Multi-satellite task scheduling method and system for dynamic observation task

Abstract

The invention discloses a multi-satellite task scheduling method and system for a dynamic observation task. The method comprises the following steps: judging a task type, if the task is a regional task, taking sampling points with the number in direct proportion to the size of a task region at a random position of the task region, and generating a point task set with multiple time windows; calculating all visible time window lists of each satellite for each task in workable time, constructing an optimized objective function according to a multi-time window meta-task set, task constraints, resource constraints and a relationship between the task constraints and the resource constraints, and establishing a multi-satellite task planning mathematical model; performing optimization solution on the multi-satellite task planning mathematical model by utilizing a genetic search algorithm to generate an executable observation plan scheme; and performing dynamic task window insertion verification, determining a dynamic task execution window, and generating a final multi-satellite dynamic task scheduling scheme. According to the method, dynamic task planning can be completed, satellite resources are effectively utilized, and task planning efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite task allocation, and in particular to a multi-satellite task scheduling method and system for dynamic observation tasks. Background Art

[0002] Satellite systems have a wide range of applications, and their tasks are diverse and complex. Simple tasks can be efficiently performed by a single intelligent satellite, while high-constraint tasks require the collaboration of multiple satellites. In order to cope with the diversity of tasks and increase feedback speed, satellite systems must have both intelligent allocation and efficient coordination capabilities. The system needs to autonomously identify mission characteristics, accurately match the optimal satellite, and improve execution efficiency. At the same time, in the face of complex tasks, the system needs to comprehensively evaluate the resources of the constellation and the status of the system, flexibly deploy satellite groups, and ensure that tasks are completed collaboratively and efficiently. This intelligent and efficient task allocation mechanism is the key to satellite systems adapting to diverse application needs and improving overall performance.

[0003] With the rapid development of satellite technology, the number of satellites has increased dramatically, while measurement and control resources are relatively limited, resulting in increasingly severe resource conflicts and contention. The need for multiple satellites to pass through at the same time has put forward higher requirements for the measurement and control system, and it is necessary to ensure sufficient resources to support complex and changeable measurement and control tasks. However, the growth of measurement and control resources is constrained by multiple factors, making it difficult to meet the growing demand, further exacerbating the resource shortage. In the same measurement and control network, managing satellites of various types has become a major challenge. In addition, the execution of measurement and control tasks also needs to consider multiple constraints. Faced with the current situation of many types of satellites, large numbers, diverse mission types and limited measurement and control resources, how to efficiently and scientifically schedule resources to ensure the smooth completion of measurement and control tasks has become a major problem facing the field of satellite measurement and control.

[0004] Traditional dynamic programming and heuristic algorithms have many shortcomings when dealing with dynamic observation tasks. For example, they lack flexibility and have low computational efficiency, making it difficult to meet the needs of real-time or near-real-time planning and scheduling of dynamic observation tasks, which can easily lead to decision-making lags. In addition, they lack adaptability and require manual intervention to adjust parameters and optimize solutions, and cannot automatically adapt to changing environments and task requirements. Finally, they have a single goal and are difficult to balance multiple goals, such as observation quality, resource utilization, and task priority, and may perform poorly in some aspects. Summary of the invention

[0005] Purpose of the invention: The present invention provides a multi-satellite task scheduling method and system for dynamic observation tasks, which effectively utilizes satellite resources and improves task planning efficiency.

[0006] Technical solution: In order to achieve the above invention objectives, the present invention adopts the following technical solution:

[0007] A multi-satellite task scheduling method for dynamic observation tasks comprises the following steps:

[0008] S1, obtain task information, determine the task type, and process it according to the task type. If it is an area task, take a number of sampling points proportional to the size of the task area at random positions in the task area to generate a point task set with multiple time windows; if it is a point task, do not process the task area;

[0009] S2, combining satellite orbit information and mission information, calculates a list of all visible time windows for each satellite for each mission within the working time, and obtains the mission number, satellite number, start time, end time and satellite deflection angle of each time window;

[0010] S3, based on the point task set of multiple time windows and the information of each time window, builds an optimization objective function according to the task constraints, resource constraints and the relationship between them, and establishes a mathematical model for multi-satellite task planning. The optimization goal is to maximize the benefits of completing the task. The constraint model includes satellite solar altitude angle constraints, maximum deflection angle constraints, task execution time constraints, power-on time constraints and task resolution constraints;

[0011] S4, using genetic algorithm to solve the multi-satellite mission planning mathematical model, and determine whether the established constraint model is satisfied, and generate an executable observation plan;

[0012] S5, based on the executable observation plan, perform dynamic task window insertion verification, determine the dynamic task execution window, and generate the final multi-satellite dynamic task scheduling plan.

[0013] As a preferred embodiment, the optimization objective function is specifically:

[0014]

[0015] Among them, MAX_score is the optimization objective function, i represents the i-th task, there are n tasks in total, among which there are α point tasks and n-α regional tasks; score i_point is the score of the point task, M_frequency is the number of times the point task is required, and frequency i is the number of tasks completed, score i_area is the score of the regional task, area i is the total area of ​​the regional mission, area_actual i It is the area covered by the completed regional tasks.

[0016] As a preferred embodiment, the satellite solar altitude angle constraint model is: the optical satellite satisfies the solar altitude angle of not less than 10;

[0017] The maximum deflection angle constraint model is: the deflection angle of the satellite when performing the observation mission is less than the given limit range;

[0018] The task execution time constraint model is as follows: the sum of satellite attitude adjustment and observation time meets the time window constraint of adjacent observation tasks; after task planning, the specific time period during which the target task is executed by the satellite meets the constraint within the effective observation time period of the task; the time interval between two adjacent observations of the same observation task is not less than the shortest time interval; the available time period of the same satellite cannot be allocated to two or more tasks at the same time;

[0019] The power-on time constraint model is: the longest continuous power-on time of the satellite meets the given time threshold;

[0020] The mission resolution constraint model is: the imaging resolution of the selected observation satellite sensor does not exceed the resolution required by the mission objective.

[0021] As a preferred embodiment, the task execution time constraint model is expressed as:

[0022] The sum of satellite attitude adjustment and observation duration meets the time window constraints of adjacent observation tasks:

[0023] trantime_10 m +startup_min im ≤stsm in

[0024] Among them, transtime_10 m is the satellite attitude adjustment time, startup_min im is the observation time, stsm in It is the start time of the satellite to perform adjacent tasks;

[0025] After mission planning, the specific period during which the target mission is performed by the satellite must meet the constraints within the mission's effective observation period:

[0026] stsm i ,estm i ,tsm i ∈d i

[0027] Among them, stsm i is the start time of the satellite's adjacent mission, estm i is the satellite mission end time, tsm i It is the specific time period during which the satellite performs its mission;

[0028] The time interval between two adjacent observations of the same observation task needs to satisfy the constraints:

[0029] stsm i+1 -estm i ≥time_interval

[0030] Among them, time_interval is the shortest time interval between two adjacent observations of the same observation task;

[0031] The selected observation satellite sensor imaging resolution meets the resolution constraints required by the mission objectives:

[0032] S_resolution i ≤M_resolution j

[0033] Among them, S_resolution i is the satellite resolution, M_resolution j is the task requirement resolution;

[0034] The same satellite cannot be assigned to two or more tasks at the same time during its available time period:

[0035]

[0036] Among them, SD is the set of available satellites, sch ij It is the satellite scheduling scheme;

[0037] The deflection angle of the satellite during the observation mission is less than the given limit:

[0038] left_roll <roll<right_roll

[0039] Among them, roll is the deflection angle of the satellite when it is working, left_roll is the maximum left deflection angle, and right_roll is the maximum right deflection angle of the satellite.

[0040] Preferably, the genetic algorithm solution step includes:

[0041] S41, initializing N chromosomes to form an initial population, wherein the chromosomes are encoded in binary, and each character corresponds to the selection state of a time window, 0 represents that the time window is excluded in the current solution, and 1 represents that the time window is selected;

[0042] S42, selecting the initial population, selecting N-1 chromosomes as parent chromosomes, and performing crossover on the selected parent chromosomes to form N-1 daughter chromosomes;

[0043] S43, mutate N-1 offspring chromosomes, insert the best individuals after mutation into the offspring chromosomes, and use the population consisting of N offspring chromosomes as the initial population;

[0044] S44, calculate the cost of each chromosome in the new initial population according to the optimization objective function, obtain the chromosome with the smallest cost, and execute S42 until the convergence condition is met, that is, the number of times the chromosome with the smallest cost is the same chromosome reaches a preset number, and an executable observation plan is obtained.

[0045] As a preferred method, a dynamic task window insertion check is performed to determine the dynamic task execution window and generate a final multi-satellite dynamic task scheduling scheme, including:

[0046] S51, sorting the dynamic task set to be inserted from early to late according to the start time;

[0047] S52, examine each visible time window in turn, calculate the overlap degree of each time point in the time window, and arrange them in non-descending order, where the overlap degree is the overlap degree of the specified task i to be inserted with other tasks in the visible time window of the satellite;

[0048] S53, for each time point after sorting, try to directly insert task i without conflict. If successful, end and output the final task planning plan; if unsuccessful, re-check until all time windows are checked, select the window with the smallest overlap to insert, and output the best task planning plan.

[0049] As a preferred method, the overlap calculation method is as follows: the visible time window of task i in satellite r [stsm i ,estm i ], from stsm i At the beginning, for each time t i Try to arrange and calculate the overlap between task i and other tasks in each case. Among them, b j Overlapping time period.

[0050] A multi-satellite task scheduling system for dynamic observation tasks, comprising:

[0051] The task preprocessing module is used to obtain task information, determine the task type, and process it according to the task type. If it is an area task, a number of sampling points proportional to the size of the task area are randomly selected at the task area to generate a point task set with multiple time windows; if it is a point task, the task area is not processed;

[0052] The time window calculation module is used to combine satellite orbit information and mission information to calculate a list of all visible time windows for each satellite for each mission within the working time, and obtain the mission number, satellite number, start time, end time and satellite deflection angle of each time window;

[0053] The task planning model building module is used to build an optimization objective function based on the point task set of multiple time windows and the information of each time window, according to the task constraints, resource constraints and the relationship between them, and establish a multi-satellite task planning mathematical model. The optimization goal is to maximize the benefits of completing the task. The constraint model includes satellite solar altitude angle constraints, maximum deflection angle constraints, task execution time constraints, power-on time constraints and task resolution constraints;

[0054] The preliminary planning solution module is used to solve the multi-satellite mission planning mathematical model using genetic algorithms, determine whether the established constraint model is satisfied, and generate an executable observation plan;

[0055] The final planning scheme determination module is used to perform dynamic task window insertion verification on the basis of the executable observation plan, determine the dynamic task execution window, and generate the final multi-satellite dynamic task scheduling scheme.

[0056] The present invention also provides a computer device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described above are implemented.

[0057] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described above are implemented.

[0058] The beneficial effects of the present invention are as follows: in solving the initial executable multi-satellite scheduling scheme, the present invention adopts an area geometric decomposition method for processing regional tasks, that is, sampling points with a number proportional to the size of the task area are taken at random positions in the task area to generate a point task set with multiple time windows, thereby reducing the difficulty of simultaneously planning multiple types of tasks and improving the efficiency of task planning; secondly, when establishing the optimization model, the constraints in the planning process are fully considered from the two perspectives of tasks and satellites to achieve accurate modeling; finally, on the basis of the initial executable multi-satellite scheduling scheme, the parameter of execution time point overlap is introduced to minimize the impact of the insertion of dynamic tasks on the original satellite scheduling scheme, so that the multi-satellite task planning scheme finally outputted can achieve the optimal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of a multi-satellite task scheduling method for dynamic observation tasks;

[0060] Figure 2 Insert flow charts for dynamic tasks based on the overlap of execution time points;

[0061] Figure 3 This is the simulation effect diagram of satellite orbit and initial mission distribution;

[0062] Figure 4 This is a comparison chart of the average running time and average profit results of the four algorithms in the scenario with a dynamic task number of 200;

[0063] Figure 5 Iteration graph of the objective function optimized for the present invention. DETAILED DESCRIPTION

[0064] In order to have a clearer understanding of the features and advantages of the technical solution of the present invention, the composition and implementation of the specific solution are explained below in conjunction with the accompanying drawings.

[0065] Different types of satellites have significant differences in performance, orbit, and measurement and control requirements. For example, high-orbit geosynchronous satellites and low-Earth orbit satellites have long visibility time with ground stations and flexible task scheduling, while the latter have short visibility time, are restricted by ground station resource characteristics and geographical location, and have high task scheduling complexity. Therefore, how to accurately select and reasonably allocate measurement and control resources based on satellite type and measurement and control task characteristics has become a key issue that needs to be solved. The present invention provides a multi-satellite task scheduling method for dynamic observation tasks, referring to Figure 1 , the method comprises the following steps:

[0066] Step S1, obtain task information, determine the task type, and process according to the task type;

[0067] Determine the task type. If it is a point task, that is, the execution location of the task is a point, the task area will not be processed. If it is an area task, that is, the execution location of the task is an area, then a number of sampling points proportional to the size of the task area are taken at random positions in the task area to generate a point task set with multiple time windows to fully consider the coverage of the task area. Suppose the area of ​​the task area in the embodiment is area square meters, then 2×area points are randomly sampled in this area, and these points are used to replace the task area. The following operations are all performed on point tasks.

[0068] Step S2, calculating the available time window for the satellite to perform the mission in combination with the constraint conditions;

[0069] Combine the satellite orbit information and mission information in the dataset to calculate all available time windows for the specified mission. The time window includes the mission number, satellite number, start time, end time and satellite deflection angle. i =[m i ,sat i ,stsm i ,estm i ,roll i], where timewindow i is the time window, m i is the task number, sat i is the satellite number, stsm i is the start time, estm i It's the end time, roll i is the satellite deflection angle.

[0070] Step S3, constructing an optimization objective function according to the task constraints, resource constraints and the relationship between them, wherein the constraints include a satellite solar altitude angle constraint model, a maximum deflection angle constraint model, a task execution time constraint model, a power-on time constraint model and a task resolution constraint model;

[0071] S31, optimization objective function model;

[0072]

[0073] Among them, MAX_score is the optimization objective function, score i_point is the pre-set point task score, M_frequency is the number of point task requirements, and frequency i is the number of times the i-th point task is completed, α is the number of point tasks, n-α is the number of regional tasks, and score i_area is the score of the pre-set regional task. i is the total area of ​​the regional task, which here represents all sampling points after regional decomposition, area_actual i is the coverage area, which represents the actual number of completed task points, through the ratio Approximately calculate the area covered by the regional task. Here, the point task and regional task scores refer to the rewards that the algorithm can obtain by completing the point task and the regional task. The scores are usually set in advance during simulation. For example, the reward value for completing the point task is 3, and the regional task score is 5. These scores are used as inputs to the optimization objective function model.

[0074] S32, the execution of the measurement and control task also needs to consider multiple constraints, including time windows, states, logical relationships and resources themselves, etc. These constraints must be fully considered in the resource scheduling process. The constraint model of the present invention is as follows:

[0075] MD: represents the task set, MD = {m1,m2,...,m α ,m α+1 ,...,m n}, there are n tasks in total, including α point tasks and n-α area tasks;

[0076] And the duration of each task is D = {d1, d2, ..., d n}(d i =max_time i -min_time i ), max_time i Indicates the latest possible time for task execution, min_time i Indicates the earliest possible time for task execution. The task must be within min_time i and max_time i The required frequency is M_frequency i ;

[0077] S: represents the satellite set, S = {sat1, sat2, ..., sat 162 ,sat 163 ,...,sat m}, a total of m satellites;

[0078] T: represents the task decision variable, T={T1,T2,...,T n}, where T i =0 means the task is not planned, T i =1 means the task is planned;

[0079] C: represents the constraint set, which mainly includes the following:

[0080] ① Degree constraint

[0081] Optical satellites meet the solar altitude angle greater than or equal to 10:

[0082] sun_elevation_angle i ≥10

[0083] ②Time constraints

[0084] The sum of satellite attitude adjustment and observation duration meets the time window constraints of adjacent observation tasks:

[0085] trantime_10 m +startup_min im ≤stsm in

[0086] Among them, transtime_10 m is the satellite attitude adjustment time, startup_min im is the observation time, stsm in It is the start time of the satellite to perform adjacent tasks;

[0087] After mission planning, the specific period during which the target mission is performed by the satellite needs to meet the constraints within the mission's effective observation period, namely:

[0088] stsm i ,estm i ,tsm i ∈d i

[0089] Among them, stsm i is the start time of the satellite's adjacent mission, estm i is the satellite mission end time, tsm i It is the specific time period during which the satellite performs its mission.

[0090] The time interval between two adjacent observations of the same observation task needs to satisfy the constraints, namely:

[0091] stsm i+1 -estm i ≥time_interval

[0092] Among them, time_interval is the shortest time interval between two adjacent observations of the same observation task.

[0093] ③ Resolution constraints

[0094] The selected observation satellite sensor imaging resolution meets the resolution constraints required by the mission objectives:

[0095] S_resolution i ≤M_resolution j

[0096] Among them, S_resolution i is the satellite resolution, M_resolution j It is the mission required resolution. The satellite resolution refers to the size of the smallest ground feature that the sensor carried by the satellite can identify; the mission resolution refers to the resolution required for imaging; therefore, the satellite resolution must be smaller than the mission resolution in order to meet the imaging requirements of the mission.

[0097] ④ Mutually exclusive constraints

[0098] The available time period of a satellite cannot be allocated to two or more tasks at the same time, namely:

[0099]

[0100] Among them, SD is the set of available satellites, sch ij is the satellite scheduling scheme, sch ijThe value is 0 or 1, where 0 means the satellite is not selected to perform this task, and 1 means it is selected to perform the task.

[0101] ⑤ Boot time constraint

[0102] The longest continuous boot time of the satellite satisfies the given duration constraint:

[0103]

[0104] Among them, orbit_max is the given longest continuous boot time of the satellite, opt represents the optical satellite, and sar represents the SAR (Synthetic Aperture Radar) satellite. Here, the working time is determined by the type of sensor carried by the satellite. The continuous boot times of the optical satellite and the SAR satellite are fixed. The time in the present invention is obtained by querying the satellite type in the common satellite data selected in the simulation experiment. In practical applications, it can be queried according to the satellite type.

[0105] ⑥ Maximum deflection angle constraint

[0106] The maximum deflection angle constraint model means that the calculated deflection angle of the satellite during operation cannot exceed its limit range, expressed as: left_roll < roll < right_roll, where roll is the deflection angle of the satellite during operation, left_roll is the maximum left deflection angle, and right_roll is the maximum right deflection angle of the satellite.

[0107] Step S4, solve by genetic search algorithm;

[0108] According to the implementation manner of the present invention, the genetic algorithm solving process includes the following steps:

[0109] S41, initialize an initial population composed of N chromosomes;

[0110] Encode the chromosomes through the genetic algorithm encoding rule to generate N chromosomes. N can vary according to requirements. Among them, the chromosome adopts a specific encoding method, namely binary encoding (0 / 1 encoding). This encoding method represents the chromosome as a series of strings composed of 0 and 1, where each character (0 or 1) corresponds to the selection state of a time window. Specifically, the string is fully arranged in the established order of all time windows, and the selection state of each time window is identified by a character in the string: 0 means the time window is excluded (i.e., not selected) in the current solution, and 1 means the time window is included (i.e., selected). In this way, each chromosome uniquely represents a specific set composed of selected time windows, thus becoming a candidate solution in the solution space searched by the algorithm.

[0111] S42, selecting the initial population, selecting N-1 chromosomes as parent chromosomes, and performing crossover on the selected parent chromosomes to form N-1 daughter chromosomes;

[0112] Select N-1 chromosomes in the initial population as parent chromosomes for crossover, randomly select two crossover points of the parent chromosome, use the gene segment between the two crossover points as the gene segment to be crossed, search for the gene segment in the other parent chromosomes that is at the same position as the gene segment to be crossed of the parent chromosome, replace the corresponding gene segments with each other, and generate N-1 daughter chromosomes after all chromosomes are crossed.

[0113] S43, mutate N-1 offspring chromosomes, insert the best individuals after mutation into the offspring chromosomes, and use the population consisting of N offspring chromosomes as the initial population;

[0114] The mutation method is: randomly select a mutation point on the chromosome to be mutated, reverse the state of whether the time window on the gene of this mutation point is selected or not, and complete the mutation. The N-1 offspring chromosomes are mutated, and according to the optimization objective function, the best individual after mutation is selected and inserted into the offspring chromosome, and the population composed of N offspring chromosomes is used as the initial population.

[0115] S44, calculating the cost of each chromosome in the new initial population according to the optimization objective function, obtaining the chromosome with the smallest cost, executing S42 until the convergence condition is met, and outputting the best solution;

[0116] The convergence condition is: when the number of times the chromosome with the smallest cost value is the same chromosome reaches the preset number, the convergence condition is met, the iteration is stopped, and the initial executable observation plan is output.

[0117] Step S5, dynamic task insertion based on the overlap of execution time points;

[0118] Based on the preliminary executable multi-satellite scheduling scheme (initial executable observation scheme), the present invention introduces the parameter of execution time point overlap to minimize the impact of dynamic task insertion on the original satellite scheduling scheme. Figure 2 , dynamic task insertion based on the overlap of execution time points includes the following steps:

[0119] S51, sort the set of dynamic tasks to be executed from early to late according to the start time. Because there may be more than one dynamic task, these dynamic tasks constitute a set, called the set of dynamic tasks to be executed, which is sorted.

[0120] S52. Calculate the coincidence degree of execution time points. Each task needs to determine its start time and end time of execution. Since the duration of each task is fixed, only the start time of each task needs to be determined. If the earliest start principle is used, in some cases, some tasks cannot be executed. Therefore, the coincidence degree of task execution time points is introduced, that is, the visible time window [stsm i , estm i of task i on satellite r. Starting from stsm i , for each moment t i , try to arrange and calculate the coincidence degree between task i and other tasks in each case, that is , which is also called the time point coincidence degree. Among them, b j is the coincidence time period. In the coincidence degree expression , the superscript t i refers to a moment in the visible time window of the task; the subscript i, r indicates that this formula calculates the coincidence of task i under the time window of satellite r.

[0121] S53. Examine each visible time window in turn, calculate the coincidence degree of each time point in this time window, and arrange them in non-decreasing order. For each time point after sorting, try to directly insert task i without conflict. Inserting without conflict means arranging the start time of task i at the end time of the visible time window. If successful, end and output the final task planning scheme; if failed, re-detect, that is, recalculate the coincidence degree between the new dynamic task to be inserted and the time window that has not been arranged until all time windows are detected, select the window with the smallest coincidence degree to insert, and output the best task planning scheme.

[0122] To verify the performance of the method of the present invention, the following is illustrated by a specific simulation experiment.

[0123] The scenario settings in this embodiment are as follows: The number of initial scheduling task sets in the experiment is 450, including 400 point tasks and 50 area tasks; the number of satellites to be scheduled is 200, including 132 optical satellites (opt) and 68 SAR satellites (sar). Generate the corresponding satellite observation task set through the Satellite Tool Kit (STK). The simulation experiment scheduling period is 24 hours, and the dynamic scheduling task time points are set at three moments: 6 o'clock, 12 o'clock, and 18 o'clock; The basic parameter information of some satellites is shown in Table 1:

[0124] Table 1 Basic Parameter Table of Some Satellites

[0125]

[0126] In order to verify the effectiveness of the design of execution time point overlap, this example compares four situations: adding and not adding execution time point overlap, and decomposing and not decomposing the task area in the genetic algorithm. Experimental group ① does not add execution time point overlap and does not decompose the task area, experimental group ② adds time point overlap but does not decompose the task area, experimental group ③ does not add execution time point overlap but decomposes the task area, and experimental group ④ adds execution time point overlap and decomposes the task area.

[0127] Experimental group ④, that is, the partial task scheduling results of the algorithm of the present invention are shown in Table 2:

[0128] Table 2: Results of some task scheduling plans

[0129]

[0130]

[0131] In Table 2, the task numbered a represents the initial area task, s represents the initial point task, da represents the dynamic area task, and ds represents the dynamic point task.

[0132] In addition, while keeping the same number of initial tasks, the above four algorithms are grouped for simulation experiments by setting three scenarios with different numbers of dynamic task sets (50, 100 and 200). Each algorithm is iterated 5 times to compare the three indicators of average algorithm benefit, average running time and dynamic task completion rate. The results when the number of dynamic task sets is 50 and 100 are shown in Table 3:

[0133] Table 3: Simulation results of dynamic insertion task planning for each algorithm

[0134]

[0135] It can be seen from Table 3 that although the genetic algorithm has a certain ability to handle dynamic tasks, as the number of dynamic tasks increases, the shortcomings of the algorithm without increasing the overlap of execution time points begin to be exposed, while the genetic algorithm with increased overlap of execution time points can better adapt to the increase in the number of dynamic tasks, reflecting the effectiveness of the dynamic task insertion method based on the overlap of execution time points.

[0136] Figure 3 The satellite orbits and initial mission distribution are given. Point missions and regional missions are mainly distributed in the west coast of North America. The red color in the figure represents point missions and the green color represents regional missions.

[0137] Figure 4 The comparison of the four experimental groups is given in the scenario where the number of dynamic tasks is 200 and the number of initial tasks is 450. The average running time is in seconds (s). Figure 4It can be seen that the regional task decomposition method can effectively improve the algorithm's operating efficiency and algorithm benefits. At the same time, combined with the overlap of execution time points, it can greatly improve the genetic algorithm's ability to handle a large number of dynamic tasks.

[0138] Figure 5 The curve of the algorithm fitness value of the present invention changing with the number of iterations is given.

[0139] In summary, the multi-satellite dynamic task planning method based on execution time point overlap and genetic algorithm proposed in the present invention has the characteristics of high observation benefit and high adaptability of dynamic task scheduling.

[0140] Based on the same technical concept as the method embodiment, the present invention also provides a multi-satellite task scheduling system for dynamic observation tasks, including:

[0141] The task preprocessing module is used to obtain task information, determine the task type, and process it according to the task type. If it is an area task, a number of sampling points proportional to the size of the task area are randomly selected at the task area to generate a point task set with multiple time windows; if it is a point task, the task area is not processed;

[0142] The time window calculation module is used to combine satellite orbit information and mission information to calculate a list of all visible time windows for each satellite for each mission within the working time, and obtain the mission number, satellite number, start time, end time and satellite deflection angle of each time window;

[0143] The task planning model building module is used to build an optimization objective function based on the point task set of multiple time windows and the information of each time window, according to the task constraints, resource constraints and the relationship between them, and establish a multi-satellite task planning mathematical model. The optimization goal is to maximize the benefits of completing the task. The constraint model includes satellite solar altitude angle constraints, maximum deflection angle constraints, task execution time constraints, power-on time constraints and task resolution constraints;

[0144] The preliminary planning solution module is used to solve the multi-satellite mission planning mathematical model using genetic algorithms, determine whether the established constraint model is satisfied, and generate an executable observation plan;

[0145] The final planning scheme determination module is used to perform dynamic task window insertion verification on the basis of the executable observation plan, determine the dynamic task execution window, and generate the final multi-satellite dynamic task scheduling scheme.

[0146] The present invention also provides a computer device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described above are implemented.

[0147] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described above are implemented.

[0148] It should be understood that the multi-satellite task scheduling system for dynamic observation tasks in the embodiment of the present invention can implement all the technical solutions in the above method embodiment, and the functions of its various functional modules can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can refer to the relevant description in the above embodiment, which will not be repeated here.

[0149] The present invention also provides a computer device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described above are implemented.

[0150] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described above are implemented.

[0151] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices (systems), computer equipment or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0152] The present invention is described with reference to a flowchart of a method according to an embodiment of the present invention. It should be understood that each process in the flowchart and a combination of processes in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1A device that specifies functions in a process or multiple processes.

[0153] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

Claims

1. A multi-satellite task scheduling method for dynamic observation tasks, characterized in that: The following steps are involved: S1, obtain task information, determine the task type, and process it according to the task type. If it is a regional task, take a number of sampling points proportional to the size of the task area at random positions in the task area to generate a point task set with multiple time windows; If it is a point task, the task area will not be processed; S2, combining satellite orbit information and mission information, calculates a list of all visible time windows for each satellite for each mission within the working time, and obtains the mission number, satellite number, start time, end time and satellite deflection angle of each time window; S3, based on the point task set of multiple time windows and the information of each time window, builds an optimization objective function according to the task constraints, resource constraints and the relationship between them, and establishes a mathematical model for multi-satellite task planning. The optimization goal is to maximize the benefits of completing the task. The constraint model includes satellite solar altitude angle constraints, maximum deflection angle constraints, task execution time constraints, power-on time constraints and task resolution constraints; S4, using genetic algorithm to solve the multi-satellite mission planning mathematical model, and determine whether the established constraint model is satisfied, and generate an executable observation plan; S5, based on the executable observation plan, perform dynamic task window insertion verification, determine the dynamic task execution window, and generate the final multi-satellite dynamic task scheduling plan.

2. The method according to claim 1, characterized in that: The optimization objective function is specifically: Among them, MAX_score is the optimization objective function, i represents the i-th task, there are n tasks in total, among which there are α point tasks and n-α regional tasks; score i_point is the score of the point task, M_frequency is the number of times the point task is required, and frequency i is the number of tasks completed, score i_area is the score of the regional task, area i is the total area of ​​the regional mission, area_actual i It is the area covered by the completed regional tasks.

3. The method according to claim 1, characterized in that The satellite sun altitude angle constraint model is: the optical satellite satisfies the sun altitude angle of not less than 10; The maximum deflection angle constraint model is: the deflection angle of the satellite when performing the observation mission is less than the given limit range; The task execution time constraint model is as follows: the sum of satellite attitude adjustment and observation time meets the time window constraint of adjacent observation tasks; after task planning, the specific time period during which the target task is executed by the satellite meets the constraint within the effective observation time period of the task; the time interval between two adjacent observations of the same observation task is not less than the shortest time interval; the available time period of the same satellite cannot be allocated to two or more tasks at the same time; The power-on time constraint model is: the longest continuous power-on time of the satellite meets the given time threshold; The mission resolution constraint model is: the imaging resolution of the selected observation satellite sensor does not exceed the resolution required by the mission objective.

4. The method according to claim 3, characterized in that The task execution time constraint model is expressed as: The sum of satellite attitude adjustment and observation duration meets the time window constraints of adjacent observation tasks: trantime_10 m +startup_min im ≤stsm in Among them, transtime_10 m is the satellite attitude adjustment time, startup_min im is the observation time, stsm in It is the start time of the satellite to perform adjacent tasks; After mission planning, the specific period during which the target mission is performed by the satellite must meet the constraints within the mission's effective observation period: stsm i ,estm i ,tsm i ∈d i Among them, stsm i is the start time of the satellite's adjacent mission, estm i is the satellite mission end time, tsm i It is the specific time period during which the satellite performs its mission; The time interval between two adjacent observations of the same observation task needs to satisfy the constraints: stsm i+1 -estm i ≥time_interval Among them, time_interval is the shortest time interval between two adjacent observations of the same observation task; The selected observation satellite sensor imaging resolution meets the resolution constraints required by the mission objectives: S_resolution i ≤M_resolution j Among them, S_resolution i is the satellite resolution, M_resolution j is the task requirement resolution; The same satellite cannot be assigned to two or more tasks at the same time during its available time period: Among them, SD is the set of available satellites, sch ij It is the satellite scheduling scheme; The deflection angle of the satellite during the observation mission is less than the given limit: left_roll <roll<right_roll Among them, roll is the deflection angle of the satellite when it is working, left_roll is the maximum left deflection angle, and right_roll is the maximum right deflection angle of the satellite.

5. The method according to claim 1, characterized in that The genetic algorithm solution steps include: S41, initializing N chromosomes to form an initial population, wherein the chromosomes are encoded in binary, and each character corresponds to the selection state of a time window, 0 represents that the time window is excluded in the current solution, and 1 represents that the time window is selected; S42, selecting the initial population, selecting N-1 chromosomes as parent chromosomes, and performing crossover on the selected parent chromosomes to form N-1 daughter chromosomes; S43, mutate N-1 offspring chromosomes, insert the best individuals after mutation into the offspring chromosomes, and use the population consisting of N offspring chromosomes as the initial population; S44, calculate the cost of each chromosome in the new initial population according to the optimization objective function, obtain the chromosome with the smallest cost, and execute S42 until the convergence condition is met, that is, the number of times the chromosome with the smallest cost is the same chromosome reaches a preset number, and an executable observation plan is obtained.

6. The method according to claim 1, characterized in that Perform dynamic task window insertion verification, determine the dynamic task execution window, and generate the final multi-satellite dynamic task scheduling plan, including: S51, sorting the dynamic task set to be inserted from early to late according to the start time; S52, examine each visible time window in turn, calculate the overlap degree of each time point in the time window, and arrange them in non-descending order, where the overlap degree is the overlap degree of the specified task i to be inserted with other tasks in the visible time window of the satellite; S53, for each time point after sorting, try to directly insert task i without conflict. If successful, end and output the final task planning plan; if unsuccessful, re-check until all time windows are checked, select the window with the smallest overlap to insert, and output the best task planning plan.

7. The method according to claim 6, characterized in that The overlap calculation method is as follows: the visible time window of task i in satellite r [stsm i ,estm i ], from stsm i At the beginning, for each time t i Try to arrange and calculate the overlap between task i and other tasks in each case. Among them, b j Overlapping time period.

8. A multi-satellite task scheduling system for dynamic observation tasks, characterized in that: include: The task preprocessing module is used to obtain task information, determine the task type, and process it according to the task type. If it is an area task, a number of sampling points proportional to the size of the task area are randomly selected at the task area to generate a point task set with multiple time windows. If it is a point task, the task area will not be processed; The time window calculation module is used to combine satellite orbit information and mission information to calculate a list of all visible time windows for each satellite for each mission within the working time, and obtain the mission number, satellite number, start time, end time and satellite deflection angle of each time window; The task planning model building module is used to build an optimization objective function based on the point task set of multiple time windows and the information of each time window, according to the task constraints, resource constraints and the relationship between them, and establish a multi-satellite task planning mathematical model. The optimization goal is to maximize the benefits of completing the task. The constraint model includes satellite solar altitude angle constraints, maximum deflection angle constraints, task execution time constraints, power-on time constraints and task resolution constraints; The preliminary planning solution module is used to solve the multi-satellite mission planning mathematical model using genetic algorithms, determine whether the established constraint model is satisfied, and generate an executable observation plan; The final planning scheme determination module is used to perform dynamic task window insertion verification on the basis of the executable observation plan, determine the dynamic task execution window, and generate the final multi-satellite dynamic task scheduling scheme.

9. A computer device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-satellite task scheduling method for dynamic observation tasks as described in any one of claims 1 to 7 are implemented.

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