CN106951312B - Multi-satellite task scheduling method and system - Google Patents
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Abstract
The invention provides a multi-satellite task scheduling method and a system, wherein the method comprises the following steps: acquiring an initial preset number of task sequences, and selecting the task sequence with the maximum fitness as an initial target task sequence from the initial preset number of task sequences; and performing operation through a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, selecting a new target task sequence from the new preset number of task sequences, and performing iterative operation through the quantum genetic algorithm according to the new target task sequence to obtain a scheduled task sequence, so that the service efficiency of the satellite is improved.
Description
Technical Field
The invention relates to the technical field of satellite observation, in particular to a multi-satellite task scheduling method and system.
Background
Earth observation satellites are important image acquisition platforms, and can observe ground targets on a running orbit through a remote sensor, and transmit acquired image data to a ground station to form image products through post processing. Currently, earth observation satellites are often transmitted as a series, such as the high-score series of China, which plans to transmit 7 civil satellites, and currently 2 satellites are transmitted. These satellites can form a relatively complete observation system that serves the information needs of a particular domain.
Task scheduling of earth observation satellites refers to scheduling a plurality of earth observation tasks (observation tasks for short) according to a certain optimization target to determine specific satellites and specific time for executing each task, and since the task scheduling is limited by the capacity of an onboard memory, image data needs to be returned to a ground station to release onboard storage capacity when a certain number of observation tasks are executed. Therefore, earth observation and data downloading are always performed in a interspersed manner, and task scheduling should also include scheduling of data downloading tasks (simply downloading tasks). The two tasks are the biggest difference between the observation tasks which are generated according to the requirements of users and are determined before dispatching, and the download tasks which are dynamically generated according to the dispatching situation and the satellite storage amount and cannot be determined before dispatching.
Task scheduling of satellites is one of the key technologies that affect the efficiency of satellite applications. However, many current researches are to separately schedule the observation task and the download task, and the separate scheduling reduces the use efficiency of the satellite, so how to improve the use efficiency of the satellite by integrated scheduling of the observation task and the download task becomes a problem to be solved urgently.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-satellite task scheduling method and system, which improve the service efficiency of satellites.
In a first aspect, the present invention provides a method for scheduling multi-star tasks, including:
acquiring an initial preset number of task sequences, and selecting the task sequence with the maximum fitness as an initial target task sequence from the initial preset number of task sequences;
calculating by a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, and selecting a new target task sequence from the new preset number of task sequences;
and obtaining a task sequence after scheduling through iterative operation of the quantum genetic algorithm according to the new target task sequence.
Optionally, the calculating by the quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences includes:
and updating the quantum bit probability of observation tasks in the initial target task sequence through a quantum revolving gate strategy, and obtaining a new preset number of task sequences according to the quantum bit probability.
Optionally, the task sequence includes an observation task and a download task, and the method further includes:
and performing real number coding and quantum bit coding on each observation task in the initial preset number of task sequences, wherein the quantum bit coding comprises the probability that the observation task is observed and the probability that the observation task is not observed.
Optionally, the selecting, as the initial target task sequence, the task sequence with the maximum fitness from the initial preset number of task sequences includes:
acquiring the fitness of the task sequence according to the proportion of the number of executed observation tasks in the number of all observation tasks in the task sequence and the proportion of the sum of the weights of the executed observation tasks in the sum of the weights of all observation tasks;
and selecting the task sequence with the maximum fitness from the initial preset number of task sequences as an initial target task sequence.
Optionally, the obtaining a new preset number of task sequences according to the qubit probability includes:
and obtaining a new preset number of task sequences through an incomplete collapse measurement strategy according to the quantum bit probability.
In a second aspect, the present invention further provides a multi-star task scheduling system, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring task sequences with initial preset quantity and selecting the task sequence with the maximum fitness from the task sequences with the initial preset quantity as an initial target task sequence;
the operation module is used for performing operation through a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, and selecting a new target task sequence from the new preset number of task sequences;
and the scheduling module is used for obtaining a scheduled task sequence through iterative operation of the quantum genetic algorithm according to the new target task sequence.
Optionally, the operation module is configured to:
and updating the quantum bit probability of observation tasks in the initial target task sequence through a quantum revolving gate strategy, and obtaining a new preset number of task sequences according to the quantum bit probability.
Optionally, the task sequence includes an observation task and a download task, and the system further includes:
and the encoding module is used for carrying out real number encoding and quantum bit encoding on each observation task in the initial preset number of task sequences, wherein the quantum bit encoding comprises the observed probability of the observation task and the probability that the observation task is not observed.
Optionally, the obtaining module is configured to:
acquiring the fitness of the task sequence according to the proportion of the number of executed observation tasks in the number of all observation tasks in the task sequence and the proportion of the sum of the weights of the executed observation tasks in the sum of the weights of all observation tasks;
and selecting the task sequence with the maximum fitness from the initial preset number of task sequences as an initial target task sequence.
Optionally, the operation module is configured to:
and obtaining a new preset number of task sequences through an incomplete collapse measurement strategy according to the quantum bit probability.
According to the technical scheme, the multi-satellite task scheduling method and system provided by the invention have the advantages that the task sequence after scheduling is obtained by selecting the target task sequence and carrying out iterative operation through the quantum genetic algorithm according to the target task sequence, and the service efficiency of the satellite is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of satellite observation and download according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a target time window according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating transition times provided by an embodiment of the present invention;
fig. 4 is a flowchart illustrating a method for scheduling a multi-star task according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a quantum genetic algorithm provided in an embodiment of the present invention;
FIG. 6 is a diagram illustrating quantum state encoding according to an embodiment of the present invention;
FIG. 7 is a diagram of quantum chromosomal encoding provided in accordance with an embodiment of the invention;
fig. 8 is a schematic structural diagram of a system for scheduling lazy tasks according to an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Before describing in detail the details of embodiments of the present invention, the satellite observation process will be described.
The satellite images a ground target on a running orbit through a remote sensor, and each imaging action forms an imaging strip with a certain width on the ground, such as the area in fig. 1. One ground target only needs to be imaged once to complete observation. The data obtained from each imaging is temporarily stored in the memory of the satellite, and after a certain number of targets are observed by the satellite, the image data needs to be downloaded to the ground station to release the memory capacity. The whole process is shown in fig. 1.
Several definitions are explained below:
time window: the satellites are in constant motion in orbit and have different orbital turns within a given scheduling period. Imaging of a terrestrial target must be performed when the satellite moves over the target in an orbital revolution, where the satellite's remote sensor will be able to see the target for a period of time called a time window (as shown in fig. 2). In a given planning period, there is generally more than one time window between the satellite and the target, the observation of the target by the satellite needs to be completed within one of the time windows, and the time window for the target to observe is generally smaller than the visible time window, and the start time and the end time of the observation time window are shown in fig. 2.
Observing the transition time: when one satellite executes 2 successive observation tasks, a certain transition time is needed in the middle to make the satellite remote sensor well adjust. I.e. the observed start time of the following task minus the observed end time of the previous task is greater than a transition time, as shown in fig. 3.
Data storage: the satellite has a fixed capacity on-board memory, and temporarily stores the observed target image data in the memory. After the data is transmitted back to the ground station, the memory capacity of the memory is released. The real-time capacity of the memory is thus dynamically changed throughout the observation.
Energy consumption: the satellite consumes energy both during the observation of the target and during the data download, and the energy available to the satellite in each orbital turn is limited, so that the energy consumption in each turn cannot exceed this maximum energy limit during the scheduling process.
Data downloading: the ground station can receive data downloaded by the satellite. As with the observation task, data download needs to be completed within a time window. Since data downloading consumes satellite energy and occupies the working time of the satellite, on-satellite storage should be fully utilized as much as possible, and the data downloading times should be reduced as much as possible.
Downloading transition time: a ground station can only receive one satellite's download at a time. If 2 satellites need to download the same ground station one after another, a transition time is needed for the ground station to adjust the receiving antenna.
Modeling is carried out on the multi-satellite observation and download task scheduling problem, parameters, variables and mathematical signs used in the model are given firstly, and then the mathematical model is given to complete scheduling. Some parameters of this embodiment may be as follows:
the mathematical model of the multi-satellite observation and download task integrated scheduling problem specifically comprises the following constraint conditions:
the formula (1) is an objective function and consists of two parts, namely the sum of the number of executed observation tasks and the sum of the weight of the executed observation tasks. The scheduling objective is to maximize their weighted sum, where Rnum，RwgtIs a scaling factor.
Constraint (2) indicates that each observation task can be performed only once at most.
Constraint (3) indicates that the ground station can only receive data download from one satellite at a time.
Constraint (4) indicates that if two observation tasks are executed by the same satellite one after the other, there is a need for sufficient transition time between the two tasks.
If two satellites successively download data to the same ground station, the ground station needs a certain transition time between receiving the data downloading of the two satellites.
Constraint (6) indicates that the energy consumed by the satellite in each orbital revolution cannot exceed the maximum energy limit.
The constraints (7) and (8) indicate that the storage capacity at the start of observation and the storage capacity at the end of observation of the satellite are the maximum storage capacity in a given planning period.
After the constrained (9) satellite performs the observation task, the occupied on-satellite memory increases.
The constraint (10) indicates that the data stored on the satellite must not exceed the maximum capacity of the on-board memory.
Fig. 4 is a flowchart illustrating a method for scheduling a multi-star task according to an embodiment of the present invention, where as shown in fig. 4, the method includes the following steps:
401. acquiring an initial preset number of task sequences, and selecting the task sequence with the maximum fitness as an initial target task sequence from the initial preset number of task sequences;
the task sequence comprises an observation task and a download task, and the method further comprises the following steps:
and performing real number coding and quantum bit coding on each observation task in the initial preset number of task sequences, wherein the quantum bit coding comprises the probability that the observation task is observed and the probability that the observation task is not observed.
Specifically, the selecting a task sequence with the maximum fitness from the initial preset number of task sequences as an initial target task sequence includes:
acquiring the fitness of the task sequence according to the proportion of the number of executed observation tasks in the number of all observation tasks in the task sequence and the proportion of the sum of the weights of the executed observation tasks in the sum of the weights of all observation tasks;
and selecting the task sequence with the maximum fitness from the initial preset number of task sequences as an initial target task sequence.
402. Calculating by a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, and selecting a new target task sequence from the new preset number of task sequences;
403. and obtaining a task sequence after scheduling through iterative operation of the quantum genetic algorithm according to the new target task sequence.
It can be understood that, in the method, the task sequence after scheduling is obtained by continuously selecting a new target task sequence and carrying out an iterative process of quantum genetic algorithm operation according to the target task sequence.
Preferably, the operation is performed by a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, and the method includes:
and updating the quantum bit probability of observation tasks in the initial target task sequence through a quantum revolving gate strategy, and obtaining a new preset number of task sequences according to the quantum bit probability.
Preferably, the obtaining of the new preset number of task sequences according to the qubit probability includes:
and obtaining a new preset number of task sequences through an incomplete collapse measurement strategy according to the quantum bit probability.
According to the method, the target task sequence is selected, iterative operation is carried out through a quantum genetic algorithm according to the target task sequence, the task sequence after scheduling is obtained, and the service efficiency of the satellite is improved.
It should be noted that, in the above method, the process of continuously iteratively calculating and selecting the target task sequence is the same as the above method of selecting the target task sequence from the initial task sequence, and this embodiment does not describe this in detail.
The above method is described in detail by the following specific examples, and as shown in fig. 5, the method comprises the following steps:
501. setting initial parameters;
initializing a population: inserting download task at first, and inserting one download task at each time by pgAnd determining whether the downloading task is executed or not according to the probability, wherein the time window of the downloading task which is not executed is as follows:reinitializing the measurement task, wherein the quantum bit code of each individual of the measurement task is initialized to
And measuring each observation task once to obtain a determined solution, evaluating the fitness and recording the optimal individual and the fitness.
The tasks are coded, and a quantum-induced genetic algorithm (quantum-induced genetic algorithm) combines the traditional genetic algorithm with a quantum theory, so that the method has a better effect than the traditional genetic algorithm. The quantum genetic algorithm has the advantages of better keeping population diversity, good exploration capability, high convergence rate and strong global optimization capability.
The basis of quantum computation is explained below.
Generally, a quantum state is formed by the superposition of multiple eigenstates of a certain mechanical quantity. In quantum computing, information is stored and processed using qubits, which can be in both |0> and |1> states simultaneously.
Qubit advancement is that it can be in the superposition of two quantum states at the same time:
alpha and beta are two amplitude constants satisfying
|α>2+|β>2＝1 (12)
Wherein, |0>And |1>Representing spin-down and spin-up states, so a qubit can contain both states |0>And |1>The information of (1). If a system has m qubits, the system can describe 2 simultaneouslymAnd (4) a state.
With equation (11), it is generally not possible to know the values of α, β correctly. An observation is required to change a qubit state to a definite state (|0> or |1>) in a probabilistic manner.
Quantum coding
For the multi-star earth observation model, quantum bit coding is mainly carried out on observation tasks. We specify that an observation task can only be observed once in a programming cycle, each observation task having two states, unappreciated and observed, corresponding to the two states |0> and |1> in a qubit. For target i there are:
the qubit coding structure is explained in 10 observation tasks (denoted by 1-10), as shown in fig. 6, where α, β are the qubit probabilities for each target.
502. Reading target task, ground station and satellite information;
as shown in fig. 7, in combination with the qubit encoding of the target, a task sequence of a group of satellites can be obtained through a measurement strategy. We explain our real number based quantum coding structure by taking as an example 10 observation tasks (denoted by 1-10), 2 ground stations (denoted by-1, -2), 2 satellites Sat1, Sat2, where virtual satellites are used to store observation tasks that cannot be performed temporarily.
503. De-indexing to obtain whether the number of task sequences is greater than or equal to a preset threshold, if so, executing step 504 and 509; if not, go to step 510-514;
504. obtaining an optimal individual;
specifically, the optimal individual is obtained by calculating through a fitness function.
The fitness function considers the proportion of the number of executed observation tasks to the number of all observation tasks and the proportion of the sum of the weights of the executed observation tasks to the sum of the weights of all observation tasks, and the expression is shown as a formula (14):
505. quantum revolving door updating
And (4) adjusting the individuals through a revolving gate strategy to obtain a new quantum bit probability.
Quantum initialization is firstly needed, N chromosomes are initialized, and the encoding length of quantum bits is the number of observation tasks and is expressed by m. Initializing each qubit probability of each chromosome toFor chromosome i, its qubits are initially encoded as:
and selecting the quantum rotating gate as a quantum bit adjusting strategy aiming at the model. The adjustment operation of the quantum revolving door is as follows:
the updating process is as follows:
wherein,andrepresenting the probability amplitude before and after the jth quantum bit revolving gate updating of the ith chromosome of the tth generation; θ is the rotation angle, whose magnitude is determined by the adjustment strategy, a general adjustment strategy being used in this project, as shown in table 1 below:
TABLE 1 Quantum revolving door renewal table
In the above table, f (x) is an objective function; 0 represents no observation and 1 represents observation; s (. alpha.) ofiβi) Is thetaiThe symbol of (a); biAnd xiAre respectively optimalThe solution and the ith value in the current solution. For example, when f (x) > f (b), xi＝1，biWhen equal to 0, Δ θi0.025 pi, and s (alpha)iβi) Can be according to alphaiβiIs set to +1, -1 or 0, respectively, to increase the probability amplitude of the quantum state |1|, i.e. to make the observation task as selective as possible to be observed.
506. Inserting a downloading task;
the step is to insert the task sequence with the same number as the de-index number after the target task sequence selected based on the steps, generally insert a certain number of downloading tasks first, so as to insert the observation task next step, and form the task sequence with the observation task and the downloading task.
507. Measuring an observation task;
and measuring according to the quantum bit probability to obtain the offspring population.
A new measurement strategy for incomplete collapse is used in conjunction with the model. The i-th observation task selection probability of the i-th chromosome of the t generation is
Or
According to the basic measurement rule, first a [0,1 ] is generated]Random number of interval, if random number
And selecting an observation task i, otherwise, not observing the observation task i. In combination with an actual model, the problem that the actual problem is more restricted and whether the observation task is observed cannot be considered by using a simple binary thought is considered, so that an incomplete collapse measurement strategy is adopted, and a collapse object is a time window of each observation task rather than the observation task. Under the improved measurement strategy, if random number
The selection range of the time window of the observation task i is the observation taskAnd all time windows of the task i, otherwise, the time window of the observation task i is collapsed, and the selection range of the time window is narrowed to one time window. And finally, reconstructing the task sequence according to the available time window to obtain a feasible solution.
508. Evaluating individual fitness;
and evaluating the fitness and recording the optimal individual and the corresponding fitness value.
509. If the termination condition? is met, the process ends, otherwise, the process continues to step 504.
In this step, there is a preset number of iterations, that is, if the number of iterations through the quantum genetic algorithm does not reach the preset number of iterations, the process continues to step 504.
510. Inserting a downloading task;
511. initializing an observation task;
512. measuring an observation task;
513. evaluating individual fitness;
514. the index is incremented by 1 and step 503 is performed.
Pseudo code 1: task insertion algorithm
Pseudo-code 2 target insertion algorithm (take i as an example):
Pseudo-code 4 measurement strategy
Pseudo-code 5 quantum revolving gate strategy
An embodiment of the present invention further provides a multi-star task scheduling system, as shown in fig. 8, the system includes:
an obtaining module 81, configured to obtain an initial preset number of task sequences, and select a task sequence with the highest fitness from the initial preset number of task sequences as an initial target task sequence;
the operation module 82 is configured to perform operation by a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, and select a new target task sequence from the new preset number of task sequences;
and the scheduling module 83 is configured to obtain a scheduled task sequence through iterative operation of the quantum genetic algorithm according to the new target task sequence.
Optionally, the operation module is configured to:
and updating the quantum bit probability of observation tasks in the initial target task sequence through a quantum revolving gate strategy, and obtaining a new preset number of task sequences according to the quantum bit probability.
Optionally, the task sequence includes an observation task and a download task, and the system further includes:
and the encoding module is used for carrying out real number encoding and quantum bit encoding on each observation task in the initial preset number of task sequences, wherein the quantum bit encoding comprises the observed probability of the observation task and the probability that the observation task is not observed.
Optionally, the obtaining module is configured to:
acquiring the fitness of the task sequence according to the proportion of the number of executed observation tasks in the number of all observation tasks in the task sequence and the proportion of the sum of the weights of the executed observation tasks in the sum of the weights of all observation tasks;
and selecting the task sequence with the maximum fitness from the initial preset number of task sequences as an initial target task sequence.
Optionally, the operation module is configured to:
and obtaining a new preset number of task sequences through an incomplete collapse measurement strategy according to the quantum bit probability.
It should be noted that the above-mentioned systems and the above-mentioned methods are in a one-to-one correspondence relationship, and the implementation details of the above-mentioned methods are also applicable to the above-mentioned systems, and the above-mentioned systems will not be described in detail in this embodiment.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the devices in an embodiment may be adaptively changed and placed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a device of a browser terminal according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A multi-satellite task scheduling method is characterized by comprising the following steps:
acquiring an initial preset number of task sequences, and selecting the task sequence with the maximum fitness as an initial target task sequence from the initial preset number of task sequences;
calculating by a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, and selecting a new target task sequence from the new preset number of task sequences;
obtaining a scheduled task sequence through iterative operation of the quantum genetic algorithm according to the new target task sequence;
the specific method for acquiring the initial preset number of task sequences and selecting the task sequence with the maximum fitness as the initial target task sequence from the initial preset number of task sequences comprises the following steps:
inserting the downloading tasks in the initial preset number of task sequences, and adding p when inserting one downloading taskgAnd determining whether the downloading task is executed or not according to the probability, wherein the time window of the downloading task which is not executed is as follows:
reinitializing the measurement task, wherein the quantum bit code of each individual of the measurement task is initialized to
Measuring each observation task once to obtain a determined solution, evaluating the fitness, and recording the optimal individual and the fitness;
encoding the task based on a quantum genetic algorithm;
reading target task, ground station and satellite information;
performing index solving to obtain whether the number of the task sequences is greater than or equal to a preset threshold value or not, and then obtaining the task sequence with the maximum fitness as an initial target task sequence; if not, executing the following steps:
inserting a downloading task; initializing an observation task; measuring an observation task; evaluating individual fitness; and adding 1 to the index, and judging whether the number of the obtained task sequences is greater than or equal to a preset threshold value again.
2. The method of claim 1, wherein the obtaining a new preset number of task sequences by performing an operation according to the initial target task sequence through a quantum genetic algorithm comprises:
and updating the quantum bit probability of observation tasks in the initial target task sequence through a quantum revolving gate strategy, and obtaining a new preset number of task sequences according to the quantum bit probability.
3. The method of claim 1, further comprising:
and performing real number coding and quantum bit coding on each observation task in the initial preset number of task sequences, wherein the quantum bit coding comprises the probability that the observation task is observed and the probability that the observation task is not observed.
4. The method according to claim 1, wherein the selecting a task sequence with the highest fitness from the initial preset number of task sequences as an initial target task sequence comprises:
acquiring the fitness of the task sequence according to the proportion of the number of executed observation tasks in the number of all observation tasks in the task sequence and the proportion of the sum of the weights of the executed observation tasks in the sum of the weights of all observation tasks;
and selecting the task sequence with the maximum fitness from the initial preset number of task sequences as an initial target task sequence.
5. The method of claim 2, wherein the obtaining a new preset number of task sequences according to the qubit probability comprises:
and obtaining a new preset number of task sequences through an incomplete collapse measurement strategy according to the quantum bit probability.
6. A multi-star task scheduling system, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring task sequences with initial preset quantity and selecting the task sequence with the maximum fitness from the task sequences with the initial preset quantity as an initial target task sequence; the specific method for selecting the task sequence with the maximum fitness as the initial target task sequence comprises the following steps:
inserting the downloading tasks in the initial preset number of task sequences, and adding p when inserting one downloading taskgAnd determining whether the downloading task is executed or not according to the probability, wherein the time window of the downloading task which is not executed is as follows:
reinitializing the measurement task, wherein the quantum bit code of each individual of the measurement task is initialized to
Measuring each observation task once to obtain a determined solution, evaluating the fitness, and recording the optimal individual and the fitness;
encoding the task based on a quantum genetic algorithm;
reading target task, ground station and satellite information;
performing index solving to obtain whether the number of the task sequences is greater than or equal to a preset threshold value or not, and then obtaining the task sequence with the maximum fitness as an initial target task sequence; if not, executing the following steps:
inserting a downloading task; initializing an observation task; measuring an observation task; evaluating individual fitness; adding 1 to the index, and judging whether the number of the obtained task sequences is greater than or equal to a preset threshold value again;
the operation module is used for performing operation through a quantum genetic algorithm according to the initial target task sequence to obtain a new preset number of task sequences, and selecting a new target task sequence from the new preset number of task sequences;
and the scheduling module is used for obtaining a scheduled task sequence through iterative operation of the quantum genetic algorithm according to the new target task sequence.
7. The system of claim 6, wherein the computing module is configured to:
and updating the quantum bit probability of observation tasks in the initial target task sequence through a quantum revolving gate strategy, and obtaining a new preset number of task sequences according to the quantum bit probability.
8. The system of claim 6, further comprising:
and the encoding module is used for carrying out real number encoding and quantum bit encoding on each observation task in the initial preset number of task sequences, wherein the quantum bit encoding comprises the observed probability of the observation task and the probability that the observation task is not observed.
9. The system of claim 6, wherein the acquisition module is configured to:
acquiring the fitness of the task sequence according to the proportion of the number of executed observation tasks in the number of all observation tasks in the task sequence and the proportion of the sum of the weights of the executed observation tasks in the sum of the weights of all observation tasks;
and selecting the task sequence with the maximum fitness from the initial preset number of task sequences as an initial target task sequence.
10. The system of claim 7, wherein the computing module is configured to:
and obtaining a new preset number of task sequences through an incomplete collapse measurement strategy according to the quantum bit probability.
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| CN111176807B (en) * | 2019-12-09 | 2023-12-12 | 中国空间技术研究院 | Multi-star collaborative task planning method |
| CN111913785B (en) * | 2020-06-10 | 2022-09-30 | 合肥工业大学 | Multi-satellite task scheduling method and system |
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