CN112529317A - Satellite imaging task planning method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a satellite imaging task planning method, which comprises the following steps: determining constraint conditions and an objective function according to the requirements of satellite imaging task planning; constructing a constraint satisfaction model of the satellite imaging task plan based on the constraint conditions and the objective function; constructing a coding sequence of a solution of the constraint satisfaction model; and selecting a strip combination from a preset strip set by using a cuckoo algorithm based on the coding sequence of the solution, wherein the strip combination is a solution of the constraint satisfaction model. The disclosure also provides a satellite imaging task planning device, an electronic device and a storage medium.
Description
Technical Field
The disclosure relates to the technical field of satellite remote sensing, in particular to a satellite imaging task planning method and device, electronic equipment and a storage medium.
Background
Remote sensing technology has become an important means for acquiring earth resource and environment information. The imaging satellite is an earth observation satellite which acquires ground image information from the outer space by using a satellite-borne remote sensor. The method has the advantages of long operation time, wide coverage range, no need of considering personnel safety and the like. However, as the requirements of users on the data demand and the data quality of imaging satellites are gradually increased, the satellite resources need to be reasonably distributed by means of mathematical models and computer technologies, and the problem of task planning of imaging satellites is solved, which is an important research subject.
The satellite imaging planning problem is a complex combined optimization problem, and the solving algorithm of the common satellite imaging task planning problem comprises a genetic algorithm, a simulated annealing algorithm, a hybrid algorithm and the like, but the algorithms are easy to generate local optimization in the solving process to obtain a suboptimal solution.
Disclosure of Invention
In view of the above, in order to overcome at least one aspect of the above problems, the present disclosure provides a satellite imaging task planning method, including: determining constraint conditions and an objective function according to the requirements of satellite imaging task planning; constructing a constraint satisfaction model of the satellite imaging task plan based on the constraint conditions and the objective function; constructing a coding sequence of a solution of the constraint satisfaction model; and selecting a strip combination from a preset strip set by using a cuckoo algorithm based on the coding sequence of the solution, wherein the strip combination is a solution of the constraint satisfaction model.
Optionally, the coding sequence of the solution whose constraint satisfies the model is
M＝[(1，q1)，(2，q2)，…，(m，qm)]
Where M is the coding sequence of the solution for which the constraint satisfies the model, 1, 2, …, M is the number of the transit trajectory that produced the imaged strip, q is the number of the transit trajectory that produced the imaged stripm＝0，1，…，Qm，QmThe Q th stripe generated under the mth transit track, and the total number of the stripes generated under the mth transit track is Q, QmIs any one of Q strips generated under the mth transit orbit, and m and Q are positive integers.
Optionally, the constraint condition includes: the method comprises the following steps of strip number constraint, strip imaging time point constraint, satellite single-time starting-up time constraint, satellite load working time constraint, satellite sensor working constraint and satellite sensor conversion time constraint.
Optionally, the objective function is
Wherein G is the comprehensive coverage rate of the imaging strip to the target area, S1The comprehensive coverage area of the imaging strip on the target area is shown, and S is the total area of the target area.
Optionally, the selecting, based on the coding sequence of the solution, a combination of strips from a preset set of strips by using a cuckoo algorithm, where the combination of strips is a solution of the constraint satisfaction model, includes:
initializing a bird nest of a cuckoo algorithm;
randomly generating n bird nests A based on the coding sequence of the solutionN＝{A1，A2，…，An}；
Computing each bird nest based on the objective functionAyFitness F ofAySelecting A with maximum fitnessyThe optimal solution of the constraint satisfaction model is taken as the optimal solution of the constraint satisfaction model;
updating the positions of the bird nests through the Laiwei flight to generate n bird nests BN＝{B1，B2，…，BnCalculating each bird nest ByFitness F ofBy；
Eliminating the bird nest B according to elimination probabilityNAnd updating the positions of the eliminated bird nests by a random preference walk algorithm to generate a new bird nest CN＝{C1，C2，…，Cn}；
Calculate each bird nest CyFitness F ofCyComparison FAy、FByAnd FCySelecting the bird nest with the maximum fitness as the optimal solution of the constraint satisfaction model;
judging whether an iteration stop condition is met;
if the iteration stopping condition is met, selecting the bird nest with the maximum fitness as the optimal solution of the constraint satisfaction model;
if the iteration stop condition is not met, repeatedly executing the generation of n bird nests B through the Laevir flightN＝{B1，B2，…，BnCalculating each bird nest ByFitness F ofByThe operation of (1);
wherein, the bird nest AN、BN、CNRespectively n solutions, A, satisfying the format of the coding sequencey、By、CyAre respectively AN、BN、C N1, 2, 3, …, n, n is a positive integer, FAy、FByAnd FCyAre respectively bird nest Ay、By、CyThe fitness of (2).
Optionally, the formula of the lave flight is
Wherein the content of the first and second substances,
the position of the nth bird nest in the tth generation is shown, t is the current iteration number, k is the iteration number meeting the iteration stop condition, t is 1, 2, 3, …, k, k is a positive integer, alpha is the step length direction, L (u, v) is a random step length path, and u and v are the Levis flight parameters which obey normal distribution.
Optionally, before selecting a combination of strips from a preset set of strips by using a cuckoo algorithm based on the coding sequence of the solution, where the combination of strips is a solution for which the constraint satisfies a model, the method includes:
acquiring observation requirements and schedulable satellite resources;
determining the geographic position and satellite orbit information of a target observation area based on the observation requirement and preset satellite resources;
calculating all imaging strips of the satellite according to the geographic position of the target observation region and the satellite orbit information;
and screening the imaging strips with imaging quality meeting a preset value from all the imaging strips to form a preset strip set.
The present disclosure also provides a satellite imaging task planning device, including:
the determining module is used for determining constraint conditions and an objective function according to the requirements of the satellite imaging task planning;
the first construction module is used for constructing a constraint satisfaction model of the satellite imaging task plan based on the constraint conditions and the objective function;
the second construction module is used for constructing a coding sequence of a solution of the constraint satisfaction model;
and the selection module is used for selecting a strip combination from a preset strip set by using a cuckoo algorithm based on the coding sequence of the solution, wherein the strip combination is the solution of the constraint satisfaction model.
The present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above satellite imaging task planning methods when executing the program.
The present disclosure also provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements any of the satellite imaging task planning methods described above.
Compared with the prior art, the method has the following beneficial effects:
1. the constraint of the cuckoo algorithm on the satellite imaging task planning satisfies model solution, the solution process of the cuckoo algorithm requires few parameters, and the global search capability and the adjustability of the solution process can be improved.
2. And constructing a coding sequence of a solution of a constraint satisfaction model which accords with the satellite imaging task plan of the satellite imaging scene based on part of constraint conditions, realizing the classification of imaging strips, simplifying the coding sequence and improving the transportability of the coding sequence.
3. The coding sequence of the constructed solution is adaptive to the satellite imaging occasion and the solving process of the cuckoo algorithm, and the solving efficiency and accuracy of the whole solving process are improved.
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For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
FIG. 1 schematically illustrates a flow chart of a method of satellite imaging mission planning according to an embodiment of the present disclosure;
FIG. 2 schematically shows a schematic diagram of a coding sequence according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a block diagram of a satellite imaging mission planning apparatus, in accordance with an embodiment of the present disclosure;
fig. 4 schematically shows a hardware structure diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order to more clearly illustrate the embodiments or prior art solutions of the present disclosure, reference will now be made briefly to the drawings that are used in the description of the embodiments or prior art, and it should be understood that these descriptions are merely illustrative and are not intended to limit the scope of the present disclosure. For a person skilled in the art, without inventive effort, further figures can be derived from these figures. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
Fig. 1 schematically shows a flow chart of a method of satellite imaging mission planning according to an embodiment of the present disclosure.
As shown in fig. 1, the present disclosure provides a satellite imaging mission planning method, the method comprising:
step S101, determining constraint conditions and a target function according to requirements of satellite imaging task planning;
step S102, constructing a constraint satisfaction model of the satellite imaging task planning based on the constraint conditions and the objective function;
step S103, constructing a coding sequence of a solution of the constraint satisfaction model;
and S104, selecting a strip combination from a preset strip set by using a cuckoo algorithm based on the coding sequence of the solution, wherein the strip combination is the solution of the constraint satisfaction model.
Specifically, the constraint conditions include:
and (3) restricting the number of strips: the total number of imaging bands actually participated in by the satellite each time is not more than the maximum total number of the imageable bands of the satellite;
strip imaging time constraint: the imaging time of the imaging strip of any satellite is longer than the single shortest shooting time required by the satellite;
strip imaging time point constraint: the imaging latest ending time point of all imaging strips is earlier than the specified ending time point;
satellite single boot time constraints: the observation time window of the satellite is smaller than the single longest starting time range;
satellite load operating time constraint: the working time of each orbit and each day of the imaging satellite load is less than the maximum limit value;
satellite sensor work constraint: different sensors of the same satellite cannot work simultaneously, and the same sensor cannot adopt different imaging modes for imaging simultaneously;
satellite sensor switching time constraint: the sensor side swing conversion time of the satellite is larger than the specified conversion time, and the radar satellite cannot simultaneously image left and right views.
The input data of the constraint satisfaction model are all preset imaging strips, and the input imaging strips comprise information such as imaging satellite identification, sensor models, sensor imaging modes, yaw angles, imaging start and end time, imaging ranges and the like. The output data is a strip set which meets the constraint conditions, and the output imaging strips comprise information such as whether the satellite is imaged in each orbit, and a sensor, a sensor mode, a sensor yaw angle, imaging start time, imaging end time and the like selected during imaging.
It should be noted that the satellite imaging planning problem is a complex problem, and it is impossible to consider all the constraints in the actual modeling process. In order to facilitate modeling and solve the main practical problems, some assumptions about imaging conditions are needed. For example, the storage capacity of the satellite for data is not considered in the model, and all data can be smoothly transmitted back to the ground station after the satellite is imaged; the model assumes that the battery power of the satellite meets all observation tasks and data transmission tasks; the model does not consider the three-dimensional imaging of the target, namely, each observation activity is independent and not constrained, and the time relation and the logic relation do not need to be considered.
According to the requirements of users for satellite imaging task planning, an index for evaluating the imaging scheme, namely an objective function, needs to be determined. Specifically, the objective function is
Wherein G is the comprehensive coverage rate of the imaging strip to the target area, S1The comprehensive coverage area of the imaging strip on the target area is shown, and S is the total area of the target area.
In the embodiment of the present disclosure, based on the objective function, the coverage rate of the target area in the finally obtained imaging scheme is calculated as an index for evaluating the imaging scheme, that is, the fitness of each imaging scheme is calculated.
In this embodiment, a constraint satisfaction model may be established based on the constraint conditions, where the constraint satisfaction model may be:
Max f(x)＝G(x)＝G(x1，x2，…xN)；
G(x)，s.t.(1)，(2a)，(2b)，(3a)，(3b)，(4)，(5a)，(5b)，(5c)；
stripNumber≤H (1)
endTimei-startTimei≥min Satpictime (2a)
endTimej-startTimej≥min Satpictime (2b)
startTimei≥StartTime，startTimej≥StartTime (3a)
endTimei≤EndTime，endTimej≤EndTime (3b)
max{endTimei，endTimej}-min{startTimei，startTimej}≤max Satontime (4)
when satNamei＝satNamej，senNamei＝senNamejWhen the temperature of the water is higher than the set temperature,
endTimei-startTimei≤maxSenontimeperpas (5a)
endTimej-startTimej≤maxSenontimeperpas (5b)
(endTimei-startTimei)+…+(endTimej-startTimej)≤maxSenontimeperpas (5c)
wherein, satNamei、satNamejSenName for i and j satellite identification respectivelyi、senNamejThe sensor model, startTime, corresponding to the i and j satellites respectivelyi、startTimejThe earliest starting point of the i and j satellites, endTime, respectivelyi、endTimejThe imaging time points are the latest imaging ending time points of the i satellite and the j satellite respectively, the stripNumber is the total number of strips actually participating in imaging of the satellite each time, H is the maximum imageable strip total number of the satellite, min Satpictme is the single shortest shooting time required by the satellite, and startTime and endTime are respectively the appointed starting time point and ending time point of imaging planning. maxsattime is the single longest boot time of the satellite, and maxSenontimeperpas is the maximum limit value of the load per-orbit working time.
As an alternative embodiment, in step S102, before using the cuckoo algorithm to select a combination of strips from a preset set of strips based on the coding sequence of the solution, where the combination of strips is a solution that the constraint satisfies the model, the method further includes preprocessing all strips imaged by the satellite, where the preprocessing includes at least the following operations:
step S111, acquiring observation requirements and schedulable satellite resources;
step S112, determining the geographic position and the satellite orbit information of the target observation area based on the observation requirement and the preset satellite resources;
step S113, calculating all imaging strips of the satellite according to the geographical position of the target observation area and the satellite orbit information;
and step S114, screening imaging strips with imaging quality meeting a preset value from all the imaging strips to form a preset strip set.
In the embodiment of the disclosure, when an imaging planning problem model is actually established, a demand observation set is formed by collecting demands provided by users, the geographic position of a target area and the orbit information of a satellite are determined according to the demand observation set and the attributes of satellite resources which can be scheduled, and a satellite imaging task is further decomposed and normalized to provide a data base for later-stage problem modeling. In the preprocessing process of the imaging planning problem, the method further comprises the following steps: according to the two lines of orbit data of the satellite and the geographic position of the target area, calculating a transit time window and an intersatellite point track of the satellite, meanwhile, decomposing and calculating the area of the strips by combining an imaging mode and a sidesway angle of the satellite to obtain all possible imaging strips, and finally, screening all the imaging strips according to preset screening conditions to reserve the strips with high imaging quality.
Specifically, the preset screening conditions may include:
(1) and (4) constraint of coverage rate. Some of the bands may be generated by satellites with short transit times and therefore the coverage of the band is low. Meanwhile, even satellites with long transit times may produce bands with low coverage of the target area.
(2) And (4) constraining the sidesway angle. The resolution of satellite imaging is affected by the yaw angle, and when the side view angle is too large, the resolution of the remote sensing image is too low, so that the requirement of a user on the resolution cannot be met.
(3) Constraints on weather conditions. When the cloud amount is large or in rainy or snowy weather, the optical satellite images the target area, and the imaging quality and resolution are affected. It is therefore necessary to remove the bands imaged when the cloud is excessive.
(4) Constraints on imaging time. For an optical satellite, a certain illumination condition is required for satellite imaging, so that a time range in which the satellite can image a target region needs to be considered, and a strip which does not accord with the imaging time is removed.
In the actual satellite imaging process, each imaging satellite can generate a plurality of optional observation activities, namely a plurality of strips according to different yaw angles in a single transit, but only one strip or no strip can be selected for each satellite in the single transit to participate in final imaging. Thus, the imaged swaths for a single transit orbit for each satellite may be selected to combine or optimize the swath combination scheme. In this case, the satellite imaging task planning problem can be converted into the selection of the optimal band combination scheme.
As an alternative embodiment, the coding sequence of the solution whose constraint satisfies the model is
M＝[(1，q1)，(2，q2)，…，(m，qm)]
Where M is the coding sequence of the solution for which the constraint satisfies the model, 1, 2, …, M is the number of the transit trajectory that produced the imaged strip, q is the number of the transit trajectory that produced the imaged stripm＝0，1，…，Qm，QmThe Q th stripe generated under the mth transit track, and the total number of the stripes generated under the mth transit track is Q, QmIs any one of Q strips generated under the mth transit orbit, and m and Q are positive integers.
In the constraint satisfaction model for solving the satellite imaging task plan, the solution efficiency is provided by the interaction of the encoding mode of the solution of the model and the solution algorithm. In the embodiment of the present disclosure, the strips in the strip set are grouped according to the imaging orbits of the satellite, that is, there may be several available imaging strips in each transit orbit, and several available imaging strips generated in the same transit orbit are grouped into one group. And respectively selecting one or 0 strips from each strip group as a code in decoding, and finally constructing a coding sequence of a solution of which the constraint of the satellite imaging task plan meets the model. When 0 stripe is selected, the code is 0, which indicates that the transit orbit is not imaged. The constructed coding sequence is a feasible imaging scheme for the target region, namely a solution of a satellite imaging task planning problem.
Fig. 2 schematically shows a schematic diagram of a coding sequence according to an embodiment of the present disclosure. As shown in FIG. 2, the transit orbit code includes a satellite orbit number m corresponding to the imaged slice, and the imaged slice code includes the numbers { (m, 0), (m, 1), …, (m, Q) of several slices generated under each transit orbitm)}. Selecting one of the strips generated in each transit trackSpecific strip (m, q)m) And finally forming a solution M ═ 1, q ═ of a constraint satisfaction model of the satellite imaging mission plan1)，(2，q2)，…，(m，qm)]. When q is 0, it means that no image is formed in the transit orbit.
In the disclosed embodiment, the coding sequence of the solution is composed of a set of integer pairs, each integer pair (m, q)m) Representing one strip. It should be noted that, in the actual stripe selection process, the Levy (Levy) flight is performed on the second integer of all the integer pairs, and the neighborhood range of the second integer is obtained by adding 1 to the total number of the stripes that can be generated by the transit orbit corresponding to the first integer of the integer pair, so that it is ensured that the generated new stripe is still within the constraint range after Levy flight update, and the significance is practical. That is, if there are q imaging bands under the transit orbit, since the selection manner of each time is to select 1 or 0 from the q imaging bands, the selection manner of the second integer is q + 1. In order to ensure that the numerical value after the Levy flight is still an integer, the updated numerical value is rounded, the neighborhood of the Levy flight is converted into a discrete space, and the coded sequence can be directly updated through the position of the cuckoo algorithm to obtain a new solution. For example, a slice is coded as (3, q)3) The updated code is (m, p)3)，q3、p3All are the stripes generated by the transit track No. 3 if p3When p is 0, it means that No. 3 transit orbit does not participate in imaging after updating3＝q3Then it means that the selected stripe is unchanged by this update. If p is3Not equal to 0 and p3≠q3Then it indicates that the updated strip is participating in imaging. The lewy flight is performed on each integer pair of the solution encoding sequence to generate a new encoding sequence, i.e., a new solution.
As an alternative embodiment, in step S104, the selecting a strip combination from the strip set by using the cuckoo algorithm based on the coding sequence of the solution and the objective function at least includes the following steps:
step S141, initializing a bird nest of a cuckoo algorithm;
step S142, based on the coding sequence of the solution, randomly generating n bird nests AN＝{A1，A2，…，An}；
Step S143, calculating each bird nest A based on the objective functionyFitness F ofAySelecting A with maximum fitnessyThe optimal solution of the constraint satisfaction model is taken as the optimal solution of the constraint satisfaction model;
step S144, updating the positions of the bird nests through Laiwei flight to generate n bird nests BN＝{B1，B2，…，BnCalculating each bird nest ByFitness F ofBy；
Step S145, eliminating the bird nest B according to elimination probabilityyAnd updating the positions of the eliminated bird nests by a random preference walk algorithm to generate a new bird nest CN＝{C1，C2，…，Cn}；
Step S146, calculating bird nest CyFitness F ofCyComparison FAy、FByAnd FCySelecting the bird nest with the maximum fitness as the optimal solution of the constraint satisfaction model;
step S147, judging whether an iteration stop condition is met, if the iteration stop condition is met, executing step S148, and if the iteration stop condition is not met, repeatedly executing S144-S147;
and S148, stopping iteration, and selecting the bird nest with the maximum fitness as the optimal solution of the constraint satisfaction model.
Wherein, the bird nest AN、BN、CNRespectively n solutions, A, satisfying the format of the coding sequencey、By、CyAre respectively AN、BN、C N1, 2, 3, …, n, n is a positive integer, FAy、FByAnd FCyAre respectively bird nest Ay、By、CyThe fitness of (2).
Specifically, the formula of the Laevir flight is
Wherein the content of the first and second substances,
the position of the nth bird nest in the tth generation is shown, t is the current iteration number, k is the iteration number meeting the iteration stop condition, t is 1, 2, 3, …, k, k is a positive integer, alpha is the step length direction, L (u, v) is a random step length path, and u and v are the Levis flight parameters which obey normal distribution. k is the maximum number of iterations performed, i.e. when t equals k, the iteration is stopped. The specific value of k can be set by one skilled in the art according to actual iteration needs and model characteristics, and the specific value of k is not limited by the disclosure.
The Mantegna algorithm is adopted to execute the Levy flight, and the specific calculation formula of the random step length path is
Wherein, beta is ∈ [1, 2 ]]U and v are the lavian flight parameters obeying a normal distribution,
further, the air conditioner is provided with a fan,
in the disclosed embodiment, after the cuckoo search is updated by the Levy flight, in order to simulate the process of abandoning the found bird nest, the position is set to [0, 1 ]]Random numbers R which are subjected to uniform distribution are generated in intervals and used as the possibility that the nest master finds the foreign eggs, namely the possibility that new solutions are reserved, and the probability P that the foreign eggs and the nests are eliminatedaA comparison is made. If R > PaThen using a random preference algorithm to change the bird's nest position
Producing new solutions, otherwise, not changing
And finally, reserving the best group of bird nest positions, namely updating the optimal solution again.
The step size β in the levey flight equation is typically 1.5. In step S144, to ensure that the best solution of the previous round is retained in the next round when performing the lavi flight update, the step factor is set
Pre-multiplication by coefficient
y is the point coordinate of the corresponding step size,
in order to solve the problem in the previous round,
the optimal solution in the previous round of solution. The step direction used alpha is [0, 1 ]]Uniform probability distribution is generated by multiplying the step direction alpha point by the random step path L (u, v) and then by the coefficient
Is the step increment.
Fig. 3 schematically shows a block diagram of a satellite imaging mission planning apparatus according to an embodiment of the present disclosure. As shown in fig. 3, the present disclosure also provides a satellite imaging task planning apparatus 300, where the planning apparatus 300 at least includes:
a determining module 310, configured to determine a constraint condition and an objective function according to requirements of a satellite imaging task planning;
a first constructing module 320, configured to construct a constraint satisfaction model of the satellite imaging task plan based on the constraint condition and the objective function;
a second construction module 330 for constructing a coding sequence of a solution for the constraint satisfaction model;
a selecting module 340, configured to select a stripe combination from a preset stripe set by using a cuckoo algorithm based on the coding sequence of the solution, where the stripe combination is a solution for which the constraint satisfies the model.
Referring to fig. 4, fig. 4 is a hardware structure diagram of an electronic device.
The electronic device described in this embodiment includes:
a memory 401, a processor 402 and a computer program stored on the memory 401 and executable on the processor 402, the processor 402 when executing the program implements the satellite imaging mission planning method described in the foregoing embodiment shown in fig. 1.
Further, the electronic device further includes:
at least one input device 403; at least one output device 404.
The memory 401, processor 402 input device 403 and output device 404 are connected by a bus 405.
The input device 403 may be a camera, a touch panel, a physical button, a mouse, or the like. The output device 404 may specifically be a display screen.
The Memory 401 may be a high-speed Random Access Memory (RAM) Memory or a non-volatile Memory (non-volatile Memory), such as a disk Memory. The memory 401 is used to store a set of executable program code and the processor 402 is coupled to the memory 401.
Further, the embodiment of the present disclosure also provides a computer-readable storage medium, where the computer-readable storage medium may be an electronic device provided in the foregoing embodiments, and the computer-readable storage medium may be the electronic device in the foregoing embodiment shown in fig. 4. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the satellite imaging mission planning method described in the foregoing embodiment of fig. 1. Further, the computer-readable storage medium may be various media that can store program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that each functional module in each embodiment of the present disclosure may be integrated into one processing module, or each module may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, or all or part of the technical solution that contributes to the prior art.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In view of the above description of the satellite imaging mission planning method, apparatus, electronic device and readable storage medium provided by the present invention, those skilled in the art will recognize that there may be variations in the embodiments and applications of the invention, and accordingly, the present description should not be construed as limiting the invention.
Claims (10)
1. A method for planning a satellite imaging task, comprising:
determining constraint conditions and an objective function according to the requirements of satellite imaging task planning;
constructing a constraint satisfaction model of the satellite imaging task plan based on the constraint conditions and the objective function;
constructing a coding sequence of a solution of the constraint satisfaction model;
and selecting a strip combination from a preset strip set by using a cuckoo algorithm based on the coding sequence of the solution, wherein the strip combination is a solution of the constraint satisfaction model.
2. The method of claim 1, wherein the coding sequence for which the constraint satisfies the solution of the model is a sequence of codes
M＝[(1，q1)，(2，q2)，…，(m，qm)]
Where M is the coding sequence of the solution for which the constraint satisfies the model, 1, 2, …, M is the number of the transit trajectory that produced the imaged strip, q is the number of the transit trajectory that produced the imaged stripm＝0，1，…，Qm，QmThe Q th stripe generated under the mth transit track, and the total number of the stripes generated under the mth transit track is Q, QmIs any one of Q strips generated under the mth transit orbit, and m and Q are positive integers.
3. The method of claim 1, wherein the constraints comprise: the method comprises the following steps of strip number constraint, strip imaging time point constraint, satellite single-time starting-up time constraint, satellite load working time constraint, satellite sensor working constraint and satellite sensor conversion time constraint.
4. The method of claim 1, wherein the objective function is
Wherein G isComprehensive coverage of target area for imaging strip, S1The comprehensive coverage area of the imaging strip on the target area is shown, and S is the total area of the target area.
5. The method according to claim 1, wherein the selecting, based on the encoded sequence of solutions, a combination of strips from a preset set of strips using a cuckoo algorithm, the combination of strips being a solution for which the constraint satisfies a model, comprises:
initializing a bird nest of a cuckoo algorithm;
randomly generating n bird nests A based on the coding sequence of the solutionN＝{A1，A2，…，An}；
Calculating each bird nest A based on the objective functionyFitness F ofAySelecting A with maximum fitnessyThe optimal solution of the constraint satisfaction model is taken as the optimal solution of the constraint satisfaction model;
updating the positions of the bird nests through the Laiwei flight to generate n bird nests BN＝{B1，B2，…，BnCalculating each bird nest ByFitness F ofBy；
Eliminating the bird nest B according to elimination probabilitynAnd updating the positions of the eliminated bird nests by a random preference walk algorithm to generate a new bird nest CN＝{C1，C2，…，Cn}；
Calculate each bird nest CyFitness F ofCyComparison FAy、FByAnd FCySelecting the bird nest with the maximum fitness as the optimal solution of the constraint satisfaction model;
judging whether an iteration stop condition is met;
if the iteration stopping condition is met, selecting the bird nest with the maximum fitness as the optimal solution of the constraint satisfaction model;
if the iteration stop condition is not met, repeatedly executing the generation of n bird nests B through the Laevir flightN＝{B1，B2，…，BnCalculating each bird nest ByFitness F ofByThe operation of (1);
wherein, the bird nest AN、BN、CNRespectively n solutions, A, satisfying the format of the coding sequencey、By、CyAre respectively AN、BN、CN1, 2, 3, …, n, n is a positive integer, FAy、FByAnd FCyAre respectively bird nest Ay、By、CyThe fitness of (2).
6. The method of claim 3, wherein said Levy flight has the formula
Wherein the content of the first and second substances,
the position of the nth bird nest in the tth generation is shown, t is the current iteration number, k is the iteration number meeting the iteration stop condition, t is 1, 2, 3, …, k, k is a positive integer, alpha is the step length direction, L (u, v) is a random step length path, and u and v are the Levis flight parameters which obey normal distribution.
7. The method according to claim 1, wherein before selecting a combination of strips from a preset set of strips using a cuckoo algorithm based on the coding sequence of the solution, the combination of strips being a solution for which the constraint satisfies a model, comprising:
acquiring observation requirements and schedulable satellite resources;
determining the geographic position and satellite orbit information of a target observation area based on the observation requirement and preset satellite resources;
calculating all imaging strips of the satellite according to the geographic position of the target observation region and the satellite orbit information;
and screening the imaging strips with imaging quality meeting a preset value from all the imaging strips to form a preset strip set.
8. A satellite imaging mission planning apparatus, comprising:
the determining module is used for determining constraint conditions and an objective function according to the requirements of the satellite imaging task planning;
the first construction module is used for constructing a constraint satisfaction model of the satellite imaging task plan based on the constraint conditions and the objective function;
the second construction module is used for constructing a coding sequence of a solution of the constraint satisfaction model;
and the selection module is used for selecting a strip combination from a preset strip set by using a cuckoo algorithm based on the coding sequence of the solution, wherein the strip combination is the solution of the constraint satisfaction model.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 7.
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