Multi-Satellite Joint Observation Mission Planning Method and System Based on Neighborhood Iterative Search

Abstract

The present invention provides a multi-satellite joint observation mission planning method and system based on neighborhood iterative search, which relates to the technical field of mission planning. Based on the simulated annealing algorithm, the present invention first randomly generates a task arrangement sequence as the initial solution, plans all tasks in the task set according to the task arrangement sequence, and calculates the total revenue; on this basis, perturbs the task arrangement sequence through neighborhood perturbation to generate a new task arrangement sequence, plans all tasks in the task set according to the new task arrangement sequence to obtain a planning scheme, calculates the new total revenue, and updates the task arrangement sequence by comparing the change in the total revenue of the task set planning scheme before and after perturbation. Utilize the advantages of the simulated annealing algorithm to jump out of the local optimum and find a more suitable task arrangement sequence. By sorting the tasks in the task set in the above manner, the present invention can obtain more total revenue, and has high planning efficiency and low cost.

Description

Technical Field

[0001] The present invention relates to the technical field of mission planning, and particularly to a multi-satellite joint observation mission planning method and system based on neighborhood iterative search. Background Art

[0002] Earth observation satellites are platforms that orbit the Earth and can observe and image the Earth's surface. For satellites equipped with only one sensor, according to the types of sensors they carry, they can be divided into various types such as infrared satellites, visible light satellites, synthetic aperture radar satellites, etc. With the wide application of Earth observation satellites in many fields such as ground exploration, environmental monitoring, and military reconnaissance, the needs of users are becoming more and more complex and diverse, generating a large number of complex Earth imaging requirements. For example, in the handling of emergencies such as earthquakes and floods, multiple types of satellites are required to jointly observe the disaster area to obtain accurate disaster data and provide a basis for timely and effective disaster relief operations. Such tasks are called multi-satellite joint Earth observation missions.

[0003] In recent years, the number and types of Earth observation satellites owned by our country have increased rapidly, which has greatly increased the complexity of satellite mission planning. In addition, when planning a task set composed of multi-satellite joint Earth observation missions, different tasks will compete for limited satellite resources. The tasks planned first will occupy some satellite resources, thereby affecting the available time window of the tasks planned later. The tasks with a later planning order are more likely to be unable to meet the observation requirements of all specified types of satellites. Therefore, the order of task planning is crucial for the total benefit that the final planning scheme can bring.

[0004] However, for a task set composed of multiple multi-satellite joint Earth observation missions, currently, mainly relying on the experience of dispatchers, the method of manual arrangement is used to plan each multi-satellite joint Earth observation mission in the task set one by one, without considering the sorting between tasks to find a better planning scheme, resulting in a low benefit of multi-satellite joint Earth observation mission planning. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a multi-satellite joint observation mission planning method and system based on neighborhood iterative search, which solves the technical problem of the low benefit of the existing planning method.

[0007] (2) Technical Solutions

[0008] To achieve the above object, the present invention is realized through the following technical solutions:

[0009] In a first aspect, the present invention provides a multi-satellite joint observation mission planning method based on neighborhood iterative search, including:

[0010] S1. Obtain a task set, a set of earth observation satellites, a set of time windows, and a minimum temperature A min , and initialize temperature A;

[0011] S2. Randomly generate a task arrangement sequence B according to the task set, the set of earth observation satellites, and the set of time windows;

[0012] S3. Initialize the current iteration number a = 1;

[0013] S4. Judge whether a ≤ N A holds, where N A represents the iteration number at temperature A. If so, go to step S5; otherwise, go to step S9;

[0014] S5. Plan all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme, and calculate the total benefit f(B) of the planning scheme;

[0015] S6. Perturb the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B', and plan all tasks in the task set according to the task arrangement sequence B' to obtain a planning scheme, and calculate the total benefit f(B');

[0016] S7. Judge whether f(B') ≥ f(B) holds. If so, update B = B', f(B) = f(B'); otherwise, accept the new solution according to the Metropolis criterion;

[0017] S8. Update a = a + 1, and go to step S4;

[0018] S9. Update temperature A = αA, where α is a constant between 0 and 1, representing the annealing rate;

[0019] S11. Judge whether A ≥ A min holds, where A min is the minimum temperature for terminating iteration. If so, go to step S11; otherwise, go to step S12;

[0020] S12. Update a = 1, and go to step S4;

[0021] S13. Output the task arrangement sequence B and its corresponding planning scheme.

[0022] Preferably, the perturbing the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B' includes:

[0023] Randomly select one operator from the task swapping operator, single-task insertion operator, dual-task bundling insertion operator, and dual-task splicing insertion operator to perturb the task arrangement sequence B to generate a new task arrangement sequence B';

[0024] Among them,

[0025] The perturbation process of the task swapping operator includes:

[0026] a1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0027] a2: Randomly select two different positions l1 and l2 in the task arrangement sequence B';

[0028] a3: Swap the task numbers stored at positions l1 and l2 in the task arrangement sequence B';

[0029] a4: Output the task arrangement sequence B';

[0030] The perturbation process of the single-task insertion operator includes:

[0031] b1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0032] b2: Randomly select a position l1 in the task arrangement sequence B';

[0033] b3: Take out the task number at position l1 in the task arrangement sequence B';

[0034] b4: Randomly select a position l2 in the task arrangement sequence B';

[0035] b5: Insert the taken-out task number into the position immediately before position l2;

[0036] b6: Output the task arrangement sequence B';

[0037] The perturbation process of the dual-task bundling insertion operator includes:

[0038] c1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0039] c2: Randomly select a position l1 that is not the first position in the task arrangement sequence B';

[0040] c3: Take out the two task numbers at position l1 and the position immediately before l1 in the task arrangement sequence B', without changing the sorting between the two task numbers;

[0041] c4: Randomly select a position l2 in the task arrangement sequence B';

[0042] c5: Insert the two retrieved task numbers together into the position immediately before the position l2;

[0043] c6: Output the task arrangement sequence B';

[0044] The perturbation process of the dual-task splicing insertion operator includes:

[0045] d1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0046] d2: Randomly select two different positions l1 and l2 in the task arrangement sequence B';

[0047] d3: Retrieve the two task numbers at positions l1 and l2 in the task arrangement sequence B', without changing the sorting between the two task numbers;

[0048] d4: Randomly select a position l3 in the task arrangement sequence B';

[0049] d5: Insert the two retrieved task numbers together into the position immediately before the position l3;

[0050] d6: Output the task arrangement sequence B'.

[0051] Preferably, the planning the tasks in the task set according to the task arrangement sequence B to obtain a planning scheme includes:

[0052] S501. Initialize the observation task set T, the time window set W, and the task arrangement sequence B;

[0053] S502. Initialize the planning scheme D B as an empty set;

[0054] S503. Copy the time window set W to generate a time window set W';

[0055] S504. Initialize i = 1;

[0056] S505. Read the task t corresponding to the task number at position l i in the task arrangement sequence B;

[0057] S506. Initialize k = 1;

[0058] S507. Determine whether there is a time window of the k-th satellite type of task t in the time window set W', if so, go to step S508, otherwise go to step S511;

[0059] S508. Calculate the benefits of all time windows of the k-th satellite type of task t through the formula where g m represents the benefit of time window m, and c mIndicates the usage cost of time window m;

[0060] S509. Add the time window with the maximum benefit to the planning scheme D B ;

[0061] S510. Delete the time window with the maximum benefit and the time window that forms an incompatible time window pair with the time window with the maximum benefit in the time window set W';

[0062] S511: Update k = k + 1, and determine whether k ≤ K i holds. If so, go to step S507; otherwise, go to step S512;

[0063] S512. Update i = i + 1, and determine whether i ≤ N T holds. If so, go to step S505; otherwise, go to step S513;

[0064] S513. Output the planning scheme D B .

[0065] Preferably, the incompatible time window pair includes:

[0066] b, c ∈ W j respectively represent two different time windows of the jth satellite. If the switching-on and switching-off transition time requirements of the sensors carried by the satellite are not satisfied between b and c, they form a group of incompatible time window pairs.

[0067] Preferably, calculating the total benefit f(B) of the planning scheme includes:

[0068] Let n i represent the actual imaging times of the task t B in the planning scheme D i Calculate the total benefit of the planning scheme D B where c represents the usage cost of the time window h, p h represents the benefit of completing the ith task, K i represents the number of times of imaging the observation target required by the task t i N i represents the total number of task sets, and w T represents the hth time window. h h

[0069] Preferably, accepting the new solution according to the Metropolis criterion includes:

[0070] Judge Whether it holds. If so, accept the new task arrangement sequence B', update B = B', f(B) = f(B'). Otherwise, do not accept the new solution and retain the original solution, where Δf = f(B') - f(B), and A is the current temperature.

[0071] In a second aspect, the present invention provides a multi-satellite joint observation task planning system based on neighborhood iterative search. The system includes:

[0072] At least one storage unit;

[0073] At least one processing unit;

[0074] Wherein, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit to implement the following steps:

[0075] S1. Obtain a task set, a set of earth observation satellites, a set of time windows, and the lowest temperature A min , and initialize the temperature A;

[0076] S2. Randomly generate a task arrangement sequence B according to the task set, the set of earth observation satellites, and the set of time windows;

[0077] S3. Initialize the current iteration number a = 1;

[0078] S4. Judge whether a ≤ N A holds, where N A represents the number of iterations at temperature A. If so, go to step S5; otherwise, go to step S9;

[0079] S5. Plan all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme, and calculate the total benefit f(B) of the planning scheme;

[0080] S6. Perturb the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B', and plan all tasks in the task set according to the task arrangement sequence B' to obtain a planning scheme, and calculate the total benefit f(B');

[0081] S7. Judge whether f(B') ≥ f(B) holds. If so, update B = B', f(B) = f(B'); otherwise, accept the new solution according to the Metropolis criterion;

[0082] S8. Update a = a + 1, and go to step S4;

[0083] S9. Update the temperature A = αA, where α is a constant between 0 and 1, representing the annealing rate;

[0084] S10. Judge whether A ≥ A minWhether it holds, where A min is the lowest temperature at which iteration terminates. If so, go to step S11; otherwise, go to step S12;

[0085] S11. Update a = 1 and go to step S4;

[0086] S12. Output the task arrangement sequence B and its corresponding planning scheme.

[0087] Preferably, the task arrangement sequence B is perturbed by neighborhood perturbation to generate a new task arrangement sequence B', including:

[0088] Randomly select one operator from the task exchange operator, single-task insertion operator, double-task bundling insertion operator, and double-task splicing insertion operator to perturb the task arrangement sequence B to generate a new task arrangement sequence B';

[0089] Among them,

[0090] The perturbation process of the task exchange operator includes:

[0091] a1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0092] a2: Randomly select two different positions l1 and l2 in the task arrangement sequence B';

[0093] a3: Exchange the task numbers stored at positions l1 and l2 in the task arrangement sequence B';

[0094] a4: Output the task arrangement sequence B';

[0095] The perturbation process of the single-task insertion operator includes:

[0096] b1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0097] b2: Randomly select a position l1 in the task arrangement sequence B';

[0098] b3: Take out the task number at position l1 in the task arrangement sequence B';

[0099] b4: Randomly select a position l2 in the task arrangement sequence B';

[0100] b5: Insert the taken-out task number into the position immediately before position l2;

[0101] b6: Output the task arrangement sequence B';

[0102] The perturbation process of the double-task bundling insertion operator includes:

[0103] c1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0104] c2: Randomly select a non-first position l1 in the task arrangement sequence B';

[0105] c3: Take out the two task numbers at the l1 position and the position immediately before l1 in the task arrangement sequence B', without changing the sorting between the two task numbers;

[0106] c4: Randomly select a position l2 in the task arrangement sequence B';

[0107] c5: Insert the two taken-out task numbers together immediately before the l2 position;

[0108] c6: Output the task arrangement sequence B';

[0109] The perturbation process of the dual-task splicing and insertion operator includes:

[0110] d1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0111] d2: Randomly select two different positions l1 and l2 in the task arrangement sequence B';

[0112] d3: Take out the two task numbers at the l1 and l2 positions in the task arrangement sequence B', without changing the sorting between the two task numbers;

[0113] d4: Randomly select a position l3 in the task arrangement sequence B';

[0114] d5: Insert the two taken-out task numbers together immediately before the l3 position;

[0115] d6: Output the task arrangement sequence B'.

[0116] Preferably, the planning of all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme includes:

[0117] S501. Initialize the observed task set T, the time window set W, and the task arrangement sequence B;

[0118] S502. Initialize the planning scheme D B as an empty set;

[0119] S503. Copy the time window set W to generate the time window set W';

[0120] S504. Initialize i = 1;

[0121] S505. Read the position l in the task arrangement sequence B iThe task t corresponding to the task number at

[0122] S506. Initialize k = 1;

[0123] S507. Determine whether there is a time window of the k-th satellite type of task t in the time window set W'. If so, go to step S508; otherwise, go to step S511.

[0124] S508. Calculate the benefits of all time windows of the k-th satellite type of task t through the formula where g m represents the benefit of time window m, and c m represents the usage cost of time window m.

[0125] S509. Add the time window with the maximum benefit to the planning scheme D B ;

[0126] S510. Delete the time window with the maximum benefit and the time window that forms an incompatible time window pair with the time window with the maximum benefit in the time window set W'.

[0127] S511: Update k = k + 1, and determine whether k ≤ K i holds. If so, go to step S507; otherwise, go to step S512.

[0128] S512. Update i = i + 1, and determine whether i ≤ N T holds. If so, go to step S505; otherwise, go to step S513.

[0129] S513. Output the planning scheme D B .

[0130] Preferably, calculating the total benefit f(B) of the planning scheme includes:

[0131] Let n i represent the actual imaging times of task t planned in the planning scheme D B , calculate the total benefit of the planning scheme D i where c B represents the usage cost of time window h, p where c h represents the usage cost of time window h, p i represents the benefit of completing the i-th task, K i represents the number of times of imaging the observation target required for task t i , N T represents the total number of task sets, and w h represents the h-th time window.

[0132] (III) Beneficial effects

[0133] The present invention provides a multi-satellite joint observation mission planning method and system based on neighborhood iterative search. Compared with the prior art, it has the following beneficial effects:

[0134] Based on the simulated annealing algorithm, the present invention first randomly generates a task arrangement sequence as the initial solution, plans all tasks in the task set according to the task arrangement sequence, and calculates the total revenue; on this basis, the task arrangement sequence is perturbed through neighborhood perturbation to generate a new task arrangement sequence, and all tasks in the task set are planned according to the new task arrangement sequence to obtain a planning scheme, calculate the new total revenue, and update the task arrangement sequence by comparing the change in the total revenue of the task set planning scheme before and after perturbation. When updating the task arrangement sequence, for the case where the total revenue of the perturbed planning scheme is less than the total revenue of the unperturbed planning scheme, the Metropolis criterion is used to accept the new solution, and the advantage of the simulated annealing algorithm is utilized to jump out of the local optimum and find a more suitable task arrangement sequence. By sorting the tasks in the task set in the above manner, the present invention can obtain more total revenue, and has high planning efficiency and low cost. Description of the Drawings

[0135] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0136] Figure 1 It is a block diagram of a multi-satellite joint observation mission planning method based on neighborhood iterative search in an embodiment of the present invention. Detailed Embodiments

[0137] To make the objectives, 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. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0138] By providing a multi-satellite joint observation mission planning method and system based on neighborhood iterative search, the embodiments of the present application solve the technical problem of low revenue of the existing planning methods, and realize reasonably arranging the order of tasks in the task set to obtain more revenue and improve the planning efficiency.

[0139] The technical solutions in the embodiments of the present application for solving the above technical problems have the following general idea:

[0140] Satellite mission planning technology aims to meet the multi-source and time-varying user requirements, and is a technology for collaborative optimization of the operation processes of a large number of heterogeneous space-ground resources. In recent years, with the rapid growth in the quantity and variety of Earth observation satellites owned by our country, the complexity of satellite mission planning has increased significantly. Due to the higher timeliness, information acquisition accuracy, and mission observation coordination requirements of multi-satellite joint Earth observation missions, it is more difficult to plan them. In addition, for a mission set composed of multiple multi-satellite joint Earth observation missions, the given satellite resources have a limited observation time window for the ground, and different missions will compete for these limited time windows, resulting in the inability to fully meet the multi-type satellite joint observation requirements of some missions. For a mission that can be observed by all specified types of satellites, all the benefits corresponding to the mission can be obtained, while for a mission in which some of the specified satellite types cannot complete the observation, the benefits of the unobserved part of the mission cannot be obtained. For a mission set composed of multiple multi-satellite joint Earth observation missions, currently, mainly relying on the experience of dispatchers, the method of manual arrangement is used to plan each multi-satellite joint Earth observation mission in the mission set one by one, without considering the sorting between missions to find a better planning scheme.

[0141] However, when planning a mission set composed of multi-satellite joint Earth observation missions, different missions will compete for limited satellite resources. The missions planned first will occupy some satellite resources, thereby affecting the available time window of the missions planned later. The missions with a later planning order are more likely to be unable to meet the observation requirements of all specified types of satellites. Therefore, the order of mission planning is crucial for the total benefits that the final planning scheme can bring. To address the above problems, the embodiments of the present invention are proposed.

[0142] To better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0143] The embodiments of the present invention provide a multi-satellite joint observation mission planning method based on neighborhood iterative search, as Figure 1 shown, including:

[0144] S1. Obtain a mission set, an Earth observation satellite set, a time window set, and a lowest temperature A min , and initialize the temperature A;

[0145] S2. Randomly generate a mission arrangement sequence B according to the mission set, the Earth observation satellite set, and the time window set;

[0146] S3. Initialize the current iteration number a = 1;

[0147] S4. Judge whether a ≤ N AWhether it holds, where N A represents the number of iterations at temperature A (N A is a set value). If so, go to step S5; otherwise, go to step S9;

[0148] S5. Plan all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme, and calculate the total benefit f(B) of the planning scheme;

[0149] S6. Perturb the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B', and plan all tasks in the task set according to the task arrangement sequence B' to obtain a planning scheme, and calculate the total benefit f(B');

[0150] S7. Determine whether f(B') ≥ f(B) holds. If so, update B = B' and f(B) = f(B'); otherwise, accept the new solution according to the Metropolis criterion;

[0151] S8. Update a = a + 1 and go to step S4;

[0152] S9. Update the temperature A = αA, where α is a constant between 0 and 1, representing the annealing rate;

[0153] S10. Determine whether A ≥ A min holds, where A min is the lowest temperature at which iteration terminates. If so, go to step S11; otherwise, go to step S12;

[0154] S11. Update a = 1 and go to step S4;

[0155] S12. Output the task arrangement sequence B and its corresponding planning scheme.

[0156] Based on the simulated annealing algorithm, the embodiment of the present invention first randomly generates a task arrangement sequence as the initial solution, plans all tasks in the task set according to the task arrangement sequence, and calculates the total benefit; on this basis, perturbs the task arrangement sequence through neighborhood perturbation to generate a new task arrangement sequence, plans all tasks in the task set according to the new task arrangement sequence to obtain a planning scheme, calculates the new total benefit, and updates the task arrangement sequence by comparing the change in the total benefit of the task set planning scheme before and after perturbation. When updating the task arrangement sequence, for the case where the total benefit of the planning scheme after perturbation is less than the total benefit of the planning scheme before perturbation, the Metropolis criterion is used to accept the new solution, and the advantage of the simulated annealing algorithm is used to jump out of the local optimum and find a more suitable task arrangement sequence. The embodiment of the present invention sorts the tasks in the task set in sequence through the above method, can obtain more total benefits, and has high planning efficiency and low cost.

[0157] The following describes each step in detail:

[0158] In step S1, a task set, an earth observation satellite set, a time window set, and a minimum temperature A are obtained min , and the temperature A is initialized. The specific implementation process is as follows:

[0159] In the embodiment of the present invention, obtaining data includes:

[0160] A task set composed of N T point target tasks and an earth observation satellite set of N S satellites The task planning scenario formed by these two sets includes the following data:

[0161] t i represents the i-th task, 1 ≤ i ≤ N T ; s j represents the j-th earth observation satellite, and each earth observation satellite is equipped with an imaging sensor; represents the time window set of all satellites for all tasks, w h represents the h-th time window, 1 ≤ h ≤ N w ; O represents the set of all incompatible time window pairs; p i represents the benefit of completing the i-th task; K i represents the number of times the task t i requires imaging of the observation target. At the same time, an incompatible time window pair is defined: let b, c ∈ W j respectively represent two different time windows of the j-th satellite. If the switching time requirements of the sensor carried by the satellite are not met between b and c, it is a set of incompatible time window pairs, denoted as (b, c).

[0162] Initialize the temperature A.

[0163] In step S2, a task arrangement sequence B is randomly generated according to the task set, the earth observation satellite set, and the time window set. The specific implementation process is as follows:

[0164] According to the data in the task planning scenario in step S1, a task arrangement sequence B is randomly generated as the initial solution. The task numbers of each task are stored in sequence in B. Let l i represent the i-th position in the task arrangement sequence B.

[0165] In step S5, all tasks in the task set are planned according to the task arrangement sequence B to obtain a planning scheme, and the total benefit f(B) of the planning scheme is calculated. The specific implementation process is as follows:

[0166] S501. Initialize the observation task set T, the time window set W, and the task arrangement sequence B;

[0167] S502. Initialize the planning scheme D B as an empty set;

[0168] S503. Copy the time window set W to generate the time window set W';

[0169] S504. Initialize i = 1;

[0170] S505. Read the task t corresponding to the task number at position l i in the task arrangement sequence B;

[0171] S506. Initialize k = 1;

[0172] S507. Determine whether there is a time window of the k-th satellite type of task t in the time window set W'. If so, go to step S508; otherwise, go to step S511;

[0173] S508. Calculate the revenue of all time windows of the k-th satellite type of task t through the formula where g m represents the revenue of time window m, and c m represents the usage cost of time window m;

[0174] S509. Add the time window with the maximum revenue to the planning scheme D B ;

[0175] S510. Delete the time window with the maximum revenue and the time window that forms an incompatible time window pair with the time window with the maximum revenue in the time window set W';

[0176] S511: Update k = k + 1, and determine whether k ≤ K i holds. If so, go to step S507; otherwise, go to step S512;

[0177] S512. Update i = i + 1, and determine whether i ≤ N T holds. If so, go to step S505; otherwise, go to step S513;

[0178] S513. Output the planning scheme D B ;

[0179] S514. Let n i represent the actual imaging times of task t planned in the planning scheme D B , and calculate the total revenue of the planning scheme D i where c B is the total revenue where, c hRepresents the usage cost of the time window h.

[0180] In step S6, the task arrangement sequence B is perturbed through neighborhood perturbation to generate a new task arrangement sequence B', and a planning scheme is obtained by planning all tasks in the task set according to the task arrangement sequence B', and the total revenue f(B') is calculated. The specific implementation process is as follows:

[0181] In the embodiment of the present invention, four operators, namely a task exchange operator, a single-task insertion operator, a double-task bundling insertion operator, and a double-task splicing insertion operator, are designed to perform neighborhood perturbation.

[0182] S601. Randomly select one operator from the task exchange operator, the single-task insertion operator, the double-task bundling insertion operator, and the double-task splicing insertion operator to perturb the task arrangement sequence B to generate a new task arrangement sequence B'. Specifically, it includes:

[0183] The perturbation process of the task exchange operator includes:

[0184] a1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0185] a2: Randomly select two different positions l1 and l2 in the task arrangement sequence B';

[0186] a3: Exchange the task numbers stored at positions l1 and l2 in the task arrangement sequence B';

[0187] a4: Output the task arrangement sequence B'.

[0188] The perturbation process of the single-task insertion operator includes:

[0189] b1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0190] b2: Randomly select a position l1 in the task arrangement sequence B';

[0191] b3: Take out the task number at position l1 in the task arrangement sequence B';

[0192] b4: Randomly select a position l2 in the task arrangement sequence B';

[0193] b5: Insert the taken-out task number into the position immediately before position l2;

[0194] b6: Output the task arrangement sequence B'.

[0195] The perturbation process of the double-task bundling insertion operator includes:

[0196] c1: Copy the task arrangement sequence B to obtain the task arrangement sequence B';

[0197] c2: Randomly select a position l1 that is not the first position in the task arrangement sequence B'.

[0198] c3: Take out the two task numbers at the l1 position and the position immediately before l1 in the task arrangement sequence B', without changing the sorting between the two task numbers.

[0199] c4: Randomly select a position l2 in the task arrangement sequence B'.

[0200] c5: Insert the two taken-out task numbers together in the position immediately before the l2 position.

[0201] c6: Output the task arrangement sequence B'.

[0202] The perturbation process of the dual-task splicing and insertion operator includes:

[0203] d1: Copy the task arrangement sequence B to obtain the task arrangement sequence B'.

[0204] d2: Randomly select two different positions l1 and l2 in the task arrangement sequence B'.

[0205] d3: Take out the two task numbers at the l1 and l2 positions in the task arrangement sequence B', without changing the sorting between the two task numbers.

[0206] d4: Randomly select a position l3 in the task arrangement sequence B'.

[0207] d5: Insert the two taken-out task numbers together in the position immediately before the l3 position.

[0208] d6: Output the task arrangement sequence B'.

[0209] S602. Plan all tasks in the task set according to the task arrangement sequence B' to obtain a planning scheme, and calculate the total revenue f(B'). This calculation process is the same as the calculation process in step S5 and will not be elaborated here.

[0210] In step S7, judge whether f(B') ≥ f(B) holds. If so, update B = B' and f(B) = f(B'); otherwise, accept the new solution according to the Metropolis criterion. The specific implementation process is as follows:

[0211] The specific method of accepting the new solution according to the Metropolis criterion includes:

[0212] Judge Whether it holds. If so, accept the new task arrangement sequence B', update B = B', f(B) = f(B'). Otherwise, do not accept the new solution and retain the original solution, where Δf = f(B') - f(B).

[0213] An embodiment of the present invention provides a multi-satellite joint observation task planning system based on neighborhood iterative search. The system includes:

[0214] At least one storage unit;

[0215] At least one processing unit;

[0216] Wherein, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit to implement the following steps:

[0217] S1. Obtain a task set, an earth observation satellite set, a time window set, and a minimum temperature A min , and initialize the temperature A;

[0218] S2. Randomly generate a task arrangement sequence B according to the task set, the earth observation satellite set, and the time window set;

[0219] S3. Initialize the current iteration number a = 1;

[0220] S4. Judge whether a ≤ N A holds, where N A represents the number of iterations at temperature A. If so, go to step S5; otherwise, go to step S9;

[0221] S5. Plan all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme, and calculate the total benefit f(B) of the planning scheme;

[0222] S6. Perturb the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B', and plan all tasks in the task set according to the task arrangement sequence B' to obtain a planning scheme, and calculate the total benefit f(B');

[0223] S7. Judge whether f(B') ≥ f(B) holds. If so, update B = B', f(B) = f(B'); otherwise, accept the new solution according to the Metropolis criterion;

[0224] S8. Update a = a + 1, and go to step S4;

[0225] S9. Update the temperature A = αA, where α is a constant between 0 and 1, representing the annealing rate;

[0226] S10. Judge whether A ≥ A min holds, where Amin is the lowest temperature at which iteration terminates. If so, go to step S11; otherwise, go to step S12;

[0227] S11. Update a = 1 and go to step S4;

[0228] S12. Output the task arrangement sequence B and its corresponding planning scheme.

[0229] It can be understood that the multi - satellite joint observation task planning system based on neighborhood iterative search provided by the embodiments of the present invention corresponds to the above - mentioned multi - satellite joint observation task planning method based on neighborhood iterative search. For the explanations, examples, beneficial effects, etc. of relevant contents, reference can be made to the corresponding contents in the multi - satellite joint observation task planning method based on neighborhood iterative search, which will not be elaborated here.

[0230] In summary, compared with the prior art, the following beneficial effects are achieved:

[0231] 1. Based on the simulated annealing algorithm, the embodiments of the present invention first randomly generate a task arrangement sequence as the initial solution, plan all tasks in the task set according to the task arrangement sequence, and calculate the total revenue. On this basis, the task arrangement sequence is perturbed through neighborhood perturbation to generate a new task arrangement sequence, and all tasks in the task set are planned according to the new task arrangement sequence to obtain a planning scheme, calculate the new total revenue, and update the task arrangement sequence by comparing the change in the total revenue of the planning schemes of the task set before and after perturbation. When updating the task arrangement sequence, for the case where the total revenue of the perturbed planning scheme is less than that of the unperturbed planning scheme, the Metropolis criterion is used to accept the new solution, and the advantage of the simulated annealing algorithm is utilized to jump out of the local optimum and find a more suitable task arrangement sequence. By the above - mentioned method, the embodiments of the present invention sort the tasks in the task set successively, and can obtain more total revenue. At the same time, the existing method of manual arrangement plans tasks one by one, which requires a large amount of manpower and material resources, has a high planning cost and low efficiency, while the method of the embodiments of the present invention has a high planning efficiency and low cost.

[0232] 2. According to the characteristics of multi - satellite joint earth observation task planning, the embodiments of the present invention design four new operators for neighborhood perturbation, so that the neighborhood perturbation has better global search ability, improves the accuracy of problem solving, and thus obtains more total revenue. At the same time, the neighborhood perturbation shortens the running time of the algorithm and further improves the planning efficiency.

[0233] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0234] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-satellite joint observation mission planning method based on neighborhood iterative search, characterized in that Including: S1. Obtain a task set, an earth observation satellite set, a time window set, and the lowest temperature A min , and initialize temperature A; S2. Randomly generate a task arrangement sequence B according to the task set, the earth observation satellite set, and the time window set; S3. Initialize the current iteration number a = 1; S4. Determine whether a ≤ N A holds, where N A represents the number of iterations at temperature A. If so, go to step S5; otherwise, go to step S9; S5. Plan all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme, and calculate the total benefit f(B) of the planning scheme; S6. Perturb the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B', and plan all tasks in the task set according to the task arrangement sequence B' to obtain a planning scheme, and calculate the total benefit f(B'); S7. Judge whether f(B') ≥ f(B) holds. If so, update B = B' and f(B) = f(B'); otherwise, accept the new solution according to the Metropolis criterion; S8. Update a = a + 1, and go to step S4; S9. Update the temperature A = αA, where α is a constant between 0 and 1, representing the annealing rate; S10. Determine whether A≥A holds, where A is the lowest temperature for terminating the iteration. If so, go to step S11; otherwise, go to step S12; min min ​​ S11. Update a = 1, and go to step S4; S12. Output the task arrangement sequence B and its corresponding planning scheme.

2. The multi-satellite joint observation mission planning method based on neighborhood iterative search according to claim 1, wherein, The perturbation of the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B' includes: Randomly select an operator from the task exchange operator, the single-task insertion operator, the double-task bundling insertion operator, and the double-task splicing insertion operator to perturb the task arrangement sequence B to generate a new task arrangement sequence B'; Wherein, The perturbation process of the task exchange operator includes: a1. Copy the task arrangement sequence B to obtain the task arrangement sequence B'; a2. Randomly select two different positions l1 and l2 in the task arrangement sequence B'; a3. Exchange the task numbers stored at the positions l1 and l2 in the task arrangement sequence B'; a4. Output the task arrangement sequence B'; The perturbation process of the single-task insertion operator includes: b1. Copy the task arrangement sequence B to obtain the task arrangement sequence B'; b2. Randomly select a position l1 in the task arrangement sequence B'; b3. Take out the task number at the position l1 in the task arrangement sequence B'; b4. Randomly select a position l2 in the task arrangement sequence B'; b5. Insert the taken-out task number into the position immediately before the position l2; b6. Output the task arrangement sequence B'; The perturbation process of the double-task bundling insertion operator includes: c1. Copy the task arrangement sequence B to obtain the task arrangement sequence B'; c2. Randomly select a position l1 that is not the first position in the task arrangement sequence B'; c3. Take out the two task numbers at the position l1 and the position immediately before l1 in the task arrangement sequence B', without changing the sorting between the two task numbers; c4. Randomly select a position l2 in the task arrangement sequence B'; c5. Insert the taken-out two task numbers together into the position immediately before the position l2; c6. Output the task arrangement sequence B'; The perturbation process of the double-task splicing insertion operator includes: d1. Copy the task arrangement sequence B to obtain the task arrangement sequence B'; d2. Randomly select two different positions l1 and l2 in the task arrangement sequence B'; d3: Take out the task numbers at positions l1 and l2 in the task arrangement sequence B', without changing the sorting between the two task numbers; d4: Randomly select a position l3 in the task arrangement sequence B'; d5: Insert the two taken-out task numbers together immediately before the position l3; d6: Output the task arrangement sequence B'.

3. The multi-satellite joint observation mission planning method based on neighborhood iterative search according to claim 1, wherein, The planning of all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme includes: S501: Initialize the observation task set T, the time window set W, and the task arrangement sequence B; S502. Initialize the planning scheme D B is an empty set; S503: Copy the time window set W to generate the time window set W'; S504: Initialize i = 1; S505. Read the task t corresponding to the task number at position l in the task arrangement sequence B i ; S506: Initialize k = 1; S507: Determine whether there is a time window of the k-th satellite type of task t in the time window set W'. If so, go to step S508; otherwise, go to step S511; S508. Calculate the revenue of all time windows of the k-th satellite type for task t through the formula where g m represents the revenue of time window m, and c m represents the usage cost of time window m; S509. Add the time window with the maximum benefit to the planning scheme D B ; S510: Delete the time window with the maximum benefit and the time window that forms an incompatible time window pair with the time window with the maximum benefit in the time window set W'; S511: Update k = k + 1, and determine whether k ≤ K i holds. If so, go to step S507; otherwise, go to step S512; S512. Update \(i = i + 1\), and determine whether \(i\leq N\) T holds. If so, go to step S505; otherwise, go to step S513. S513. Output planning scheme D B .

4. The multi-satellite joint observation mission planning method based on neighborhood iterative search according to claim 3, characterized in that, The incompatible time window pair includes: b, c ∈ W j respectively represent two different time windows of the j-th satellite. If the switching-on and switching-off transition time requirements of the sensors carried by the satellite are not met between b and c, it is a pair of incompatible time windows.

5. The multi-satellite joint observation mission planning method based on neighborhood iterative search according to claim 1, wherein The calculation of the total benefit f(B) of the planning scheme includes: Let n i represent the actual number of imaging times planned for task t B in the planning scheme D i , and calculate the total benefit B of the planning scheme D where c h represents the usage cost of time window h, p i represents the benefit of completing the i-th task, K i represents the number of imaging times required for task t i to image the observation target, N T represents the total number of task sets, and w h represents the h-th time window.

6. The multi-satellite joint observation mission planning method based on neighborhood iterative search according to claim 1, characterized in that The acceptance of the new solution according to the Metropolis criterion includes: Judge whether it holds. If so, accept the new task arrangement sequence B', update B = B', f(B) = f(B'); otherwise, do not accept the new solution and retain the original solution, where Δf = f(B') - f(B), and A is the current temperature.

7. A multi-satellite joint observation mission planning system based on neighborhood iterative search, characterized in that The system includes: At least one storage unit; At least one processing unit; Wherein, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit to implement the following steps: S1. Obtain a task set, an earth observation satellite set, a time window set, and a minimum temperature A min , and initialize temperature A; S2: Randomly generate a task arrangement sequence B according to the task set, the earth observation satellite set, and the time window set; S3: Initialize the current iteration number a = 1; S4. Determine whether a ≤ N A holds, where N A represents the number of iterations at temperature A. If yes, go to step S5; otherwise, go to step S9; S5: Plan all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme, and calculate the total benefit f(B) of the planning scheme; S6: Perturb the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B', and plan all tasks in the task set according to the task arrangement sequence B' to obtain a planning scheme, and calculate the total benefit f(B'); S7: Determine whether f(B') ≥ f(B) holds. If so, update B = B' and f(B) = f(B'); otherwise, accept the new solution according to the Metropolis criterion; S8: Update a = a + 1, and go to step S4; S9: Update the temperature A = αA, where α is a constant between 0 and 1, representing the annealing rate; S10. Determine whether A ≥ A holds, where A is the lowest temperature at which iteration terminates. If so, go to step S11; otherwise, go to step S12; min min ​​ S11: Update a = 1, and go to step S4; S12: Output the task arrangement sequence B and its corresponding planning scheme.

8. The multi-satellite joint observation mission planning system based on neighborhood iterative search according to claim 7, wherein, The perturbation of the task arrangement sequence B through neighborhood perturbation to generate a new task arrangement sequence B' includes: Randomly select an operator from the task exchange operator, the single-task insertion operator, the double-task bundling insertion operator, and the double-task splicing insertion operator to perturb the task arrangement sequence B to generate a new task arrangement sequence B'; Wherein, The perturbation process of the task exchange operator includes: a1: Copy the task arrangement sequence B to obtain the task arrangement sequence B'; a2: Randomly select two different positions l1 and l2 in the task arrangement sequence B'; a3: Swap the task numbers stored at positions l1 and l2 in the task arrangement sequence B'. a4: Output the task arrangement sequence B'. The perturbation process of the single-task insertion operator includes: b1: Copy the task arrangement sequence B to obtain the task arrangement sequence B'. b2: Randomly select a position l1 in the task arrangement sequence B'. b3: Take out the task number at position l1 in the task arrangement sequence B'. b4: Randomly select a position l2 in the task arrangement sequence B'. b5: Insert the taken-out task number into the position immediately before position l2. b6: Output the task arrangement sequence B'. The perturbation process of the double-task bundling insertion operator includes: c1: Copy the task arrangement sequence B to obtain the task arrangement sequence B'. c2: Randomly select a non-first position l1 in the task arrangement sequence B'. c3: Take out the two task numbers at position l1 and the position immediately before l1 in the task arrangement sequence B', without changing the sorting between the two task numbers. c4: Randomly select a position l2 in the task arrangement sequence B'. c5: Insert the two taken-out task numbers together into the position immediately before position l2. c6: Output the task arrangement sequence B'. The perturbation process of the double-task splicing insertion operator includes: d1: Copy the task arrangement sequence B to obtain the task arrangement sequence B'. d2: Randomly select two different positions l1 and l2 in the task arrangement sequence B'. d3: Take out the two task numbers at positions l1 and l2 in the task arrangement sequence B', without changing the sorting between the two task numbers. d4: Randomly select a position l3 in the task arrangement sequence B'. d5: Insert the two taken-out task numbers together into the position immediately before position l3. d6: Output the task arrangement sequence B'.

9. The multi-satellite joint observation mission planning system based on neighborhood iterative search according to claim 7, characterized in that, The step of planning all tasks in the task set according to the task arrangement sequence B to obtain a planning scheme includes: S501. Initialize the observed task set T, the time window set W, and the task arrangement sequence B. S502. Initialize the planning scheme D B to be an empty set; S503. Copy the time window set W to generate the time window set W'. S504. Initialize i = 1. S505. Read the task t corresponding to the task number at position l in the task arrangement sequence B i ; S506. Initialize k = 1. S507. Determine whether there is a time window of the k-th satellite type of task t in the time window set W'. If yes, go to step S508; otherwise, go to step S511. S508. Calculate the revenue of all time windows of the k-th satellite type for task t through the formula , where g m represents the revenue of time window m, and c m represents the usage cost of time window m; S509. Add the time window with the maximum benefit to the planning scheme D B ; S510. Delete the time window with the maximum benefit and the time window that forms an incompatible time window pair with the time window with the maximum benefit in the time window set W'. S511: Update k = k + 1, and determine whether k ≤ K holds. If it holds, go to step S507; otherwise, go to step S512; i ​ S512. Update \(i = i + 1\) and determine whether \(i\leq N\) holds. If it holds, go to step S505; otherwise, go to step S513; T ​ S513. Output planning scheme D B .

10. The multi-satellite joint observation mission planning system based on neighborhood iterative search according to claim 7, characterized in that, The calculation of the total benefit f(B) of the planning scheme includes: Let n i represent the number of actual planned imaging times for task t B in the planning scheme D i , and calculate the total revenue of the planning scheme D B . Among them, c h represents the usage cost of the time window h, p i represents the revenue for completing the i-th task, K i represents the number of times of imaging the observation target required for task t i , N T represents the total number of task sets, and w h represents the h-th time window.

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