Remote sensing satellite task planning method and system for disaster prevention and reduction

Abstract

The invention discloses a remote sensing satellite task planning method and system for disaster prevention and reduction. The method comprises the following steps: 1) acquiring satellite information and task information; 2) setting an objective function based on the satellite information and the task information, and setting constraint conditions; 3) constructing a satellite task planning model based on the objective function and the constraint conditions; and 4) solving the satellite task planning model by adopting an improved genetic algorithm to obtain an optimal satellite task planning method. According to the method, the satellite task planning model is solved through the improved genetic algorithm, and the problems that spatial-temporal resolution constraints are not fully considered in current satellite task planning and the universality of the algorithm in scenes with different difficulties is low are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite observation, and particularly provides a remote sensing satellite mission planning method and system for disaster prevention and mitigation. Background Art

[0002] Natural disasters generally refer to events and phenomena that cause damage to human life, property, social functions, ecological environment, etc. due to natural factors. There are many types of natural disasters, mainly including typhoons, earthquakes, floods, droughts, fires, snow disasters, etc. The formation times of these natural disasters vary, and they are sometimes sudden and sometimes slow, causing different degrees of harm to people's lives and psychology. The early warning of natural disasters has become a very important issue. Remote sensing satellites are key tools for disaster prevention and mitigation. Currently, remote sensing satellites have the characteristics of long observation time and large coverage area, and have the ability to work all day and all weather, being basically unrestricted by time, climate, and scope. They can obtain high-resolution remote sensing images better when dealing with disaster monitoring tasks. Strengthening the construction of the remote sensing satellite earth observation system is of great significance for maintaining social order, economic development, national defense construction, etc.

[0003] At present, relevant scholars have carried out many studies on satellite mission planning problems. Due to the scale of resources and tasks, the complexity of their solutions has increased sharply. The relevant algorithm research mainly focuses on: exact algorithms, heuristic algorithms, and meta-heuristic algorithms. Exact algorithms often solve problems in a mathematical analysis manner and have the ability to find the global optimal solution. However, due to their computational complexity, they are difficult to apply to large-scale problems. Heuristic algorithms mainly guide the construction process of solutions through heuristic rules designed based on domain knowledge. Heuristic algorithms are relatively fast and have good applicability to large-scale satellite scheduling problems. Meta-heuristic algorithms mainly include swarm algorithms such as ant colony algorithms, simulated annealing algorithms, genetic algorithms, etc. and neighborhood search algorithms. Compared with heuristic algorithms, this type of algorithm adds a random search process on the basis of human experience rules and usually requires a long time for iterative convergence. Its optimization ability and solution efficiency are between exact algorithms and heuristic algorithms, and it is one of the most commonly used satellite scheduling algorithms.

[0004] Most of the existing satellite mission planning methods treat tasks as meta-tasks, without considering the complex spatio-temporal resolution constraints existing in disaster prevention and mitigation tasks, which cannot be processed independently, nor considering the universality of algorithms in different difficulty scenarios. Therefore, there are relatively large limitations. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a remote sensing satellite mission planning method and system for disaster prevention and mitigation, which solves the problem of satellite planning under the constraints of complex spatio-temporal resolutions and in different difficulty scenarios.

[0006] Technical solution of the present invention:

[0007] A remote sensing satellite mission planning method for disaster prevention and mitigation, comprising the following steps:

[0008] S1. Obtain satellite information and mission information;

[0009] S2. Set an objective function based on the satellite information and the mission information, and set constraint conditions;

[0010] S3. Construct a satellite mission planning model based on the objective function and the constraint conditions;

[0011] S4. Use an improved genetic algorithm to solve the satellite mission planning model to obtain an optimal satellite mission planning method.

[0012] Among them, the objective function described in step S2 is:

[0013]

[0014] Wherein:

[0015] RF i represents the actual number of times the i-th task is executed in the mission planning scheme;

[0016] F i represents the number of times the i-th task is required to be executed within the specified time;

[0017] S i represents the observation benefit of the i-th task.

[0018] Among them, the constraint conditions described in step S2 include:

[0019] (1) Satellite maximum side-sway angle constraint:

[0020]

[0021] (2) Satellite minimum solar altitude angle constraint at the sub-satellite point during the satellite's on-time:

[0022]

[0023] (3) Satellite side-sway angle deflection constraint during the satellite's off-time stage:

[0024]

[0025] (4) Mission spatial resolution constraint:

[0026] Resolution i ≥SR j

[0027] (5) Task observation time constraints:

[0028]

[0029] (6) Task time resolution constraints:

[0030]

[0031] in:

[0032] LR j represents the maximum left swing angle of the jth satellite;

[0033] RR j represents the maximum right swing angle of the jth satellite;

[0034] represents the roll angle of the jth satellite performing the kth observation plan of the ith mission;

[0035] represents the solar altitude angle of the kth observation plan performed by the jth satellite in the ith mission;

[0036] SL j represents the minimum solar altitude angle required for the jth satellite to effectively image;

[0037] Indicates the observation end time of the kth observation plan executed by the jth satellite;

[0038] It indicates the observation start time of the k'th observation plan of the jth satellite performing the i+1th mission; is a piecewise function, which indicates that the jth satellite deflects from the Swing to yaw angle The time required, no imaging can be performed during the satellite's side swing;

[0039] Resolution i represents the minimum spatial resolution of the i-th task;

[0040] SR j represents the minimum spatial resolution of the jth satellite;

[0041] MINT i represents the earliest possible time for the execution of the i-th task;

[0042] MAXT i Indicates the latest possible time for the execution of the i-th task;

[0043] I i Indicates the minimum time difference between two valid imaging tasks of the i-th task.

[0044] Preferably, the improved genetic algorithm in step S4 includes the following steps:

[0045] S401. Construct a satellite-task planning candidate set according to the satellite information and task information;

[0046] S402. The chromosome coding rule adopts dynamic tuple coding: the length of the chromosome is N t , each gene of the chromosome is a sequence of four-tuples, representing a planning scheme for a task. The four-tuple is expressed as (wb, we, s, roll), where wb represents the planned start time, we represents the planned end time, s represents the satellite number, and roll represents the satellite deflection angle; each four-tuple corresponds to the execution of a task and is arranged in the order of the planned start time. The number of four-tuples changes dynamically and does not exceed the number of times the task needs to be executed; the chromosome is selected from the satellite-task planning candidate set according to the dynamic tuple coding and generated, and finally an initial population with a size of N is formed;

[0047] S403. Calculate the fitness value of each chromosome according to the objective function, and sort the individuals in the population according to the fitness value;

[0048] S404. Determine whether the dual termination conditions are met: the iterative termination condition or the early stopping termination condition. If one of the termination conditions is met, output the optimal planning scheme; if not, go to step S405;

[0049] S405. Execute the selection operator: select suitable individuals to be retained in the next generation, adopt the elite retention method to retain the excellent individuals in the population, and combine the roulette wheel selection method to maintain the diversity of the population;

[0050] S406. Execute the crossover operator: randomly pair the individuals in the population in pairs according to the crossover probability to form parental and maternal chromosomes. The parental and maternal chromosomes generate offspring individuals through partial gene segment exchange, and the finite backtracking method is used to avoid gene defects in the offspring individuals;

[0051] S407. Execute the mutation operator: select an individual for mutation operation according to the mutation probability to generate a new individual;

[0052] S408. Combine the individuals obtained by selection, crossover, and mutation of the population to form a new population, and go to step S403.

[0053] Preferably, the early stopping termination condition in step S404 is that the fitness value of the optimal individual has not improved in consecutive p rounds of iteration.

[0054] Preferably, the limited backtracking method in S406 is to avoid that a certain execution of a task does not meet the constraint conditions. It traverses the quadruple sequence of the gene segments after crossover and exchange in a loop, and sequentially determines whether the task execution represented by the current quadruple meets all the constraints. If the constraints are met, the quadruple is retained; if not, the quadruple is removed, and the next quadruple is continued to be checked.

[0055] Preferably, the mutation in S407 includes the following steps:

[0056] S407a. Randomly select a task from the selected individuals for mutation;

[0057] S407b. Traverse the satellite-task candidate set to obtain the available windows of this task on all satellites;

[0058] S407c. Traverse the set of available windows, and each available window is used as a candidate window with a certain probability until the available window is empty or the number of planned windows has met the task execution frequency. If the current available window is used as a candidate window, go to step S407d; otherwise, go to step S407c;

[0059] S407d. Determine whether the planned start time of the candidate window and the latest planned end time in the planned window meet the task front and back coverage time interval constraints. If the constraints are met, go to step S407e; otherwise, go to step S407c;

[0060] S407e. Determine whether the insertion of the candidate window meets the satellite shutdown time stage side-sway angle deflection constraint. If the constraints are met, update the candidate window as the planned window, and go to step S407c after the judgment.

[0061] The second aspect of the present invention relates to a remote sensing satellite mission planning system for disaster prevention and mitigation, including a computer, and the computer includes:

[0062] At least one storage unit;

[0063] At least one processing unit;

[0064] 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 steps of the method for planning a remote sensing satellite mission for disaster prevention and mitigation of the present invention.

[0065] The present invention has the following beneficial effects:

[0066] (1) The present invention addresses the problem of remote sensing satellite mission planning for disaster prevention and mitigation. Considering the time resolution constraint, the mission is not further decomposed and processed into meta-tasks. The present invention adopts a dynamic tuple coding rule and uses chained tuples to solve the time resolution constraint problem. Among them, each tuple represents the specific execution of a mission, and it is determined whether the mission meets the time resolution constraint by the tuple information corresponding to the genes in the chromosome.

[0067] (2) The present invention addresses the problem of satellite planning in scenarios with different difficulties and designs an improved genetic algorithm for solution. The finite backtracking method is combined in the mutation factor to ensure better mutation effects of the algorithm in simple environments and to solve the problem of slow convergence of the algorithm in complex environments; a double stopping condition is adopted to directly end the algorithm when the fitness value of the optimal individual does not improve, effectively improving the convergence speed of the algorithm in scenarios with different difficulties. Description of the Drawings

[0068] Figure 1 is a schematic flowchart of the method of the present invention.

[0069] Figure 2 is a schematic diagram of the dynamic tuple coding method of the chromosome of the present invention.

[0070] Figure 3 is a schematic flowchart of the improved genetic algorithm of the present invention.

[0071] Figure 4 is a schematic diagram of the system of the method of the present invention. Detailed Embodiments

[0072] 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 are 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 shall fall within the protection scope of the present invention.

[0073] Embodiment 1

[0074] This Embodiment 1 provides a remote sensing satellite mission planning method for disaster prevention and mitigation, which solves complex technical problems such as the insufficient consideration of spatio-temporal resolution constraints in current satellite mission planning and the low universality of algorithms in scenarios with different difficulties.

[0075] The technical solutions in the embodiments of the present application for solving the above technical problems are generally as follows:

[0076] As Figure 1As shown in the figure, for the problem of remote sensing satellite mission planning for disaster prevention and mitigation, the present invention first obtains satellite and mission-related information and models based on this information. In the design of the objective function, it aims to maximize the mission planning benefit, and the constraint conditions mainly consider the limitations of the satellite observation time window. In addition, an improved genetic algorithm is proposed to solve this problem. The dynamic tuple coding method is used to represent chromosomes, effectively solving the problems brought by complex spatio-temporal resolution constraints. The convergence speed of the algorithm is improved by the double-stop condition judgment and the finite backtracking method, and the universality of the algorithm in different difficulty scenarios is satisfied.

[0077] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0078] A remote sensing satellite mission planning method for disaster prevention and mitigation includes the following steps:

[0079] S1. Obtain satellite information and mission information;

[0080] S2. Set an objective function based on the satellite information and the mission information, and set constraint conditions;

[0081] S3. Build a satellite mission planning model based on the objective function and the constraint conditions;

[0082] S4. Use an improved genetic algorithm to solve the satellite mission planning model to obtain the optimal satellite mission planning method.

[0083] For the problem of remote sensing satellite mission planning for disaster prevention and mitigation, considering the complex spatio-temporal resolution constraints brought by the mission, after obtaining satellite information and mission information and modeling, an improved genetic algorithm is designed for solution. This genetic algorithm uses the dynamic tuple coding method to handle the problems brought by complex spatio-temporal resolution constraints, and the convergence speed of the algorithm is improved by the double-stop condition judgment and the finite backtracking method, and the universality of the algorithm in different difficulty scenarios is satisfied.

[0084] Next, each step of the above solution will be introduced in detail:

[0085] In step S3, a satellite mission planning model is built based on the objective function and the constraint conditions. The satellite mission planning model includes:

[0086] The objective function is:

[0087]

[0088] Where:

[0089] RF iIt represents the actual number of times the i-th task is executed in the task planning scheme;

[0090] F i It represents the number of times the i-th task is required to be executed within the specified time;

[0091] S i It represents the observed benefit of the i-th task.

[0092] The constraint conditions include:

[0093] (1) Satellite maximum side-sway angle constraint:

[0094]

[0095] (2) Satellite power-on time minimum solar altitude angle constraint:

[0096]

[0097] (3) Satellite power-off time stage side-sway angle deflection constraint:

[0098]

[0099] (4) Task spatial resolution constraint:

[0100] Resolution i ≥SR j

[0101] (5) Task observation time constraint:

[0102]

[0103] (6) Task time resolution constraint:

[0104]

[0105] Among them:

[0106] LR j It represents the maximum left side-sway angle of the j-th satellite;

[0107] RR j It represents the maximum right side-sway angle of the j-th satellite;

[0108] It represents the side-sway angle of the j-th satellite for the k-th observation plan of the i-th task;

[0109] It represents the solar altitude angle of the j-th satellite for the k-th observation plan of the i-th task; SL j It represents the minimum solar altitude angle required for the j-th satellite to effectively image;

[0110] Indicates the end time of the k-th observation plan executed by the j-th satellite for the i-th task;

[0111] Indicates the start time of the k'-th observation plan executed by the j-th satellite for the (i + 1)-th task;

[0112] is a piecewise function, indicating the time required for the j-th satellite to slew from the deflection angle to the deflection angle During the satellite slew, imaging cannot be performed;

[0113] Resolution i Indicates the lowest spatial resolution of the i-th task;

[0114] SR j Indicates the lowest spatial resolution of the j-th satellite;

[0115] MINT i Indicates the earliest possible time for the i-th task to be executed;

[0116] MAXT i Indicates the latest possible time for the i-th task to be executed;

[0117] I i Indicates the minimum time difference that must be maintained between two effective imaging tasks of the i-th task.

[0118] In step S4, an improved genetic algorithm is used to solve the satellite task planning model described above to obtain an optimal satellite task planning method:

[0119] As Figure 2 , the chromosome encoding rule of the improved genetic algorithm described above uses dynamic tuple encoding. The length of the chromosome is N t , each gene represents a task planning scheme and is composed of a sequence of quadruples. A quadruple consists of a planned start time, a planned end time, a satellite number, and a satellite deflection angle. Each quadruple represents one task execution, and the quadruples are sorted according to the planned start time. The number of quadruples is dynamically variable and does not exceed the number of times the task needs to be executed.

[0120] As Figure 3 , the improved genetic algorithm in step S4 described above includes the following steps:

[0121] S401, generating a satellite-task planning candidate set according to the satellite information and the mission information. The satellite-task planning candidate set is a set of mission planning sequences of all single satellites, and the mission planning sequences of the single satellites contain all executable tasks that meet the constraints and are sorted by the start time of the task execution;

[0122] S402, randomly generating chromosomes from the satellite-mission planning candidate set according to the chromosome encoding rule to form an initial population of size N;

[0123] S403, calculating the fitness value of each chromosome according to the objective function, and sorting the individuals in the population according to the fitness value;

[0124] S404, determine whether the dual termination conditions are met: the iteration termination condition or the early stopping termination condition. If one of the termination conditions is met, the optimal planning scheme is output. If not, proceed to S405. The iteration termination condition is that the number of iterations reaches the specified number. The early stopping termination condition is that the fitness value of the optimal individual does not increase in consecutive p rounds of iterations.

[0125] S405, execute the selection operator. Select suitable individuals to be retained for the next generation, use the elite retention method to retain the excellent individuals in the population, and combine the roulette selection method to maintain the diversity of the population;

[0126] S406, executing the crossover operator. According to the crossover probability, the individuals in the population are randomly paired to form paternal and maternal chromosomes. The paternal and maternal chromosomes generate offspring individuals by exchanging some gene fragments, and the gene defects of the offspring individuals are avoided by the limited backtracking method;

[0127] S407, execute the mutation operator. Select an individual to perform mutation operation according to the mutation probability to generate a new individual;

[0128] S408, the individuals obtained through selection, crossover and mutation of the population are combined into a new population and transferred to S403.

[0129] Among them, in order to avoid a certain execution of the task not satisfying the constraints, the limited backtracking method of S406 loops through the quadruple sequence of the gene fragments after the cross-exchange, and determines in turn whether the task execution represented by the current quadruple satisfies all constraints. If the constraints are satisfied, the quadruple is retained; if not, the quadruple is discarded and the next quadruple is checked.

[0130] The variation of S407 comprises the following steps:

[0131] S407a, randomly select a task from the selected individuals to mutate;

[0132] S407b. Traverse the satellite-task candidate set to obtain the available windows of this task on all satellites;

[0133] S407c. Traverse the set of available windows. Each available window is used as a candidate window with a certain probability until the available windows are empty or the number of scheduled windows has met the task execution frequency. If the current available window is used as a candidate window, go to step S407d; otherwise, go to step S407c;

[0134] S407d. Determine whether the scheduled start time of the candidate window and the latest scheduled end time in the scheduled windows meet the task's front and back coverage time interval constraints. If the constraints are met, go to step S407e; otherwise, go to step S407c;

[0135] S407e. Determine whether the insertion of the candidate window meets the satellite shutdown time phase side-sway angle deflection constraints. If the constraints are met, update the candidate window as a scheduled window, and after the determination, go to step S407c.

[0136] Embodiment 2

[0137] As Figure 4 , this embodiment provides a remote sensing satellite mission planning system for disaster prevention and mitigation, including a computer, and the computer includes:

[0138] At least one storage unit;

[0139] At least one processing unit;

[0140] 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 steps of the method according to any one of claims 1 to 5.

[0141] It should be noted that in the present invention, 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 including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0142] 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 make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote sensing satellite mission planning method for disaster prevention and mitigation, characterized in that: The following steps are involved: S1. Obtain satellite information and mission information; S2. Setting an objective function based on the satellite information and the mission information, and setting constraint conditions; S3, constructing a satellite mission planning model based on the objective function and the constraint conditions; S4. Using an improved genetic algorithm to solve the satellite mission planning model to obtain the optimal satellite mission planning solution.

2. The remote sensing satellite mission planning method for disaster prevention and mitigation as claimed in claim 1, characterized in that: The objective function described in step S2 is: in: RF i Indicates the actual number of times the i-th task is executed in the task planning scheme; F i Indicates the number of times the i-th task needs to be executed within the specified time; S i represents the observed return of the ith task.

3. The remote sensing satellite mission planning method for disaster prevention and mitigation as claimed in claim 1, characterized in that: The constraints described in step S2 include: (1) Satellite maximum roll angle constraint: (2) Minimum solar altitude angle constraint under the satellite during satellite startup time: (3) Satellite shutdown period side swing angle deflection constraint: (4) Task space resolution constraints: Resolution i ≥SR j (5) Any observation time constraint: (6) Task time resolution constraints: in: LR j represents the maximum left swing angle of the jth satellite; RR j represents the maximum right swing angle of the jth satellite; represents the roll angle of the jth satellite performing the kth observation plan of the ith mission; represents the solar altitude angle of the kth observation plan performed by the jth satellite on the ith mission; SL j represents the minimum solar altitude angle required for the jth satellite to effectively image; Indicates the observation end time of the kth observation plan executed by the jth satellite; It indicates the observation start time of the k'th observation plan of the jth satellite performing the i+1th mission; is a piecewise function, which indicates that the jth satellite deflects from the Swing to yaw angle The time required, no imaging can be performed during the satellite's side swing; Resolution i represents the minimum spatial resolution of the i-th task; SR j represents the minimum spatial resolution of the jth satellite; MINT i represents the earliest possible time for the execution of the i-th task; MAXT i Indicates the latest possible time for the execution of the i-th task; I i Indicates the minimum time difference that must occur between two valid imaging tasks of the i-th task.

4. The remote sensing satellite mission planning method for disaster prevention and mitigation as claimed in claim 1, characterized in that: The improved genetic algorithm described in step S4 comprises the following steps: S401, constructing a satellite-mission planning candidate set according to the satellite information and mission information; S402, chromosome encoding rules use dynamic tuple encoding: the length of the chromosome is N t , each gene of the chromosome is a four-tuple sequence, representing a task planning scheme, the four-tuple is expressed as (wb, we, s, roll), where wb represents the planned start time, we represents the planned end time, s represents the satellite number, and roll represents the satellite deflection angle; each four-tuple corresponds to the execution of a task and is arranged in the order of the planned start time. The number of four-tuples is dynamically changing and does not exceed the number of times the task needs to be executed; the chromosome selects and generates tasks from the satellite-task planning candidate set according to the dynamic tuple encoding, and finally forms an initial population of size N; S403, calculating the fitness value of each chromosome according to the objective function, and sorting the individuals in the population according to the fitness value; S404, determine whether the dual termination conditions are met: the iteration termination condition or the early stopping termination condition. If one of the termination conditions is met, output the optimal planning scheme. If not, proceed to step S405; S405, executing the selection operator: selecting suitable individuals to be retained for the next generation, using the elite retention method to retain excellent individuals in the population, and combining the roulette selection method to maintain the diversity of the population; S406, executing the crossover operator: randomly pairing individuals in the population in pairs according to the crossover probability to form paternal and maternal chromosomes, and generating offspring individuals by exchanging partial gene fragments of the paternal and maternal chromosomes, and avoiding gene defects of offspring individuals by limited backtracking method; S407, executing mutation operator: selecting an individual to perform mutation operation according to the mutation probability to generate a new individual; S408. The individuals obtained through selection, crossover and mutation of the population are combined to form a new population, and the process goes to step S403.

5. The remote sensing satellite mission planning method for disaster prevention and mitigation as claimed in claim 4, characterized in that: The satellite-mission planning candidate set described in step S401 is a set of mission planning sequences including all single satellites. The mission planning sequences of single satellites include all executable tasks that meet the constraints and are sorted by the start time of task execution.

6. The remote sensing satellite mission planning method for disaster prevention and mitigation as claimed in claim 4, characterized in that: The early stopping termination condition described in step S404 is that the fitness value of the optimal individual does not increase in consecutive p rounds of iterations.

7. The remote sensing satellite mission planning method for disaster prevention and mitigation as claimed in claim 4, characterized in that: In order to avoid a certain execution of a task not satisfying the constraints, the limited backtracking method described in step S406 loops through the quadruple sequence of the gene fragments after the cross-exchange, and determines in turn whether the task execution represented by the current quadruple satisfies all the constraints. If the constraints are satisfied, the quadruple is retained; if not, the quadruple is discarded and the next quadruple is checked.

8. The remote sensing satellite mission planning method for disaster prevention and mitigation as claimed in claim 4, characterized in that: The variation of step S407 includes the following steps: S407a, randomly select a task from the selected individuals to mutate; S407b, traverse the satellite-task candidate set to obtain the available windows of the task on all satellites; S407c, traverse the available window set, and each available window is used as a candidate window with a certain probability, until the available window is empty or the number of planned windows has satisfied the task execution frequency. If the current available window is used as a candidate window, go to step S407d; otherwise, go to step S407c; S407d, determine whether the planned start time of the candidate window and the latest planned end time in the planned window meet the time interval constraint before and after the task. If the constraint is met, proceed to step S407e; otherwise, proceed to step S407c; S407e, determine whether the candidate window insertion satisfies the side swing angle deflection constraint during the satellite shutdown time period. If the constraint is satisfied, update the candidate window to the planned window. After the determination is completed, proceed to step S407c.

9. A remote sensing satellite mission planning system for disaster prevention and mitigation, characterized in that: The system includes a computer, wherein the computer 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 any method described in claim 1 to claim 8.

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