CN114239986A - Satellite time window allocation method, device, equipment and readable storage medium - Google Patents
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Abstract
The invention provides a satellite time window allocation method, a device, equipment and a readable storage medium, which relate to the technical field of satellite allocation and comprise the steps of obtaining all time windows of a satellite task, wherein each time window is a time window of a detection task executed by a satellite; discretizing the time window to obtain an observation time window, establishing an objective function according to the observation time window and a preset satellite task priority, and obtaining a satellite task model based on the objective function and a preset constraint condition; solving the satellite task model to obtain a satellite task population; and performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme. The planning and scheduling aiming at the satellite imaging task can improve the utilization rate of satellite resources, reasonably distribute imaging resources, fully exert the capability of an imaging system and maximize the completion rate of the imaging task.
Description
Technical Field
The invention relates to the technical field of satellite deployment, in particular to a method, a device, equipment and a readable storage medium for deploying a satellite time window.
Background
With the continuous development of satellite technology, an agile earth observation satellite emerges, the earth observation satellite can rotate in three dimensions of pitching, yawing and rotating to image a ground target, the observation opportunity of the satellite on a task is increased, the satellite generates a plurality of time windows for a certain task to be observed in one orbit circle, and the satellite can execute the observation task without running right above the target. Therefore, in the prior art, various tasks are observed by adopting agile earth observation satellites.
The multi-satellite and multi-station integrated scheduling relates to a plurality of satellites, a plurality of observation tasks and a plurality of ground stations, and means that on the basis of comprehensively considering satellite resource capacity, ground receiving station resource capacity and user requirements, resources are allocated to imaging tasks and data downloading tasks corresponding to a plurality of competing requirements without conflict, and starting and stopping time of each task is determined so as to meet the requirements of users to the maximum extent.
Disclosure of Invention
The present invention is directed to a method, an apparatus, a device and a readable storage medium for allocating a satellite time window to solve the above problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for deploying a satellite time window, including:
acquiring all time windows of a satellite task, wherein each time window is a time window of a detection task executed by the satellite, and the detection task is a task of detecting a ground target by the satellite;
discretizing the time window to obtain an observation time window, establishing an objective function according to the observation time window and a preset satellite task priority, and obtaining a satellite task model based on the objective function and a preset constraint condition;
solving the satellite task model to obtain a satellite task population;
and performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme.
Preferably, discretizing the time window to obtain an observation time window includes:
sequencing all the time windows according to the time sequence visible to the satellite by the satellite, and discretizing each sequenced time window according to equal parts;
judging whether the time window of each discretized satellite task has an overlapping part or not;
if no overlapping part exists between all the time windows, taking the length of the most middle time window in all the time windows as the observation time window of the satellite task;
if the time windows have overlapping parts, segmenting all the overlapping parts between the time windows according to the preset observation time length of the satellite task to obtain the observation time windows of the satellite task.
Preferably, the dividing the overlapped part between all the time windows according to the preset observation duration of the satellite task to obtain the observation time window of the satellite task includes:
extracting all overlapping parts between the time windows;
according to the overlapping part between the time windows, calculating to obtain the entry and exit azimuth of the satellite corresponding to the overlapping part and the maximum time length covered by the overlapping part;
calculating according to the entry-exit azimuth angle and the maximum duration by using an image splicing algorithm to obtain
Splicing a task time window;
judging the observation duration of the splicing task time window to obtain a comparison result;
and segmenting according to the comparison result of the observation duration to obtain the observation time window of the satellite task.
Preferably, the cross operation processing is performed on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme, which includes:
initializing parameters of an iterative genetic algorithm, and constructing a satellite task table;
coding the satellite task population based on a real number permutation coding method;
calculating a fitness value of each individual in the encoded satellite task population based on a preset fitness function, and updating a satellite task table according to the fitness value;
performing variation operation, crossover operation, secondary variation operation and selection operation on individuals in the satellite task population in sequence according to the updated satellite task table to obtain a task observation sequence;
and judging whether the iteration termination condition is reached in the task observation sequence, if so, outputting an optimal solution to obtain a satellite task scheduling scheme.
In a second aspect, the present application further provides a satellite time window deployment device, including a first obtaining module, a first processing module, a solving module, and a second processing module, wherein:
a first obtaining module: the system comprises a plurality of time windows, a plurality of time windows and a plurality of detection tasks, wherein the time windows are used for acquiring all satellite tasks, each time window is used for executing a detection task by a satellite, and the detection task is used for detecting a ground target by the satellite;
a first processing module: the time window discretization processing module is used for discretizing the time window to obtain an observation time window, establishing an objective function according to the observation time window and a preset satellite task priority, and obtaining a satellite task model based on the objective function and a preset constraint condition;
a solving module: the satellite task model is used for solving to obtain a satellite task population;
a second processing module: and the method is used for performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme.
Preferably, the first processing module includes a sorting unit, a first judging unit, an identifying unit and a first dividing unit, wherein:
a sorting unit: the satellite time sequencing system is used for sequencing all the time windows according to the time sequence visible to the satellite and discretizing each sequenced time window according to equal parts;
a first judgment unit: the time window is used for judging whether the time window of each discretized satellite task has an overlapping part;
an identification unit: if there is no overlapping part between all the time windows, the length of the most middle time window in all the time windows is used as the observation time window of the satellite task;
a first dividing unit: and if the time windows have overlapping parts, segmenting all the overlapping parts between the time windows according to the preset observation time length of the satellite task to obtain the observation time window of the satellite task.
Preferably, the first dividing unit includes an extracting unit, a first calculating unit, a second judging unit and a second dividing unit, wherein:
an extraction unit: for extracting all overlapping parts between the time windows;
the first calculation unit: the time window is used for calculating and obtaining the entry and exit azimuth of the satellite corresponding to the overlapping part and the maximum time length covered by the overlapping part according to the overlapping part between the time windows;
a second calculation unit: the image splicing algorithm is used for calculating to obtain a splicing task time window according to the entry and exit azimuth and the maximum duration;
a second judgment unit: the system is used for judging the observation duration of the splicing task time window to obtain a comparison result;
a second dividing unit: and the observation time window is used for segmenting according to the comparison result of the observation time length to obtain the observation time window of the satellite task.
Preferably, the second processing module includes a constructing unit, a processing unit, a solving unit, an operating unit, and a third determining unit, wherein:
a construction unit: the parameters are used for initializing the iterative genetic algorithm and constructing a satellite task table;
a processing unit: the satellite task population is coded based on a real number permutation coding method;
a solving unit: the system is used for solving fitness values of individuals in the encoded satellite task population based on a preset fitness function and updating a satellite task table according to the fitness values;
an operation unit: the system is used for sequentially carrying out variation operation, crossover operation, secondary variation operation and selection operation on individuals in the satellite task population according to the updated satellite task table to obtain a task observation sequence;
a third judging unit: and the method is used for judging whether the iteration termination condition is reached in the task observation sequence, and if so, outputting an optimal solution to obtain a satellite task scheduling scheme.
In a third aspect, the present application further provides a satellite time window deployment apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the satellite time window deployment method when executing the computer program.
In a fourth aspect, the present application further provides a readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the satellite-based time window scheduling method.
The invention has the beneficial effects that: the method constructs the task observation model by combining the observation income and the priority of the satellite task, improves the efficiency of the satellite observation task, improves the genetic algorithm aiming at the defects of the prior art, and optimizes the scheduling scheme of the satellite task; and the iterative genetic algorithm is utilized to increase the space of solutions in the genetic algorithm and improve the efficiency of satellite observation tasks.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic flow chart illustrating a method for allocating a satellite time window according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a satellite time window adjusting apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a satellite time window deployment apparatus according to an embodiment of the present invention.
In the figure, 701, a first acquisition module; 7011. a first acquisition unit; 7012. an input unit; 702. a first processing module; 7021. a sorting unit; 7022. a first judgment unit; 7023. an identification unit; 7024. a first dividing unit; 70241. an extraction unit; 70242. a first calculation unit; 70243. a second calculation unit; 70244. a second judgment unit; 70245. a second dividing unit; 7025. a decomposition unit; 7026. a third calculation unit; 7027. an analysis unit; 7028. a binding unit; 703. a solving module; 704. a second processing module; 7041. a building unit; 7042. a processing unit; 7043. a solving unit; 7044. an operation unit; 7045. a third judgment unit; 800. satellite time window deployment equipment; 801. a processor; 802. a memory; 803. a multimedia component; 804. an input/output (I/O) interface; 805. a communication component.
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 will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1:
the embodiment provides a satellite time window allocation method.
Referring to fig. 1, it is shown that the method comprises step S100, step S200, step S300 and step S400.
S100, acquiring all time windows of a satellite task, wherein each time window is a time window of a detection task executed by the satellite, and the detection task is a task of detecting a ground target by the satellite.
It is understood that, in this step, the time window of the probe task is a time range from the start of the probe task to the end of the probe task.
In this step, S100 includes S101 and S102, in which:
s101: acquiring first information, wherein the first information comprises satellite orbit operation information corresponding to each satellite task, geographical position information of a to-be-observed transit ground station task and imaging target type information;
s102: and inputting the first information into STK software, and calculating all time windows of the satellite tasks.
S200, discretizing the time window to obtain an observation time window, establishing an objective function according to the observation time window and a preset satellite task priority, and obtaining a satellite task model based on the objective function and a preset constraint condition.
It is understood that in this step, S200 includes S201, S202, S203, S204, and S205, where:
s201: setting a planning task set to be planned as M { (M1, M2, L L, mn }, setting a time window set as TW { [ Ts1, Te1], [ Ts2, Te2], L L, [ Tsn, Ten ] }, and sequencing the tasks in the planning task set according to the time sequence visible by the satellites; discretizing the time window of each observation task according to the equal part a, namely dividing the visible time window into a equal parts, totaling a time points, and taking each time point as the imaging start time of the satellite for the observation task; calculating the attitude angle and the imaging duration corresponding to the observation task when the satellite starts imaging the observation task at each time point, defining each time point as a node point, and sequencing according to the time sequence, wherein each node comprises an observation task number, imaging start time, a corresponding imaging attitude angle and imaging duration, namely: wherein i is an observation task number, j is a node serial number, that is, point (i, j) is the jth node of the ith observation task, β ij is a yaw angle of the satellite imaging the observation task, and qij is a pitch angle of the satellite imaging the observation task; and sequentially expanding the nodes of the first observation task to feasible nodes of the subsequent observation task from each node of the first observation task according to the serial number of the node.
S202, discretizing the time window of each detection task according to equal parts;
specifically, the visible time window VWijk of the k-th orbit turn on the observation satellite Sj is calculated by the task ti, and the visible time window is discretized.
S203, judging whether the time window of each discretized satellite task has an overlapping part;
s204, if all the time windows have no overlapped part, taking the length of the most middle time window in all the time windows as an observation time window of the satellite task;
and S205, if the time windows have overlapping parts, segmenting the overlapping parts among all the time windows according to the preset observation time length of the satellite task to obtain the observation time windows of the satellite task.
In step S205, steps S2051, S2052, S2053, S2054, and S2055 are further included, where:
s2051: extracting all overlapping parts between time windows;
s2052: according to the overlapping part between the time windows, calculating to obtain the entry and exit azimuth of the satellite corresponding to the overlapping part and the maximum time length covered by the overlapping part;
specifically, according to the judgment condition of time overlap, extracting the overlap part between time windows, and obtaining the time length of the previous time window, the time length of the next time window, the entry-exit azimuth of the previous time and the entry-exit azimuth of the next time according to the overlap part between the time windows; and obtaining the maximum time required by the rotation of the antenna according to the speed of the azimuth angle of the antenna, the entry-exit azimuth angle at the previous time and the entry-exit azimuth angle at the later time, and performing splicing analysis on the tracking task for the maximum time required by the rotation of the antenna to obtain the effective communication time of the splicing task.
S2053: calculating to obtain a splicing task time window by using an image splicing algorithm according to the entry-exit azimuth and the maximum duration;
s2054: judging the observation duration of the splicing task time window to obtain a comparison result;
s2055: and segmenting according to the comparison result of the observation duration to obtain an observation time window of the satellite task.
Specifically, the visible time window of each task is divided according to the observed execution time length peri to obtain a plurality of observed time windows of the corresponding tasks. For example, if the observation time length of task i is peri, and the division step size is 1, the visible time window of task i is divided into [0, peri ] [1,1+ peri ] [2,2+ peri ] … … specifically: if the observation duration peri of the satellite for task i is 1 and the step size is 0.5, the interval [0,9] of the visible time window of task i can be decomposed into [0,1] [0.5,1.5] [1,2] … … [8,9], and the division of the visible time window is completed.
S200 further includes S206, S207, S208, and S209, wherein:
s206: decomposing the satellite task into a plurality of meta tasks according to the imaging target type information, and acquiring input conditions of the plurality of meta tasks;
s207: based on the input conditions of the multiple meta-tasks, comprehensively calculating the task priorities of the multiple meta-tasks according to the benefit factors and imaging feasibility factors of the preset satellite tasks;
s208: under the extended observation working mode and the emergency observation working mode, analyzing based on task priority to obtain constraint conditions of the data transmission load in the satellite imaging process;
s209: and solving the planning results of the multiple element tasks based on the objective function and in combination with the constraint conditions to obtain a satellite task model.
S300, solving the satellite task model to obtain a satellite task population.
It is understood that, in this step, the following steps are specifically included:
and respectively randomly generating a task sequence in the first 50% and the last 50% of the satellite task sequence based on a preset constraint condition.
Wherein the preset constraint conditions comprise:
OTWis+peri＝OTWie
VTWis≤OTWis＜OTWie≤VTWie
in the formula: ai is a binary variable of 0/1, which when taken to 1 indicates that the ith task was performed on the satellite; when 0 is taken, it means that the ith task is not executed on the satellite; n represents the number of satellite tasks; peri represents the observation duration of the satellite performing the ith task; the OTWis represents the starting time of an observation time window for the satellite to execute the ith task; specifically, s represents a start time; the OTWie represents the end time of an observation time window for the satellite to execute the ith task; specifically, e represents an end time; VTWis represents the start time of the satellite's visible time window for the ith task; VTWie represents the end time of the satellite's visible time window for the ith task.
Connecting two randomly generated task sequences to form a complete chromosome; judging the feasibility of the chromosome based on the time constraint condition, and if the condition is met, reserving the chromosome; if the condition is not satisfied, the chromosome is regenerated.
The time constraint conditions are as follows: cij represents the satellite's attitude adjustment time between two successive observation tasks; and representing the starting time of the observation time window for the satellite to execute the jth task, and repeating the steps until an initial population of m chromosomes with a preset scale is generated, namely the satellite task population.
S400, performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme.
It is understood that in this step, S400 includes S401, S402, S403, S404, and S405, where:
s401: initializing parameters of an iterative genetic algorithm and constructing a satellite task table.
Specifically, task information and genetic algorithm parameters are initialized. And constructing a satellite task table, and setting the table length to be L. And for the satellite task table, storing the better individuals and the fitness value thereof generated in the iterative process into the satellite task table, wherein the number of the stored individuals is determined by the length L of the satellite task table, and the individuals in the satellite task table are arranged from large to small according to the fitness value. And updating the satellite task table when the fitness value of the iteration generated individual is larger than the fitness value of the existing individual in the satellite task table.
S402: and coding the satellite task population based on a real number permutation coding method.
In the present example, the initial genetic population size is S. That is, time windows are randomly distributed according to the priority of the observation task, and a coding individual Q group is generated. Specifically, the real permutation encoding method includes: for the satellite task population, each chromosome of the population represents a scheduling scheme, the tasks on each chromosome are arranged according to the sequence of the satellite observation time windows, and virtual satellites are added into the chromosomes for storing the tasks which cannot be observed temporarily, so that the lengths of the chromosomes are kept consistent.
S403: and solving fitness values of individuals in the encoded satellite task population based on a preset fitness function, and updating a satellite task table according to the fitness values.
Specifically, the higher the priority, the smaller the temporal and spatial resources, the higher the fitness value of the task with the smaller available time window; updating the satellite task table, specifically: each chromosome in the population represents a scheduling scheme, i.e., a task observation sequence. In an iterative process, individuals are generated and fitness values are calculated. And when the generated individual fitness value is larger than the existing individual fitness in the satellite task table, adding the generated individual and the generated individual fitness value into the satellite task table, and deleting the individual with the minimum fitness value in the satellite task table. The number of individuals stored in the satellite task list is determined by the length L of the satellite task list, and the individuals in the satellite task list are arranged from large to small according to the fitness value.
S404: and performing mutation operation, crossover operation, secondary mutation operation and selection operation on individuals in the satellite task population in sequence according to the updated satellite task table to obtain a task observation sequence.
Specifically, the method comprises the following steps: and (4) copying operation. All parent individuals are copied and kept in the same time as the child individuals are selected. The situation that better individuals are destroyed due to genetic manipulation is reduced. A mutation operation comprising: and calculating the fitness value of the population individuals, and carrying out variation on individual chromosomes lower than the average fitness value so as to improve the local searching capability of the genetic algorithm.
For satellite task a, if the start time of the execution of satellite task a is before the mutation time point and the mutation time point is during the execution of satellite task a, then: and performing mutation operation on the next satellite task B of the satellite task A (the satellite task B is considered to be the task closest to the mutation time point at the moment), removing the satellite task B, and moving the satellite task B to the virtual satellite task sequence.
Wherein the second mutation operation comprises: and calculating the fitness value of each chromosome individual after the crossover operation by adopting a roulette selection mechanism, and selecting and reserving the chromosome individual according to roulette.
In particular, roulette is chosen as the prior art, indicating that the probability of each individual entering the next generation is equal to the ratio of its fitness value to the sum of the fitness values of the individuals in the overall population. The roulette selects an observation task sequence with a high probability and a good reservation, and accelerates the convergence of the population, thereby improving the operation efficiency of the algorithm.
Wherein the selecting operation comprises: keeping the individuals with the maximum fitness value in the parent chromosomes and the offspring chromosomes; for the population at the moment, except that the individual with the largest fitness value is not processed, two individuals are randomly selected from all the remaining individuals to perform pairwise competition selection operation, and the individual with the larger fitness value is reserved to the next generation of population; repeating the operation until all the remaining individuals are subjected to pairwise competitive selection to obtain the next generation of population.
S405: and judging whether an iteration termination condition is reached in the task observation sequence, if so, outputting an optimal solution to obtain a satellite task scheduling scheme.
Specifically, the iteration termination condition set in the embodiment of the present invention is population stability. And setting the maximum iteration number, and if the population continuously iterates for 10 times in the iteration process, and the difference value between the maximum fitness value and the average fitness value of the population in the 10 generations is less than 10 < -5 > or the iteration reaches the set maximum iteration number, terminating the iteration.
Specifically, the optimal solution obtained according to the genetic algorithm is an optimal task observation sequence, and is used as a satellite task scheduling scheme for implementation.
Example 2:
as shown in fig. 2, the present embodiment provides a satellite time window deployment apparatus, and referring to fig. 2, the apparatus includes a satellite time window deployment apparatus, which includes a first obtaining module 701, a first processing module 702, a solving module 703 and a second processing module 704, where:
the first obtaining module 701: the system comprises a time window acquisition module, a data processing module and a data processing module, wherein the time window acquisition module is used for acquiring all time windows of satellite tasks, each time window is used for executing a detection task by a satellite, and the detection task is used for detecting a ground target by the satellite;
preferably, the first obtaining module 701 includes a first obtaining unit 7011 and an input unit 7012, wherein:
first obtaining unit 7011: the system comprises a plurality of pieces of information acquisition equipment, a plurality of pieces of information acquisition equipment and a plurality of pieces of information acquisition equipment, wherein the information acquisition equipment is used for acquiring first information, and the first information comprises satellite orbit operation information corresponding to each satellite task, geographical position information of a to-be-observed transit ground station task and imaging target type information;
input unit 7012: and the first information is input into the STK software, and all time windows of the satellite tasks are calculated.
The first processing module 702: the method is used for discretizing the time window to obtain an observation time window, establishing an objective function according to the observation time window and a preset satellite task priority, and obtaining a satellite task model based on the objective function and a preset constraint condition.
Preferably, the first processing module 702 comprises a decomposition unit 7025, a third calculation unit 7026, an analysis unit 7027 and a combination unit 7028, wherein:
decomposition unit 7025: the system comprises a satellite task input module, a satellite task output module and a satellite task output module, wherein the satellite task input module is used for decomposing a satellite task into a plurality of meta tasks according to imaging target type information and acquiring input conditions of the plurality of meta tasks;
third calculation unit 7026: the method comprises the steps that task priorities of a plurality of meta tasks are obtained through comprehensive calculation according to benefit factors and imaging feasibility factors of preset satellite tasks based on input conditions of the plurality of meta tasks;
analysis unit 7027: the method is used for analyzing and obtaining the constraint condition of the data transmission load in the satellite imaging process based on the task priority under the extended observation working mode and the emergency observation working mode;
combining unit 7028: and solving the planning results of the multiple element tasks based on the objective function and in combination with the constraint conditions to obtain a satellite task model.
A solving module 703: the satellite task model is used for solving to obtain a satellite task population;
the second processing module 704: and the method is used for performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme.
Preferably, the first processing module 702 further comprises a sorting unit 7021, a first judging unit 7022, an identifying unit 7023 and a first dividing unit 7024, wherein:
sorting unit 7021: the satellite-based time sequencing system is used for sequencing all the time windows according to the time sequence visible to the satellites, and discretizing each sequenced time window according to equal parts;
first determining unit 7022: the time window is used for judging whether the time window of each discretized satellite task has an overlapping part;
identification unit 7023: if there is no overlapping part between all time windows, the length of the most middle time window in all time windows is used as the observation time window of the satellite task;
first segmentation unit 7024: and if the time windows have overlapping parts, segmenting the overlapping parts among all the time windows according to the preset observation duration of the satellite task to obtain the observation time windows of the satellite task.
Specifically, the first segmentation unit 7024 includes an extraction unit 70241, a first calculation unit 70242, a second calculation unit 70243, a second judgment unit 70244, and a second segmentation unit 70245, wherein:
extraction unit 70241: for extracting all overlapping parts between time windows;
first calculation unit 70242: the satellite positioning system is used for calculating and obtaining an entry and exit azimuth angle of a satellite corresponding to the overlapping part and the maximum time length covered by the overlapping part according to the overlapping part between the time windows;
second calculation unit 70243: the image splicing algorithm is used for calculating to obtain a splicing task time window according to the entry and exit azimuth and the maximum duration;
second determination unit 70244: the system is used for judging the observation duration of the splicing task time window to obtain a comparison result;
second dividing unit 70245: and the method is used for segmenting according to the comparison result of the observation duration to obtain the observation time window of the satellite task.
Preferably, second processing module 704 includes a constructing unit 7041, a processing unit 7042, a solving unit 7043, an operating unit 7044, and a third determining unit 7045, where:
construction unit 7041: the parameters are used for initializing the iterative genetic algorithm and constructing a satellite task table;
processing unit 7042: the method is used for encoding the satellite task population based on a real number permutation encoding method;
solving unit 7043: the system is used for solving fitness values of individuals in the encoded satellite task population based on a preset fitness function and updating a satellite task table according to the fitness values;
operation unit 7044: the system comprises a satellite task group, a task selection module and a task selection module, wherein the satellite task group is used for carrying out mutation operation, cross operation, secondary mutation operation and selection operation on individuals in the satellite task group in sequence according to an updated satellite task table to obtain a task observation sequence;
third determining unit 7045: and the method is used for judging whether the iteration termination condition is reached in the task observation sequence, and if so, outputting an optimal solution to obtain a satellite task scheduling scheme.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3:
corresponding to the above method embodiments, the present embodiment further provides a satellite time window deployment apparatus, and a satellite time window deployment apparatus described below and a satellite time window deployment method described above may be referred to in correspondence.
Fig. 3 is a block diagram illustrating a satellite time window deployment apparatus 800 in accordance with an exemplary embodiment. As shown in fig. 3, the satellite time window adjusting apparatus 800 may include: a processor 801, a memory 802. The satellite time window deployment apparatus 800 may further comprise one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the satellite time window deployment apparatus 800, so as to complete all or part of the steps of the satellite time window deployment method. The memory 802 is used to store various types of data to support the operation of the satellite time window deployment apparatus 800, such data may include, for example, instructions for any application or method operating on the satellite time window deployment apparatus 800, as well as application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen, for example, may be a touch screen, and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the satellite time window allocating apparatus 800 and other apparatuses. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 805 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the satellite time window allocating apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above-mentioned satellite time window allocating method.
In another exemplary embodiment, a computer readable storage medium comprising program instructions for implementing the steps of the satellite time window deployment method described above when executed by a processor is also provided. For example, the computer readable storage medium may be the memory 802 described above comprising program instructions that are executable by the processor 801 of the satellite time window deployment apparatus 800 to perform the satellite time window deployment method described above.
Example 4:
corresponding to the above method embodiments, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and a satellite time window deployment method described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the satellite time window deployment method of the above-mentioned method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for scheduling a satellite time window, comprising:
acquiring all time windows of a satellite task, wherein each time window is a time window of a detection task executed by the satellite, and the detection task is a task of detecting a ground target by the satellite;
discretizing the time window to obtain an observation time window, establishing an objective function according to the observation time window and a preset satellite task priority, and obtaining a satellite task model based on the objective function and a preset constraint condition;
solving the satellite task model to obtain a satellite task population;
and performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme.
2. The method for deploying a satellite time window according to claim 1, wherein discretizing the time window to obtain an observation time window comprises:
sequencing all the time windows according to the time sequence visible to the satellite by the satellite, and discretizing each sequenced time window according to equal parts;
judging whether the time window of each discretized satellite task has an overlapping part or not;
if no overlapping part exists between all the time windows, taking the length of the most middle time window in all the time windows as the observation time window of the satellite task;
if the time windows have overlapping parts, segmenting all the overlapping parts between the time windows according to the preset observation time length of the satellite task to obtain the observation time windows of the satellite task.
3. The method for deploying a satellite time window according to claim 2, wherein the step of segmenting all overlapping portions of the time window according to a preset observation duration of the satellite task to obtain the observation time window of the satellite task comprises:
extracting all overlapping parts between the time windows;
according to the overlapping part between the time windows, calculating to obtain the entry and exit azimuth of the satellite corresponding to the overlapping part and the maximum time length covered by the overlapping part;
calculating according to the entry-exit azimuth angle and the maximum duration by using an image splicing algorithm to obtain
Splicing a task time window;
judging the observation duration of the splicing task time window to obtain a comparison result;
and segmenting according to the comparison result of the observation duration to obtain the observation time window of the satellite task.
4. The method for deploying the satellite time window according to claim 1, wherein the step of performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task deployment scheme comprises:
initializing parameters of an iterative genetic algorithm, and constructing a satellite task table;
coding the satellite task population based on a real number permutation coding method;
calculating a fitness value of each individual in the encoded satellite task population based on a preset fitness function, and updating a satellite task table according to the fitness value;
performing variation operation, crossover operation, secondary variation operation and selection operation on individuals in the satellite task population in sequence according to the updated satellite task table to obtain a task observation sequence;
and judging whether the iteration termination condition is reached in the task observation sequence, if so, outputting an optimal solution to obtain a satellite task scheduling scheme.
5. A satellite time window deployment apparatus, comprising:
a first obtaining module: the system comprises a plurality of time windows, a plurality of time windows and a plurality of detection tasks, wherein the time windows are used for acquiring all satellite tasks, each time window is used for executing a detection task by a satellite, and the detection task is used for detecting a ground target by the satellite;
a first processing module: the time window discretization processing module is used for discretizing the time window to obtain an observation time window, establishing an objective function according to the observation time window and a preset satellite task priority, and obtaining a satellite task model based on the objective function and a preset constraint condition;
a solving module: the satellite task model is used for solving to obtain a satellite task population;
a second processing module: and the method is used for performing cross operation processing on the satellite task population based on a preset iterative genetic algorithm to obtain a satellite task allocation scheme.
6. The satellite time window deployment apparatus of claim 5, wherein said first processing module comprises:
a sorting unit: the satellite time sequencing system is used for sequencing all the time windows according to the time sequence visible to the satellite and discretizing each sequenced time window according to equal parts;
a first judgment unit: the time window is used for judging whether the time window of each discretized satellite task has an overlapping part;
an identification unit: if there is no overlapping part between all the time windows, the length of the most middle time window in all the time windows is used as the observation time window of the satellite task;
a first dividing unit: and if the time windows have overlapping parts, segmenting all the overlapping parts between the time windows according to the preset observation time length of the satellite task to obtain the observation time window of the satellite task.
7. The satellite time window deployment apparatus of claim 6, wherein said first partitioning unit comprises:
an extraction unit: for extracting all overlapping parts between the time windows;
the first calculation unit: the time window is used for calculating and obtaining the entry and exit azimuth of the satellite corresponding to the overlapping part and the maximum time length covered by the overlapping part according to the overlapping part between the time windows;
a second calculation unit: the image splicing algorithm is used for calculating to obtain a splicing task time window according to the entry and exit azimuth and the maximum duration;
a second judgment unit: the system is used for judging the observation duration of the splicing task time window to obtain a comparison result;
a second dividing unit: and the observation time window is used for segmenting according to the comparison result of the observation time length to obtain the observation time window of the satellite task.
8. The satellite time window deployment apparatus of claim 5, wherein said second processing module comprises:
a construction unit: the parameters are used for initializing the iterative genetic algorithm and constructing a satellite task table;
a processing unit: the satellite task population is coded based on a real number permutation coding method;
a solving unit: the system is used for solving fitness values of individuals in the encoded satellite task population based on a preset fitness function and updating a satellite task table according to the fitness values;
an operation unit: the system is used for sequentially carrying out variation operation, crossover operation, secondary variation operation and selection operation on individuals in the satellite task population according to the updated satellite task table to obtain a task observation sequence;
a third judging unit: and the method is used for judging whether the iteration termination condition is reached in the task observation sequence, and if so, outputting an optimal solution to obtain a satellite task scheduling scheme.
9. A satellite time window deployment apparatus, comprising:
a memory for storing a computer program;
processor for implementing the steps of the satellite time window adaptation method according to any one of claims 1 to 4 when executing said computer program.
10. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the satellite time window adaptation method according to any one of claims 1 to 4.
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