates j The near-Earth space event starts at and end time The duration interval of the composition.

4. The method for analyzing near-Earth space game situation based on improved intelligent pig game theory according to claim 3 is characterized in that: Step S3 includes: Step S31: for all single near-Earth space events The dominant subject set Integrate into a composite set to obtain the leading subject collaboration set for near-Earth space historical events { N domin },satisfy: ； in, Indicates j The dominant entity set of near-Earth space events, ; Step S32: In the leading subject collaboration set { N domin }, for any dominant subject that has appeared, count its dominant times and cooperative subject sequence to satisfy: ； in, Indicates that in the leading subject collaboration set { N domin } i A leading subject, Indicates that i The number of times the dominant subject appears; Indicates that i A sequence of collaborative entities of a leading entity, Representation and Appearing in the same near-Earth space historical event, that is, the leading subject who cooperates in executing the same near-Earth space historical event the most times, is the number of collaborations between the two. Representation and Appearing in the same near-Earth space historical event, that is, the second most dominant subject in the same near-Earth space historical event that the two cooperated to execute, is the number of cooperation between the two, and so on, it decreases in this way, satisfying: ； in, and k The dominant subjects have cooperated to execute historical events in near-Earth space. The dominant subject with the least cooperation is represented by ; Step S33: Determine a strong dominant subject, a medium dominant subject, and a weak dominant subject according to the number of times the dominant subject appears in the dominant subject collaboration set.

5. The method for analyzing near-Earth space game situation based on improved intelligent pig game theory according to claim 4 is characterized in that: Step S33 includes: Step S331: Based on the number of times the dominant subject appears in the dominant subject collaboration set, a dominant subject strength degree series is established { S domin },satisfy: ； in, Respectively For the maximum value, the second largest value, and finally the minimum value, the middle elements of the sequence are arranged in descending order; Step S332: the dominant subject strength degree series { S domin } cluster analysis, and classify the dominant subjects according to the number of times they dominate the near-Earth space The values ​​are divided into three groups from large to small. The first group is the strong dominant subject set. , the second group is a medium dominant subject set , the third group is the weak dominant subject .

6. The method for analyzing near-Earth space game situation based on improved intelligent pig game theory according to claim 1, characterized in that: Step S4 includes: Step S41: For a strong dominant subject set , determine the dominant subject Whether the collaborative subject is also a strong subject, the judgment formula is: ； If the above formula is satisfied, then Be a strong and leading subject of collaboration; If the above is not met, then the judgment A single strong dominant entity; Step S42: The leading subjects with different cooperation degrees are grouped together to obtain a strong cooperation leading subject group. and a single strong dominant entity set .

7. The method for analyzing near-Earth space game situation based on improved intelligent pig game theory according to claim 1, characterized in that: Step S5 includes: Step S51: For any medium dominant subject , to determine whether its collaborative leading subject is a strong leading subject, the determination formula is: ； If the above formula is not satisfied, the medium-dominant subject is judged to be an autonomous medium-dominant subject; If the above formula is satisfied, and its collaborative dominant subject includes only one strong dominant subject, or includes multiple strong dominant subjects but all strong dominant subjects are mutually collaborative strong dominant subjects, then the medium dominant subject is determined to be a follower medium dominant subject; If the above formula is satisfied and it includes multiple strong dominant subjects, but all the strong dominant subjects are not mutually cooperative strong dominant subjects, then the medium dominant subject is judged to be a broad-type medium dominant subject.

8. The method for analyzing near-Earth space game situation based on improved intelligent pig game theory according to claim 1, characterized in that: The weighted coefficient matrix of the risk estimation is determined through expert discussion and evaluation, and satisfies the matrix form: ； ； ； ； ； ； ； ； ； ； in, , , , , They represent the weighted coefficients of the confrontation risk estimation of the current game behavior of the target subject by the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject, and the weak dominant subject respectively; , , , , They respectively represent the confrontation risk coefficients of the strong dominant subject, the follower medium dominant subject, the autonomous medium dominant subject, the extensive medium dominant subject, and the weak dominant subject against the current game behavior of the target subject.

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