Multi-Satellite Cooperative Task Scheduling and Planning Method Based on Adaptive Genetic Algorithm with Integrated and Merged Observations

Abstract

The present invention discloses an adaptive genetic algorithm-based dynamic scheduling and planning method for multi-satellite collaborative tasks based on fusion and combined observations. First, the regional tasks are decomposed by geometric sampling to construct discrete observation points. Secondly, the tasks are transformed into a weighted meta-task set, and combined with satellite resources, task and attitude constraints to calculate the optional satellites and time windows. Further, the meta-tasks that meet the conditions are detected and merged to construct a combined observation model with the maximum observation benefit as the goal. Finally, the adaptive genetic algorithm is used for optimization and solution to ensure that the constraints are met and an executable multi-satellite collaborative observation scheduling scheme is generated. In the process of regional task planning and scheduling, the present invention can quickly decompose regional tasks into point tasks by using the method of regional discrete adaptive sampling, improving the completion rate of regional observation tasks. In addition, an effective method for merging and observing meta-tasks is designed, effectively improving the benefit of multi-satellite dynamic task planning.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-satellite mission planning and scheduling, and in particular to a multi-satellite collaborative mission scheduling and planning method based on an adaptive genetic algorithm for fusion and merger of observations. By classifying all tasks, setting corresponding priorities, and in the process of regional mission planning and scheduling, using the method of regional discrete adaptive sampling to quickly decompose regional tasks into point tasks, the completion rate of regional observation tasks is improved; further, when converting all tasks into weighted meta-tasks for processing and planning, an effective method for merging observations of meta-tasks is designed, and an adaptive genetic algorithm is used to solve the constructed multi-satellite mission planning model, effectively improving the benefit of multi-satellite dynamic mission planning and scheduling. The algorithm can generate a mission observation plan that simultaneously maximizes the objective function and satisfies different constraint analyses, effectively utilizes satellite resources, and improves the mission planning efficiency, belonging to the technical field of mission planning and scheduling. Background Art

[0002] With the rapid progress of space technology and the in-depth exploration of the global space, the satellite network, as an important bridge connecting the Earth and the universe, is expanding at an alarming rate. This trend is not only reflected in the significant increase in the number of satellites, but also in the diversification of satellite types and the comprehensive improvement of their capabilities such as observation, communication, and navigation. From high-resolution remote sensing satellites to deep space probes, from communication relay satellites to scientific experiment platforms, various satellites play irreplaceable roles in their respective fields.

[0003] However, with the complexity of the satellite network, the task requirements also show an explosive growth trend. These tasks may involve multiple aspects such as Earth observation, environmental monitoring, disaster warning, resource exploration, and military reconnaissance, posing unprecedented high requirements for the real-time, accuracy, and flexibility of mission planning. Traditional mission planning methods, such as rule-based scheduling and simple optimization algorithms, often struggle to cope with such a complex and changing task environment, especially when dealing with multi-satellite collaborative mission planning, their limitations are more obvious.

[0004] Multi-satellite collaborative mission planning is a highly complex system engineering problem, which requires the algorithm to optimize task allocation, resource scheduling, and path planning while considering various constraint conditions such as satellite orbits, attitudes, payload capabilities, task priorities, and time windows to ensure that all tasks can be completed efficiently and accurately. In this process, not only the capabilities and limitations of individual satellites need to be considered, but also the cooperation and competition relationships between satellites need to be considered to maximize the overall mission benefit.

[0005] Traditional task planning methods are often inefficient when dealing with multi-satellite systems and difficult to meet the requirements of complex tasks. The adaptive genetic algorithm provides an effective solution for multi-satellite task planning and scheduling. The genetic algorithm is an optimization algorithm based on natural selection and genetic variation, which searches for the optimal solution by simulating the biological evolution process. In multi-satellite task planning and scheduling, the adaptive genetic algorithm can automatically adjust the algorithm parameters according to the characteristics of the tasks and the status of satellite resources, improving the search efficiency and solution quality. It can effectively handle complex constraint conditions, taking into account the task priorities and time windows at the same time, realizing the reasonable allocation of multi-satellite resources and the efficient execution of tasks. Summary of the Invention

[0006] To overcome the above defects in the prior art, in the process of regional task planning and scheduling, the present invention adopts the method of regional discrete adaptive sampling to quickly decompose regional tasks into point tasks, improving the completion rate of regional observation tasks; when processing and planning all point tasks, an effective method for merging and observing meta-tasks is designed; in the process of solving the task planning model by the adaptive genetic algorithm, an adaptive mutation and crossover probability is designed based on the population iteration times and individual fitness, and the constructed multi-satellite task planning optimization objective function is solved, effectively improving the multi-satellite dynamic task planning revenue and the algorithm solution efficiency; the present invention can improve the utilization rate of satellite resources and solve the problem of the timeliness of multi-satellite task execution. For different task situations, different processing methods are selected to achieve the reasonable allocation of satellite resources, increase the number of single-orbit observation tasks, and improve the response timeliness.

[0007] To achieve the above object, the present invention includes the following technical solutions:

[0008] A multi-satellite collaborative task dynamic scheduling and planning method based on an adaptive genetic algorithm for fusion and merging observations, including the following steps

[0009] Step 1: Task classification stage, construct a set of tasks to be planned according to task attributes;

[0010] Step 2: Regional task decomposition stage, analyze task characteristics, and transform complex large-area observation tasks into simple point tasks;

[0011] Step 3: Data preprocessing stage, convert all tasks into meta-tasks, and analyze and calculate the optional satellite resources and corresponding time windows for each meta-task according to the schedulable satellite resources;

[0012] Step 4: Task merging and observation processing stage, sequentially perform merging observation constraint detection on all meta-tasks, and perform merging observation processing on the meta-tasks that meet the merging conditions;

[0013] Step 5: Optimize the model construction, taking the maximum observation benefit as the optimization goal, establish the conflict resolution principle in the scheduling planning process, and build a multi-satellite multi-task combined observation scheduling planning model;

[0014] Step 6: Solve using the adaptive genetic algorithm, optimize and solve the parameters of the established scheduling planning model, and obtain the combined observation scheduling combination result that meets the task planning constraints;

[0015] Preferably, in the above step 1, constructing the set of tasks to be planned includes classifying and setting priorities for any tasks, specifically including the following steps:

[0016] S11, Task classification;

[0017] The input of the scheduling planning task mainly includes two parts: task information and satellite information. According to the task attributes, all task sets T = {T 1 , T 2 , T 3 , …, T M} are classified into the point task set T poi = {T 1_poi , T 2_poi , T 3_poi , …, T m1_poi}, the area task set T are = {T 1_are , T 2_are , T 3_are , …, T m2_are} and the dynamic task set T dyn = {T 1_dyn , T 2_dyn , T 3_dyn , …, T m3_dyn}. Among them, the point task is determined by the longitude (lon i ) and latitude (lat i ) of the target point. Then, the specific position of the subtask T i_poi in the point task set is expressed as T i_poi = (lat i , lon i ); the position and shape of the area observation task are determined by the longitude and latitude of each vertex. Then, the subtask T i_are in the area task set is expressed as T i_are = {(lat 1 , lon 1 ), …, (lat i , lon i )|i = 1, 2, …, N}.

[0018] S12, Priority setting;

[0019] According to the rule that the priority of dynamic tasks is higher than that of point tasks, and the priority of point tasks is higher than that of area tasks, corresponding priorities are set for all tasks in the set of all tasks to be planned, where the priorities of subtasks in the same set are the same.

[0020] Preferably, the decomposition of the area task in step 2 specifically includes the following steps:

[0021] S21. Read the latitude and longitude ranges [lat 1 , lat i and longitude range [lon 1 , lon i of the area task, and respectively set the discrete granularity △λ of latitude decomposition lat_i and discrete granularity △λ of longitude lon_i . The longitude and latitude discrete granularities conform to the following geometric ratio relationship According to the above area adaptive discrete sampling method, the task area is divided into grids, and each sampling point is the vertex of the grid and also corresponds to a position on the ground.

[0022] S22. Screen the sampling points within the area task range. After dividing the task area by the geometric ratio sampling method, there will still be some sampling points outside the area. At this time, these points need to be removed. Taking one sampling point as an example, it is explained as follows: Let the coordinates of one sampling point be (a, b). If lat 1 ≤a≤lat i and lon 1 ≤b≤lon i , then set the flag of this sampling point to true, otherwise to false, so as to judge all the sampling points within the task area. The specific process is as Figure 2 shown.

[0023] S23. For the multi-satellite scheduling and planning problem of the area task, the area coverage rate is an important indicator to measure the quality of the planning scheme. For the given satellite imaging strip and each sampling point in the area task, the imaging strip covers the sampling points of the discrete task area. According to the position relationship between the sampling point and the imaging strip, it is determined whether the discrete grid is covered. If all vertices of the grid are covered, it is judged that the grid is covered by the imaging strip; otherwise, it is judged that the grid is not covered. Count all the covered grid points, and then calculate the coverage rate of the task area.

[0024] S24. The area observation tasks in the dynamic tasks are also decomposed by performing steps S21 - 23 on them.

[0025] Preferably, in step 3, all tasks are converted into meta-tasks, and based on the schedulable satellite resources, the optional satellite resources and the corresponding time windows for each meta-task are analyzed and calculated, which specifically include the following steps:

[0026] S31, after the processing of steps S1 to S2, all tasks are decomposed into three types of point tasks with different priorities. Now, all point tasks are converted into meta-tasks with three different weights, and the converted meta-task set is T init = 0.5·T dyn ∪0.3·T are ∪0.2·T poi 。

[0027] S32, after the conversion, the constraints of the meta-tasks are the same as those of the original tasks. Before that, some constraint variables and parameters involved are defined:

[0028] Table 1 Definition Table of Satellite-related Parameters

[0029]

[0030] Table 2 Definition Table of Task-related Parameters

[0031]

[0032]

[0033] S33, convert the constraints of the original tasks and satellite constraints into the constraints of the meta-tasks, mainly including:

[0034] 1) The synthetic aperture radar can penetrate clouds and observe all-weather without requirements. When an optical imaging satellite images, it must meet the requirement of the minimum solar altitude angle: sun_angle>sunlight;

[0035] 2) The imaging resolution of the selected observation satellite sensor cannot be less than the resolution required by the meta-task: T init _resolution≤S_resolution;

[0036] 3) The time interval between two adjacent time windows of the satellite needs to meet the constraint of being greater than the satellite attitude adjustment time: et j-1 +trantime_i≤st j ,where et j-1 represents the end time of the previous task on the satellite, and trantime_i represents the attitude adjustment time of satellite i;

[0037] 4) After the task is planned, the specific time window for the target task to be executed by the satellite needs to meet the constraint within the effective observation time period of the task, that is: sti , and i ∈ d i ;

[0038] 5) The time interval between two adjacent observations of the same observation task needs to meet the minimum time interval constraint, that is: st i+1 - et i ≥ time_interval;

[0039] 6) In the available time period of the same satellite, it cannot be allocated to two or more tasks at the same time, that is, the mutual exclusion constraint cannot be violated: Among them, C i is the set of task decision variables, which are boolean variables. The value of 0 means the task is not planned, and the value of 1 means the task is planned;

[0040] 7) The continuous working time of the satellite cannot exceed the longest continuous power-on time constraint:

[0041]

[0042] In the formula, sat_type is the satellite type variable. The continuous working time of the opt type is 300s, and the continuous working time of the sar type satellite is 600s.

[0043] S34. Calculate the optional time window of the task. First, calculate the satellites that can execute the task according to the position of each meta-task. Further, according to the constraints between the tasks and satellites in S32, calculate the satellite working time window that can be selected for the task, and form the set of optional time windows of the task.

[0044] Preferably, in step 4, the combined observation constraints of all meta-tasks will be detected in sequence, and the meta-tasks that meet the combination conditions will be processed for combined observation, which specifically includes the following steps:

[0045] S41. There are mainly three conditions for the meta-tasks to be combined for observation: 1) Multiple meta-tasks must all be within the swath width of the satellite scan on the ground; 2) When performing combined observation, the resolution of the same satellite must meet the resolution requirements of multiple meta-tasks; 3) The time window after combining multiple meta-tasks must be less than the longest single working time of the satellite.

[0046] S42. Processing of the satellite attitude angle after combined observation: The observation angle difference between the observation satellite for any two meta-tasks should be within the satellite field of view angle range. In order to cover all meta-tasks for synthetic observation, the mean value of the maximum observation angle and the minimum observation angle is selected as the attitude angle of the satellite for the combined observation time window.

[0047] S43. Pairwise detect all tasks of satellite i that have visible time windows in the first orbital cycle. If the deflection angle and observation time constraints are both satisfied, merge the two meta-tasks; otherwise, do not merge them.

[0048] S43. Perform a merge detection on the merged observation tasks obtained in S42 with the remaining meta-tasks in this cycle again. If the merge constraint conditions are satisfied, further merge them; otherwise, jump to S45.

[0049] S44. Sequentially perform merge detections on the merged tasks with other tasks in the orbital cycle until all meta-task merge detections on the first orbital cycle of this satellite are completed, and then go to S45.

[0050] S45. Sequentially traverse other orbital cycles of this satellite.

[0051] S46. Sequentially traverse all satellites.

[0052] S47. The merging is completed. Calculate the merged time window and satellite attitude.

[0053] Preferably, in step 5, with the maximum observation benefit as the optimization goal, establish the conflict resolution principle in the scheduling planning process, and establish a multi-satellite multi-task merged observation scheduling planning model, which specifically includes the following steps:

[0054] S51. Design an optimization objective function based on the benefits of meta-tasks:

[0055] maxF = f(T dyn ) + f(T are ) + f(T poi ) + f P

[0056] where f(T dyn ) is the dynamic task benefit; f P is the negative benefit obtained for violating the constraints; f(T are ) is the regional task benefit, and the specific calculation method is:

[0057] In the formula, score i_area is the benefit that regional task i should obtain, p_a i is the actual number of sampling points executed after the regional task is discretized, and spt i is the total number of sampling points after regional i is discretized;

[0058] f(T poi ) is the point task benefit, and the specific calculation method is:

[0059] In the formula, score i_pointis the income deserved for point task i, frequency is the execution frequency of the point task, T init _frequency is the required execution frequency of the point task for the task.

[0060] S52. Adopt the conflict resolution principle combining priority and income, that is, preferentially execute the meta-task with a higher weight. When the weights of two meta-tasks are the same, preferentially execute the task with a higher task income.

[0061] Preferably, in step 6, the parameters of the established scheduling planning model are optimized and solved using an adaptive genetic algorithm to obtain a combined observation scheduling combination result that meets the task planning constraints, specifically including the following steps:

[0062] S61. Encoding method. Use a matrix M to represent the visible windows of multiple satellites and multiple tasks, that is:

[0063]

[0064] Among them, the element W ij (i = 1, 2,..., N s ; j = 1, 2,..., N t ) is the set of visible windows of satellite i for task j, N t is the number of tasks, and N S is the number of satellites. Based on the matrix M, create an ordered set of visible time windows corresponding to each task: Among them the visible windows of each satellite are sorted according to the order of visible time, and the order remains fixed throughout the calculation process. Let the dimension of the chromosome be two-dimensional. The first dimension represents the satellite number of the observation task, and the second dimension represents the start time of the satellite to execute the task. According to the above description, the visible time window of the satellite for the task is encoded as:

[0065]

[0066] Among them, x i = 0, 1, 2,..., N s represents the satellite number of the task j to be executed, and y i = 0, 1, 2,..., N t represents the serial number of the visible time window of the satellite to execute the task.

[0067] S62. Determination of the initial solution. The initial solution is determined in sequence according to the start time of the point task, and the time windows of the satellites are sorted in time order. Specifically, the task with the earliest start is preferentially assigned to the satellite that can perform the observation earliest.

[0068] S63, Adaptive crossover and mutation operators. To avoid the reduction of the convergence speed and accuracy of the algorithm caused by fixed crossover and mutation probabilities, an adaptive crossover probability P c and mutation probability P m are designed based on the population iteration number and individual fitness. The specific calculation methods are as follows:

[0069]

[0070]

[0071] Among them, P c1 , P c2 are the maximum and minimum values of the initially set crossover probability, f′ is the maximum fitness of the two crossover parties, P m1 , P m2 are the maximum and minimum values of the initially set mutation probability, f is the fitness of the mutated individual, f avg is the average fitness of the current population, f max is the maximum fitness of the current population, g is the current population iteration number, and G is the maximum population iteration number.

[0072] S64, Selection operator. Sort the population chromosomes in descending order of fitness, and successively select a new population with the same scale as the initial population, which can reduce the population scale and the computational pressure of the algorithm, and can further screen out chromosomes with higher fitness to accelerate the convergence speed.

[0073] S65, Solve to obtain the optimal individual, and further output the optimal multi-satellite observation scheduling combination that meets the planning constraints.

[0074] In the process of regional mission planning and scheduling, the present invention can quickly decompose regional missions into point missions by using the method of regional discrete adaptive sampling, improving the completion rate of regional observation missions; in addition, an effective method for merging and observing meta-tasks is designed, and an effective method for merging and observing meta-tasks is designed, and the constructed multi-satellite merged observation mission planning model is solved by using an adaptive genetic algorithm, effectively improving the benefits of multi-satellite dynamic mission planning. Experimental results show that the multi-satellite collaborative mission planning and scheduling algorithm based on the adaptive genetic algorithm for fusion and merging observation can generate a mission observation plan that simultaneously maximizes the objective function and meets different constraint analyses, effectively utilizes satellite resources, and improves the mission planning efficiency.

[0075] An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: when the processor executes the program, it implements the multi-satellite collaborative task dynamic scheduling and planning method based on the adaptive genetic algorithm for fusion and merging observation.

[0076] A computer-readable storage medium stores computer instructions thereon, and when the computer instructions are executed by a processor, the multi-satellite collaborative task dynamic scheduling and planning method of the adaptive genetic algorithm based on fusion and merging observations is implemented.

[0077] The advantages of the present invention are as follows:

[0078] In dealing with multi-type observation tasks, the present invention adopts a method of regional discrete adaptive sampling for regional tasks to decompose regional observation tasks, converts them into discrete observation points, and further converts all tasks into meta-tasks with weights; fully considers the constraints related to tasks and satellites, and calculates the optional time windows for each meta-task; in order to improve the efficiency of satellites in executing tasks, a constraint-based multi-task merging observation method is proposed to perform merging detection on all time windows of satellites; an optimization model is constructed with the maximum observation benefit of multi-satellites as the goal. Based on the traditional genetic algorithm, according to the number of population iterations and individual fitness values, adaptive crossover and mutation probabilities are designed, and an adaptive genetic algorithm is proposed to solve the function to be optimized, which improves the flexibility of the update strategy, makes the algorithm have good convergence, and can simultaneously handle the planning and scheduling of multiple different types of tasks of multi-satellites. The simulation scheduling experiment shows that as the number of different types of tasks increases, the advantage of this algorithm in terms of operation efficiency becomes more obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flowchart of the multi-satellite collaborative task planning method of the adaptive genetic algorithm based on fusion and merging observations of the present invention.

[0080] Figure 2 It is a flowchart of the regional task decomposition of the present invention.

[0081] Figure 3 It is a comparison chart of the average benefit results of the traditional genetic algorithm, the adaptive genetic algorithm, the genetic algorithm considering merging observations, and the adaptive genetic algorithm based on fusion and merging observations in this example.

[0082] Figure 4 It is a comparison chart of the average running time of the traditional genetic algorithm, the adaptive genetic algorithm, the genetic algorithm considering merging observations, and the adaptive genetic algorithm based on fusion and merging observations in this example. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0083] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0084] As shown by Figure 1 the multi-satellite cooperative mission planning method based on the adaptive genetic algorithm of fusion and combined observation of the present invention includes the following specific steps:

[0085] S1. Task classification stage: construct a set of tasks to be planned according to task attributes;

[0086] S11. Task classification: divide tasks into point tasks, area tasks, and dynamic tasks;

[0087] S12. Set priorities for dynamic tasks, area tasks, and point tasks in descending order of priority;

[0088] S2. Area task decomposition stage: analyze task characteristics;

[0089] S21. Adopt the area discrete adaptive sampling method to discretize the task area into grids;

[0090] S22. Screen sampling points within the area task range;

[0091] S23. Calculate the coverage rate of the area task;

[0092] S24. Loop S21 - S23 to discretize all area tasks;

[0093] S3. Data preprocessing stage: convert all tasks into meta-tasks, and calculate the optional satellite resources and corresponding windows for all meta-tasks;

[0094] S31. Assign different weight coefficients to the benefits of all point tasks according to priority and convert them into meta-tasks;

[0095] S32. Define constraint variables and parameters;

[0096] S33. Equivalently convert task constraints and satellite constraints into meta-task constraints;

[0097] S34. Calculate the set of optional time windows for tasks.

[0098] S4. Task merging and observation processing stage: sequentially perform merging observation constraint detection on all meta-tasks, and perform merging observation processing on the meta-tasks that meet the merging conditions;

[0099] S41. Merging observation constraint conditions for meta-tasks;

[0100] S42. Satellite attitude angle processing after merging observations;

[0101] S43. Perform merging observation constraint detection on all meta-tasks with visible time windows in the first orbital cycle of satellite i, and merge the meta-tasks that meet the constraints;

[0102] S44. Merge the combined observation tasks obtained in S43 with the remaining meta-tasks of this orbit cycle again for combined detection, merge the meta-tasks that meet the constraints, and if not, jump to S46.

[0103] S45. Conduct combined detection on the meta-tasks in all orbit cycles of this satellite in sequence, and then transfer to S46.

[0104] S46. Traverse the other orbit cycles of this satellite in sequence.

[0105] S47. Traverse all satellites in sequence.

[0106] S48. The merging is completed, and calculate the time window and satellite attitude after merging.

[0107] S5. Establish conflict resolution principles and establish a multi-satellite multi-task combined observation scheduling planning model.

[0108] S51. Design an optimization objective function based on task benefits.

[0109] S52. Adopt a conflict resolution principle that combines priority and benefits.

[0110] S6. Solve using an adaptive genetic algorithm to optimize the parameters of the established scheduling planning model.

[0111] S61. Two-dimensional coding method.

[0112] S62. Determination of the initial solution.

[0113] S63. Design adaptive crossover and mutation operators based on the population iteration times and individual fitness values.

[0114] S64. Selection operator.

[0115] S65. Solve for the optimal individual and output a planning and scheduling plan that meets the task and satellite constraints.

[0116] In this embodiment, a dataset is made using the publicly available satellite TLE two-line ephemeris data. Each TLE contains two lines of text, with each line having a total of 69 characters, and it can provide sufficient information to determine the orbital position and velocity of Earth-orbiting satellites. These data include the longitude, latitude, and altitude of the satellite at each time point, as well as the solar elevation angle of the satellite relative to the sub-satellite point. The calculation is completed using SGP4. SGP4 is a general and widely accepted standard algorithm applicable to most Earth-orbiting satellites, mainly used to predict the position and velocity of low-Earth orbit satellites, calculate the satellite's orbit based on TLE data, and can approximate the orbital perturbations caused by factors such as the Earth's gravity, solar and lunar perturbations, and atmospheric drag. The algorithm test scenario is described in JSON format. The scenario file contains the satellites used, key strategic observation points and regions, as well as possible dynamic adjustment information. The length and structure of this information may vary greatly. The area of the regional task is in square kilometers. This area is calculated under the EPSG:6933 coordinate reference system. This coordinate system is an equal-area projection, suitable for calculating the actual area on the Earth. The scenario settings are shown in Table 3:

[0117] Table 3: Detailed Scenario Settings

[0118] Scene complexity Low Medium High Number of strategic observation points 45 180 450 Number of strategic observation areas 5 20 50 Number of additional points 5 20 50 Number of additional areas 1 5 10 Number of optical satellites 20 52 132 Number of SAR satellites 10 28 68

[0119] Set up simulation experiments for three different scenarios with high, medium, and low complexity. Considering the timeliness requirements of multi-satellite mission planning, the simulation experiment sets the observation time requirement for one mission as 24 hours, that is, the simulation time is from 00:00:00 to 23:59:59, and conduct single-day mission planning. Part of the scheduling planning results for the low-complexity scenario are shown in Table 4:

[0120] Table 4 Results of Part of the Planning Scheme for the Low-Difficulty Scenario

[0121] Satellite ID Mission number Start time End time Roll angle Opt01 [28 / 46] 22:21:17 22:22:07 13.37° Opt02 [18 / 11 / 12 / 27] 18:31:47 18:33:10 28.08° Opt03 [44 / 47 / 40] 14:39:54 14:40:53 13.77° Opt04 [55 / 1 / 16 / 6 / 5 / 29] 17:56:58 17:58:57 28.94° Opt05 [4] 16:33:56 16:34:06 21.22° Opt06 [19 / 31 / 43 / 37 / 50] 14:36:21 14:36:41 -13.21° Sar01 [5] 2:42:20 2:42:40 2.15° Sar02 [33 / 21 / 2 / 24 / 17] 18:43:08 18:46:03 12.74° Sar03 [7 / 32 / 56 / 13] 5:15:41 5:15:56 13.34° Sar04 [45 / 22 / 38 / 3 / 39] 5:47:11 5:49:03 3.91° Sar05 [14 / 37 / 9 / 40 / 42] 10:20:14 10:21:44 4.18° Sar06

[54] 18:30:40 18:31:00 3.35°

[0122] To evaluate the effectiveness and solution effect of the algorithm proposed in the present invention, the genetic algorithm (GA) and the adaptive genetic algorithm without regional segmentation (AGA) are selected for comparison. Each algorithm is run five times in scenarios of the same difficulty, and the average running time and average benefit of the algorithm for the task planning scheme are selected as the evaluation criteria.

[0123] The parameter settings of the algorithms are as follows:

[0124] Genetic algorithm (GA): population size 20, number of iterations 100;

[0125] Genetic algorithm with region-adaptive discretization (S-GA): population size 20, number of iterations 100;

[0126] Adaptive genetic algorithm (AGA): population size 20, iteration number 100;

[0127] Adaptive genetic algorithm combined with regional adaptive discretization (S-AGA): population size is 20 and number of iterations is 100.

[0128] Figure 3 The average benefits of four algorithms in five experiments in three scenarios of different complexity are given. It can be seen from the figure that in scenarios of the same complexity, the adaptive genetic algorithm obtains higher benefits than the traditional genetic algorithm; in addition, for the same algorithm, the use of the combined observation method will further improve the score of the algorithm.

[0129] Figure 4 The average running time results of four algorithms in five experiments in three scenarios of different complexity are given. It can be seen from the figure that in scenarios of the same complexity, the efficiency of the adaptive genetic algorithm is higher than that of the traditional genetic algorithm; after the two algorithms introduce the design of combined observation, they can greatly shorten the running time of the algorithm and effectively improve the running efficiency of the algorithm.

[0130] In summary, the multi-satellite collaborative task planning method based on the adaptive genetic algorithm of fusion and merged observations of the present invention has the characteristics of observation benefits and fast convergence speed. While ensuring that the constraints are not violated as much as possible, it can effectively solve the problem of simultaneous planning of multiple satellites and multiple tasks.

[0131] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-satellite collaborative task dynamic scheduling planning method based on an adaptive genetic algorithm with fusion and merged observations, characterized in that: The method comprises the following steps S1: Task classification stage, based on task attributes, a set of tasks to be planned is constructed; S2: Regional task decomposition stage, analyzing the task characteristics and transforming the complex large-area observation task into a simple point task; S3: Data preprocessing stage, convert all tasks into meta-tasks, and analyze and calculate the optional satellite resources and corresponding time windows of each meta-task based on the schedulable satellite resources; S4: Task merging observation processing stage, merge observation constraint detection is performed on all meta-tasks in turn, and meta-tasks that meet the merging conditions are merged and observed; S5: Optimize model construction, take the maximum observation benefit as the optimization goal, and establish the conflict resolution principle in the scheduling planning process. Establish a multi-satellite multi-task combined observation scheduling planning model; S6: Adaptive genetic algorithm is used to solve the established scheduling planning model and optimize the parameters to obtain the combined observation scheduling result that meets the task planning constraints; Among them, S5 is as follows: S51, design and optimize the objective function based on the benefits of the meta-task: maxF=f(T dyn )+f(T are )+f(T poi )+f P Among them, f(T dyn ) is the dynamic task benefit; f P Negative payoff for violating constraints; f(T are ) is the regional mission income, and the specific calculation method is: Where score i_area is the revenue that regional task i deserves, p_a i is the number of sampling points actually executed after the regional task is discretized, spt i is the total number of sampling points after discretization of region i; f(T poi ) is the point task income, and the specific calculation method is: Where score i_point is the revenue that task i deserves, frequency is the frequency at which the task is executed, T init _frequency is the frequency at which the task needs to be executed. S52 adopts the conflict resolution principle combining priority and benefit, that is, the meta-task with higher weight is executed first. When the weights of two meta-tasks are the same, the task with higher benefit is executed first.

2. The method for dynamic scheduling and planning of multi-satellite collaborative tasks based on an adaptive genetic algorithm with fusion and combined observations according to claim 1 is characterized in that: S1, task classification stage, constructs a set of tasks to be planned based on task attributes. The specific steps are as follows; S11, task classification, the input of the scheduling planning task includes two parts: task information and satellite information. According to the task attributes, all task sets T = {T1, T2, T3, ..., T M }, classified into point task set T poi ={T 1_poi ,T 2_poi ,T 3_poi ,…,T m1_poi }、Regional task set T are ={T 1_are ,T 2_are ,T 3_are ,…,T m2_are } and the dynamic task set T dyn ={T 1_dyn ,T 2_dyn ,T 3_dyn ,…,T m3_dyn }, where the point mission is the longitude lon of the target point i and latitude lat i If it is determined, then the subtask T in the task set is i_poi The specific position is represented by T i_poi =(lat i ,lon i ); The position and shape of the regional observation task are determined by the longitude and latitude of each vertex, so the subtask T in the regional task set i_are Represented as T i_are ={(lat1,lon1),…,(lat i ,lon i )|i=1,2,…,N}, S12, according to the rule that the priority of dynamic tasks is higher than the priority of point tasks, and the priority of point tasks is higher than the priority of regional tasks, corresponding priorities are set for all tasks in all task sets to be planned, wherein subtasks in the same set have the same priority.

3. The method for dynamic scheduling and planning of multi-satellite collaborative tasks based on an adaptive genetic algorithm with fusion and combined observations according to claim 1 is characterized in that ,S2, regional task decomposition stage, analyze the task characteristics, transform the complex large-area observation task into a simple task, which specifically includes the following steps; S21, read the latitude range of the regional task [lat1,lat i ], longitude range [lon1,lon i ], respectively set the discrete granularity △λ of the latitudinal decomposition of the region lat_i , longitude discrete granularity △λ lon_i , the longitude and latitude discrete granularity conforms to the following geometric relationship According to the above geometric sampling method, the task area is divided into grids. Each sampling point is a vertex of the grid and corresponds to a position on the ground. S22, filter the sampling points within the task area. After dividing the task area by geometric sampling, there will still be some sampling points outside the area. At this time, these points need to be eliminated. Suppose the coordinates of one of the sampling points are (a, b). If lat1≤a≤lat i and lon1≤b≤lon i , then set the flag of this sampling point to true, otherwise to false, so as to determine all the sampling points in the task area. S23, for the multi-satellite scheduling planning problem of regional tasks, the regional coverage rate is an important indicator to measure the quality of the planning scheme. For each sampling point in a given satellite imaging strip and regional task, the imaging strip covers the sampling points of the discrete task area. According to the positional relationship between the sampling point and the imaging strip, it is determined whether the discrete grid is covered. If all vertices of the grid are covered, the grid is judged to be covered by the imaging strip; otherwise, the grid is judged to be uncovered. All covered grid points are counted, and then the coverage rate of the task area is calculated. S24, performing steps S21 to S23 on the regional observation task in the dynamic task to decompose it.

4. The method for dynamic scheduling and planning of multi-satellite collaborative tasks based on an adaptive genetic algorithm with fusion and combined observations according to claim 2 is characterized in that ,S3,In the data preprocessing stage, all tasks are converted into meta-tasks. According to the schedulable satellite resources, the optional satellite resources and the corresponding time windows of each meta-task are analyzed and calculated. The specific steps include: S31, after processing through steps S1-2, all tasks are decomposed into three types of point tasks with different priorities. Now all point tasks are converted into meta-tasks with three different weights. The converted meta-task set is T init =0.5·T dyn ∪0.3·T are ∪0.2·T poi , S32, after the conversion is completed, the constraints of the meta-task are consistent with the constraints of the original task. Before this, some constraint variables and parameters involved are defined: S: Satellite resource set S = {sat1, sat2, ..., sat n }, SD: Available satellite set; S_geometry: the latitude and longitude of the subsatellite point, that is, the geographical location of the point on the Earth's surface directly below the satellite at a certain moment in orbit; sun_angle: the solar altitude angle of the subsatellite point; sunlight: the minimum solar altitude angle required for effective imaging by optical satellites; S_resolution: The spatial resolution of the satellite sensor, which indicates the size of the smallest ground feature that the sensor can identify, in meters; startup_min: the shortest time required for the satellite to start executing a mission and end the current mission, unit: s; roll: maximum deflection angle, which indicates the angle by which satellite i can rotate left or right relative to its forward direction; orbit_max: the longest continuous working time of satellite i in one orbit around the earth; visualfield: The width of the ground covered by the satellite sensor, in km. transtime: the time required for satellite attitude adjustment, unit: s; T init _resolution: the sensor spatial resolution required by the task, unit: m; T init _frequency: represents the number of times the task should be executed within the specified time; time_interval: the time interval between two effective imaging tasks, unit: h; min_time: the earliest time at which the task can be executed; max_time: the latest time at which the task can be executed; score: the maximum score of the task; TSM i : The set of mission execution time of satellite i; The start time of satellite i's mission j; The end time of satellite i performing mission j; The time window in which satellite i performs mission j; The duration set of each task i in the meta-task set; The deflection angle of satellite i when performing mission j; S33, convert the original task constraints and satellite constraints into meta-task constraints, 1) Synthetic aperture radar can penetrate clouds and observe all-weather without any requirements, while optical imaging satellites must meet the minimum solar altitude angle requirement when imaging: sun_angle>sunlight; 2) The imaging resolution of the selected observation satellite sensor cannot be less than the resolution required by the meta-task: T init _resolution ≤ S_resolution; 3) The time interval between two adjacent time windows of the satellite needs to satisfy the constraint of being greater than the satellite attitude adjustment time: et j-1 +trantime_i≤st j , among which, et j-1 It indicates the time when the last mission of the satellite ended, and trantime_i indicates the time when the attitude of satellite i was adjusted; 4) After the mission is planned, the specific time window for the target mission to be executed by the satellite needs to meet the constraints within the mission's effective observation period, that is: i ,et i ∈d i ; 5) The time interval between two adjacent observations of the same observation task needs to meet the minimum time interval constraint, that is: i+1 -et i ≥ time_interval; 6) The same satellite available time period cannot be assigned to two or more tasks at the same time, that is, it cannot violate the mutual exclusion constraint: Among them, C i is a set of task decision variables, which is a Boolean variable. A value of 0 indicates that the task is not planned, and a value of 1 indicates that the task is planned; 7) The satellite continuous working time cannot exceed the maximum continuous power-on time constraint: In the formula, sat_type is the satellite type variable, the continuous working time of opt type satellite is 300s, and the continuous working time of sar type satellite is 600s. S34, calculate the optional time window of the task. First, according to the position of each meta-task, calculate the satellite that can execute the task; further, according to the constraints of the task and the satellite in S32, calculate the optional satellite working time window of the task to form a set of optional time windows for the task.

5. The method for dynamic scheduling and planning of multi-satellite collaborative tasks based on an adaptive genetic algorithm with fusion and combined observations according to claim 1 is characterized in that ,S4, task merging observation processing stage, all meta-tasks are tested for merging observation constraints in turn, and meta-tasks that meet the merging conditions are processed for merging observations, which specifically includes the following steps; S41, the conditions for meta-tasks to conduct combined observations are mainly the following three: 1) multiple meta-tasks must be within the width of the satellite scan on the ground; 2) When performing combined observations, the resolution of the same satellite must meet the resolution requirements of multiple meta-tasks; 3) The time window after combining multiple meta-tasks must be less than the maximum single working time of the satellite. S42, satellite attitude angle processing after combined observation: the difference in observation angles of any two meta-tasks of the observation satellite should be within the satellite field of view. In order to cover all meta-tasks for synthetic observation, the average of the maximum observation angle and the minimum observation angle is selected as the attitude angle of the satellite that performs the combined observation time window. S43, perform pairwise detection on all tasks with visible time windows of satellite i in the first orbital cycle. If both the deflection angle and observation time constraints are satisfied, the two meta-tasks are merged; if not, they are not merged. S43, the combined observation task obtained in S42 is combined again with the remaining meta-tasks of the round for detection. If the combined constraint condition is met, it is further combined with the meta-tasks; if not, jump to S45. S44, sequentially merge and detect the merged tasks with other tasks in the orbital cycle until the merge detection of all meta-tasks on the first orbital cycle of the satellite is completed, and then go to S45, S45, traverse the other orbits of the satellite in turn, S46, traverse all satellites in turn, S47, the merging is completed, and the merged time window and satellite attitude are calculated.

6. The method for dynamic scheduling and planning of multi-satellite collaborative tasks based on an adaptive genetic algorithm with fusion and combined observations according to claim 1 is characterized in that , S6, solving by adaptive genetic algorithm, performing parameter optimization on the established scheduling planning model, and obtaining a combined observation scheduling result that meets the task planning constraints, specifically including the following steps; S61, encoding method, uses a matrix M to represent the multi-satellite multi-task visible window, that is: Among them, the element W ij (i=1,2,...,N s ; j = 1, 2, ..., N t ) is the set of satellite visible windows for mission j, N t is the number of tasks, N S is the number of satellites, and an ordered set of visible time windows corresponding to each mission is created based on the matrix M: in The visible windows of each satellite are sorted in order of visible time, and the order remains unchanged during the entire calculation process. Let the dimension of the chromosome be two-dimensional. The first dimension represents the satellite serial number of the observation mission, and the second dimension represents the start time of the satellite's mission. According to the above statement, the visible time window of the satellite for the mission is encoded as: Among them, x i =0,1,2,...,N s represents the satellite number of mission j, y i =0,1,2,...,N t Indicates the visible time window number of the satellite mission. S62, determination of the initial solution. The initial solution is determined in chronological order based on the start time of the point tasks, and the time windows of the satellites are sorted in chronological order. Specifically, the earliest started task is preferentially assigned to the satellite that can make observations the earliest. S63, adaptive crossover and mutation operator, in order to avoid the convergence speed and accuracy of the algorithm caused by constant crossover and mutation probability, the adaptive crossover probability P is designed according to the number of population iterations and individual fitness. c and mutation probability P m , the specific calculation methods are as follows: Among them, P c1 ,P c2 is the maximum and minimum value of the initial crossover probability, f′ is the maximum fitness of both parties in the crossover, P m1 ,P m2 is the maximum and minimum value of the initial mutation probability, f is the fitness of the mutant individual, and f avg is the average fitness of the contemporary population, f max is the maximum fitness of the contemporary population, g is the number of iterations of the current population, G is the maximum number of iterations of the population, S64, selection operator, sorting the population chromosomes in descending order according to their fitness, and selecting new populations of the same size as the initial population in turn, reducing the population size and reducing the algorithm calculation pressure, and further screening chromosomes with higher fitness to speed up the convergence speed. S65, solve and obtain the optimal individual, and further output the optimal multi-satellite observation scheduling combination that meets the planning constraints.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the multi-satellite collaborative task dynamic scheduling planning method based on the adaptive genetic algorithm of fusion and combined observation as described in any one of claims 1 to 6 above is implemented.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, the method for dynamic scheduling and planning of multi-satellite collaborative tasks based on an adaptive genetic algorithm with fused and merged observations as described in any one of claims 1 to 6 is implemented.

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