Low-Earth Orbit Large-Scale Remote Sensing Constellation Mission Scheduling Method for Continuous Observation Applications of Ground-Oriented Multiple Focus Targets

Abstract

The present invention discloses a task scheduling method for a low-earth orbit large-scale remote sensing constellation for continuous observation applications of multiple ground targets of interest, including: S1. Construction of a task allocation model: Establish a task allocation model for continuous observation of multiple ground targets of interest by a low-earth orbit large-scale remote sensing satellite constellation. The task allocation model describes the allocation process of observation tasks, stipulates the conditions and objective function satisfied by the optimal observation plan, comprehensively considers the maximum observation gap window constraint and various strong constraints, and maximizes the overall task benefit value of the optimal observation plan; S2. Solving the task allocation model: Use a solving algorithm formed by adding a constraint check and correction operator to the genetic algorithm to solve the task allocation model and obtain the optimal observation plan. The advantages are: Aiming at the characteristics of continuous observation task requirements, using the maximum observation gap as an index to measure the continuity of observation tasks, having the advantage of taking into account both observation time and observation continuity, and improving the accuracy and efficiency of task solving.

Description

Technical Field

[0001] The present invention relates to the technical field of constellation mission planning, and particularly to a low-earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground focus targets. Background Art

[0002] Currently, the earth observation satellite system has shown a trend of satellite miniaturization and constellation clustering. With the increase in the number of constellation satellites and the variety of imaging payloads, the coverage frequency of the constellation for ground targets increases, the revisit period shortens, and the ability to complete earth observation tasks is continuously enhanced. At the same time, with the continuous expansion of the application fields of remote sensing technology, the needs of users are more diversified. For example, in disaster disposal and environmental monitoring, there is a need to monitor forest fire situations in real time to reduce casualties and improve work efficiency. To meet such needs, it is necessary to improve the ability of the earth observation satellite constellation to continuously observe ground focus targets over a period of time. However, on the one hand, in the field of imaging satellite task allocation, there is little research on the continuous observation task allocation of constellations for ground targets, and no clear indicators for measuring the continuity of constellations performing ground target observation tasks are given in the research; on the other hand, the continuous expansion of the constellation scale has led to an increasing computational workload for task allocation solution and low computational efficiency. In view of this situation, there is an urgent need to study a task allocation model and solution method that uses multiple low-earth orbit remote sensing satellites to work in relays to achieve continuous observation of multiple key targets, so as to improve the cost-effectiveness of the low-earth orbit earth observation constellation in performing continuous observation tasks for multiple key targets and improve the continuity.

[0003] In summary, there is an urgent need to propose a task allocation model for continuous observation of multiple key ground targets by a remote sensing satellite constellation and a task solution method based on an intelligent optimization algorithm. Summary of the Invention

[0004] The purpose of the present invention is to provide a low-earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground focus targets, so as to solve the foregoing problems existing in the prior art.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A low-earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground focus targets includes the following steps,

[0007] S1. Construction of the task allocation model:

[0008] Establish a task assignment model for continuous observation of multiple target areas on the ground by a constellation of low Earth orbit large-scale remote sensing satellites. The task assignment model describes the process of task assignment, stipulates the conditions and objective function that the optimal observation plan should meet, comprehensively considers the maximum observation gap window constraint and various strong constraints, and maximizes the overall task benefit value of the optimal observation plan;

[0009] S2. Solve the task assignment model:

[0010] Use a solution algorithm formed by adding a constraint check and correction operator to the genetic algorithm to solve the task assignment model and obtain the optimal observation plan.

[0011] Preferably, the input of the task assignment model includes,

[0012] A satellite resource set, which includes six orbital elements, the type of imaging payload, the optimal resolution of the imaging payload, and the cost of a satellite to perform an observation task;

[0013] A task information set, which includes the geographical location of the observed target, the required observation time period of the task, the task priority, the required first observation gap of the task, and the required minimum resolution of the task;

[0014] An available observation data set, which includes the satellite to which the observation data belongs, the observed target to which the observation data belongs, and the observation time window.

[0015] Preferably, step S1 specifically includes the following contents,

[0016] S11. Calculate the actual time coverage rate of each task and the cost consumed by the satellite to perform the task, normalize the time coverage rate and cost of each task and find the difference, then weight the corresponding tasks using the task priority, and sum the weights of all tasks to obtain the overall task benefit value;

[0017] S12. Based on the overall task benefit value, establish a task assignment model with maximizing the overall task benefit value as the goal and the maximum observation gap window constraint and various strong constraints as the limiting conditions.

[0018] Preferably, the various strong constraints include task priority constraint, observation time constraint, lighting condition constraint, meteorological condition constraint, image type constraint, resolution constraint;

[0019] The observation time constraint means considering the required observation time of the task; the lighting condition constraint means that the optical imaging payload must be used when the lighting conditions meet the imaging requirements; the meteorological condition constraint means that the optical imaging payload can only obtain effective images on sunny days; the image type constraint means that the image type must match the required image type of the task; the resolution constraint means that the image resolution must be less than or equal to the required minimum resolution of the task.

[0020] Preferably, a mathematical description is given to the task allocation model. For m different imaging payloads, n observation tasks are arranged. On the premise of meeting the user requirements of each task, the efficiency of the overall task is improved;

[0021] If task i and task j occupy the same imaging payload k during the execution process, and task i is executed before task j, then after task i is completed, based on the basic simplification and assumptions, task j can be immediately executed;

[0022] The basic simplification and assumptions include simplifying the observation target to a point target; assuming that the satellite is not restricted by energy; assuming that the command upload and data downlink during the task process are not restricted; assuming that the attitude conversion of the optical earth observation satellite is an instantaneous process;

[0023] When different tasks are observed by the same imaging payload within the same period of time, if they are simultaneously observed by the SAR imaging payload, these tasks can be executed simultaneously; if they are simultaneously observed by the visible light imaging payload, if the distance between the observed targets does not exceed the swath width of the visible light imaging payload, these tasks can be executed simultaneously. If the observed targets are relatively scattered and the distance exceeds the swath width of the visible light imaging payload, the task with the higher priority is executed first. If the priorities are the same, the task with the smaller current task coverage rate is executed;

[0024] Among them, the imaging mode of the SAR imaging payload is scanning imaging; the imaging mode of the visible light imaging payload is frame imaging, considering the side-sway in its rolling direction.

[0025] Preferably, the conditions that the optimal observation plan should meet include that each task can only be executed within its time window; each task can occupy any one of the imaging payload set that meets its requirements and can be executed by multiple imaging payloads simultaneously; each imaging payload can only execute one observation task at any time; the execution time of each observation task can be less than its required execution time, but it must meet the maximum observation gap requirement of the task and the overall task benefit value is the largest.

[0026] Preferably, step S2 specifically includes the following contents,

[0027] S21. The user puts forward the observation task requirements, including the geographical coordinates of the observation target, the observation time period, the maximum observation gap, the task priority, the image type requirement, and the minimum resolution requirement;

[0028] S22. Obtain the remote sensing satellite resource data, including the number of satellites in the remote sensing satellite constellation, the six orbital elements of the satellites, the imaging payload types of the satellites, the imaging payload resolutions of the satellites, and the maximum side-sway angle of the optical satellite in the rolling direction;

[0029] S23. Without considering any constraints, calculate the observation data of all satellites for all mission objectives; each piece of observation data includes the imaging payload, the observation target, the start time of imaging, and the end time of imaging;

[0030] S24. Preprocess all the observation data to screen out the observation data that meet the maximum gap window of the observation task;

[0031] S25. For the observation data that meet the maximum gap window of the observation task, use a genetic algorithm with a constraint checking and correction operator to solve it and obtain the optimal observation plan;

[0032] S26. Output a report of the optimal observation plan, and the report includes the satellite name, the target name, the start time of observation, the end time of observation, and the usage status.

[0033] Preferably, step S24 specifically includes the following contents.

[0034] S241. Receive the observation task requirements proposed by the user;

[0035] S242. Judge the observation task requirements. When the image type requirement of a task is an optical image and the meteorological conditions for observation are not suitable for using an optical imaging satellite for observation, this task requirement cannot be met, give relevant prompts and this task requirement does not participate in subsequent calculations;

[0036] S243. Read all the observation data that meet the requirements of the observation task;

[0037] S244. Screen the observable time windows that meet the task requirements according to the observation time constraint, the lighting condition constraint, the meteorological condition constraint, the image type constraint, and the resolution constraint;

[0038] S245. Calculate the maximum observation gap window of each task when all the observable time windows that meet the task requirements are used. If the maximum observation gap window is greater than the maximum observation gap window required by the task, then under the existing conditions, the task requirements cannot be met; if the maximum observation gap window is less than or equal to the maximum observation time window required by the task, then under the existing conditions, the task requirements can be met and further optimization calculations can be carried out.

[0039] Preferably, step S25 specifically includes the following contents.

[0040] S251. Input the number of evolutionary times and the population size of the genetic algorithm;

[0041] S252. Initialize the population;

[0042] S253. Conduct constraint checking on the initial population and correct the individuals whose maximum observation gap window does not meet the task requirements.

[0043] S254. Perform iterative calculations. Each iterative calculation includes calculating the fitness of each individual in the population, selection, crossover, mutation, recombination, constraint checking, and correction.

[0044] S255. Select the individual with the maximum fitness from the last generation of the population as the calculation result of the genetic algorithm.

[0045] S256. According to the result of the genetic algorithm, decode the genotype of the optimal individual to form an optimal observation plan. This observation plan includes the satellites used for each task and the corresponding observation time windows.

[0046] Preferably, step S253 is specifically as follows: First, receive the population, then read the genes of each individual one by one, calculate the actual maximum observation gap window for each observation task in this individual, and determine whether the actual maximum observation gap window for each observation task meets the requirements. If it meets the requirements, save this individual until all individuals in the population are traversed; if it does not meet the requirements, change one of the genotypes of this individual from 0 to 1, recalculate and check, and save this individual after meeting the task requirements until all individuals in the population are traversed.

[0047] The beneficial effects of the present invention are as follows: 1. In view of the characteristics of continuous observation task requirements, the present invention uses the maximum observation gap as an index to measure the continuity of observation tasks. Compared with the traditional task time coverage rate index, it has the advantage of taking into account both observation time and observation continuity, improving the accuracy and efficiency of task solving. 2. For the continuous observation task allocation model of low-orbit constellations for multiple focus targets, the present invention mainly uses the maximum gap window constraint and comprehensively combines various strong constraints, such as task priority, weather conditions, observation time period, minimum resolution, and image type requirements, making the task allocation model closer to the actual engineering situation. 3. For the solution of the continuous observation task allocation under strong constraints constructed by the present invention, the genetic algorithm is used to solve the problem of low solution efficiency; by constructing a constraint detection and correction operator, the problem that the population evolves in the direction of increasing the maximum observation gap is solved. Description of the Drawings

[0048] Figure 1 It is a schematic diagram of the side swing of the optical imaging payload in the rolling direction in an embodiment of the present invention;

[0049] Figure 2 It is a schematic diagram of the optical imaging payload satellite simultaneously observing multiple targets in an embodiment of the present invention;

[0050] Figure 3 It is a flowchart of the task scheduling method in an embodiment of the present invention;

[0051] Figure 4 It is a flowchart of the data preprocessing of the observation time window in an embodiment of the present invention;

[0052] Figure 5 It is the flow chart of optimizing and solving by genetic algorithm in the embodiments of the present invention;

[0053] Figure 6 It is the schematic diagram of two evolution directions of the population in the embodiments of the present invention;

[0054] Figure 7 It is the flow chart of constraint checking and correction in the embodiments of the present invention;

[0055] Figure 8 It is the schematic diagram of the crossover operator in the embodiments of the present invention;

[0056] Figure 9 It is the schematic diagram of the mutation operator in the embodiments of the present invention;

[0057] Figure 10 It is the schematic diagram of the recombination operator in the embodiments of the present invention;

[0058] Figure 11 It is the schematic diagram of selecting the time window within the required task time in the embodiments of the present invention;

[0059] Figure 12 It is the schematic diagram of the maximum observation gap window in the embodiments of the present invention;

[0060] Figure 13 It is the schematic diagram of the earth observation constellation used in the embodiments of the present invention;

[0061] Figure 14 It is the schematic diagram of the convergence curve of the genetic algorithm in the embodiments of the present invention. Detailed implementation manners

[0062] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific implementation manners described herein are only used to explain the present invention, and are not used to limit the present invention.

[0063] Currently, the satellite remote sensing industry is developing rapidly, and it can provide users with high-precision products within an extremely short response time. Moreover, with the development of technologies such as artificial intelligence and big data, the application fields of remote sensing technology are constantly expanding. With the development of remote sensing technology, the needs of users are more diversified, such as larger data volume, higher precision, larger coverage area, etc. Among them, the task demand for continuous observation of multiple ground target focuses is particularly urgent. However, there is currently little research on how to meet the task demand for continuous observation of multiple ground target focuses, and there is a lack of research on how to set indicators for measuring the continuity of ground target observation by the constellation.

[0064] Therefore, the present invention proposes a task allocation model for a low-earth orbit large-scale remote sensing satellite constellation for continuous observation of multiple ground focus targets and a solution algorithm. The model describes the allocation process of observation tasks, stipulates the conditions and objective function that the optimal observation plan should satisfy, comprehensively considers the maximum observation gap window constraint and other strong constraints, and maximizes the overall task benefit value of the optimal observation plan. The solution algorithm adds a constraint check and correction operator on the basis of the genetic algorithm to improve the accuracy and efficiency of the solution, providing a reference for the allocation of earth observation tasks under a large-scale satellite constellation in the future. The method of the present invention mainly includes two aspects:

[0065] I. Construction of the task allocation model:

[0066] Establish a task allocation model for a low-earth orbit large-scale remote sensing satellite constellation to continuously observe multiple ground focus targets. The task allocation model describes the allocation process of observation tasks, stipulates the conditions and objective function that the optimal observation plan should satisfy, comprehensively considers the maximum observation gap window constraint and various strong constraints, and maximizes the overall task benefit value of the optimal observation plan.

[0067] In this embodiment, a task allocation model for a low-earth orbit large-scale remote sensing satellite constellation to continuously observe multiple ground focus targets is established. Among them, the maximum observation gap window of the observation task is proposed as an index to measure the continuity of the observation task. The main contents include:

[0068] 1.1. Make simplifications and assumptions. Simplify the observation target into a point target. Assume that the satellite is not subject to energy constraints, assume that the command upload and data return are not restricted during the task process, and assume that the attitude conversion of the optical earth observation satellite is an instantaneous process.

[0069] 1.2. Model input, including satellite resource set, task information set, and available observation data set.

[0070] (1) The satellite resource set includes: the six orbital elements of the satellite, the type of imaging payload, the optimal resolution of the imaging payload, and the cost of the satellite to perform an observation task. The satellite resource set is denoted as Sat: Sat = {sat k | k ∈ M = {1,..., m}}, sat k represents the kth satellite, and m represents the number of satellites. For its related parameters include: tle k = {a k , i k , e k , Ω k , ω k , τ k} the six orbital elements of the satellite, img_s k ∈ {O, S}, where O represents a visible light imaging payload satellite and S represents a SAR imaging payload satellite; res_sk Represents the optimal resolution of the imaging payload; sat_cost k Represents the cost incurred for the satellite to perform one observation mission. In the present invention, this cost is considered a fixed value.

[0071] (2) The task information set includes: the geographical location of the observed target, the required observation time period for the task, the task priority, the required earliest observation gap for the task, and the required minimum resolution for the task. The task information set is denoted as Task: Task = {task j |j ∈ J = {1,..., n}}, task j represents the j-th task, and n represents the number of tasks. For its related parameters include: q j = [qs j , qe j represents the required observation time period for the task, qs j represents the start time, and qe j represents the end time; pri j represents the task priority, which is used to distinguish the importance of different tasks. res_t j represents the required minimum image resolution for the task, img_t j ∈ {O, S, OS} represents the required image type for the task, where O represents that the task requires visible light images, S represents SAR images, OS represents no requirement for the image type, atmo j = {0, 1} represents the local meteorological conditions of the observed target, where 0 represents cloudy weather and 1 represents clear weather, δ j represents the required maximum observation gap time.

[0072] (3) The available observation data set includes: the satellite to which the observation data belongs, the observed target to which the observation data belongs, and the observation time window. The observation data set is denoted as TW: TW = {tw i,j |i ∈ {1,..., K all}}, j ∈ J}, tw i,j represents the i-th observation data, and this observation data belongs to the j-th task, K all represents the number of observation data. For its related parameters include: sat k,j represents the observation satellite number to which this observation data belongs, task j represents the task number to which this observation data belongs, p i,j represents the observation time window of this observation data, where p i,j = [ps i,j , pe i,j represents the observation time window, ps i,j represents the start time, pei,j Indicates the end time. x i : x i ∈{0,1} represents the usage status of the window, where 1 indicates that the window is in use and 0 indicates that the window is not in use.

[0073] 1.3. Describe the satellite earth observation model used. For example Figure 1 As shown, the present invention targets SAR imaging payload satellites and visible light imaging payload satellites. Among them, the imaging mode of the SAR imaging payload satellite is scanning imaging; the visible light imaging payload satellite is frame imaging, considering the side-sway in its rolling direction.

[0074] See Appendix Figure 13 , which is a schematic diagram of the low-earth orbit large-scale constellation used in this embodiment. The constellation includes 2308 satellites, among which there are 1154 optical imaging payload satellites and 1154 SAR imaging payload satellites.

[0075] 1.4. Mathematically describe the task assignment model: Arrange n observation tasks (task set Task) for m different imaging payloads (satellite set Sat), and improve the overall task efficiency on the premise of meeting the user requirements of each task.

[0076] In this embodiment, the specific requirements of four observation tasks are shown in Table 1.

[0077] Table 1 Observation task requirements

[0078]

[0079] 1.5. If tasks i and j occupy the same imaging payload k during execution, and task i is executed before task j, then after task i is completed, according to the basic assumption that satellite attitude conversion time is not considered, task j can be executed immediately.

[0080] Due to the limitations of the optimal resolution of the imaging payload, the type of imaging payload, and the imaging time, some tasks may not be executable.

[0081] When different tasks are observed by the same imaging payload within the same period of time. If they are simultaneously observed by the SAR imaging payload, these tasks can be executed simultaneously. If they are simultaneously observed by the visible light imaging payload, if the distance between the observed targets does not exceed the swath width of the visible light imaging payload (as shown in Figure 2 left), then these tasks can be executed simultaneously; if the observed targets are relatively scattered and the distance exceeds the swath width of the visible light imaging payload (as shown in Figure 2 right), at this time, the task with higher priority is executed first. If the priorities are the same, the task with a smaller current task coverage rate is executed.

[0082] 1.6. The conditions that an optimal task allocation plan (observation plan) should meet are as follows. Each task can only be executed within its time window; each task can occupy any imaging payload in the set of imaging payloads that meet its requirements and can be executed by multiple imaging payloads simultaneously; each imaging payload can only execute one observation task at any time; the execution time of each task can be less than its required execution time, but it must meet the maximum observation gap requirement of the task and maximize the overall task benefit.

[0083] 1.7. Calculation process of the overall task benefit value:

[0084] First, calculate the actual time coverage rate of a task;

[0085] qrt j = (∪ j p i,j x i ) ∩ qt j

[0086] Then, calculate the cost consumed by the satellite to execute this task;

[0087] cost j = ∑ j x i sat_cost k,j , j ∈ J

[0088] Normalize the time coverage and cost and then find the difference; then weight it using the task priority;

[0089]

[0090] Finally, sum up the weights of all tasks;

[0091]

[0092] 1.8. According to the above symbol definitions and mathematical descriptions, establish a task allocation model;

[0093]

[0094]

[0095] or img_s k,j = img_t j

[0096] or img_s k,j = S

[0097] II. Solution of the task allocation model:

[0098] The task assignment model is solved by using a solution algorithm formed by adding a constraint check and correction operator on the basis of a genetic algorithm to obtain an optimal observation plan.

[0099] In this embodiment, on the basis of the genetic algorithm, in view of the problem that the population may evolve in the wrong direction, a constraint check and correction operator is added, and a task solution method is designed, as Figure 3 shown, specifically including the following contents,

[0100] 2.1. The user puts forward the observation task requirements, including the geographical coordinates of the observation target, the observation time period, the maximum observation gap, the task priority, the image type requirements, and the minimum resolution requirements.

[0101] 2.2. Obtain remote sensing satellite resource data, including the number of satellites in the remote sensing satellite constellation, the six orbital elements of the satellites, the imaging payload types of the satellites, the imaging payload resolutions of the satellites, and the maximum side-sway angle of the optical satellite in the rolling direction.

[0102] 2.3. Without considering any constraints, calculate the observation data of all satellites for all task targets; each observation data includes the imaging payload, the observation target, the start time of imaging, and the end time of imaging.

[0103] 2.4. Preprocess all the observation data to screen out the observation data that meet the maximum gap window of the observation task.

[0104] As Figure 4 shown, the specific execution process of this step is as follows,

[0105] (1). Receive the observation task requirements put forward by the user.

[0106] (2). Judge the observation task requirements. When the image type requirement of a task is an optical image and the meteorological conditions for observation are not suitable for using an optical imaging satellite for observation, this task requirement cannot be met, and relevant prompts are given and this task requirement does not participate in the subsequent calculations.

[0107] (3). Read all the observation data that meet the requirements of the observation task.

[0108] (4). Screen the observable time windows that meet the task requirements according to the observation time constraint, illumination condition constraint, meteorological condition constraint, image type constraint, and resolution constraint. The observation time constraint means considering the task-required observation time; the illumination condition constraint means that the optical imaging payload must be used when the illumination conditions meet the imaging requirements; the meteorological condition constraint means that the optical imaging payload can only obtain effective images on sunny days; the image type constraint means that the image type must match the task-required image type; the resolution constraint means that the image resolution must be less than or equal to the task-required minimum resolution.

[0109] For the interception process of the observation window, refer to Figure 11 . For the observation windows that are completely within the task time window, select them all; for the observation windows that are partially within the task time window, take the intersection with the task time window; for the observation windows that are completely outside the task time window, do not select them at all.

[0110] For the maximum observation gap window, refer to Figure 12 . That is, take the union of all the observation windows of this task, and then take the complement with the task time window. The largest window in this set is the maximum observation gap window.

[0111] In this embodiment, after data preprocessing, the available observation data for each task is shown in Table 2.

[0112] Table 2 Available Observation Data after Data Preprocessing

[0113]

[0114]

[0115]

[0116] (5). Calculate the maximum observation gap window for each task when all observation time windows are used. If the maximum observation gap window is greater than the maximum observation gap window required by the task, then under the existing conditions, the task requirements cannot be met; if the maximum observation gap window is less than or equal to the maximum observation time window required by the task, then under the existing conditions, the task requirements can be met and further optimization calculations can be performed.

[0117] 2.5. For the observation data that meets the maximum gap window of the observation task, use the genetic algorithm with constraint checking and correction operators to solve and obtain the optimal observation plan.

[0118] As Figure 5 shown, the specific execution process of this step is as follows.

[0119] (1). Input the number of genetic algorithm evolutions and the size of the population.

[0120] (2). Initialize the population.

[0121] (3). Perform constraint checking on the initial population and correct the individuals whose maximum observation gap window does not meet the task requirements.

[0122] (4). Perform iterative calculations. Each iterative calculation includes calculating the fitness of each individual in the population, selection, crossover, mutation, recombination, constraint checking and correction. Among them, Figure 8Shows the crossover process used in the genetic algorithm, and the operator used is the single-point crossover operator. Figure 9 Shows the mutation process used in the genetic algorithm, and the operator used is the single-point mutation operator. Figure 10 Shows the recombination process used in the genetic algorithm. The number of individuals in the offspring population may decrease. At this time, individuals with high fitness in the parent generation are selected and added to the offspring population to keep the number of individuals in each generation of the population stable.

[0123] (5), Select the individual with the highest fitness from the last generation of the population as the calculation result of the genetic algorithm;

[0124] (6), According to the result of the genetic algorithm, decode the genotype of the optimal individual to form the optimal observation plan; this observation plan includes the satellites used for each task and the corresponding observation time windows.

[0125] In this embodiment, as Figure 6 shown, when the population evolves and calculates, it will develop in two directions: one is that the number of observation gap windows increases and the maximum observation gap window shrinks, improving the time coverage rate and the continuity of observation tasks; the other is that the number of observation gap windows decreases, but the maximum observation gap window expands. Although the time coverage rate is improved, the continuity of observation tasks decreases, resulting in the task plan not meeting the maximum observation gap window constraint condition and becoming an infeasible solution. In order to suppress the second development direction, a constraint detection and correction operator is proposed to check the constraints of the population after operations such as crossover and mutation, and correct the detected infeasible solutions.

[0126] As Figure 7 shown, the specific correction process is as follows: First, receive the population, then read the genes of each individual one by one, calculate the actual maximum observation gap window of each observation task in this individual, and judge whether the actual maximum observation gap window of each observation task meets the requirements. If it meets, save this individual until all individuals in the population are traversed; if it does not meet, change one of the genotypes of this individual from 0 to 1, recalculate and check, and save this individual after meeting the task requirements until all individuals in the population are traversed. For the convergence curve of the genetic algorithm solution, see Figure 14 .

[0127] 2.6. Output the optimal observation plan report, and the report includes the satellite name, target name, observation start time, observation end time, and usage status.

[0128] In this embodiment, after solving, the obtained observation plan is shown in Table 3.

[0129] Table 3 Observation task allocation plan obtained by solving

[0130]

[0131]

[0132]

[0133] By adopting the above technical solutions disclosed in the present invention, the following beneficial effects are obtained:

[0134] The present invention provides a low-orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground focus targets. In view of the characteristics of continuous observation task requirements, the present invention uses the maximum observation gap as an index to measure the continuity of observation tasks. Compared with the traditional task time coverage rate index, it has the advantage of taking into account both observation time and observation continuity, and improves the accuracy and efficiency of task solving. The present invention aims at the continuous observation task allocation model of multiple ground focus targets for low-orbit constellations, mainly uses the maximum gap window constraint, and comprehensively considers various strong constraints, such as task priority, weather conditions, observation time period, minimum resolution and image type requirements, to make the task allocation model closer to the actual engineering situation. For the solution of the continuous observation task allocation under the strong constraints constructed by the present invention, a genetic algorithm is used to solve the problem of low solution efficiency; by constructing a constraint detection and correction operator, the problem that the population evolves in the direction of increasing the maximum observation gap is solved.

[0135] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A low-earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground focus targets, characterized in that: Including the following steps, S1. Construction of task allocation model: Establish a task allocation model for continuous observation tasks of multiple ground attention targets by a constellation of low-orbit large-scale remote sensing satellites. The task allocation model describes the allocation process of observation tasks, stipulates the conditions and objective function that the optimal observation plan should meet, and comprehensively considers the maximum observation gap window constraint and various strong constraints to maximize the overall task benefit value of the optimal observation plan; Step S1 specifically includes the following content, S11. Calculate the actual time coverage rate of each task and the cost consumed by the satellite to execute the task. Normalize the time coverage rate and cost of each task, find the difference, then weight the corresponding tasks using task priorities, and sum the weights of all tasks to obtain the overall task benefit value; S12. Based on the overall task benefit value, establish a task allocation model with the goal of maximizing the overall task benefit value and the maximum observation gap window constraint and various strong constraints as limiting conditions; The various strong constraints include task priority constraint, observation time constraint, lighting condition constraint, meteorological condition constraint, image type constraint, resolution constraint; The observation time constraint means considering the required observation time of the task; the lighting condition constraint means that the optical imaging payload must be used when the lighting conditions meet the imaging requirements; the meteorological condition constraint means that the optical imaging payload can only obtain effective images on sunny days; the image type constraint means that the image type must conform to the required image type of the task; the resolution constraint means that the image resolution must be less than or equal to the required minimum resolution of the task; S2. Solving the task allocation model: Use a solution algorithm formed by adding a constraint check and correction operator to the genetic algorithm to solve the task allocation model and obtain the optimal observation plan; Step S2 specifically includes the following content, S21. The user puts forward observation task requirements, including the geographical coordinates of the observation target, the observation time period, the maximum observation gap, task priority, image type requirements, and minimum resolution requirements; S22. Obtain remote sensing satellite resource data, including the number of satellites in the remote sensing satellite constellation, the six orbital elements of the satellites, the types of imaging payloads of the satellites, the imaging payload resolutions of the satellites, and the maximum lateral swing angle of the optical satellites in the rolling direction; S23. Without considering any constraints, calculate the observation data of all satellites for all task targets; each observation data includes the imaging payload, the observation target, the start time of imaging, and the end time of imaging; S24. Preprocess all observation data to screen out the observation data that meet the maximum gap window of the observation task; S25. For the observation data that meet the maximum gap window of the observation task, use the genetic algorithm with a constraint check and correction operator to solve and obtain the optimal observation plan; S26. Output a report of the optimal observation plan, and the report includes satellite names, target names, observation start times, observation end times, and usage status.

2. The low-earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground-based focus targets according to claim 1, wherein: The input of the task allocation model includes, A satellite resource set, which includes six orbital elements, the type of imaging payload, the optimal resolution of the imaging payload, and the cost for the satellite to execute an observation task; Task information set, where the task information set includes the geographical location of the observed target, the required observation time period of the task, the task priority, the required minimum observation gap of the task, and the required minimum resolution of the task; Available observation data set, where the available observation data set includes the satellite to which the observation data belongs, the observed target to which the observation data belongs, and the observation time window.

3. The low-earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground-oriented targets according to claim 1, characterized in that: Mathematically describe the task allocation model, arrange n observation tasks for m different imaging payloads, and improve the overall task efficiency on the premise of meeting the user requirements of each task; If tasks i and j occupy the same imaging payload k during execution, and task i is executed before task j, then after task i is completed, based on the basic simplification and assumptions, task j can be immediately executed; The basic simplification and assumptions include simplifying the observed target to a point target; assuming that the satellite is not constrained by energy; assuming that command upload and data downlink are not constrained during the task process; Assume that the attitude conversion of the optical earth observation satellite is an instantaneous process; When different tasks are observed by the same imaging payload within the same time period, if they are simultaneously observed by the SAR imaging payload, these tasks can be executed simultaneously; if they are simultaneously observed by the visible light imaging payload, if the distance between the observed targets does not exceed the swath width of the visible light imaging payload, these tasks can be executed simultaneously. If the observed targets are scattered and the distance exceeds the swath width of the visible light imaging payload, the task with the higher priority is executed first. If the priorities are the same, the task with the smaller current task coverage rate is executed; Among them, the imaging mode of the SAR imaging payload is scanning imaging; the imaging mode of the visible light imaging payload is frame imaging, considering the side swing in its rolling direction.

4. The low-earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground-oriented targets according to claim 1, characterized in that: The conditions that the optimal observation plan should meet include that each task can only be executed within its time window; each task can occupy any one of the imaging payloads that meet its requirements and can be executed by multiple imaging payloads simultaneously; each imaging payload can only execute one observation task at any time; the execution time of each observation task can be less than its required execution time, but it must meet the maximum observation gap requirement of the task and the overall task efficiency value is the largest.

5. The task scheduling method for a low-earth orbit large-scale remote sensing constellation for continuous observation of multiple ground target objects according to claim 1, characterized in that: Step S24 specifically includes the following content, S241. Receive the observation task requirements proposed by the user; S242. Judge the observation task requirements. When the image type requirement of a task is an optical image and the observed meteorological conditions are not suitable for observation by an optical imaging satellite, this task requirement cannot be met, and relevant prompts are given and this task requirement does not participate in subsequent calculations; S243. Read all the observation data that meet the requirements of the observation task; S244. Screen the observable time windows that meet the task requirements according to the observation time constraint, illumination condition constraint, meteorological condition constraint, image type constraint, and resolution constraint; S245. Calculate the maximum observation gap window of each task when all the observable time windows that meet the task requirements are used. If the maximum observation gap window is greater than the required maximum observation gap window of the task, the task requirements cannot be met under the existing conditions; If the maximum observation gap window is less than or equal to the maximum observation time window required by the task, then under the existing conditions, the task requirements can be met and further optimization calculations can be carried out.

6. The low-Earth orbit large-scale remote sensing constellation mission scheduling method for continuous observation applications of multiple ground-oriented targets according to claim 5, characterized in that: Step S25 specifically includes the following content: S251. Input the number of genetic algorithm evolutions and the population size. S252. Initialize the population. S253. Perform constraint checks on the initial population and correct individuals whose maximum observation gap windows do not meet the task requirements. S254. Perform iterative calculations. Each iterative calculation includes calculating the fitness of each individual in the population, selection, crossover, mutation, recombination, constraint checks, and corrections. S255. Select the individual with the maximum fitness from the last generation of the population as the result of the genetic algorithm calculation. S256. According to the genetic algorithm result, decode the genotype of the optimal individual to form an optimal observation plan; this observation plan includes the satellites used for each task and the corresponding observation time windows.

7. The task scheduling method for a low-earth orbit large-scale remote sensing constellation for continuous observation of multiple ground-based target applications according to claim 6, characterized in that: Specifically, step S253 is as follows: First, receive the population, then read the genes of each individual one by one, calculate the actual maximum observation gap window of each observation task in the individual, and judge whether the actual maximum observation gap window of each observation task meets the requirements. If it meets the requirements, save the individual until all individuals in the population are traversed; if it does not meet the requirements, change one of the genotypes of the individual from 0 to 1, recalculate and check, and save the individual after meeting the task requirements until all individuals in the population are traversed.

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