CN111077770B - Method and system for configuring multi-satellite cooperative resources - Google Patents

Method and system for configuring multi-satellite cooperative resources Download PDF

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CN111077770B
CN111077770B CN201911038392.XA CN201911038392A CN111077770B CN 111077770 B CN111077770 B CN 111077770B CN 201911038392 A CN201911038392 A CN 201911038392A CN 111077770 B CN111077770 B CN 111077770B
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satellite
task
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CN111077770A (en
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靳鹏
张凯
胡笑旋
夏维
罗贺
马华伟
王国强
任送莲
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Hefei University of Technology
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Abstract

The invention provides a configuration method and a configuration system of multi-satellite cooperative resources, and relates to the field of aerospace. The method comprises the following steps: acquiring satellite resources; selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed; vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity; obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed; acquiring demand energy constraint based on vectorized task demand and resource cluster capacity; acquiring resource energy constraints based on all satellite resources and resource clusters; acquiring a capability energy constraint based on the vectorized satellite capability and the resource cluster capability; constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint; and acquiring the resource alliance based on the dynamic resource alliance model. The invention improves the utilization rate of satellite resources.

Description

Method and system for configuring multi-satellite cooperative resources
Technical Field
The invention relates to the technical field of aerospace, in particular to a configuration method and a configuration system of multi-satellite cooperative resources.
Background
With the development of aerospace technology, earth observation satellites are increasingly concerned by people. The earth observation satellite acquires relevant information by detecting the earth surface and the lower atmosphere. The earth observation satellite has the unique advantages of wide coverage area, long duration, no limitation of airspace national boundaries and the like, so the earth observation satellite is widely applied to the fields of military reconnaissance, meteorological observation, environmental protection and the like.
For some complex observation tasks, a single satellite is used for observing a task target, only partial effective information of the task target can be obtained, and the task requirement cannot be completed efficiently. Therefore, in the prior art, the same task is observed by arranging a plurality of satellites, so that the observation task is completely and completely finished.
However, the inventor of the present application finds that the prior art does not consider reasonable allocation of satellite resources and observation tasks in practical application, thereby causing waste of satellite resources. Therefore, the prior art has the defect of low utilization rate of satellite resources.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a configuration method and a configuration system of multi-satellite cooperative resources, and solves the technical problem of low utilization rate of satellite resources in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention provides a configuration method of multi-satellite cooperative resources, which solves the technical problem and is executed by a computer, and the configuration method comprises the following steps:
acquiring satellite resources;
selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed;
vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity;
obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed;
acquiring a demand energy constraint based on the vectorized task demand and the resource cluster capacity; acquiring resource energy constraints based on all satellite resources and the resource clusters; obtaining a capability energy constraint based on the vectorized satellite capability and the resource cluster capability;
constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint;
and acquiring resource alliances based on the dynamic resource alliance model.
Preferably, the method for obtaining the profit of each task to be executed includes:
S(tk)＝E(tk)-V(Gpk)-C(Gpk)
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
E(tk) Indicating completion of task tkAn available reward;
C(Gpk) Representing a resource cluster GpkThe cost of conversion of all resources in the system;
V(Gpk) Indicating the mutual cooperation completion t of resources in the resource clusterkCost of consumption in the process; the method for acquiring the target function comprises the following steps:
Figure BDA0002252176550000031
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
and N is the number of tasks to be executed.
Preferably, the required energy constraint is:
Figure BDA0002252176550000032
Figure BDA0002252176550000033
wherein:
Figure BDA0002252176550000034
representing a resource cluster GpkThe ability of (c);
Figure BDA0002252176550000035
indicating the kth task to be performed tkThe task requirements of (1);
Figure BDA0002252176550000036
representing a resource cluster GpkCapability component in jth dimension
Figure BDA0002252176550000037
Representing a task tkA demand component in the j-th dimension.
Preferably, the resource energy constraint is:
Gp1∪Gp2∪...∪GpN＝R
|Gp1|+|Gp2|+...+|GpN|≥|R|
wherein:
Gpkrepresents the kth task to be executed tkThe resource cluster of (2);
r represents the total satellite resources.
Preferably, the capacity energy constraint comprises: the resource clusters constitute a capacity energy constraint and a satellite resource contribution capacity energy constraint.
Preferably, the resource cluster formation capability energy constraint is:
Figure BDA0002252176550000041
Figure BDA0002252176550000042
wherein:
Figure BDA0002252176550000043
representing a resource cluster GpkThe ability of (c);
Figure BDA0002252176550000044
denotes the ith satellite riIn the resource cluster GpkThe contributing capability of (1);
Figure BDA0002252176550000045
representing a resource cluster GpkA capability component in the j-th dimension;
Figure BDA0002252176550000046
representing a satellite riIn the resource cluster GpkA capability component of the contributing capability in the j-th dimension;
m represents the number of satellites.
Preferably, the satellite resource contribution capacity energy constraint is:
Figure BDA0002252176550000047
wherein:
Figure BDA0002252176550000048
denotes the ith satellite riIn the resource cluster GpkThe contributing capability of (1);
Figure BDA0002252176550000049
representing a satellite riThe ability of itself;
when satellite riParticipate in performing task tkWhen xikIs 1; otherwise, xikIs 0.
Preferably, the resource alliance acquisition method comprises the following steps:
inputting satellite resource information and task information to be executed into the dynamic resource alliance model to obtain resource alliances corresponding to each task to be executed;
the resource alliance corresponding to each task to be executed is as follows:
G＝{(Ga1，t1)，(Ga2，t2)，...，(GaN，tN)}
wherein:
g represents a matching set of all tasks to be executed and corresponding resource alliances;
tkrepresenting the kth task to be executed;
Gakindicating a task to be performed tkThe resources of (2) are allied.
The invention provides a system for configuring multi-satellite cooperative resources, which solves the technical problem, and comprises a computer, wherein the computer comprises:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
acquiring satellite resources;
selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed;
vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity;
obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed;
acquiring a demand energy constraint based on the vectorized task demand and the resource cluster capacity; acquiring resource energy constraints based on all satellite resources and the resource clusters; obtaining a capability energy constraint based on the vectorized satellite capability and the resource cluster capability;
constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint;
and acquiring resource alliances based on the dynamic resource alliance model.
Preferably, the method for obtaining the profit of each task to be executed includes:
S(tk)＝E(tk)-V(Gpk)-C(Gpk)
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
E(tk) Indicating completion of task tkAn available reward;
C(Gpk) Representing a resource cluster GpkThe cost of conversion of all resources in the system;
V(Gpk) Indicating the completion of mutual cooperation of resources tkCost of consumption in the process;
the method for acquiring the target function comprises the following steps:
Figure BDA0002252176550000061
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
and N is the number of tasks to be executed.
(III) advantageous effects
The invention provides a method and a system for configuring multi-satellite cooperative resources. Compared with the prior art, the method has the following beneficial effects:
the invention obtains satellite resources; selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed; vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity; obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed; acquiring demand energy constraint based on vectorized task demand and resource cluster capacity; acquiring resource energy constraints based on the satellite resources and the resource clusters; acquiring a capability energy constraint based on the vectorized satellite capability and the resource cluster capability; constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint; and acquiring the resource alliance based on the dynamic resource alliance model. According to the invention, the constraint conditions are determined by comprehensively considering the requirements of the tasks and the capability of the satellite, the objective function is determined according to the income of the tasks, the dynamic resource alliance model is constructed, the configuration results of the tasks and the satellite resources are further obtained, the efficient configuration of the satellite resources and the tasks is realized, and the utilization rate of the satellite resources is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is an overall flowchart of a configuration method of multi-satellite cooperative resources according to an embodiment of the present invention.
Detailed Description
In order to make the objects, 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, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a method and a system for configuring multi-satellite cooperative resources, solves the technical problem of low satellite resource utilization rate in the prior art, and improves the satellite resource utilization rate.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
the embodiment of the invention obtains satellite resources; selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed; vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity; obtaining the income of each task to be executed; acquiring an objective function based on the profits of all tasks to be executed; acquiring demand energy constraint based on vectorized task demand and resource cluster capacity; acquiring resource energy constraints based on the satellite resources and the resource clusters; acquiring a capability energy constraint based on the vectorized satellite capability and the resource cluster capability; constructing a dynamic resource alliance model based on an objective function, a demand energy constraint, a resource energy constraint and a capacity energy constraint; and acquiring the resource alliance based on the dynamic resource alliance model. According to the embodiment of the invention, the constraint conditions are determined by comprehensively considering the requirements of the tasks and the capability of the satellite, the objective function is determined according to the income of the tasks, the dynamic resource alliance model is constructed, the configuration results of the tasks and the satellite resources are further obtained, the efficient configuration of the satellite resources and the tasks is realized, and the utilization rate of the satellite resources is improved.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
The embodiment of the invention provides a method for configuring multi-satellite cooperative resources, which is executed by a computer and comprises the following steps as shown in figure 1:
s1, acquiring satellite resources;
s2, selecting satellite resources based on task requirements of the task to be executed to obtain a resource cluster corresponding to the task to be executed;
s3, vectorizing the satellite capacity of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity;
s4, obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed;
s5, acquiring required energy constraint based on vectorized task requirements and the resource cluster capacity; acquiring resource energy constraints based on all satellite resources and the resource clusters; acquiring a capability energy constraint based on the vectorized satellite capability and the resource cluster capability;
s6, constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint;
and S7, acquiring resource alliance based on the dynamic resource alliance model.
The embodiment of the invention obtains satellite resources; selecting satellite resources based on task requirements of the tasks to be executed to obtain resource clusters corresponding to the tasks to be executed; vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity; obtaining the income of each task to be executed; acquiring an objective function based on the profits of all tasks to be executed; acquiring demand energy constraint based on vectorized task demand and resource cluster capacity; acquiring resource energy constraints based on the satellite resources and the resource clusters; acquiring a capability energy constraint based on the vectorized satellite capability and the resource cluster capability; constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint; and acquiring the resource alliance based on the dynamic resource alliance model. According to the embodiment of the invention, the constraint conditions are determined by comprehensively considering the requirements of the tasks and the capability of the satellite, the objective function is determined according to the income of the tasks, the dynamic resource alliance model is constructed, the configuration results of the tasks and the satellite resources are further obtained, the efficient configuration of the satellite resources and the tasks is realized, and the utilization rate of the satellite resources is improved.
The following is a detailed analysis of each step.
In step S1, satellite resources are acquired.
Specifically, the method comprises the following steps of obtaining at a satellite control center: tasks to be observed and available satellite resources.
All available satellite resources are gathered together to form a resource pool, M satellites capable of executing observation tasks are arranged, and r represents the satellite resources respectively1、r2、...、rMIs represented by wherein riRepresenting the ith satellite, resource pool R ═ R1,r2,...,rM}。
There are N complex tasks to be performed, denoted t1、t2、...、tNWherein t iskWhen the k-th complex task is represented, the task set T ═ T1,t2,...,tN}。
In step S2, satellite resources are selected based on the task requirements of the task to be executed, and a resource cluster corresponding to the task to be executed is obtained.
Specifically, in the embodiment of the invention, a multi-satellite cooperative resource organization form facing to complex tasks is designed and named as a resource cluster. The method is a resource set which can cooperatively finish complex task observation and respectively belongs to different satellite resources. The complex tasks which can not be completed by single resources are completed through mutual negotiation and cooperation of different types of resources of different satellites. The resource clusters and the complex tasks are in a one-to-one correspondence relationship, and the same resource is allowed to belong to a plurality of resource clusters from the perspective of resource optimal utilization.
For task t to be executedkThe corresponding resource cluster is Gpk。
In step S3, vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; resource cluster capabilities are obtained based on the vectorized satellite capabilities.
Specifically, the method comprises the following steps:
and S301, vectorizing the satellite capability.
For satellite riUsing an S-dimensional energy vector
Figure BDA0002252176550000101
Quantitative description of satellite riThe size of the execution capacity. Wherein:
Figure BDA0002252176550000111
1≤i≤M，1≤j≤S。
specifically, in the embodiment of the invention, the capacity of the satellite for executing the observation task can be described quantitatively by adopting a seven-dimensional demand vector, each dimension of demand is sequentially provided with a satellite imaging coverage area, a satellite imaging image type, a satellite continuous observation time capacity, a satellite imaging resolution, a satellite total resource, energy resources, storage capacity and the like, a satellite execution task priority is provided, the satellite imaging width is provided, the capacity strength of each dimension is divided into 6 grades, the grades are respectively represented by numbers 1 to 6, and the higher the number is, the higher the capacity grade is, the stronger the capacity is.
The method comprises the steps of determining the strength grade corresponding to each dimension of the satellite through analyzing the capability of an inviting expert for executing an observation task on the satellite, obtaining a corresponding grade value, and converting qualitative requirement strength into quantitative representation according to each dimension of the satellite to form a satellite capability vector.
And S302, vectorizing task requirements.
Specifically, an S-dimensional demand vector is employed:
Figure BDA0002252176550000112
the amount of required capacity to quantitatively describe complex tasks. Wherein:
Figure BDA0002252176550000113
(1≤k≤N，1≤j≤S)。
in the embodiment of the invention, the magnitude of the task demand capacity can be quantitatively described by adopting a seven-dimensional demand vector, and each dimension demand is respectively the requirements of firstly, the task coverage area, secondly, the task imaging type, thirdly, the task duration observation time, fourthly, the task imaging resolution, thirdly, the task resource consumption, fourthly, the task execution priority and fourthly, the task coverage width. The demand strength of each dimension is divided into 6 grades, which are respectively represented by numbers 1 to 6, and the larger the number is, the higher the demand grade is, the stronger the demand is.
The embodiment of the invention invites experts to analyze the tasks to be executed. And through inviting experts to analyze the task, determining the strength grade corresponding to each dimension of the task, obtaining a corresponding grade value, and converting the qualitative requirement strength into quantitative representation according to the grade of each dimension of the task to form a task requirement vector.
S303, acquiring the resource cluster capability of the resource cluster.
In particular, for the resource cluster GpkQuantitatively describing the capacity of the resource cluster by using an S-dimensional capacity vector,
Figure BDA0002252176550000121
specifically, in the embodiment of the invention, the resource cluster capacity can be described quantitatively by adopting a seven-dimensional demand vector, each dimensional demand is respectively provided with (i) imaging coverage area capacity, (ii) imaging picture type, (iv) imaging duration observation time capacity, (iv) imaging resolution, (v) total resource (c) task priority, (v) imaging width (c) dividing the demand strength of each dimension into 6 grades, which are respectively represented by numbers 1 to 6, and the larger the number is, the higher the demand grade is, the stronger the demand is.
Vectorizing and expressing the contribution capacity of different satellites in the resource cluster, correspondingly accumulating the capacity value of each dimension, and if the capacity value exceeds 6, expressing the capacity value according to 6 to obtain the capacity value of each dimension of the resource cluster so as to form a resource cluster capacity vector.
It should be noted that the resource cluster capability is an accumulated value of all satellite resource capabilities constituting the resource cluster capability, and specifically includes:
Figure BDA0002252176550000122
wherein:
Figure BDA0002252176550000123
representing a resource cluster GpkThe ability of (c);
Figure BDA0002252176550000124
denotes the ith satellite riIn the resource cluster GpkContribution ability in (1)
In step S4, a benefit of each task to be performed is acquired; and acquiring an objective function based on the profits of all tasks to be executed.
Specifically, the method for acquiring the profit of each task to be executed includes:
S(tk)＝E(tk)-V(Gpk)-C(Gpk)
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
E(tk) Indicating completion of task tkAn available reward;
C(Gpk) Representing a resource cluster GpkThe cost of conversion of all resources in the system;
V(Gpk) Indicating the mutual cooperation completion t of resources in the resource clusterkCost of consumption in the process;
the objective function is specifically:
Figure BDA0002252176550000131
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
and N is the number of tasks to be executed.
The objective function is: the total profit after all tasks are completed is maximized.
In step S5, acquiring a demand energy constraint based on the vectorized task demand and the resource cluster capability; acquiring resource energy constraints based on all satellite resources and the resource clusters; capability energy constraints are obtained based on the vectorized satellite capabilities and the resource cluster capabilities described above.
Specifically, the method comprises the following steps:
s501, acquiring required energy constraint
The method specifically comprises the following steps:
Figure BDA0002252176550000132
Figure BDA0002252176550000133
wherein:
Figure BDA0002252176550000134
representing a resource cluster GpkThe ability of (c);
Figure BDA0002252176550000135
indicating the kth task to be performed tkThe task requirements of (1);
Figure BDA0002252176550000141
representing a resource cluster GpkCapability component in j-th dimension
Figure BDA0002252176550000142
Representing a task tkA demand component in the j-th dimension.
The constraint conditions described above mean: the requirement of the task to be executed is less than or equal to the capability of the corresponding resource cluster.
And S502, acquiring resource energy constraint.
Complex task oriented tkIs a subset Gp of the resource pool RkE.r, from the perspective of resource optimal utilization, the same resource is allowed to belong to multiple resource clusters, i.e. the accumulation of resources in all resource clusters is equal to the total resources in the resource pool.
The resource energy constraints are specifically:
Gp1∪Gp2∪...∪GpN＝R
|Gp1|+|Gp2|+...+|GpN|≥|R|
wherein:
Gpkindicating the kth task to be performed tkThe resource cluster of (2);
r represents the total satellite resources.
S503, obtaining capacity energy constraint.
Specifically, the capacity energy constraints include: the resource clusters constitute a capacity energy constraint and a satellite resource contribution capacity energy constraint.
Wherein the resource cluster formation capability energy constraint is:
Figure BDA0002252176550000143
Figure BDA0002252176550000144
wherein:
Figure BDA0002252176550000151
representing a resource cluster GpkThe ability of (c);
Figure BDA0002252176550000152
denotes the ith satellite riIn the resource cluster GpkThe contributing capability of (1);
Figure BDA0002252176550000153
representing a resource cluster GpkA capability component in the j-th dimension;
Figure BDA0002252176550000154
representing a satellite riIn the resource cluster GpkThe capability component M in the j-th dimension represents the number of satellites.
The constraint conditions described above mean: the total capacity of the resource cluster is equal to the sum of the capacities contributed by each satellite resource in the resource cluster.
The energy constraint of the contribution capacity of the satellite resources is specifically as follows:
Figure BDA0002252176550000155
wherein:
Figure BDA0002252176550000156
denotes the ith satellite riIn the resource cluster GpkThe contributing capability of (1);
Figure BDA0002252176550000157
representing a satellite riThe ability of itself;
when satellite riParticipating in executing task tkWhen xikIs 1; otherwise, xikIs 0.
The constraint conditions described above mean: for each satellite, the sum of the contribution capacities of the single satellite resource in all the resource clusters participating in is less than or equal to the capacity of the satellite.
In step S6, a dynamic resource alliance model is constructed based on the objective function, the required energy constraint, the resource energy constraint and the capability energy constraint.
In step S7, the resource federation is acquired based on the dynamic resource federation model.
Specifically, satellite resource information and task information to be executed are input into the dynamic resource alliance model to obtain resource alliances.
Specifically, the resource alliance corresponding to each task to be executed is as follows:
Gak＝best{Gpk}
wherein:
Gpkindicating the kth task to be performed tkThe resource cluster of (2);
Gakindicating a task to be performed tkThe resource of (2) is allied as an optimal resource cluster.
For task t to be executedkThe optimal resource cluster refers to a final resource cluster which is obtained after screening of the dynamic resource alliance model and meets the conditions, namely the resource cluster which meets the conditions can be used as a task to be executedThe resources of the service are allied.
The resource alliance corresponding to all the tasks to be executed is obtained by the following steps:
G＝{(Ga1，t1)，(Ga2，t2)，...，(GaN，tN)}
wherein:
g represents a matching set of all tasks to be executed and corresponding resource alliances;
tkrepresenting the kth task to be executed;
Gakindicating a task to be performed tkThe resource of (2) is allied.
The embodiment of the invention also provides a configuration system of multi-satellite cooperative resources, which comprises a computer, wherein the computer comprises:
at least one memory cell;
at least one processing unit;
wherein, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit to realize the following steps:
s1, acquiring satellite resources;
s2, selecting satellite resources based on task requirements of the task to be executed to obtain a resource cluster corresponding to the task to be executed;
s3, vectorizing the satellite capacity of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity;
s4, obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed;
s5, acquiring required energy constraint based on vectorized task requirements and the resource cluster capacity; acquiring resource energy constraints based on all satellite resources and the resource clusters; acquiring a capability energy constraint based on the vectorized satellite capability and the resource cluster capability;
s6, constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint;
and S7, acquiring resource alliance based on the dynamic resource alliance model.
It can be understood that, the configuration system provided in the embodiment of the present invention corresponds to the configuration method, and the explanation, examples, and beneficial effects of the relevant contents of the configuration system provided in the embodiment of the present invention may refer to the corresponding contents in the configuration method of multi-satellite cooperative resources, which are not described herein again.
In summary, compared with the prior art, the method has the following beneficial effects:
the embodiment of the invention obtains satellite resources; selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed; vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity; obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed; acquiring demand energy constraint based on vectorized task demand and resource cluster capacity; acquiring resource energy constraints based on the satellite resources and the resource clusters; acquiring a capability energy constraint based on the vectorized satellite capability and the resource cluster capability; constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint; and acquiring the resource alliance based on the dynamic resource alliance model. According to the embodiment of the invention, the constraint conditions are determined by comprehensively considering the requirements of the tasks and the capability of the satellite, the objective function is determined according to the income of the tasks, the dynamic resource alliance model is constructed, the configuration results of the tasks and the satellite resources are further obtained, the efficient configuration of the satellite resources and the tasks is realized, and the utilization rate of the satellite resources is improved.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments. In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (5)

1. A method for configuring multi-satellite cooperative resources, wherein the method for configuring is executed by a computer and comprises the following steps:
acquiring satellite resources;
selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed;
vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of a task to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity;
obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed;
acquiring a demand energy constraint based on the vectorized task demand and the resource cluster capacity; acquiring resource energy constraints based on all satellite resources and the resource clusters; obtaining a capability energy constraint based on the vectorized satellite capability and the resource cluster capability;
constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint;
acquiring resource alliances based on the dynamic resource alliance model;
the method for acquiring the objective function comprises the following steps:
Figure FDA0003533924750000011
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
n is the number of tasks to be executed;
the required energy constraint is:
Figure FDA0003533924750000021
Figure FDA0003533924750000022
wherein:
Figure FDA0003533924750000023
representing a resource cluster GpkThe ability of (c);
Figure FDA0003533924750000024
indicating the kth task to be performed tkThe task requirements of (1);
Figure FDA0003533924750000025
representing a resource cluster GpkCapability component in j-th dimension
Figure FDA0003533924750000026
Representing a task tkA demand component in the j-th dimension;
the resource energy constraints are:
Gp1∪Gp2∪...∪GpN＝R
|Gp1|+|Gp2|+...+|GpN|≥|R|
wherein:
Gpkindicating the kth task to be performed tkThe resource cluster of (2);
r represents total satellite resources;
the capacity energy constraint includes: the resource cluster forms an ability energy constraint and a satellite resource contribution ability energy constraint;
the resource cluster formation capacity energy constraint is as follows:
Figure FDA0003533924750000027
Figure FDA0003533924750000028
wherein:
Figure FDA0003533924750000029
representing a resource cluster Gpk(ii) ability of;
Figure FDA00035339247500000210
denotes the ith satellite riIn the resource cluster GpkThe contribution ability of (1);
Figure FDA0003533924750000031
representing a resource cluster GpkA capability component in the j-th dimension;
Figure FDA0003533924750000032
representing a satellite riIn the resource cluster GpkThe ability component of the contributing ability in the j-th dimension;
m represents the number of satellites;
the satellite resource contribution capacity energy constraint is as follows:
Figure FDA0003533924750000033
wherein:
Figure FDA0003533924750000034
denotes the ith satellite riIn the resource cluster GpkThe contributing capability of (1);
Figure FDA0003533924750000035
representing a satellite riThe ability of itself;
when satellite riParticipate in performing task tkWhen xikIs 1; if not, then,xikis 0.
2. The configuration method according to claim 1, wherein the method for acquiring the profit of each task to be performed comprises:
S(tk)＝E(tk)-V(Gpk)-C(Gpk)
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
E(tk) Indicating completion of task tkAn available reward;
C(Gpk) Representing a resource cluster GpkThe cost of conversion of all resources in the system;
V(Gpk) Indicating the mutual cooperation completion t of resources in the resource clusterkThe cost of consumption in the process.
3. The configuration method according to claim 1, wherein the resource alliance acquisition method comprises:
inputting satellite resource information and task information to be executed into the dynamic resource alliance model to obtain resource alliances corresponding to each task to be executed;
the resource alliance corresponding to each task to be executed is as follows:
G＝{(Ga1，t1)，(Ga2，t2)，...，(GaN，tN)}
wherein:
g represents a matching set of all tasks to be executed and corresponding resource alliances;
tkrepresenting the kth task to be executed;
Gakindicating a task t to be performedkThe resource of (2) is allied.
4. A system for configuring multi-satellite co-resources, the system comprising a computer, the computer comprising:
at least one memory cell;
at least one processing unit;
wherein the at least one memory unit has stored therein at least one instruction that is loaded and executed by the at least one processing unit to perform the steps of:
acquiring satellite resources;
selecting satellite resources based on task requirements of a task to be executed to obtain a resource cluster corresponding to the task to be executed;
vectorizing satellite capabilities of the satellite resources; vectorizing task requirements of tasks to be executed; acquiring resource cluster capacity based on the vectorized satellite capacity;
obtaining the income of each task to be executed; obtaining an objective function based on the profits of all tasks to be executed;
acquiring a demand energy constraint based on the vectorized task demand and the resource cluster capacity; acquiring resource energy constraints based on all satellite resources and the resource clusters; obtaining a capability energy constraint based on the vectorized satellite capability and the resource cluster capability;
constructing a dynamic resource alliance model based on the objective function, the demand energy constraint, the resource energy constraint and the capacity energy constraint;
acquiring resource alliances based on the dynamic resource alliance model;
the method for acquiring the objective function comprises the following steps:
Figure FDA0003533924750000051
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
n is the number of tasks to be executed;
the required energy constraint is:
Figure FDA0003533924750000052
Figure FDA0003533924750000053
wherein:
Figure FDA0003533924750000054
representing a resource cluster GpkThe ability of (c);
Figure FDA0003533924750000055
represents the kth task to be executed tkThe task requirements of (1);
Figure FDA0003533924750000056
representing a resource cluster GpkCapability component in j-th dimension
Figure FDA0003533924750000057
Representing a task tkA demand component in the j-th dimension;
the resource energy constraints are:
Gp1∪Gp2∪...∪GpN＝R
|Gp1|+|Gp2|+...+|GpN|≥|R|
wherein:
Gpkindicating the kth task to be performed tkThe resource cluster of (2);
r represents total satellite resources;
the capacity energy constraint includes: the resource cluster forms an ability energy constraint and a satellite resource contribution ability energy constraint;
the resource cluster formation capacity energy constraint is as follows:
Figure FDA0003533924750000061
Figure FDA0003533924750000062
wherein:
Figure FDA0003533924750000063
representing a resource cluster GpkThe ability of (c);
Figure FDA0003533924750000064
denotes the ith satellite riIn the resource cluster GpkThe contributing capability of (1);
Figure FDA0003533924750000065
representing a resource cluster GpkA capability component in the j-th dimension;
Figure FDA0003533924750000066
representing a satellite riIn the resource cluster GpkA capability component of the contributing capability in the j-th dimension;
m represents the number of satellites;
the satellite resource contribution capacity energy constraint is as follows:
Figure FDA0003533924750000067
wherein:
Figure FDA0003533924750000068
denotes the ith satellite riIn the resource cluster GpkThe contributing capability of (1);
Figure FDA0003533924750000069
representing a satellite riThe ability of itself;
when satellite riParticipate in performing task tkWhen xikIs 1; otherwise, xikIs 0.
5. The configuration system according to claim 4, wherein the method for acquiring the profit of each task to be performed comprises:
S(tk)＝E(tk)-V(Gpk)-C(Gpk)
wherein:
S(tk) Finish the kth task t to be executedkThe income obtained later;
E(tk) Indicating completion of task tkAn available reward;
C(Gpk) Representing a resource cluster GpkThe cost of conversion of all resources in the system;
V(Gpk) Indicating the completion of mutual cooperation of resources tkThe cost of consumption in the process.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106685515A (en) * 2017-01-05 2017-05-17 清华大学 Allocation method and device for satellite resources in space information network
CN107276660A (en) * 2017-06-22 2017-10-20 清华大学 Resource allocation methods and device in non-orthogonal multiple air-ground coordination communication system
CN108023637A (en) * 2017-12-06 2018-05-11 中国人民解放军国防科技大学 Isomorphic multi-satellite online collaboration method
CN109657930A (en) * 2018-11-29 2019-04-19 合肥工业大学 Resource Alliance's construction method of cotasking based on dummy constellation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106685515A (en) * 2017-01-05 2017-05-17 清华大学 Allocation method and device for satellite resources in space information network
CN107276660A (en) * 2017-06-22 2017-10-20 清华大学 Resource allocation methods and device in non-orthogonal multiple air-ground coordination communication system
CN108023637A (en) * 2017-12-06 2018-05-11 中国人民解放军国防科技大学 Isomorphic multi-satellite online collaboration method
CN109657930A (en) * 2018-11-29 2019-04-19 合肥工业大学 Resource Alliance's construction method of cotasking based on dummy constellation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
多星任务统筹配置与数传规划方法研究;李兴泽;《中国优秀硕士学位论文全文数据库 基础科学辑》;20150115;A008-81 *

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