CN111913785B - Multi-satellite task scheduling method and system - Google Patents

Multi-satellite task scheduling method and system Download PDF

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CN111913785B
CN111913785B CN202010521530.6A CN202010521530A CN111913785B CN 111913785 B CN111913785 B CN 111913785B CN 202010521530 A CN202010521530 A CN 202010521530A CN 111913785 B CN111913785 B CN 111913785B
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CN111913785A (en
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靳鹏
唐晓茜
胡笑旋
罗贺
王国强
马华伟
夏维
张歆悦
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Hefei University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/48—Indexing scheme relating to G06F9/48
    • G06F2209/486—Scheduler internals

Abstract

The invention provides a multi-satellite task scheduling method and system, and relates to the field of satellite scheduling. The method comprises the following steps: matching the satellite and the satellite task, and coding to obtain a group gene; solving an initial solution based on a multi-star task model and a group gene which are constructed in advance; taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm; performing variation operation processing on the satellite task population subjected to the cross operation processing based on a multi-round iteration two-point variation method; and acquiring a satellite task scheduling scheme according to the satellite task population processed by the mutation operation. The invention improves the efficiency of satellite observation tasks.

Description

Multi-satellite task scheduling method and system
Technical Field
The invention relates to the technical field of satellite scheduling, in particular to a multi-satellite task scheduling method and system.
Background
With the development of science and technology, the aerospace field is continuously explored and developed, and various aircrafts and satellites are manufactured and widely applied to various fields, such as ground monitoring through satellites. The user puts forward the observation task requirement to the ground station, and the ground station injects the task to the satellite, surrounds the ground, and observes the task target, thereby obtains the information that the user needs. Therefore, the realization of mission observations using satellites is a current research focus.
With the continuous development of satellite technology, the prior art generally adopts a multi-satellite task scheduling method for observation. In the existing multi-satellite task scheduling, one or more tasks required by a user are allocated to one or more satellites for observation, the allocation result is a sequence observed by the satellites for the tasks, and then the tasks to be observed are imaged according to the generated sequence.
However, the inventor of the present application finds that multiple satellites are used to observe multiple tasks in multiple rounds during multi-satellite task scheduling, and the multi-satellite observation task has low efficiency because the multi-satellite, multi-round and multi-task complex observation method causes a large number of observation schemes and a complex process of the satellites, and often cannot realize optimal utilization of satellite resources.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a multi-satellite task scheduling method and a multi-satellite task scheduling system, which solve the technical problem of low efficiency when a multi-satellite observation task is utilized 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 multi-satellite task scheduling method for solving the technical problem, which is executed by a computer and comprises the following steps:
s1, matching the satellite and the satellite task, and coding to obtain a group gene;
s2, solving an initial solution based on a pre-constructed multi-star task model and the group genes;
s3, taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm;
s4, performing variation operation processing on the satellite task population after the cross operation processing based on a multi-round iteration two-point variation method;
and S5, acquiring a satellite task scheduling scheme according to the satellite task population after the mutation operation processing.
Preferably, in S1, the matching and encoding the satellite and the satellite task includes:
sorting and labeling the satellite tasks according to the priority from large to small, separating the satellite tasks by using the satellites, and coding the satellite tasks by adopting an 0/1 coding mode, wherein: for satellite task T, a 0 indicates that T is not observed and a1 indicates that T is observed by the first satellite behind T.
Preferably, in S2, the pre-constructed multi-star task model includes an objective function and a constraint condition, where the objective function is:
Figure BDA0002532333190000021
wherein:
Figure BDA0002532333190000031
representing the benefit of the ith task being performed;
Figure BDA0002532333190000032
is a variable of 0/1
Figure BDA0002532333190000033
When 1 is taken, the ith task is executed on the kth orbit of the jth satellite; when in use
Figure BDA0002532333190000034
When 0 is taken, the ith task is not executed;
n represents the number of satellites; m represents the number of satellite missions;
β ij representing the loss value of the jth satellite when the ith task is executed;
the constraint conditions include:
Figure BDA0002532333190000035
Figure BDA0002532333190000036
Figure BDA0002532333190000037
Figure BDA0002532333190000038
Figure BDA0002532333190000039
Figure BDA00025323331900000310
wherein:
Figure BDA00025323331900000311
representing the end time of an observation time window when the ith task is executed on the kth orbit of the jth satellite;
Figure BDA00025323331900000312
representing the start time of an observation time window when the ith task is executed on the kth orbit of the jth satellite;
per i representing the observation duration of the ith task;
Figure BDA00025323331900000313
representing the starting time of a visible time window of the jth satellite on the kth track for the ith task;
Figure BDA00025323331900000314
representing the end time of the visible time window of the jth satellite to the ith task on the kth track;
Figure BDA0002532333190000041
representing the attitude transition time between two successive tasks i and i' performed on the kth orbit of the jth satellite;
Capacity j representing the maximum storage capacity of each orbit of the jth satellite;
Figure BDA0002532333190000042
the storage capacity required by the image obtained by observing the ith task on the kth orbit by the jth satellite is represented;
Res j represents the maximum energy limit per orbit for the jth satellite;
Figure BDA0002532333190000043
representing the energy required by the jth satellite to image the ith task observation in the kth orbit.
Preferably, in S3, the performing a crossover operation process on the satellite task population based on a preset two-generation competitive optimization algorithm includes:
s301, sequentially selecting any two chromosomes A, B as parent chromosomes;
s302, randomly generating two satellites C, D as intersections of parent chromosomes;
obtaining a sequence A1 intermediate the intersection C, D in the parent chromosome A and a sequence B1 intermediate the intersection C, D in the parent chromosome B;
performing cross exchange on the sequence A1 and the sequence B1 to obtain two prepared chromosomes;
s303, when the cross-exchanged sequence in the prepared chromosome is repeated with the original task in the parent chromosome, confirming the codes of the repeated tasks, and if the code values of the repeated tasks are all 0, ignoring the repetition; if the coding values of the repeated tasks are all 1, selecting one of the repeated tasks to execute, and deleting the other repeated task; if the code values of the repeated tasks are 1 and 0, directly reserving;
repeating the steps until no repeated task exists in the cross-interchange sequence to obtain two offspring chromosomes;
s304, calculating fitness values of the offspring chromosomes and the parent chromosomes based on a preset fitness function, and reserving two chromosomes with the highest fitness values;
s305, judging whether the chromosomes in the satellite task population are combined pairwise, if so, finishing the cross operation; if not, go to S301.
Preferably, the preset fitness function is as follows:
Figure BDA0002532333190000051
wherein:
P s representing the total priority of executing tasks on the s-th chromosome;
p i representing the priority of the ith observation target;
Figure BDA0002532333190000052
is a variable of 0/1
Figure BDA0002532333190000053
When 1 is taken, the ith task is executed on the kth orbit of the jth satellite; when in use
Figure BDA0002532333190000054
When 0 is taken, it means that the ith task is not executed.
Preferably, step S4 specifically includes the following steps:
s401, randomly selecting a parent chromosome for the satellite task population after the cross operation;
s402, selecting two variant gene positions by roulette, carrying out variant on original codes on the two selected variant gene positions, and if the original code is 1, not needing variant; if the original code is 0, the code is changed into 1;
s403, after all the parent chromosomes are selected, performing feasibility test on the retained individuals according to the constraint conditions, deleting infeasible individuals, and retaining feasible individuals;
s404, taking the feasible individuals as parent chromosomes, and turning to S402 until the variation tends to be stable.
Preferably, the method for selecting the variant loci comprises:
setting a variant locus selection function:
Figure BDA0002532333190000061
wherein:
size indicates the total number of tasks present in the sequence, i indicates the ith gene position;
randomly generating two numbers a and b between 0 and 1;
if a>＝f i And a is<f i+1 If so, the ith gene locus is selected as the first variant gene locus;
determining the second variant locus according to the number b.
Preferably, step S5 specifically includes the following steps:
and for the satellite task population subjected to mutation operation processing, calculating objective function values of all chromosomes in the population based on the objective function, and selecting a scheme corresponding to the optimal solution as a satellite task scheduling scheme.
The invention provides a multi-satellite task scheduling system for solving the technical problem, which 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:
s1, matching the satellite and the satellite task, and coding to obtain a group gene;
s2, solving an initial solution based on a pre-constructed multi-star task model and the group genes;
s3, taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm;
s4, performing variation operation processing on the satellite task population after the cross operation processing based on a multi-round iteration two-point variation method;
and S5, acquiring a satellite task scheduling scheme according to the satellite task population after the mutation operation processing.
(III) advantageous effects
The invention provides a multi-satellite task scheduling method and system. Compared with the prior art, the method has the following beneficial effects:
the invention matches the satellite and the satellite task, and codes to obtain the group gene; solving an initial solution based on a multi-star task model and a group gene which are constructed in advance; taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm; performing mutation operation processing on the satellite task population subjected to the cross operation processing based on a multi-round iteration two-point mutation method; and acquiring a satellite task scheduling scheme according to the satellite task population processed by the mutation operation. By carrying out gene group coding on the tasks, the feasibility test of the solution in the operation process is avoided, the operation time is shortened, the cross operation is carried out by using two generations of competitive optimization modes so as to increase the understanding space, the population evolution can be prevented from being delayed and the optimal solution can be more easily found by using multiple rounds of iterative two-point variation, and the efficiency of satellite observation tasks is further improved.
Drawings
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 multi-star task scheduling method 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 solves the problem of low efficiency when a multi-satellite observation task is utilized in the prior art by providing the multi-satellite task scheduling method and system, and improves the efficiency when the multi-satellite observation task is utilized.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
the embodiment of the invention matches the satellite and the satellite task and carries out coding to obtain the group gene; solving an initial solution based on a multi-star task model and a group gene which are constructed in advance; taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm; performing variation operation processing on the satellite task population subjected to the cross operation processing based on a multi-round iteration two-point variation method; and acquiring a satellite task scheduling scheme according to the satellite task population processed by the mutation operation. The embodiment of the invention avoids the feasibility test of the solution in the operation process by carrying out the group gene coding on the task, shortens the operation time, simultaneously carries out the cross operation by using two generations of competitive optimization modes so as to increase the understanding space, can prevent the population evolution from being delayed and is more beneficial to finding the optimal solution by using multiple rounds of iterative two-point variation, and further improves the efficiency of the satellite observation task.
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 multi-star task scheduling method, which is executed by a computer and comprises the following steps as shown in figure 1:
s1, matching the satellite and the satellite task, and coding to obtain a group gene;
s2, solving an initial solution based on a pre-constructed multi-star task model and the group genes;
s3, taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm;
s4, performing variation operation processing on the satellite task population after the cross operation processing based on a multi-round iteration two-point variation method;
and S5, acquiring a satellite task scheduling scheme according to the satellite task population after the mutation operation processing.
The embodiment of the invention matches the satellite and the satellite task and carries out coding to obtain the group gene; solving an initial solution based on a multi-star task model and a group gene which are constructed in advance; taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm; performing variation operation processing on the satellite task population subjected to the cross operation processing based on a multi-round iteration two-point variation method; and acquiring a satellite task scheduling scheme according to the satellite task population subjected to the mutation operation. The embodiment of the invention avoids the feasibility test of the solution in the operation process by carrying out the group gene coding on the task, shortens the operation time, simultaneously carries out the cross operation by using two generations of competitive optimization modes so as to increase the understanding space, can prevent the population evolution from being delayed and is more beneficial to finding the optimal solution by using multiple rounds of iterative two-point variation, and further improves the efficiency of the satellite observation task.
The following is a detailed analysis of each step.
In step S1, the satellite and satellite tasks are matched and encoded to obtain a set of genes.
Specifically, the satellite tasks are sorted and labeled according to the priority from big to small, the satellite tasks are separated by the satellites, and the satellite tasks are encoded by adopting an 0/1 encoding mode, wherein: for satellite task T, a 0 indicates that T is not observed and a1 indicates that T is observed by the first satellite behind T.
The following table is given as an example:
Figure BDA0002532333190000101
satellites numbered 5 and 8 are executed on satellite 1, task 5 is executed in a first orbit of satellite 1, and task 8 is executed in a second orbit of satellite 1.
In step S2, an initial solution is found based on the previously constructed multi-star task model and the above-described set of genes.
Specifically, the pre-constructed multi-star task model comprises an objective function and a constraint condition.
The objective function is:
Figure BDA0002532333190000102
wherein:
Figure BDA0002532333190000103
representing the benefit of the ith task being performed;
Figure BDA0002532333190000104
is a variable of 0/1
Figure BDA0002532333190000105
When 1 is taken, the ith task is executed on the kth orbit of the jth satellite; when in use
Figure BDA0002532333190000106
When 0 is taken, the ith task is not executed;
n represents the number of satellites; m represents the number of satellite tasks;
β ij indicating the loss value of the jth satellite when the ith task is executed.
The constraint conditions include:
Figure BDA0002532333190000111
Figure BDA0002532333190000112
Figure BDA0002532333190000113
Figure BDA0002532333190000114
Figure BDA0002532333190000115
Figure BDA0002532333190000116
wherein:
Figure BDA0002532333190000117
representing the end time of an observation time window when the ith task is executed on the kth orbit of the jth satellite;
Figure BDA0002532333190000118
is shown asThe starting time of an observation time window when i tasks are executed on the kth orbit of the jth satellite;
per i representing the observation duration of the ith task;
Figure BDA0002532333190000119
representing the starting time of a visible time window of the jth satellite on the kth track for the ith task;
Figure BDA00025323331900001110
representing the end time of the visible time window of the jth satellite to the ith task on the kth track;
Figure BDA00025323331900001111
representing the attitude transition time between two successive tasks i and i' performed on the kth orbit of the jth satellite;
Capacity j representing the maximum storage capacity of each orbit of the jth satellite;
Figure BDA00025323331900001112
the storage capacity required by the image obtained by observing the ith task on the kth orbit by the jth satellite is represented;
Res j represents the maximum energy limit per orbit for the jth satellite;
Figure BDA0002532333190000121
indicating the energy required by the jth satellite to image the ith task observation in the kth orbit.
In the embodiment of the present invention, an initial solution may be obtained according to the encoding mode of the group genes and the multi-star task model in S1 for subsequent operations.
In step S3, the initial solution is used as a satellite task population, and the satellite task population is subjected to cross operation processing based on a preset two-generation competition optimization algorithm.
The crossover operation specifically comprises the following steps:
s301, selecting any two chromosomes A, B as parent chromosomes in sequence.
S302, randomly generating two satellites C, D as the cross points of the parent chromosomes.
The sequence a1 was obtained in the parent chromosome a intermediate the intersection C, D, and the sequence B1 was obtained in the parent chromosome B intermediate the intersection C, D.
The sequence A1 and the sequence B1 were cross-exchanged to obtain two preliminary chromosomes.
S303, when the cross-exchanged sequence in the prepared chromosome is repeated with the original task in the parent chromosome, confirming the codes of the repeated tasks, and if the code values of the repeated tasks are all 0, ignoring the repetition; if the code values of the repeated tasks are all 1, selecting one of the repeated tasks to execute, and deleting the other repeated task; if the code values of the repeated tasks are 1 and 0, the repeated tasks are directly reserved.
And repeating the steps until no repeated tasks exist in the cross-interchange sequences, and obtaining two offspring chromosomes.
S304, calculating fitness values of the offspring chromosomes and the parent chromosomes based on a preset fitness function, and reserving the two chromosomes with the highest fitness values.
Specifically, the preset fitness function is as follows:
Figure BDA0002532333190000131
wherein:
P s representing the total priority of executing tasks on the s-th chromosome;
p i indicating the priority of the ith observation target;
Figure BDA0002532333190000132
is a variable of 0/1
Figure BDA0002532333190000133
When 1 is taken, the ith task is executed on the kth orbit of the jth satellite; when the temperature is higher than the set temperature
Figure BDA0002532333190000134
When 0 is taken, it means that the ith task is not executed.
It should be noted that, when designing the fitness function, the idea of the embodiment of the present invention is as follows:
for general tasks, the priority and the target profit are positively correlated, so that designing the fitness function by using the priority can lead the convergence direction and accelerate the convergence speed. However, for the urgent task, the priority may be higher but not positively correlated with the profit, and in this case, the observed profit should not be limited, and the priority should be given as the main priority, and the fitness function is still appropriate. And the fitness function is simple and convenient to calculate, and the time complexity of algorithm calculation can be reduced.
S305, judging whether the chromosomes in the satellite task population are combined pairwise, if so, finishing the cross operation; if not, go to S301.
It should be noted that, in the method provided by the embodiment of the present invention, the individuals used for crossing are all individuals in the initial population, and the crossing operation is performed by taking the group genes as a unit, so that the integrity of the genes is ensured, the feasibility of generating the individuals is ensured, the phenomenon of premature convergence is avoided, and the feasibility of understanding is ensured.
In step S4, a mutation operation process is performed on the satellite task population after the crossover operation process based on a multi-round iterative two-point mutation method.
The method specifically comprises the following steps:
s401, randomly selecting a parent chromosome for the satellite task population after the cross operation.
S402, selecting two variant gene positions by roulette, carrying out variant on original codes on the two selected variant gene positions, and if the original code is 1, not needing variant; if the original code is 0, the code is changed to 1.
And selecting the satellite orbit with more surplus storage capacity and energy to arrange the task according to the following constraint conditions.
Figure BDA0002532333190000141
Figure BDA0002532333190000142
Specifically, the method for selecting the variant loci comprises the following steps:
setting a variant locus selection function:
Figure BDA0002532333190000143
wherein:
size indicates the total number of tasks present in the sequence, i indicates the ith gene position;
randomly generating two numbers a and b between 0 and 1;
if a>＝f i And a is<f i+1 Then the ith locus is selected to determine the first variant locus.
Similarly, the second variant locus is determined according to the number b.
S403, after all the parent chromosomes are selected, performing feasibility test on the retained individuals according to constraint conditions, deleting infeasible individuals, and retaining feasible individuals;
s404, taking the feasible individuals as parent chromosomes, and turning to S402 until the variation tends to be stable.
It should be noted that the mutation operation in the embodiment of the present invention performs the mutation in smaller units in the group of genes, and by designing the mutation rule, it is ensured that the solution converges in a better direction, and it is not necessary to calculate the fitness function, but only the feasibility of the solution needs to be determined, so as to reduce the algorithm execution time, and ensure that the new individual obtained by the mutation is better than the original individual.
In step S5, a satellite task scheduling plan is obtained according to the satellite task population after the mutation operation processing.
The method specifically comprises the following steps:
and (4) performing preferential treatment on the satellite task population subjected to mutation operation treatment. Specifically, the objective function value of an individual tending to be stable in the population is calculated according to the objective function, and an optimal solution is obtained. And selecting a scheme corresponding to the optimal solution as a satellite task scheduling scheme.
It should be noted that, in the embodiment of the present invention, the feasibility of the offspring chromosome in the algorithm crossing process is ensured by the coding mode of the group gene, and the time for feasibility judgment is reduced. Through two generations of competitive cross operation, the phenomenon of premature convergence of results caused by the fact that individual genes cannot be effectively copied in the traditional genetic algorithm is avoided. By means of multiple rounds of iterative two-point mutation operations, the population diversity is increased, the difference between chromosomes is increased, the phenomenon that evolution is not stopped is avoided, the search efficiency is improved, and local optimization is avoided.
The embodiment of the invention also provides a multi-satellite task scheduling system, 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, matching the satellite and the satellite task, and coding to obtain a group gene;
s2, solving an initial solution based on a pre-constructed multi-star task model and the group genes;
s3, taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm;
s4, performing variation operation processing on the satellite task population after the cross operation processing based on a multi-round iteration two-point variation method;
and S5, acquiring a satellite task scheduling scheme according to the satellite task population after the mutation operation processing.
It can be understood that, the scheduling system provided in the embodiment of the present invention corresponds to the scheduling method, and the explanation, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the multi-star task scheduling method, 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 matches the satellite and the satellite task and carries out coding to obtain the group gene; solving an initial solution based on a multi-star task model and a group gene which are constructed in advance; taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competitive optimization algorithm; performing variation operation processing on the satellite task population subjected to the cross operation processing based on a multi-round iteration two-point variation method; and acquiring a satellite task scheduling scheme according to the satellite task population processed by the mutation operation. The embodiment of the invention avoids the feasibility test of the solution in the operation process by carrying out the group gene coding on the task, shortens the operation time, simultaneously carries out the cross operation by using two generations of competitive optimization modes so as to increase the understanding space, can prevent the population evolution from being delayed and is more beneficial to finding the optimal solution by using multiple rounds of iterative two-point variation, and further improves the efficiency of the satellite observation task.
It is noted that, herein, 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 the 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 should 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 (7)

1. A multi-star task scheduling method, wherein the scheduling method is executed by a computer and comprises:
s1, matching the satellite and the satellite task, and coding to obtain a group gene;
s2, solving an initial solution based on a pre-constructed multi-star task model and the group genes;
s3, taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm;
s4, performing variation operation processing on the satellite task population after the cross operation processing based on a multi-round iteration two-point variation method;
s5, acquiring a satellite task scheduling scheme according to the satellite task population after the mutation operation processing;
in S2, the pre-constructed multi-star task model includes an objective function and a constraint condition, where the objective function is:
Figure FDA0003804916460000011
wherein:
Figure FDA0003804916460000012
representing the benefit of the ith task being performed;
Figure FDA0003804916460000013
is a variable of 0/1
Figure FDA0003804916460000014
When 1 is taken, the ith task is executed on the kth orbit of the jth satellite; when the temperature is higher than the set temperature
Figure FDA0003804916460000015
When 0 is taken, the ith task is not executed;
n represents the number of satellites; m represents the number of satellite tasks;
β ij representing the loss value of the jth satellite when the ith task is executed;
the constraint conditions include:
Figure FDA0003804916460000016
Figure FDA0003804916460000021
Figure FDA0003804916460000022
Figure FDA0003804916460000023
Figure FDA0003804916460000024
Figure FDA0003804916460000025
wherein:
Figure FDA0003804916460000026
representing the end time of an observation time window when the ith task is executed on the kth orbit of the jth satellite;
Figure FDA0003804916460000027
representing the start time of an observation time window when the ith task is executed on the kth orbit of the jth satellite;
per i representing the observation duration of the ith task;
Figure FDA0003804916460000028
representing the starting time of a visible time window of the jth satellite to the ith task on the kth orbit;
Figure FDA0003804916460000029
representing the end time of the visible time window of the jth satellite to the ith task on the kth track;
Figure FDA00038049164600000210
representing the attitude transition time between two successive tasks i and i' performed on the kth orbit of the jth satellite;
Capacity j representing the maximum storage capacity of each orbit of the jth satellite;
Figure FDA00038049164600000211
showing the image obtained by the observation of the ith task on the kth orbit by the jth satelliteThe required storage capacity;
Res j represents the maximum energy limit per orbit for the jth satellite;
Figure FDA0003804916460000031
representing the energy required by the jth satellite to observe and image the ith task on the kth orbit;
step S4 specifically includes the following steps:
s401, randomly selecting a parent chromosome for the satellite task population after the cross operation;
s402, selecting two variant gene positions by roulette, carrying out variant on original codes on the two selected variant gene positions, and if the original code is 1, not needing variant; if the original code is 0, the code is changed into 1;
s403, after all the parent chromosomes are selected, performing feasibility test on the retained individuals according to the constraint conditions, deleting infeasible individuals, and retaining feasible individuals;
s404, taking the feasible individuals as parent chromosomes, and turning to S402 until the variation tends to be stable.
2. The scheduling method of claim 1 wherein said matching and encoding of satellites and satellite tasks at S1 comprises:
sorting and labeling satellite tasks according to priorities from large to small, separating the satellite tasks by using the satellites, and encoding the satellite tasks by adopting an 0/1 encoding mode, wherein: for satellite task T, a 0 indicates that T is not observed and a1 indicates that T is observed by the first satellite behind T.
3. The scheduling method of claim 1 wherein in S3, the performing a crossover operation process on the satellite task population based on a pre-set two-generation competitive optimization algorithm comprises:
s301, sequentially selecting any two chromosomes A, B as parent chromosomes;
s302, randomly generating two satellites C, D as cross points of parent chromosomes;
obtaining a sequence A1 intermediate the intersection C, D in the parent chromosome A and a sequence B1 intermediate the intersection C, D in the parent chromosome B;
performing cross exchange on the sequence A1 and the sequence B1 to obtain two prepared chromosomes;
s303, when the cross-exchanged sequence in the prepared chromosome is repeated with the original task in the parent chromosome, confirming the codes of the repeated tasks, and if the code values of the repeated tasks are all 0, ignoring the repetition; if the coding values of the repeated tasks are all 1, selecting one of the repeated tasks to execute, and deleting the other repeated task; if the code values of the repeated tasks are 1 and 0 respectively, directly reserving the repeated tasks;
repeating the steps until no repeated task exists in the cross-interchange sequence, and obtaining two offspring chromosomes;
s304, calculating fitness values of the offspring chromosomes and the parent chromosomes based on a preset fitness function, and reserving two chromosomes with the highest fitness values;
s305, judging whether the chromosomes in the satellite task population are combined pairwise, if so, finishing the cross operation; if not, go to S301.
4. The scheduling method of claim 3 wherein the predetermined fitness function is:
Figure FDA0003804916460000041
wherein:
P s representing the total priority of executing tasks on the s-th chromosome;
p i representing the priority of the ith observation target;
Figure FDA0003804916460000042
is a variable of 0/1
Figure FDA0003804916460000043
When 1 is taken, the ith task is executed on the kth orbit of the jth satellite; when in use
Figure FDA0003804916460000044
When 0 is taken, it means that the ith task is not executed.
5. The scheduling method of claim 1 wherein the selection of variant loci comprises:
setting a variant locus selection function:
Figure FDA0003804916460000051
wherein:
size indicates the total number of tasks present in the sequence, i indicates the ith gene position;
randomly generating two numbers a and b between 0 and 1;
if a>＝f i And a is<f i+1 If so, the ith gene locus is selected as the first variant gene locus;
determining the second variant locus according to the number b.
6. The scheduling method of claim 1, wherein the step S5 specifically comprises the steps of:
and for the satellite task population after the mutation operation processing, calculating objective function values of all chromosomes in the population based on the objective function, and selecting a scheme corresponding to the optimal solution as a satellite task scheduling scheme.
7. A multi-star task scheduling system, 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:
s1, matching the satellite and the satellite task, and coding to obtain a group gene;
s2, solving an initial solution based on a pre-constructed multi-star task model and the group genes;
s3, taking the initial solution as a satellite task population, and performing cross operation processing on the satellite task population based on a preset two-generation competition optimization algorithm;
s4, performing variation operation processing on the satellite task population after the cross operation processing based on a multi-round iteration two-point variation method;
s5, acquiring a satellite task scheduling scheme according to the satellite task population after the mutation operation processing;
in S2, the pre-constructed multi-star task model includes an objective function and a constraint condition, where the objective function is:
Figure FDA0003804916460000061
wherein:
Figure FDA0003804916460000062
representing the benefit of the ith task being performed;
Figure FDA0003804916460000063
is a variable of 0/1
Figure FDA0003804916460000064
When 1 is taken, the ith task is executed on the kth orbit of the jth satellite; when in use
Figure FDA0003804916460000065
When 0 is taken, the ith task is not executed;
n represents the number of satellites; m represents the number of satellite tasks;
β ij representing the loss value of the jth satellite when the ith task is executed;
the constraint conditions include:
Figure FDA0003804916460000066
Figure FDA0003804916460000067
Figure FDA0003804916460000068
Figure FDA0003804916460000069
Figure FDA00038049164600000610
Figure FDA0003804916460000071
wherein:
Figure FDA0003804916460000072
representing the end time of an observation time window when the ith task is executed on the kth orbit of the jth satellite;
Figure FDA0003804916460000073
representing the start time of an observation time window when the ith task is executed on the kth orbit of the jth satellite;
per i representing the observation duration of the ith task;
Figure FDA0003804916460000074
representing the starting time of a visible time window of the jth satellite on the kth track for the ith task;
Figure FDA0003804916460000075
representing the end time of a visible time window of the jth satellite to the ith task on the kth orbit;
Figure FDA0003804916460000076
representing the attitude transition time between two successive tasks i and i' performed on the kth orbit of the jth satellite;
Capacity j representing the maximum storage capacity of each orbit of the jth satellite;
Figure FDA0003804916460000077
the storage capacity required by the image obtained by the observation of the jth satellite on the kth orbit for the ith task is represented;
Res j representing the maximum energy limit of each orbit of the jth satellite;
Figure FDA0003804916460000078
representing the energy required by the jth satellite to observe and image the ith task on the kth orbit;
step S4 specifically includes the following steps:
s401, randomly selecting a parent chromosome for the satellite task population after the cross operation;
s402, selecting two variant gene positions by roulette, carrying out variant on original codes on the two selected variant gene positions, and if the original code is 1, not needing variant; if the original code is 0, the code is changed into 1;
s403, after all the parent chromosomes are selected, performing feasibility test on the retained individuals according to the constraint conditions, deleting infeasible individuals, and retaining feasible individuals;
s404, taking the feasible individuals as parent chromosomes, and turning to S402 until the variation tends to be stable.
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