A multi-star collaborative multi-objective task allocation game-theoretic decision-making method and device

Abstract

This invention discloses a multi-satellite collaborative multi-target task allocation game-theoretic decision-making method and apparatus, belonging to the field of spacecraft control technology. The method includes: determining a first task set and task information consisting of all threat targets, and a first satellite set and satellite information for all targets observation; establishing event triggering conditions to characterize the timing of task allocation based on real-time feedback information obtained from onboard sensors and communication between satellites; determining whether the current task information and satellite information meet the event triggering conditions; if so, establishing a task allocation model based on the current second task set and second satellite set participating in task allocation; establishing a game model with the satellites participating in task allocation as participants based on the task allocation model, and interactively optimizing and adjusting the game model to obtain the optimal task allocation result. This invention can solve the problem that static task allocation in related technologies is difficult to meet the requirements of multi-target observation.

Description

Technical Field

[0001] This invention relates to the field of spacecraft control technology, and in particular to a multi-satellite collaborative multi-objective task allocation game decision-making method and device. Background Technology

[0002] The current escalation of space competition, increased harassment, and frequent orbital conflict incidents pose serious challenges to the space security of high-value spacecraft. Multi-satellite collaboration, as an important type of intelligent autonomous space system, can overcome the limitations of single-satellite observation capabilities and space environment interference problems in the observation of threatening targets, thereby improving robustness and fault tolerance.

[0003] In related technologies, task allocation methods can be divided into three categories: centralized, distributed, and hierarchical, which can solve some static task allocation problems. However, in collaborative multi-objective task allocation, since the number of targets and their threat levels within the task scope are dynamically changing, static task allocation cannot guarantee task completion.

[0004] Therefore, there is an urgent need for a multi-star collaborative multi-objective task allocation game decision-making method and device to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a multi-satellite collaborative multi-target task allocation game-theoretic decision-making method and apparatus, which can solve the problem that static task allocation in related technologies cannot meet the requirements of multi-target observation. The technical solution is as follows:

[0006] On the one hand, a multi-star collaborative multi-objective task allocation game decision-making method is provided, the method comprising:

[0007] The first mission set and mission information consisting of all threat targets, and the first satellite set and satellite information used for target observation are determined respectively;

[0008] Based on real-time feedback information obtained from onboard sensors and communication between satellites, event triggering conditions are established to characterize the timing of task allocation.

[0009] Determine whether the current task information and satellite information meet the event triggering conditions. If so, establish a task allocation model based on the second task set and the second satellite set participating in task allocation at the current time.

[0010] Based on the task allocation model, a game model is established with the satellites that perform task allocation as participants. The game model is then interactively optimized and the strategy is adjusted to obtain the optimal task allocation result.

[0011] On the other hand, a multi-star collaborative multi-objective task allocation game decision-making device is provided, the device comprising:

[0012] The first determination module is used to determine the first task set and task information consisting of all threat targets, and the first satellite set and satellite information used for target observation.

[0013] The modeling module is used to establish event triggering conditions to characterize the timing of task allocation based on real-time feedback information detected by on-board sensors and obtained from communication between satellites.

[0014] The second determining module is used to determine whether the task information and satellite information at the current moment meet the event triggering conditions. If so, a task allocation model is established based on the second task set and the second satellite set participating in the task allocation at the current moment.

[0015] The game theory module is used to establish a game theory model with the satellites that perform task allocation as participants based on the task allocation model, and to perform interactive optimization and strategy adjustment on the game theory model to obtain the optimal task allocation result.

[0016] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to implement the steps of the multi-star collaborative multi-objective task allocation game decision-making method described above.

[0017] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the multi-star collaborative multi-objective task allocation game decision-making method described above.

[0018] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the multi-star collaborative multi-objective task allocation game decision-making method described above.

[0019] The technical solution provided by this invention can bring at least the following beneficial effects: By introducing event triggering conditions, the dynamic task allocation problem is decomposed into the problem of determining the redistribution timing and the task allocation problem at the redistribution time, thereby improving the dynamic response capability of the system. In solving the single task allocation problem, the considered constraints are integrated into the utility function design, avoiding explicit handling of constraints; a game theory-based allocation algorithm is designed to quickly iterate and approximate the equilibrium solution. Compared with existing centralized task allocation, this method can fully consider the dynamic changes in the target threat level and the dynamic situations such as the addition and removal of satellites; at the same time, a distributed game theory task allocation method is constructed, which enables the algorithm to adapt to changes in the number of satellites, and the computational complexity does not increase with the increase in task scale, resulting in good scalability. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a multi-star collaborative multi-objective task allocation game decision-making method provided in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of a satellite performing a mission according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the utility values ​​of each satellite in the first round of negotiation at the trigger moment in a simulation test provided by an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the utility values ​​of each satellite in the second round of negotiation at the trigger moment in a simulation test provided by an embodiment of the present invention;

[0025] Figure 5 This is a structural diagram of a multi-star collaborative multi-objective task allocation game decision-making device provided in an embodiment of the present invention;

[0026] Figure 6 This is a hardware architecture diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] As mentioned earlier, in collaborative multi-target task allocation, since the number of targets and their threat levels within the task scope are dynamically changing, static task allocation cannot guarantee task completion, and multi-target dynamic task allocation is required for the task set.

[0029] Based on this, the concept of this invention is to construct a distributed game-theoretic task allocation method, which enables the algorithm to adapt to changes in the number of satellites, while fully considering dynamic changes in the target threat level and dynamic situations such as the addition and removal of satellites.

[0030] The following describes the specific implementation of the above concept.

[0031] Please refer to Figure 1 This invention provides a multi-star collaborative multi-objective task allocation game decision-making method, which includes:

[0032] Step 100: Determine the first mission set and mission information consisting of all threat targets, and the first satellite set and satellite information for all targets observation.

[0033] Step 102: Based on the real-time feedback information obtained from onboard sensors and communication between satellites, establish event triggering conditions to characterize the timing of task allocation.

[0034] Step 104: Determine whether the task information and satellite information at the current moment meet the event triggering conditions. If so, establish a task allocation model based on the second task set and the second satellite set participating in task allocation at the current moment.

[0035] Step 106: Establish a game model with the satellites that perform task allocation as participants based on the task allocation model, and perform interactive optimization and strategy adjustment on the game model to obtain the optimal task allocation result.

[0036] In this embodiment of the invention, by introducing event triggering conditions, the dynamic task allocation problem is decomposed into the problem of determining the reallocation timing and the task allocation problem at the reallocation time, thereby improving the dynamic response capability of the system. In solving the single task allocation problem, the considered constraints are integrated into the utility function design, avoiding explicit handling of constraints; a game theory-based allocation algorithm is designed to quickly iterate and approximate the equilibrium solution. Compared with existing centralized task allocation methods, this method can fully consider the dynamic changes in the target threat level and the dynamic situations such as the addition and removal of satellites; at the same time, a distributed game theory task allocation method is constructed, enabling the algorithm to adapt to changes in the number of satellites, and the computational complexity does not increase with the increase in task scale, exhibiting good scalability.

[0037] The following description Figure 1 The execution method of each step is shown.

[0038] First, for step 100, the first task set and task information consisting of all threat targets are determined, as well as the first satellite set and satellite information used for target observation.

[0039] like Figure 2 As shown, each threat target in different orbits within the mission space is first defined as a mission. The first mission set T for all threat target observation missions is then:

[0040]

[0041] Where, N t Indicates the number of threatening targets.

[0042] And using a quintuple Describe the task information in the first task set:

[0043]

[0044] in, For the target T i The number; Let t be the position of the threatening target in the J2000 coordinate system at time t; For threat target T i The threat level of a target is assessed based on its relative distance from the satellite's initial position. This includes the minimum number of satellites (num) required to complete the target mission. low And the maximum number of satellites required, num up ; This represents the current state of the task, which could be "Executing", "Waiting to Execute", or "No Execution Required".

[0045] The satellites performing the mission are isomorphic satellites, initially located near the parent star. All satellites used for target observation are represented as the first satellite set A:

[0046]

[0047] Where, N a This indicates the total number of satellites.

[0048] Using a triple To record satellite information:

[0049]

[0050] in, Indicates the satellite's number, This indicates the satellite's spatial position in the J2000 coordinate system at the current time t. This indicates the current status of the satellite, which may include "idle", "working", and "faulty".

[0051] Then, for step 102, based on the real-time feedback information obtained from onboard sensors and communication between satellites, event triggering conditions are established to characterize the timing of task allocation.

[0052] In this embodiment of the invention, real-time feedback information is obtained based on real-time communication between the onboard sensor and the satellite. The real-time feedback information includes the status of each satellite within the satellite array and the status of each task, and the number of satellites in an idle state is counted as N. s Based on this information, we construct and establish event triggering conditions to characterize the timing of task allocation:

[0053]

[0054] Among them, t trig+1 The time when the (trig+1)th event is triggered; inf is the infimum; N trig Assign the minimum number of satellites required for a pre-defined single mission; N s The number of satellites in an idle state; t k Let k be the k-th time.

[0055] For step 104, determine whether the task information and satellite information at the current moment meet the event triggering conditions. If so, establish a task allocation model based on the second task set and the second satellite set participating in task allocation at the current moment.

[0056] The previous step established the event triggering conditions. If the number of satellites at the current moment meets the above triggering conditions, tasks can be assigned to the satellites. The task assignment at each triggering moment can be determined based on the task coordination status between satellites.

[0057]

[0058] Among them, binary variables It is the decision variable for task allocation, representing satellite A. j Does it perform mission T with other satellites? i If yes, then it is 1; otherwise, it is 0. s The second set of satellites for task allocation at the current moment, with a quantity of N. s ; r The second set of tasks to be assigned at this moment, with a size of N. r This indicates that the tasks in the task set are in the "waiting to be executed" state, according to task T. i The top N after ranking by threat level r One task; For overall benefit;

[0059] According to satellite A j Reward function for collaborative observation missions with other satellites and the loss function caused by energy consumption during task execution Calculate the global benefit

[0060]

[0061] The reward function takes into account the constraints of task completion time and the number of satellites cooperating, and is expressed as follows:

[0062]

[0063] Where α represents the penalty term for the collaborative quantity constraint; α = 1 when the collaborative quantity constraint is satisfied, and α = 0 when the quantity constraint is violated. λ > 0 and γ > 0 are the time discount factor and priority discount factor, respectively. This represents the fixed reward for completing the task. Furthermore, due to the collaboration of multiple entities, this reward is equally shared among the N cooperating satellites.

[0064] The loss function primarily considers the energy consumption during task execution.

[0065]

[0066] Here, β ensures that the reward for an individual performing a task is not less than the loss caused by energy consumption, and fuel represents the average energy consumption of the satellite performing the task. The average energy consumption and task execution duration are determined by the formation control algorithm. The inputs are the current position and velocity of the satellite, the position of the task, and the number of coordinators. The outputs are the average energy consumption and task execution duration, thereby constructing an information matrix for the task at the current moment.

[0067] The constraints of the above task allocation model are determined based on the required number of satellites for the tasks to be performed and the threat level of the tasks to be performed:

[0068]

[0069]

[0070] The first constraint represents the number of satellites required for the execution of a single mission. The second constraint indicates that, based on the mission threat level, urgent missions are prioritized. Indicates task T i The higher the value, the greater the threat level. Indicates task T i The start time of execution.

[0071] For step 106, a game model is established based on the task allocation model, with the satellites that perform task allocation as participants. The game model is then interactively optimized and the strategy is adjusted to obtain the optimal task allocation result.

[0072] For the task allocation model obtained from the above steps, this embodiment of the invention uses game theory to transform the model, aiming to achieve coordination among satellites in task selection. Specifically, based on the task allocation model, a second satellite set A is established. s For participants, the second task set T r For strategy space, global benefit Game model E with utility function:

[0073]

[0074] Where T0 indicates that the satellites in the second satellite set do not choose to perform any missions; U j The utility function is determined by directly considering the global benefit constraints in the above steps.

[0075] For this game theory model, a distributed game task allocation process needs to be constructed based on a game negotiation mechanism, so that the satellite can approach the Nash equilibrium state through continuous information exchange and strategy adjustment. Specifically, the following steps are included:

[0076] Step S1: Have each satellite in the second satellite set randomly select a strategy s from its respective strategy space. i This forms the initial strategy combination S;

[0077] Step S2: In the current round of information exchange, each satellite receives strategy information from its neighboring individuals from the previous round.

[0078] Step S3: Based on the strategy information The utility function of all satellites is optimized to obtain the optimal response strategy for each satellite following the greedy criterion, and this optimal response strategy is used as the latest strategy information s for the current round of satellites. i [iter] :

[0079]

[0080] Step S4: Transfer the latest strategy information s of each satellite i [iter] Send to neighboring individuals to obtain the latest strategy combination S for the current round. [iter] ;

[0081] Step S5: Determine the latest strategy combination S [iter] Strategy combination S from the previous round [iter-1] If they are consistent, the latest strategy combination is determined to be the optimal task allocation result and the task allocation result is sent to the satellite's control module for trajectory planning and control strategy calculation. Otherwise, steps S2-S5 are repeated until the strategy combination is consistent.

[0082] The utility value of each satellite in each round of negotiation is as follows: Figure 3 and Figure 4 As shown.

[0083] Please refer to Figure 5 This invention provides a multi-star collaborative multi-objective task allocation game decision-making device, which includes:

[0084] The first determination module 500 is used to determine the first task set and task information consisting of all threat targets, and the first satellite set and satellite information used for target observation.

[0085] Modeling module 502 is used to establish event triggering conditions to characterize the timing of task allocation based on real-time feedback information detected by on-board sensors and obtained from communication between satellites.

[0086] The second determining module 504 is used to determine whether the task information and satellite information at the current moment meet the event triggering conditions. If so, a task allocation model is established based on the second task set and the second satellite set participating in the task allocation at the current moment.

[0087] The game module 506 is used to establish a game model with the satellites that perform task allocation as participants based on the task allocation model, and to perform interactive optimization and strategy adjustment on the game model to obtain the optimal task allocation result.

[0088] In this embodiment of the invention, the task information is represented by a quintuple. Record:

[0089]

[0090] in, For the target T i The number; Let t be the position of the threatening target in the J2000 coordinate system at time t; For threat target T i The threat level of a target is assessed based on its relative distance from the satellite's initial position. This includes the minimum number of satellites (num) required to complete the target mission. low And the maximum number of satellites required, num up ; This represents the current state of the task.

[0091] The satellite information is obtained through a triplet. Record:

[0092]

[0093] in, The satellite's designation; This indicates the satellite's spatial position in the J2000 coordinate system at the current time t. This indicates the current status of the satellite.

[0094] In this embodiment of the invention, the event triggering condition is established using the following formula:

[0095]

[0096] Among them, t trig+1 The time when the (trig+1)th event is triggered; inf is the infimum; N trig Assign the minimum number of satellites required for a pre-defined single mission; N s The number of satellites in an idle state; t k Let k be the k-th time.

[0097] In this embodiment of the invention, establishing a task allocation model based on the set of observation tasks and the set of satellites participating in task allocation at the current time includes:

[0098] According to satellite A j Reward function for collaborative observation missions with other satellites and the loss function caused by energy consumption during task execution Calculate the global benefit

[0099]

[0100] The task allocation problem is determined based on the task coordination status among the satellites:

[0101]

[0102] Among them, binary variables It is the decision variable for task allocation, representing satellite A. j Does it perform mission T with other satellites? i If yes, then it is 1; otherwise, it is 0. s The second set of satellites for task allocation at the current moment; T r The second set of tasks for which tasks are assigned at this moment;

[0103] The constraints of the task allocation model are determined based on the required number of satellites for the tasks to be performed and the threat level of the tasks.

[0104]

[0105]

[0106] in, For task T to be executed i The level of threat; For another pending task T k The level of threat; For task T i The start time of execution.

[0107] In this embodiment of the invention, the game theory model is established in the following way:

[0108] Based on the task allocation model, a game model E is established with the second satellite set as participants, the second task set as the strategy space, and the global payoff as a utility function:

[0109]

[0110] Where T0 indicates that the individual does not choose to perform any task; U j It is a utility function.

[0111] In this embodiment of the invention, the step of interactively optimizing and adjusting the strategy of the game model to obtain the optimal task allocation result includes:

[0112] Step S1: Have each satellite in the second satellite set randomly select a strategy s from its respective strategy space. i This forms the initial strategy combination S;

[0113] Step S2: In the current round of information exchange, each satellite receives strategy information from its neighboring individuals from the previous round.

[0114] Step S3: Based on the strategy information The utility function of all satellites is optimized to obtain the optimal response strategy for each satellite following the greedy criterion, and this optimal response strategy is used as the latest strategy information s for the current round of satellites. i [iter] :

[0115]

[0116] Step S4: Transfer the latest strategy information s of each satellite i [iter] Send to neighboring individuals to obtain the latest strategy combination S for the current round. [iter] ;

[0117] Step S5: Determine the latest strategy combination S [iter] Strategy combination S from the previous round [iter-1]If they are consistent, then the latest strategy combination is determined to be the optimal task allocation result; otherwise, repeat steps S2-S5 until the strategy combinations are consistent.

[0118] It should be noted that the multi-star collaborative multi-objective task allocation game decision-making device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functional allocation can be completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the multi-star collaborative multi-objective task allocation game decision-making device and the multi-star collaborative multi-objective task allocation game decision-making method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0119] Embodiments of this application also provide a computer device, please refer to... Figure 6 The computer device includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, at least one program, code set or instruction set being loaded and executed by the processor to implement the multi-star collaborative multi-objective task allocation game decision-making method provided in the above-described method embodiments.

[0120] Embodiments of this application also provide a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the multi-star collaborative multi-objective task allocation game decision-making method provided in the above-described method embodiments.

[0121] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform any of the multi-star collaborative multi-objective task allocation game decision-making methods described in the above embodiments.

[0122] For ease of description, the above systems or devices are described separately as various modules or units based on their functions. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components.

[0123] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0124] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0125] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for multi-satellite cooperative multi-target task allocation game decision, characterized in that, The method comprises: respectively determining a first task set composed of all threat targets and task information, and a first satellite set used for target observation and satellite information; establishing an event trigger condition for representing a task allocation opportunity according to real-time feedback information obtained through on-satellite sensor detection and communication between satellites; determining whether the task information and the satellite information at the current time meet the event trigger condition, and if so, establishing a task allocation model according to a second task set and a second satellite set participating in task allocation at the current time; establishing a game model with satellites performing task allocation as participants according to the task allocation model, and performing interactive optimization and strategy adjustment on the game model to obtain an optimal task allocation result.

2. The method of claim 1, wherein, The task information is through a five tuple Recorded: wherein, is the threat target T i numbered; is the threat target at the moment the threat target is located in the J2000 coordinate system; is the threat degree of the target based on the relative distance between the threat target T i and the initial position of the satellite; includes the number of satellites at least needed for the target task to be completed and the number of satellites at most needed for the target task to be completed ; is the status of the task at the current moment; The satellite information is passed through a triple Recorded: wherein, is the number of the satellite; denotes the current time is the spatial position of the satellite in the J2000 coordinate system, denotes the current state of the satellite.

3. The method of claim 1, wherein, The event trigger condition is established through the following formula: wherein, is the kth moment of the kth event trigger; inf is the infimum; is the minimum number of satellites required for the allocation of the kth single task; is the number of satellites in idle state; t k is the kth moment.

4. The method of claim 2, wherein, The task allocation model established according to the second task set and the second satellite set participating in task allocation at the current time comprises: According to the satellite Reward function for performing an observation task in cooperation with other satellites Loss function resulting from energy consumption when performing a task Global revenue is calculated : determining a task allocation problem according to a task cooperative execution state between the satellites: wherein the binary variable is a decision variable of task assignment, indicating whether the satellite performs the task together with other satellites , 1 if yes, otherwise 0; is a second satellite set for task assignment at the current time; is a second task set for task assignment at the current time; determining a constraint condition of the task allocation model according to a satellite quantity requirement required by a to-be-executed task and a threat degree of the to-be-executed task: wherein, a threat level of a task to be executed; a threat level of another task to be executed; a threat level of another task to be executed; a threat level of another task to be executed; a start execution time of a task; a start execution time of a task; denotes the number of all satellites.

5. The method of claim 4, wherein, The game model is established in the following manner: According to the task allocation model, a game model E is established with the second satellite set as participants, the second task set as a strategy space, and global benefits as a utility function. wherein, indicates that the satellites in the second set of satellites do not select to perform any tasks; is the utility function.

6. The method of claim 5, wherein, The game model is established in the following manner: Step S1, causing each satellite in the second set of satellites to randomly select a strategy from the respective policy space , to form an initial policy combination ; Step S2, in the information interaction of the current round, each satellite receives the strategy information from the adjacent individual in the last round ; Step S3, according to the policy information The utility functions of all satellites are optimized, and the optimal reaction policy of each satellite following the greedy criterion is obtained in turn, and the optimal reaction policy is taken as the latest policy information of the satellite in the current round : ； Step S4, sending the latest strategy information of each satellite to neighboring individuals to obtain the latest strategy combination of the current round Step S4, sending the latest strategy information of each satellite to neighboring individuals to obtain the latest strategy combination of the current round Step S4, sending the latest strategy information of each satellite to neighboring individuals to obtain the latest strategy combination of the current round Step S5, judging whether the latest strategy combination is consistent with the strategy combination of the last round with the strategy combination of the last round is consistent, it is determined that the latest strategy combination is the optimal task allocation result, otherwise, steps S2-S5 are repeated until the strategy combinations are consistent.

7. A multi-satellite cooperative multi-target task allocation game decision device, characterized in that, The apparatus comprises: a first determination module configured to respectively determine a first task set composed of all threat targets and task information, and a first satellite set used for target observation and satellite information; a modeling module configured to establish an event trigger condition for representing a task allocation opportunity according to real-time feedback information obtained through on-satellite sensor detection and communication between satellites; a second determination module configured to determine whether the task information and the satellite information at the current time meet the event trigger condition, and if so, establish a task allocation model according to a second task set and a second satellite set participating in task allocation at the current time; a game module configured to establish a game model with satellites performing task allocation as participants according to the task allocation model, and perform interactive optimization and strategy adjustment on the game model to obtain an optimal task allocation result.

8. A computer device, comprising: The computer device comprises a memory and a processor, the memory is configured to store a computer program, and the processor is configured to execute the computer program stored on the memory to implement the steps of the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the method of any one of claims 1-6.

10. A computer program product, characterised in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1-6.

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