CN112583470B - Optimal matching-based satellite measurement and control planning and scheduling method - Google Patents
Optimal matching-based satellite measurement and control planning and scheduling method Download PDFInfo
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Abstract
The invention provides a satellite measurement and control planning and scheduling method based on optimal matching, which is characterized in that applied limiting conditions are firstly divided into strong limiting items and weak limiting items before a genetic algorithm is executed, available arc segments are screened out according to the strong limiting items, a large number of diversity genes are generated through a shuffling algorithm and a diversity generation algorithm, initial genes and the diversity genes are combined into an initialized genome, the original genome meets the applied strong limiting items, the genes are subjected to fitness grading according to the applied weak limiting items, the genetic algorithm operations of selection, crossing and variation are carried out, and finally the result with the highest grading is output. Therefore, the method can more conveniently and quickly deal with various complex constraint conditions in the engineering.
Description
Technical Field
The invention relates to the field of satellite measurement and control, in particular to a satellite measurement and control planning and scheduling method based on optimal matching.
Background
With the development of science and technology, people continuously explore and develop the aerospace field, various aircrafts and satellites are manufactured and widely applied to various fields, the problem of satellite measurement and control scheduling is an NP-hard problem, an optimal solution cannot be generated, a currently adopted solution usually mainly adopts a Genetic Algorithm (GA), but the algorithm can only simply score according to time and equipment service duration in the execution process, an output result is only the maximum score, the judgment of certain necessary limits cannot be realized, and the requirements of complex conditions such as lifting circle requirements, equipment limits, manual assigned plans, application priorities, satellite priorities and the like in engineering cannot be met.
Disclosure of Invention
In order to solve the technical problems, the invention provides a satellite measurement and control planning and scheduling method based on optimal matching, which comprises the steps of firstly, dividing applied limiting conditions into strong limiting items and weak limiting items according to experience, screening available arc segments according to the strong limiting items before executing a genetic algorithm, generating a small amount of initial genes from the screened available arc segments through a shuffling algorithm, generating a large amount of diversity genes through a diversity generation algorithm, and combining the initial genes and the diversity genes into an initialized genome; and then, fitness scoring is carried out on the genes according to the weak restriction items, and the genetic algorithm operation of selection, intersection and variation is carried out, and finally the result with the highest score is output.
The invention provides a satellite measurement and control planning and scheduling method based on optimal matching, which comprises the following steps:
s1, determining the applied limiting conditions, and dividing the limiting conditions into strong limiting items and weak limiting items;
s2, screening available arc segments according to the applied strong limitation items, and coding each available arc segment;
s3, generating initial genes by using the screened available radians through a shuffling algorithm, generating diversity genes by using a diversity generation algorithm, and combining the initial genes and the diversity genes into an initialized genome;
s4, scoring fitness according to the applied weak restriction terms, judging whether convergence conditions are met or not, outputting a gene generation result and decoding if the convergence conditions are met, and ending the program; if the convergence condition is not satisfied, performing step S5;
s5, carrying out genetic operations of selection, crossover and mutation:
s5.1, selecting a gene with high fitness to enter the next round;
s5.2, crossing the father genes selected in the selection stage to generate new genes, and combining the new genes with the father genes to form a new gene pool;
s5.3, performing mutation operation on the basis of the new gene pool to obtain a new generation of population;
and S6, repeating the steps S4 and S5 on the new generation of population until a convergence condition is met, outputting a gene generation result, decoding, and ending the program.
Further, the strong limitation items comprise time limitation, equipment limitation, lifting ring limitation and unavailable resource limitation.
Further, the weak restriction items include lifting rail requirements, service duration and elevation coefficients.
Further, in step S4, the fitness score rule is: and the gene fitness is sigma (lifting orbit demand coefficient + service duration coefficient + elevation coefficient) (10 + application priority + satellite priority), corresponding coefficients are distributed to each weak restriction item, the sum is 1, weighting operation is carried out on the weak restriction items and the application priority and the satellite priority, and the priority degree of the application priority is higher than the satellite priority.
Further, in step S2, the encoding is performed by real number encoding, and each available arc segment of each application corresponds to one encoding.
Further, in step S5.1, the selection operator selects by combining the sorting selection operator with the elite retention operator, the sorting selection operator assigns different selection probabilities to different genes according to the fitness, and the elite retention operator directly selects a gene with the highest fitness for the next round.
Further, in step S5.2, the crossover operator employs an order crossover operator and a sysread sequence crossover operator.
Further, in step S5.3, the mutation operator employs a multi-segment inversion mutation operator and a multi-point crossover mutation operator, the multi-segment inversion mutation operator is employed in the former stage to perform mutation with a large change, and the multi-point crossover mutation operator is employed in the latter stage.
Further, the decoding mode is used for inquiring genes from beginning to end in a FIFO (first in first out) mode, and each gene is decoded respectively.
Further, the convergence condition is whether the score meets the requirement or whether the scheduling times reach an upper limit.
The invention has the following beneficial effects:
according to the method, the applied limiting conditions are divided into the strong limiting items and the weak limiting items before the genetic algorithm is executed, the strong limiting items are screened firstly to generate the initialized genome, and then the fitness scoring is carried out on the gene only according to the applied weak limiting items.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention:
FIG. 1 is a schematic flow chart of a satellite measurement and control planning and scheduling method based on optimal matching;
FIG. 2 is a schematic diagram of a Syswerda sequence crossover operator;
FIG. 3 is a diagram illustrating a multi-segment inversion mutation operator;
FIG. 4 is a diagram illustrating a multi-point crossover mutation operator.
Detailed Description
The invention will be described in further detail below with reference to the figures and specific embodiments.
As shown in fig. 1: a satellite measurement and control planning and scheduling method based on optimal matching comprises the following steps:
s1, firstly, determining applied limiting conditions, and dividing the limiting conditions into strong limiting items and weak limiting items according to actual engineering requirements;
s2, screening available arc segments according to the applied strong limitation items, coding each available arc segment, and enabling the screened available arc segments to meet the applied strong limitation items;
s3, generating a small amount of initial genes through a shuffling algorithm according to the screened available radians, generating a large amount of diversity genes through a diversity generation algorithm, and combining the initial genes and the diversity genes into an initialized genome; wherein, the shuffling algorithm flow is as follows:
step 1: taking the available arc segment as PJ to obtain the size of the PJ;
step 2: randomly selecting 1 number i from 1-size, and exchanging size positions and i positions in PJ;
Step3：size＝size-1；
step 4: if size >1, go back to Step2, otherwise end.
The diversity generation algorithm flow is as follows:
step 1: setting i to be 1, and setting the initial number of individuals to be generated to be PJ;
step 2: obtaining seed solution SD ═ SD1, SD2, …, sdn,
step 3: let QY be n/RN, RN be a random number between the intervals [5, n/5], and let j be 1;
step 4: given the array F (1, 2, …, n), a subsequence of F is defined as F (h: s) ═ s, s + h, s +2h, …, s + λ h, where h is a positive integer, s is a positive integer between 1 and h, and λ is a largest non-negative integer satisfying s + λ h < n; defining the permutation F (h) ═ (F (h: h), F (h: h-1), …, F (h:1)) for h < n, yielding sequence F (j);
step 5: sequentially placing the elements in the seed solution SD in the knife sequence NS according to the positions established by the elements in the sequence F (j), and generating NS (NS1, NS2, … and nsn), wherein NS is a diversity solution;
step 6: if j < ═ QY and i < ═ PJ, return to Step 4;
step 7: if i < ═ PJ, returning to Step2, otherwise, ending the process.
S4, scoring fitness according to the applied weak restriction terms, judging whether convergence conditions are met or not, outputting a gene generation result and decoding if the convergence conditions are met, and ending the program; if the convergence condition is not satisfied, the process proceeds to step S5. The screened genes meet the strong restriction terms of the application, and fitness scoring is carried out according to the weak restriction terms of the application, so that the genes with high fitness scoring meet both the strong restriction terms and the weak restriction terms; through the fitness scoring only according to the applied weak restriction items, the fitness scoring judgment of the strong restriction items is not needed, the time of system operation is greatly shortened, more restriction conditions can be judged and scored, and the judgment of complex restriction conditions is realized, so that various complex restriction conditions in engineering can be more conveniently and quickly responded.
S5, carrying out genetic operations of selection, crossover and mutation:
s5.1, selecting a gene with high fitness to enter the next round;
s5.2, crossing the father genes selected in the selection stage to generate new genes, and combining the new genes with the father genes to form a new gene pool;
s5.3, performing mutation operation with lower probability on the basis of the new gene pool to obtain a new generation of population;
and S6, repeating the steps S4 and S5 on the new generation of population until a convergence condition is met, outputting a gene generation result, decoding, and ending the program.
Specifically, the strong restriction items include time restriction, equipment restriction, lifting ring restriction, and unavailable resource restriction.
Specifically, the weak restriction items include a lifting rail requirement, a service duration, an elevation coefficient and the like.
Specifically, in step S4, the fitness score rule is: and (3) gene fitness sigma (ascending and descending orbit demand coefficient + service duration coefficient + elevation coefficient) (10 application priority + satellite priority), namely distributing corresponding coefficients for each weak restriction item, wherein the sum is 1, weighting calculation is carried out on the application priority and the satellite priority, the priority of the application priority is higher than the satellite priority, the sum of the fitness of each gene is obtained according to the formula, and sequencing calculation is carried out.
Specifically, in step S2, the codes are encoded by real numbers, each available arc segment of each application corresponds to one code, and the total length of the genes is the sum of the total number of available arc segments of all applications.
Specifically, in step S5.1, the selection operator is selected by combining the sorting selection operator with the elite retention operator, the sorting selection operator assigns different selection probabilities to different genes according to the fitness, and the elite retention operator directly selects a gene with the highest fitness for the next round; wherein the sorting selection operator comprises the following steps:
step 1: sorting according to the gene fitness;
step 2: assigning the probability of selection to each gene by ranking, the first being (2n)/n (n +1), the second being (2n-2)/n (n +1), and so on, the last being 2/n (n + 1);
step 3: generating a random number in the range of 1 to n (n + 1);
step 4: determining a corresponding gene according to the random number, and selecting the gene in the next round;
step 5: if the total number of choices meets the requirement, the method is ended, otherwise, the method returns to Step 3.
Specifically, in step S5.2, the crossover operator uses an order crossover operator and a sysread sequence crossover operator, and a specific operator is selected according to a fixed probability when a specific crossover operation is performed. The order crossover operator operates as follows: the method comprises the steps of firstly randomly determining two crossing positions, exchanging fragments between the crossing points, deleting genes to be exchanged from another parent individual from an original parent individual from a second crossing position, then filling in the rest genes from the second crossing position, and sequentially crossing to generate a child individual by directly exchanging the fragments between certain two gene positions of the two parent individuals, wherein the child individual inherits the absolute position relationship between certain fragments of genes of the parent individuals.
As shown in fig. 2, the sysread sequence crossover operator operates as follows: selecting a certain father of the two father individuals as a reference object (setting the father as 2), randomly selecting K positions in the father, finding K elements selected by the father 2 in the father 1, rearranging the K elements in the father 1 according to the sequence of the individuals 2, and finally directly and sequentially filling other elements in the individuals 1 into vacant positions of the individuals 1 according to the original sequence to generate a new individual, wherein the son individual generated by the operator mainly inherits the relative sequence relationship among the genes of the father.
Specifically, in step S5.3, the mutation operation is performed with a lower probability based on the new gene pool, in this embodiment, a mode of combining multiple segments of inversion mutation operators and multipoint exchange mutation operators is adopted, the multiple segments of inversion mutation operators are adopted at the early stage to perform mutation with a larger change, and the multipoint exchange mutation operators are adopted at the later stage.
As shown in fig. 3, the multi-segment inversion mutation operator randomly selects K positions (K > ═ 2) as the mutation points in the parent, and inverts the elements between every two mutation points (including the elements corresponding to the two mutation points) in sequence from front to back.
As shown in FIG. 4, the multiple point crossover mutation operator randomly selects a mutation gene site i in the parent, the corresponding gene is g i Determining the allele, randomly selecting an element g in the allele i Replacing the original gene g at position i of the parent chromosome i Ensure g i ≠g j 。
Specifically, the decoding method comprises the steps of inquiring genes from beginning to end according to FIFO (first-in first-out), decoding each gene respectively, selecting the gene if the application corresponding to the gene is not completely met, removing conflict items in the subsequent genes, and executing in a circulating mode until the gene is empty.
Specifically, the convergence condition is whether the score meets the requirement or whether the scheduling frequency reaches an upper limit.
In other embodiments, the strong restriction term and the weak restriction term may be determined and divided differently according to actual engineering requirements.
The technical solutions provided by the embodiments of the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by using specific examples, and the descriptions of the embodiments are only used to help understanding the principles of the embodiments of the present invention; meanwhile, for a person skilled in the art, according to the embodiments of the present invention, there may be variations in the specific implementation manners and application ranges, and in summary, the present disclosure should not be construed as limiting the invention.
Claims (7)
1. A satellite measurement and control planning and scheduling method based on optimal matching is characterized in that: the method comprises the following steps:
s1, determining the applied limiting conditions, and dividing the limiting conditions into strong limiting items and weak limiting items; the strong limitation items comprise time limitation, equipment limitation, lifting ring limitation and unavailable resource limitation; the weak limitation items comprise lifting rail requirements, service duration and elevation coefficients;
s2, screening available arc segments according to the applied strong limitation items, and coding each available arc segment;
s3, generating initial genes by using the screened available arc segments through a shuffling algorithm, generating diversity genes by using a diversity generation algorithm, and combining the initial genes and the diversity genes into an initialized genome;
s4, scoring fitness according to the applied weak restriction terms, judging whether convergence conditions are met or not, outputting a gene generation result and decoding if the convergence conditions are met, and ending the program; if the convergence condition is not satisfied, performing step S5;
s5, carrying out genetic operations of selection, crossover and mutation:
s5.1, selecting a gene with high fitness to enter the next round;
s5.2, crossing the father genes selected in the selection stage to generate new genes, and combining the new genes with the father genes to form a new gene pool;
s5.3, performing mutation operation on the basis of the new gene pool to obtain a new generation of population;
s6, repeating the steps S4 and S5 on the new generation of population until a convergence condition is met, outputting a gene generation result, decoding, and ending the program;
in step S4, the fitness score rule is: and the gene fitness is sigma (lifting orbit demand coefficient + service duration coefficient + elevation coefficient) (10 + application priority + satellite priority), namely corresponding coefficients are distributed to each weak restriction item, the sum is 1, weighting operation is carried out on the application priority and the satellite priority, and the priority degree of the application priority is higher than that of the satellite.
2. The optimal matching based satellite measurement and control planning and scheduling method according to claim 1, wherein: in step S2, the encoding is performed by real number encoding, and each available arc segment of each application corresponds to one encoding.
3. The optimal matching based satellite measurement and control planning and scheduling method according to claim 1, wherein: in step S5.1, the selection operator is selected by combining the sorting selection operator with the elite reservation operator, the sorting selection operator assigns different selection probabilities to different genes according to the fitness, and the elite reservation operator directly selects a gene with the highest fitness to enter the next round.
4. The optimal matching based satellite measurement and control planning and scheduling method according to claim 1, wherein: in step S5.2, the crossover operator employs an order crossover operator and a sysread sequence crossover operator.
5. The optimal matching based satellite measurement and control planning and scheduling method according to claim 1, wherein: in step S5.3, the mutation operator is a combination of a multi-segment inversion mutation operator and a multi-point crossover mutation operator, the multi-segment inversion mutation operator is used in the early stage to perform a mutation with a large change, and the multi-point crossover mutation operator is used in the later stage.
6. The optimal matching based satellite measurement and control planning and scheduling method according to claim 1, wherein: the decoding mode is used for inquiring genes from beginning to end according to an FIFO mode, and each gene is decoded respectively.
7. The optimal matching based satellite measurement and control planning and scheduling method according to claim 1, wherein: the convergence condition is whether the score meets the requirement or whether the scheduling times reach the upper limit.
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