Method and system for solving short-term task planning of astronomical satellite

Abstract

The present application relates to the technical field of astronomical satellite mission planning, in particular to a method and system for solving short-term mission planning of astronomical satellite. The method comprises: step 1) constructing a short-term mission planning mathematical model of the astronomical satellite to be planned, and abstracting the mission planning problem as a maximized optimization problem; step 2) solving by using a hybrid search strategy artificial bee colony algorithm, searching for excellent solutions by the employed bees based on an "elite solution guided search" strategy, and searching by the follower bees based on a "neighborhood optimal solution update" strategy, so as to accelerate the solution and improve the solution accuracy. Compared with the basic artificial algorithm, the method has the advantages of fast convergence speed, high solution accuracy and strong optimization ability, and has fewer control parameters than other swarm intelligence algorithms; in the short-term mission planning problem of the astronomical satellite, the method can obtain higher task completion degree and greater observation benefit.

Description

Technical Field

[0001] This invention relates to the field of astronomical satellite mission planning technology, and in particular to a method and system for solving short-term mission planning for astronomical satellites. Background Technology

[0002] The short-term mission planning problem for astronomical satellites is a complex multi-constraint, multi-objective optimization problem, but it is also a key technical problem faced by every astronomical satellite, and an important means to achieve scientific goals and maximize scientific output. Currently, the planning algorithms used in my country's astronomical satellite mission planning are usually designed specifically for specific missions. This invention is designed for the short-term planning mission of a certain space science astronomical satellite currently under development in my country.

[0003] Short-term planning involves considering more factors and using more precise models compared to other types of planning. Examples include models for calculating the orbits of the Sun and Moon, satellite orbits, the time period of transit through the South Atlantic anomaly, and data transmission opportunities. Short-term planning requires the arrangement of more specific observation tasks within a relatively short future period.

[0004] In traditional planning systems and algorithms, heuristics are the most widely used. Designing a heuristic relies on prior knowledge, and often multiple heuristics need to be designed for a single task, with the best one selected based on test results. For some complex scenarios, it may even be impossible to find a suitable heuristic. If the task constraints change, this design and selection process may need to be repeated, which is very time-consuming. Swarm intelligence-based planning algorithms are also widely used; compared to heuristic algorithms, designing the objective function is much easier. However, basic swarm intelligence algorithms often suffer from slow convergence speed or insufficient solution accuracy. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and to propose a method and system for solving short-term mission planning for astronomical satellites.

[0006] To achieve the above objectives, this invention proposes a method for solving short-term mission planning for astronomical satellites, the method comprising:

[0007] Step 1) Construct a short-term mission planning mathematical model for the astronomical satellite to be planned, and abstract the mission planning problem into a maximization optimization problem;

[0008] Step 2) The artificial bee colony algorithm with a hybrid search strategy is used to solve the problem. The hired bees search for excellent solutions based on the "elite solution-guided search" strategy, while the follower bees search based on the "neighborhood optimal solution update" strategy, so as to speed up the solution and improve the solution accuracy.

[0009] As an improvement to the above method, short-term planning must meet the following constraints:

[0010] The telescope's orientation shall be at an angle of not less than 95° to the solar vector, not less than 20° to the lunar vector, and not less than 77° to the Earth vector; the astronomical satellite shall not adjust its attitude 6 minutes before and during transit; the astronomical satellite shall not conduct observation missions when passing through the SAA area; the effective observation duration for each mission shall not be less than the shortest observation duration for that mission this week; the astronomical satellite shall adjust its attitude no more than three times per orbital cycle.

[0011] As an improvement to the above method, step 1) includes:

[0012] Multiple payloads on an astronomical satellite are equivalent to a single virtual payload, and different operating modes of multiple payloads are equivalent to different operating modes of a single virtual payload.

[0013] The objective function fit is designed as follows:

[0014]

[0015] Among them, o i Task priority is used to measure the quality of a planning outcome. If task c... i If any constraint is violated, the effective observation time of the task is reduced to [e]. i ] = 0.

[0016] As an improvement to the above method, step 2) includes:

[0017] Step 2-1) Generate SN initial solutions and set the solutions Number of searches (trial) l =0, l=1,2,…,SN, where l represents the number of solutions, and the current iteration number t=1;

[0018] Step 2-2) Repeat the following steps until t reaches the set maximum number of iterations:

[0019] Steps 2-3) Enter the hired bee stage. The hired bees use an "elite solution-guided search" strategy to search for new solutions and estimate the quality of the solutions.

[0020] Steps 2-4) Use a greedy strategy to select the better solution from the new and old solutions; if the solution is not updated, the search count is reduced. l +1, otherwise, trial l =0;

[0021] Steps 2-5) Enter the follower bee stage. The follower bees use a roulette wheel to select the solution corresponding to the hired bee. The search is carried out using the "neighborhood optimal solution update" strategy to obtain a new solution.

[0022] Steps 2-6) Use a greedy strategy to select the better solution from the new and old solutions; if the solution is not updated, the search count is reduced. l +1, otherwise, trial l =0;

[0023] Steps 2-7) If trial exists l If the value is greater than or equal to the limit, the mercenary bee corresponding to the solution is transformed into a scout bee, a new solution is generated, and the process enters the scout bee phase; where limit represents the upper limit of nectar source search.

[0024] Step 2-8) Record the current optimal solution and let the iteration number t+1.

[0025] As an improvement to the above method, step 2-1) includes:

[0026] For an N-dimensional problem, the l-th honey source in the t-th iteration is represented as:

[0027]

[0028] in, This represents the i-th dimension of the corresponding solution, i.e., task c. i The start observation time, where i represents the dimension, i = 1, 2, ..., N. L i and U i Representing task c respectively i The lower and upper bounds of the start time range, and each dimension of the initial position of the nectar source are generated according to the following formula:

[0029]

[0030] Here, rand(0,1) means generating a random number between 0 and 1.

[0031] As an improvement to the above method, step 2-3) includes:

[0032] The hired bees employ an "elite solution-guided search" strategy in the current solution. Randomly select one dimension and update the search according to the following formula:

[0033]

[0034] in, Indicates selecting the current solution The i-th dimension, It's the updated solution. The i-th dimension, i = 1, 2, ..., N; It is a disturbance factor. This means randomly selecting an elite solution from the elite population. Choose its i-th dimension, the proportion of elites in the population is η, and the number of elites is EN = ceil(η·SN), where ceil means rounding down, and q∈[1,EN].

[0035] Using fitness estimation The quality is denoted as

[0036] As an improvement to the above method, steps 2-5) include:

[0037] According to the honey source fitness value and the maximum fitness value in the population Calculate the nectar source using the following formula The probability of being selected (prob) l :

[0038]

[0039] Based on probability, a roulette wheel is used; the higher the adaptability of the nectar source, the greater the probability of it being selected.

[0040] The bees follow the selected nectar source and perform a "neighborhood-based optimal solution update" strategy according to the following formula:

[0041]

[0042] Where, N l Solution The neighborhood solution set is defined as N. l ={j|dist(l,j)≤ρ·md l}, j represents N l The solution in md l For the remaining solutions to the solution The average distance, i.e. ρ is the neighborhood coefficient, used to control the size of the neighborhood, and dist() is used to measure the Euclidean distance between two solutions.

[0043] As an improvement to the above method, step 2-7) includes:

[0044] If a trial exists l If the value is greater than or equal to the limit, then the mercenary bee corresponding to that solution becomes a scout bee, and a new solution is generated. Each dimension is calculated by the following formula:

[0045]

[0046] Where limit is the upper limit for honey source search, triall nectar source Number of times it was searched.

[0047] On the other hand, this invention proposes a system for solving short-term mission planning for astronomical satellites, the system comprising: a model construction module and a planning and solving module; wherein,

[0048] The model building module is used to build a short-term mission planning mathematical model for the astronomical satellite to be planned, and to abstract the mission planning problem into a maximization optimization problem.

[0049] The planning and solving module is used to solve the problem using a hybrid search strategy artificial bee colony algorithm. Employed bees search for excellent solutions based on an "elite solution-guided search" strategy, while follower bees search based on a "neighborhood-optimal solution update" strategy, thereby accelerating the solution and improving the solution accuracy.

[0050] Compared with the prior art, the advantages of the present invention are:

[0051] 1. Compared with basic artificial algorithms, the method of this invention has the advantages of fast convergence speed, high solution accuracy, and strong optimization ability. Compared with other swarm intelligence algorithms, it also has fewer control parameters.

[0052] 2. In the planning of short-term missions for astronomical satellites, the method of this invention results in a higher degree of mission completion and greater observational benefits. Attached Figure Description

[0053] Figure 1 This is a flowchart of the method for solving short-term mission planning for astronomical satellites according to the present invention;

[0054] Figure 2 It is a comparison of the evolutionary convergence curves of three algorithms. Detailed Implementation

[0055] Artificial bee colony algorithm (AQA) is a typical swarm intelligence algorithm, which has the advantage of fewer control parameters compared to other swarm intelligence algorithms. This invention addresses the shortcomings of the basic AQA algorithm, such as slow convergence speed and weak development capabilities, by considering the different search characteristics of different bees and proposing a novel hybrid search strategy AQA algorithm to solve the short-term mission planning problem for astronomical satellites. The steps include:

[0056] Step 1) Construct a short-term mission planning mathematical model for the astronomical satellite to be planned, and abstract the mission planning problem into a maximization optimization problem;

[0057] Step 2) The artificial bee colony algorithm with a hybrid search strategy is used to solve the problem. The hired bees search for excellent solutions based on the "elite solution-guided search" strategy, while the follower bees search based on the "neighborhood optimal solution update" strategy, so as to speed up the solution and improve the solution accuracy.

[0058] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0059] Example 1

[0060] Embodiment 1 of the present invention proposes a method for solving short-term mission planning for astronomical satellites.

[0061] The technical process of this method is as follows: First, the short-term mission planning problem of astronomical satellites is abstracted into a mathematical model, and then the improved artificial bee colony algorithm is used to solve it.

[0062] (1) Constructing a mathematical model for short-term mission planning of astronomical satellites

[0063] ① Model Assumptions

[0064] Short-term planning is influenced by many factors, including onboard energy, attitude control, storage capacity, and payload operating modes. For the sake of convenience, the following assumptions are made without loss of generality:

[0065] √ Multiple payloads on a satellite can be considered as a single virtual payload, and different operating modes of multiple payloads can be considered as different operating modes of a single virtual payload;

[0066] √ The impact of onboard memory capacity and hardware failures on mission planning is not considered;

[0067] ②Constraint Analysis and Definition

[0068] √ To protect the telescope from light damage, the angle between the telescope's direction and the solar vector must be no less than 95°. A Sun ≥95°; the angle with the lunar vector is not less than 20°, A Moon ≥20°; the angle with the Earth's vector is not less than 77°, A Earth ≥77°;

[0069] √ To ensure communication, the satellite must not be adjusted in attitude for six minutes before its passage until the start of the next observation mission.

[0070] Satellites transmit data as they pass certain ground stations. (Passage time) STK simulations show that the system includes h transit time periods. To ensure normal communication, the satellite must not adjust its attitude for the 6 minutes before and during the transit. It is an empty set.

[0071] Where x i For task c i The start time of observation, x manu Let i = 1, 2, ..., N, and k = 1, 2, ..., h be the attitude adjustment time for the two observation missions.

[0072] √ In order to protect scientific instruments, astronomical satellites are required to refrain from conducting observation missions on their payloads when passing through the SAA zone.

[0073] When the satellite passes through the SAA, the payload does not conduct observation tasks, that is, for

[0074] in For task c i The end time of observation, c i and c j It should satisfy D i,j =1; D i,j =1 indicates task c i The next observation task is c j ; Indicates the time window for satellite transit over SAA. This represents the start time of the k-th time window during the satellite's transit over SAA. This represents the end time of the satellite's k-th time window.

[0075] √ The execution of each task must meet the user's minimum observation time requirement for the task.

[0076] To ensure the planning objectives are more scientifically meaningful, the effective observation duration for each task in the plan should not be shorter than the shortest observation duration for that task this week.

[0077] Among them, e i Indicates task c i The effective observation period [e i ] indicates task c i Effective observation duration; W i For task c i The visible time window, Indicates task c i The start time of the k-th time window Indicates task c i The end time of the k-th time window; R = [r1, r2, ..., r N ], r i For task c i The shortest observation time.

[0078] √ For energy conservation purposes, the number of attitude adjustments for each satellite in orbit should not exceed three.

[0079] Right now Where D j,f ·Df,d ·D d,i =1,i,d,f,j=1,2,…,N.

[0080] b represents the satellite orbital period; This represents the set of times when the task orientation adjustment begins. c i The start time of posture adjustment.

[0081] ③ Design of the objective function

[0082] fit=∑ i [e i ]·o i (1)

[0083] Among them, o i For task c i Priority; `fit` is used to measure the quality of a planning outcome, and its purpose is to maximize task observation duration and task priority. If task c i If any of the above constraints are violated, the effective observation time of the task will be reduced to [e]. i ] = 0.

[0084] The purpose of this design is to arrange as many observation tasks as possible while giving them the highest priority, which is also for the purpose of maximizing scientific output.

[0085] (2) Solve using the hybrid search strategy artificial bee colony algorithm

[0086] Artificial colony optimization (ACO) is an intelligent optimization algorithm inspired by the cooperative nectar-gathering behavior of bee colonies. It works by allowing individual bees to find local optimizations, thereby revealing the global optimum within the population. There are three roles in the basic ACO algorithm:

[0087] √ Hire bees to search for nectar sources and then pass on information such as the quality of the nectar sources to other bees;

[0088] √ Follower bees receive information from hired bees and then choose which nectar source to collect nectar from;

[0089] √ If the hired bees cannot find a better nectar source in the vicinity while searching for nectar sources, they will abandon that nectar source and search for other nectar sources nearby.

[0090] The control parameters of the algorithm are explained below:

[0091] Table 1 Algorithm Control Parameters

[0092]

[0093] In the model, the start observation time X of the task is the object of optimization, equivalent to the nectar source that the bees are searching for. The quality of the nectar source is represented by the fitness value fit(X). The number of hired bees is equal to the number of follower bees, each accounting for half of the population size NP, denoted as SN.

[0094] For an N-dimensional problem, the l-th honey source in the t-th iteration is represented as a... in This represents the i-th dimension of the corresponding solution, i.e., task c. i The start observation time, l = 1, 2, ..., SN, i = 1, 2, ..., N, L i and U i Representing task c respectively i The lower and upper bounds of the range of values ​​for the start time, and each dimension of the initial position of the nectar source can be generated according to equation (2):

[0095]

[0096] In the basic artificial bee colony algorithm, the formula for searching hired bees is shown in equation (3):

[0097]

[0098] This is actually based on updating one dimension of the current solution with a random solution, which will cause the algorithm to converge to a good solution more slowly.

[0099] To enhance the ability of mercenary bees to explore superior solutions, an "elite solution-guided search" strategy was designed for them, and its search formula is shown in equation (4):

[0100]

[0101] in, Indicates selecting the current solution The i-th dimension, It's the updated solution. The i-th dimension, Depend on Based on the above formula, i = 1, 2, ..., N; It is a disturbance factor. This means randomly selecting an elite solution from the elite population. We choose the i-th dimension, with η representing the proportion of elites in the population, and the number of elites EN = ceil(η·SN), where ceil represents rounding down, and q∈[1,EN]. In practice, we use the current solution as the search center and guide the search with random elite solutions.

[0102] If the new nectar source is found to be more adaptable than the original nectar source, that is... Then, a greedy strategy is adopted to replace the old nectar source with the new one; otherwise, the original nectar source is retained. After all the hired bees have completed the operation of equation (4), they return to the hive to share the nectar source information with the other bees.

[0103] During the follower bee phase, the probability of a nectar source being selected (i.e., the hired bee corresponding to the nectar source being followed) is calculated according to equation (5) based on the existing information:

[0104]

[0105] in

[0106] The bees follow the roulette wheel based on probability. According to equation (5), the higher the fitness value of the nectar source, the greater the probability of it being selected.

[0107] In the basic artificial bee colony algorithm, the search method for follower bees is still Equation (3), which results in weak development capability of follower bees.

[0108] In actual bee colonies, follower bees receive information from hired bees and then select the nectar sources to search for. This means that the search methods of follower bees and hired bees are completely different. In order to enhance the development capabilities of follower bees, they adopt a "neighborhood-based optimal solution update" strategy, and their search method is shown in equation (6):

[0109]

[0110] Where, N l Solution The neighborhood solution set is defined as N. l ={j|dist(l,j)≤ρ·md l}, md l For the remaining solutions to the solution The average distance, i.e. ρ is the neighborhood coefficient, used to control the size of the neighborhood, and dist is used to measure the Euclidean distance between two solutions.

[0111] Table 2: Stage Steps and Flowchart

[0112]

[0113]

[0114] Example 2

[0115] Embodiment 2 of the present invention proposes a system for solving short-term mission planning for astronomical satellites, using the same method as in Embodiment 1. The system includes: a model construction module and a planning and solving module; wherein,

[0116] The model building module is used to construct a mathematical model for the short-term mission planning of the astronomical satellite to be planned, and to abstract the mission planning problem into a maximization optimization problem.

[0117] The planning and solving module is used to solve the problem using a hybrid search strategy artificial bee colony algorithm. Employed bees search for excellent solutions based on an "elite solution-guided search" strategy, while follower bees search based on a "neighborhood-optimal solution update" strategy, thereby accelerating the solution and improving the solution accuracy.

[0118] Simulation Example

[0119] To verify the effectiveness of the algorithm in solving short-term mission planning for astronomical satellites, a weekly observation target mission database for a certain satellite (as shown in Table 3) was used as input for testing. The database contains 63 different targets and 184 meta-tasks, of which 30 tasks have a priority of 4, 29 tasks have a priority of 3, 56 tasks have a priority of 2, and 69 tasks have a priority of 1. The higher the priority number, the more important the task.

[0120] Table 3. Weekly Planned Observation Target Task Library (Partial)

[0121]

[0122]

[0123] The hybrid search strategy artificial bee colony algorithm is compared with the basic artificial bee colony algorithm and the elite-preserving strategy genetic algorithm. Parameter settings are shown in Table 3, with 8000 iterations and 10 runs. The best single-best planning result from the 10 runs is shown in Table 5. The average performance and optimization results of the three algorithms are compared in Tables 6 and 7. Figure 2 .

[0124] Table 4 Control Parameters

[0125]

[0126] Table 5. Planning Results (Partial)

[0127]

[0128] Table 6 Comparison of Algorithm Performance and Optimization Results

[0129]

[0130] The goal of astronomical satellite mission planning is to maximize scientific output, aiming to maximize observation benefits by utilizing resources as much as possible within a limited time. As shown in Table 6, the average running time of the hybrid search strategy artificial bee colony algorithm is longer than that of other algorithms. This is because the time complexity of finding the neighborhood set of a solution in equation (6) is relatively high. However, it can converge to a relatively good solution on average in 1500 iterations. In this paper, 8000 iterations are set for ease of comparison. In actual tasks, the number of iterations can be reduced to speed up the solution. In other indicators, the hybrid search strategy artificial bee colony algorithm is greater than or equal to or slightly lower than other algorithms. In a week, it arranged an average of 174.4 effective tasks, and the task completion rate reached 94.8%. For high-priority tasks, the task completion rate of the hybrid search strategy artificial bee colony algorithm is close to 100%, and the completion rate of lower-priority tasks is also higher than that of other algorithms. In the best case, this algorithm arranged a total of 178 effective observation tasks, with a task completion rate as high as 96.7%. This demonstrates that the artificial bee colony algorithm with a hybrid search strategy achieves higher task completion rates and greater observational gains compared to other algorithms, showing promising application prospects in short-term astronomical satellite mission planning.

[0131] Figure 2 As can be seen, the genetic algorithm with the elite preservation strategy performs well in the early stages of the search, but its momentum weakens and it quickly gets trapped in local optima. The basic artificial bee colony algorithm suffers from slow convergence speed and weak development capabilities, which has been verified in simulations. The artificial bee colony algorithm with a hybrid search strategy proposed in this invention converges to an excellent solution in about 1200 generations, indicating that the artificial bee colony algorithm with the hybrid search strategy overcomes the disadvantage of slow convergence and has strong optimization and development capabilities, as well as high solution accuracy.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for solving short-term mission planning for astronomical satellites, the method comprising: Step 1) Construct a mathematical model for the short-term mission planning of the astronomical satellite to be planned, abstracting the mission planning problem into a maximization optimization problem; the mathematical model satisfies the following constraints: The angle between the telescope's direction and the solar vector is not less than And the angle between it and the lunar vector is not less than And the angle between it and the Earth's vector is not less than The astronomical satellite cannot adjust its attitude 6 minutes before and during the transit; the payload of the astronomical satellite will not conduct observation tasks when passing through the SAA area; the effective observation time for each mission shall not be less than the shortest observation time for that mission this week; the number of attitude adjustments made by the astronomical satellite in each orbital cycle shall not exceed three. Step 2) The artificial bee colony algorithm with a hybrid search strategy is used to solve the problem. The hired bees use an "elite solution-guided search" strategy to search for superior solutions. The search formula is as follows: ； in, Indicates selecting the current solution The dimension, It's the updated solution. The dimension, ; It is a disturbance factor. ; This means randomly selecting an elite solution from the elite population. Choose the first The proportion of elites in the population is Number of elites , Indicates rounding down. ; The follower bee searches using a "neighborhood-based optimal solution update" strategy, thereby accelerating the solution process and improving its accuracy. The search formula is as follows: ； in, Solution The neighborhood solution set, The optimal solution in the neighborhood is the first dimension.

2. The method for solving short-term mission planning for astronomical satellites according to claim 1, wherein step 1) comprises: Multiple payloads on an astronomical satellite are equivalent to a single virtual payload, and different operating modes of multiple payloads are equivalent to different operating modes of a single virtual payload. Design the objective function for: ； in, Task priority is used to measure the quality of a planning outcome. Violation of any constraint will reduce the effective observation time of the task. .

3. The method for solving short-term mission planning for astronomical satellites according to claim 2, wherein step 2) includes: Step 2-1) Generate An initial solution is given, and the solution is set. Number of searches , , Current iteration number ; Step 2-2) Repeat the following steps until... Reaching the set maximum number of iterations: Steps 2-3) Enter the hired bee stage. The hired bees use an "elite solution-guided search" strategy to search for new solutions and estimate the quality of the solutions. Steps 2-4) Use a greedy strategy to select the better solution from the new and old solutions; if the solution is not updated, the number of searches is reduced. ,otherwise, ; Steps 2-5) Enter the follower bee stage. The follower bee uses a roulette wheel to select the solution corresponding to the hired bee. The search is carried out using the "neighborhood optimal solution update" strategy to obtain a new solution. Steps 2-6) Use a greedy strategy to select the better solution from the new and old solutions; if the solution is not updated, the number of searches is reduced. ,otherwise, ; Steps 2-7) If it exists The mercenary bee corresponding to this solution becomes a scout bee, a new solution is generated, and the process enters the scout bee phase; among which, Indicates the upper limit for honey source searches; Steps 2-8) Record the current optimal solution, and let the iteration number be... .

4. The method for solving short-term mission planning for astronomical satellites according to claim 3, wherein step 2-1) includes: for Dimensional problem, the first In the nth iteration Each nectar source can be represented as follows: ； in, Represents the first solution Dimension, i.e., task The start time of observation, i Representing dimension, , , and Representing tasks The lower and upper bounds of the start time range, and each dimension of the initial position of the nectar source are generated according to the following formula: ； in, This indicates that a random number between 0 and 1 will be generated.

5. The method for solving short-term mission planning for astronomical satellites according to claim 3, wherein the quality of the estimated solution in step 2-3) is: Using fitness estimation The quality is denoted as .

6. In the method for solving short-term mission planning for astronomical satellites according to claim 3, in steps 2-5), the probability of the following bee selecting a nectar source... for: Calculate the nectar source using the following formula Probability of being selected : ； in, nectar source fitness value, This represents the maximum fitness value in the population. The higher the adaptability of a nectar source, the greater the probability of it being selected.

7. The method for solving short-term mission planning for astronomical satellites according to claim 4, wherein steps 2-7) include: If it exists Then the mercenary bee corresponding to the solution becomes a scout bee, and a new solution is generated. Each dimension is calculated by the following formula: ； in, This is the upper limit for honey source searches. nectar source Number of times it was searched.

8. A system for solving short-term mission planning for astronomical satellites, characterized in that, The system includes: a model building module and a planning and solving module; wherein... The model construction module is used to construct a mathematical model for the short-term mission planning of the astronomical satellite to be planned, abstracting the mission planning problem into a maximization optimization problem; the mathematical model satisfies the following constraints: The angle between the telescope's direction and the solar vector is not less than And the angle between it and the lunar vector is not less than And the angle between it and the Earth's vector is not less than The astronomical satellite cannot adjust its attitude 6 minutes before and during the transit; the payload of the astronomical satellite will not conduct observation tasks when passing through the SAA area; the effective observation time for each mission shall not be less than the shortest observation time for that mission this week; the number of attitude adjustments made by the astronomical satellite in each orbital cycle shall not exceed three. The planning and solving module is used to solve the problem using a hybrid search strategy artificial bee colony algorithm. The hired bees search for superior solutions based on an "elite solution-guided search" strategy, and the search formula is as follows: ； in, Indicates selecting the current solution The dimension, It's the updated solution. The dimension, ; It is a disturbance factor. ; This means randomly selecting an elite solution from the elite population. Choose the first The proportion of elites in the population is Number of elites , Indicates rounding down. ; The follower bee searches using a "neighborhood-based optimal solution update" strategy, thereby accelerating the solution process and improving its accuracy. The search formula is as follows: ； in, Solution The neighborhood solution set, The optimal solution in the neighborhood is the first dimension.

Images