CN110825513A - Agile single-star multi-constraint task optimal scheduling method - Google Patents
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Abstract
The invention discloses an agile single-star multi-constraint task optimization scheduling method, which comprises the steps of performing dynamic task decomposition according to a complex observation target task area to generate an attitude file with a plurality of visible time windows; establishing a task planning mathematical optimization model meeting multiple constraint conditions, which is mainly divided into the construction of an attitude maneuver time constraint model, a satellite energy constraint model, a data storage constraint model and a minimum pitch angle constraint model; and coding and initializing a plurality of visible time windows in the generated attitude file set by utilizing a genetic search algorithm. The invention solves the conflict between adjacent tasks or task satisfaction in the observation process of the satellite, reasonably distributes and utilizes resources and provides a reasonable and feasible task planning scheme. The purposes of reasonably distributing satellite resources, increasing the number of single-track observation tasks and improving the response timeliness of the satellite are achieved.
Description
Technical Field
The invention relates to an agile single-satellite multi-constraint task optimization scheduling method, which is applied to the field of agile single-satellite task planning.
Background
In the planning of the imaging task of the non-agile satellite, only the side-looking imaging capability of the satellite is considered, and scholars at home and abroad make very deep research on the side-looking imaging capability. Relevant models including point targets, regional targets, moving targets, different-orbit stereo imaging and multi-satellite task planning are established, and task synthesis observation between non-agile satellite earth observation point targets is preliminarily achieved. Meanwhile, different algorithms are established aiming at different models, the advantages and disadvantages of the corresponding algorithms are analyzed, new composite algorithms are compared and developed, and the results are quite many.
The agile satellite not only has wide application in civil use, but also plays an important role in military affairs. In military warfare, imaging satellites once played a crucial role in contending for battlefield control information rights. The imaging satellite has the advantages of large imaging area, wide imaging range and no limitation of geographical conditions and national boundaries, can provide near-real-time information, can also obtain a large amount of information which cannot be obtained by using conventional means, becomes equipment which is vital to win modern high-technology local wars, is always valued by military parties of various countries, and becomes an important guarantee for winning the modern wars.
With the continuous progress of satellite technology and sensor technology, an agile satellite is a new type of earth observation satellite, compared with the existing conventional satellite, the attitude adjustment precision is high, the attitude mobility is strong, and therefore user tasks can be flexibly executed under various working modes, and the agile satellite represents the mainstream direction of the next generation of satellite development. With the development of agile satellite technology, the networking cooperative work can complete complex user tasks according to the characteristics of various types of loads of the satellite and different orbital space positions. Therefore, agile satellite mission planning networking collaborative construction is an important direction for aerospace research, and has become a key point for competitive research in all aerospace big countries, and the research not only comprises the research on satellite hardware technology but also comprises the research on networking planning scheduling technology.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an agile single-satellite multi-constraint task optimization scheduling method.
In order to achieve the purpose, the invention adopts the following technical scheme:
the agile single-satellite multi-constraint task optimization scheduling method comprises the following steps:
(1) performing dynamic task decomposition according to the complex observation target task area to generate an attitude file with a plurality of visible time windows;
(2) establishing a task planning mathematical optimization model meeting multiple constraint conditions, which is mainly divided into the construction of an attitude maneuver time constraint model, a satellite energy constraint model, a data storage constraint model and a minimum pitch angle constraint model;
(3) and coding and initializing a plurality of visible time windows in the generated attitude file set by utilizing a genetic search algorithm.
As a further aspect of the present invention, the method of step (1) is as follows: p longitude and latitude coordinates of known point target
Satellite orbit height h, satellite orbit tilt τ, earth radius R, camera field angle φ, remote sensor yaw range [ α ]min,αmax]Planning a period T;
let satellite observation angle βtThe coordinate of the point under the star isThe principle formula is as follows:
calculating minimum observation angles β for satellitesminAnd maximum observation angle βmax：
Calculating the latitude and longitude of the subsatellite point:
wherein, central angle C is:
C＝arcsin((1+h/R)sinβt)-βt；
calculating a stagnation point:
determining a monotone interval:
if it is not
And βmax＞β0The function u-f (β) is in [ β ]min,β0]Upper monotonically decreasing at [ β0,βmax]The upper monotonic increase;
if it is not
And βmin＜β0The function u-f (β) is in [ β ]min,β0]Upper monotonically increasing, at [ β0,βmax]Upper monotonically decreasing;
if it is not
And βmax≤β0The function u-f (β) is in [ β ]min,βmax]Upper monotonically decreasing;
if it is notAnd βmin≥β0The function u-f (β) is in [ β ]min,βmax]Upper monotonically decreasing;
it is determined whether a visible time window exists.
As a further scheme of the invention, the specific steps of the step (3) are as follows: and (3) encoding: the coding mechanism is the basis of genetic operation, because the genetic algorithm usually does not process the data of the problem space directly, but carry on the code space that a certain coding mechanism corresponds, therefore the choice of the coding mechanism has determined performance and efficiency of the algorithm to a great extent, the algorithm adopts the binary system, the binary system is to use a binary string to express the solution of the problem; initializing a population: the task after coding is to set an initial population, and to use the initial population as a starting point for the generation of evolution until the termination is carried out according to a certain evolution termination criterion, and the population size adopted by the algorithm is 50. In the process of solving by using a genetic algorithm, a fitness function is used as an operation basis, and the fitness value of each individual in a solution space is used for searching. The fitness value can indicate the strength of the individual to the environmental adaptability, and is a standard for distinguishing the quality of the individuals in the population. The individual fitness is high, and the probability of selection is high; otherwise, the probability of being selected is low. The optimization target mainly considered by the algorithm comprises the task yield and the task completion rate, and the fitness function is the weighted average of the task rate and the task completion rate.
As a further scheme of the invention, the solving method is to add a constraint analysis model to the optimization objective function to solve the genetic search algorithm, and in the process of solving by using the genetic algorithm, a fitness function is used as an operation basis, and the fitness value of each individual in a solution space is used for searching, and three operators of genetic operation selection, intersection and mutation are used. (1) Selecting: fitness value is a criterion for determining whether a chromosome is good or not, and the larger the fitness value of an individual is, the more chance it has to be selected. The selection method of the present algorithm is a fitness scale selection method in which the individual selection probability is proportional to its fitness value. (2) And (3) crossing: crossover, also known as recombination or pairing, is the combination of features from parent chromosomes to create new chromosomes that search efficiently for solution space while reducing the probability of disruption to the efficient pattern. The algorithm adopts multipoint intersection. The crossover probability is used to control the probability of crossover operations occurring. When the cross probability is too high, the individual in the population is updated quickly, and the individual with high fitness is damaged quickly due to large change. If the crossover probability is small, crossover operations are rarely performed, making the search stalled. (3) Mutation: selection and crossover are the main means of generating new individuals, while mutation is an aid to generating new individuals, but it is also an essential step in genetic algorithms. When performing mutation operation, the mutation probability should not be too large.
As a further scheme of the invention, a single-star mission planning mathematical model is solved according to an algorithm of genetic search.
As a further scheme of the invention, the method comprises a complex area target dynamic task decomposition module, a single-star task planning mathematical modeling and solving module and a multi-constraint analysis model establishing module;
the complex region target dynamic task decomposition module performs dynamic task decomposition on an observed region according to the observation requirement of a user and the resource condition of the satellite, and establishes a strip division method according to a geometric relation to generate a strip target for the satellite to perform observation once;
the single-satellite mission planning mathematical modeling and solving module is used for optimizing a satellite mission planning observation plan and reasonably distributing resources;
the multi-constraint analysis model establishing module establishes an analysis model meeting multi-constraint conditions according to task constraints, resource constraints and the relation among the task constraints and the resource constraints, and analyzes energy balance, attitude maneuver time and storage.
As a further scheme of the invention, time conflict adjustment between adjacent tasks of the task planning is carried out for different scene modes to determine the specific time window of the task meeting multiple constraints.
Compared with the prior art, the invention has the beneficial effects that:
(1) adopting a complex region dynamic task decomposition strategy: and according to the pitching, rolling and yawing characteristics of the agile satellite, carrying out dynamic task decomposition on the target in the complex area by utilizing the calculated track of the point under the satellite, the maximum view field angle and the attitude maneuvering speed of the satellite to generate an executable task attitude information file.
(2) And (3) optimizing a task planning mathematical model strategy: according to the attitude information file decomposed in the complex region, the problem of time window conflict between tasks is adjusted and corrected by combining constraint analysis conditions, so that the reasonable distribution and utilization of satellite resources are realized, and the optimal task observation plan scheme meeting different constraints is provided.
(3) Different constraint analysis model construction strategies: and quantitatively constructing an attitude maneuver stabilization model and a satellite energy constraint model according to the conditions of the satellite resources and the geometric relationship between the satellite and the task, and optimizing and adjusting the observation plan scheme meeting different requirements.
The invention mainly aims at a task planning mathematical model established by an agile single satellite under the condition of meeting multiple constraints, solves the conflict between adjacent tasks or task satisfaction in the observation process of the satellite by utilizing a genetic search algorithm, reasonably distributes and utilizes resources and provides a reasonable and feasible task planning scheme. The purposes of reasonably distributing satellite resources, increasing the number of single-track observation tasks and improving the response timeliness of the satellite are achieved.
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FIG. 1 is a schematic diagram of an agile single-star multi-constraint task optimization scheduling method according to the present invention;
FIG. 2 is a flowchart of an agile single-star multi-constraint task optimization scheduling method according to the present invention.
Detailed Description
The invention is explained in further detail below with reference to the figures and the specific embodiments.
According to the task planning solving method, dynamic task decomposition is carried out on the target in the observation area according to the observation requirement of the user, a constraint analysis model is established to carry out solving of a genetic search algorithm, optimization adjustment is carried out on the metatasks with a plurality of visible time windows, and an observation plan scheme meeting constraint conditions is generated, so that the utilization efficiency of satellite resources and the observation timeliness of tasks are improved.
FIG. 1 is a schematic diagram of the agile single-star multi-constraint task optimization scheduling method of the present invention,
(a) the complex region dynamic task decomposition module dynamically decomposes an observation region into a strip target which can be observed by the satellite at one time according to the observation requirement of a user and the resource condition of the satellite; (b) the single-satellite mission planning mathematical modeling and solving module is used for optimizing a satellite mission planning observation plan and reasonably distributing resources; (c) and the constraint analysis model establishing module establishes an analysis model meeting multiple constraint conditions according to the task constraint, the resource constraint and the relationship among the task constraint and the resource constraint to generate a reasonable task observation plan scheme.
Fig. 2 is a flow chart of the agile single-satellite multi-constraint task optimization scheduling method of the present invention, and it can be seen from fig. 2 that the agile single-satellite multi-constraint task optimization scheduling method of the present invention includes the following steps:
(1) and (4) judging whether the task target type is a complex area target, if so, performing the step (2), and if not, not performing decomposition.
(2) Decomposing a target dynamic task in a complex area, calculating latitude and longitude of a satellite point, calculating a stationing point, determining a monotonous interval, and judging whether a visible time window exists or not.
(3) And (4) establishing a mathematical model of the single-satellite task planning, establishing an optimization objective function of the objective function including the yield, the completion rate and the like, and entering the step (4), otherwise, entering the step (7).
(4) And (5) encoding the meta-task time window in the step (2) by utilizing a genetic search algorithm, calculating an initial fitness value, and otherwise, entering the step (6).
(5) Selecting operation of the genetic search algorithm in the step (4), selecting: fitness value is a criterion for determining whether a chromosome is good or not, and the larger the fitness value of an individual is, the more chance it has to be selected. Step (6) is entered.
(6) And (3) crossing: crossover, also known as recombination or pairing, is the combination of features from parent chromosomes to create new chromosomes, performing efficient search of the solution space while reducing the probability of disruption to the efficient pattern, and step (7).
(7) Mutation: selection and crossover are the main means of generating new individuals, while mutation is an aid to generating new individuals, but it is also an essential step in genetic algorithms. When mutation operation is performed, the mutation probability should not be too large, and the step (8) is performed.
(8) Generating an observation plan scheme of loop iteration once, and entering the step (9)
(9) Tuning into a constraint analysis mathematical model, proceeding to step (10)
(10) Judging whether the constraint analysis conditions are met, if so, generating a final executable plan scheme, if not, turning to the step (4), continuing the steps (5), (6), (7) and (8)
(11) And finishing the selection of the task planning scheme.
In a word, the method can support agile single-star task planning, and carry out selection, intersection and variation of genetic search algorithms through solving the constructed analysis model meeting the constraint conditions to carry out conflict between adjacent tasks, optimize an observation plan scheme and generate an optimal observation plan meeting the constraint conditions.
The invention aims at single-star task planning, continuously improves the task planning modeling and solving algorithm process, and is finally suitable for solving the problem of agile multi-star collaborative task planning.
The foregoing is a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that variations, modifications, substitutions and alterations can be made in the embodiment without departing from the principles and spirit of the invention.
Claims (7)
1. The method for optimizing and scheduling the agile single-satellite multi-constraint tasks is characterized by comprising the following steps of:
(1) performing dynamic task decomposition according to the complex observation target task area to generate an attitude file with a plurality of visible time windows;
(2) establishing a task planning mathematical optimization model meeting multiple constraint conditions, which is mainly divided into the construction of an attitude maneuver time constraint model, a satellite energy constraint model, a data storage constraint model and a minimum pitch angle constraint model;
(3) and coding and initializing a plurality of visible time windows in the generated attitude file set by utilizing a genetic search algorithm.
2. The agile single-star multi-constraint task optimization scheduling method according to claim 1, wherein the method of step (1) is as follows: p longitude and latitude coordinates of known point targetSatellite orbit height h, satellite orbit tilt τ, earth radius R, camera field angle φ, remote sensor yaw range [ α ]min,αmax]Planning a period T;
let satellite observation angle βtThe coordinate of the point under the star is
The principle formula is as follows:
calculating minimum observation angles β for satellitesminAnd maximum observation angle βmax：
Calculating the latitude and longitude of the subsatellite point:
wherein, central angle C is:
C＝arcsin((1+h/R)sinβt)-βt；
calculating a stagnation point:
determining a monotone interval:
if it is not
And βmax＞β0The function u-f (β) is in [ β ]min,β0]Upper monotonically decreasing at [ β0,βmax]The upper monotonic increase;
if it is not
And βmin＜β0The function u-f (β) is in [ β ]min,β0]Upper monotonically increasing, at [ β0,βmax]Upper monotonically decreasing;
if it is notAnd βmax≤β0The function u-f (β) is in [ β ]min,βmax]Upper monotonically decreasing;
if it is not
And βmin≥β0The function u-f (β) is in [ β ]min,βmax]Upper monotonically decreasing;
it is determined whether a visible time window exists.
3. The agile single-star multi-constraint task optimization scheduling method according to claim 1, wherein the specific steps of the step (3) are as follows: and (3) encoding: the coding mechanism is the basis of genetic operation, because the genetic algorithm usually does not process the data of the problem space directly, but carry on the code space that certain coding mechanism corresponds, therefore the choice of the coding mechanism has determined performance and efficiency of the algorithm, this algorithm adopts the binary system mechanism, the binary system is to use a binary string to express the solution of the problem; initializing a population: the task after coding is to set an initial population, and to use the initial population as a starting point for the generation of evolution until the termination is carried out according to a certain evolution termination criterion, and the population size adopted by the algorithm is 50.
4. The agile single-star multi-constraint task optimization scheduling method of claim 1, wherein the solving method is to add a constraint analysis model to the optimization objective function to solve the genetic search algorithm, and in the process of solving by using the genetic algorithm, the fitness function is used as an operation basis, and the fitness value of each individual in the solution space is used to search, and the genetic operation selects, crosses and mutates three operators.
5. The agile single-star multi-constraint task optimization scheduling method according to claim 1, wherein the single-star task planning mathematical model is solved according to an algorithm of genetic search.
6. The agile single-star multi-constraint task optimization scheduling method of claim 1, comprising a complex area target dynamic task decomposition module, a single-star task planning mathematical modeling and solving module, and a multi-constraint analysis model building module;
the complex region target dynamic task decomposition module performs dynamic task decomposition on an observed region according to the observation requirement of a user and the resource condition of the satellite, and establishes a strip division method according to a geometric relation to generate a strip target for the satellite to perform observation once;
the single-satellite mission planning mathematical modeling and solving module is used for optimizing a satellite mission planning observation plan and reasonably distributing resources;
the multi-constraint analysis model establishing module establishes an analysis model meeting multi-constraint conditions according to task constraints, resource constraints and the relation among the task constraints and the resource constraints, and analyzes energy balance, attitude maneuver time and storage.
7. The agile single-star multi-constraint task optimized scheduling method of claim 1, wherein time conflict adjustments between task planning adjacent tasks are made for different scene modes to determine a specific time window for tasks that meet multiple constraints.
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