Task planning method and system for multi-satellite multi-objective optimization

Abstract

The invention provides a task planning method and system for multi-satellite and multi-target optimization, and relates to the technical field of aerospace engineering planning, and the method comprises the steps: dividing a target space region into three-dimensional grids according to satellite orbit data and target space region data, and obtaining a visible satellite list; converting the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list, forming a solution set space by the four-dimensional grid and the visible satellite list, and converting solution set individuals into hybrid coding population individuals; the population individual fitness of each hybrid coding population individual is calculated based on the target optimization function, if the current number of iterations is smaller than the total number of iterations, solution set individuals corresponding to the hybrid coding population individuals are sorted, and elite solution set individuals are output according to a time sequence; and performing matching according to the task trajectory data and target area four-dimensional grid planning data corresponding to the elite solution set individuals to obtain an optimization task planning scheme. According to the invention, the efficiency of multi-satellite area task planning can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aerospace engineering planning, and in particular to a mission planning method and system oriented to multi-satellite multi-objective optimization. Background Art

[0002] The global demand for earth observation, communication, navigation and other services continues to grow, which has prompted the exploration of diversified satellite applications. With the development of satellite technology and applications, the demand for multi-satellite multi-objective optimization mission planning is also increasing. In modern space exploration and earth observation missions, multiple satellites need to be deployed to complete complex tasks. These satellites can be of the same type or different types. Multi-satellite collaboration can provide more comprehensive data coverage and higher temporal resolution. In the mission planning of multi-satellite multi-objective optimization, traditional planning methods are usually based on deterministic mathematical models, such as linear programming, dynamic programming, etc. These methods are effective in dealing with simple problems, but when faced with complex multi-objective optimization problems, it is often difficult to find the global optimal solution, and the calculation time increases significantly with the growth of the problem size. In addition, for pre-planning in the case of unknown specific tasks, in actual operations, satellites may need to perform a certain degree of pre-planning before the mission is fully clarified. Therefore, it is crucial to design a flexible mission planning system that can make preliminary arrangements under limited information and can quickly adjust plans based on new intelligence or instructions to deal with emergencies or dynamically changing mission environments.

[0003] The Chinese patent with publication number CN115689225A discloses a satellite task synthesis, allocation and planning method based on genetic iterative search, and the above method includes: by synthesizing the tasks to be observed in the set of tasks to be observed, the number of attitude conversions and energy consumption of the satellite sensor are reduced, and the number of tasks that can be observed by the satellite sensor each time it is turned on is increased, thereby greatly improving the observation efficiency. On this basis, the observation tasks are planned based on the tree search task planning method, and each task to be observed is corresponded to a node in the search tree, so that the observation plan for each satellite can be quickly obtained. However, the above application selects known static ground points as research units, which is difficult to meet the unknown task solution of dynamic task trajectories. These goals often conflict and it is difficult to achieve the optimal solution at the same time. Therefore, it is very necessary to provide a task planning method and system for multi-satellite multi-objective optimization to improve the efficiency of multi-satellite regional task planning. Summary of the invention

[0004] In view of this, the present invention proposes a mission planning method and system for multi-satellite multi-objective optimization. By dividing the target space area into three-dimensional grids and converting them into four-dimensional grids, it is possible to perform pre-planning when the mission trajectory is unknown. At the same time, a hybrid coding method is used to encode the solution set individuals, and the crossover and mutation probabilities are dynamically adjusted to improve the efficiency of multi-satellite regional mission planning.

[0005] The present invention provides a mission planning method for multi-satellite multi-objective optimization, the method comprising:

[0006] Divide the target space region into three-dimensional grids according to the satellite orbit data and the target space region data, and obtain a list of visible satellites corresponding to the three-dimensional grids;

[0007] Convert the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid, form a solution set space composed of the four-dimensional grid and the visible satellite list, and perform mixed coding on each solution set individual in the solution set space, so that the solution set individual is converted into a mixed coding population individual;

[0008] The fitness of each individual in the mixed coding population is calculated based on the target optimization function. If the current number of iterations is less than the total number of iterations, the solution set individuals corresponding to the mixed coding population individuals are sorted, and the elite solution set individuals are output in time series.

[0009] The task trajectory data is matched with the four-dimensional grid planning data of the target area corresponding to the elite solution set individuals to obtain an optimized task planning solution.

[0010] Based on the above technical solution, preferably, obtaining the visible satellite list corresponding to the three-dimensional grid specifically includes:

[0011] Selecting the longitude and latitude position coordinates of the key points in the three-dimensional grid, and converting the longitude and latitude position coordinates from the longitude and latitude form into the geocentric coordinate form to obtain the geocentric coordinates of the key points;

[0012] Calculating a sight line vector from the satellite to the key point in the three-dimensional grid according to the satellite geocentric coordinates and the key point geocentric coordinates;

[0013] Based on the distance between the satellite and the grid key point and the sight vector, the satellite altitude angle corresponding to each key point in the three-dimensional grid is calculated to obtain a visibility satellite information list corresponding to the three-dimensional grid.

[0014] Based on the above technical solution, preferably, the converting of the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid specifically includes:

[0015] The three-dimensional grid is combined with the time series corresponding to each key point in the three-dimensional grid to obtain a four-dimensional grid and a visible satellite list corresponding to the four-dimensional grid.

[0016] More preferably, the method further comprises:

[0017] If the current number of iterations is greater than or equal to the total number of iterations, genetic crossover mutation is performed according to the dynamic mutation rate function and the dynamic crossover rate function to obtain genetic offspring individuals;

[0018] Determine whether the genetic offspring individual meets the task planning constraints. If the genetic offspring individual does not meet the task planning constraints, perform repair optimization on the genetic offspring individual that does not meet the task planning constraints.

[0019] More preferably, the expression of the objective optimization function is:

[0020] F=MaxΣαT+βL+λR

[0021]

[0022] L=Max∑a 1 f 1 +a 2 f 2 +···+a p f p

[0023] R = b 1 y 1 +b 2 y 2 +···+b q y q

[0024] Among them, F represents the target optimization function, T represents the star selection function, α represents the weight coefficient corresponding to the optimization reward function, L represents the optimization reward function, β represents the weight coefficient corresponding to the optimization reward function, R represents the constraint reward function, λ represents the weight coefficient corresponding to the constraint reward function, t i represents the star selection task at the i-th time node, n represents the total number of time nodes, S represents the star selection parameter, and a p represents the weight coefficient corresponding to the pth optimized reward sub-function, f p represents the pth optimized reward sub-function in the constraint reward function, y q represents the qth constraint reward sub-function in the constraint reward function, b q Represents the weight coefficient corresponding to the qth constraint reward sub-function.

[0025] More preferably, the optimization reward function includes one or more of a communication delay reward function, an accuracy reward function, a continuous communication duration reward function, a satellite configuration reward function, and a satellite usage quantity reward function.

[0026] More preferably, the expressions of the dynamic mutation rate function and the dynamic crossover rate function are respectively:

[0027]

[0028] Among them, MUTPB() represents the dynamic mutation rate function, gen represents the current iteration number, NGEN represents the total iteration number, inital mutpb represents the initial mutation rate, CXPB() represents the dynamic crossover rate function, inital cxpb represents the initial crossover rate, fina mutpb Represents the final mutation rate, final cxpb represents the final crossover rate.

[0029] In a second aspect of the present application, a mission planning system for multi-satellite multi-objective optimization is provided, wherein the mission planning system comprises a grid construction module, a population optimization module and a mission planning module, wherein:

[0030] The grid construction module is used to divide the target space area into three-dimensional grids according to the satellite orbit data and the target space area data, and obtain a visible satellite list corresponding to the three-dimensional grid;

[0031] The population optimization module is used to convert the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid, form a solution set space composed of the four-dimensional grid and the visible satellite list, and perform hybrid coding on each solution set individual in the solution set space so that the solution set individual is converted into a hybrid coding population individual, calculate the population individual fitness of each hybrid coding population individual based on the target optimization function, and if the current number of iterations is less than the total number of iterations, sort the solution set individuals corresponding to the hybrid coding population individuals, and output the elite solution set individuals in time series;

[0032] The task planning module is used to match the task trajectory data with the four-dimensional grid planning data of the target area corresponding to the elite solution set individuals to obtain an optimized task planning solution.

[0033] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0034] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of a mission planning method for multi-satellite multi-objective optimization.

[0035] The mission planning method and system for multi-satellite multi-objective optimization provided by the present invention have the following beneficial effects compared with the prior art:

[0036] (1) By dividing the target space area into three-dimensional grids and converting them into four-dimensional grids, it is possible to pre-plan when the mission trajectory is unknown, and obtain the visible satellite lists corresponding to the three-dimensional grids and four-dimensional grids, which can effectively identify the satellite resources available under specific time and space conditions. At the same time, the hybrid coding method is used to encode the solution set individuals, which can better represent complex mission planning problems and improve the accuracy of fitness calculation. Based on the improved genetic algorithm, the mission planning solution is solved, and the crossover and mutation probabilities are dynamically adjusted, which avoids the problems of premature convergence and insufficient population diversity that may occur in traditional algorithms, reduces observation redundancy, and thus improves the efficiency of multi-satellite regional mission planning.

[0037] (2) By converting the longitude and latitude coordinates of key points in the three-dimensional grid into geocentric coordinates, the consistency and accuracy of position calculation are ensured. By calculating the line-of-sight vector and altitude angle between the satellite and the grid key points, the satellite resources available under specific time and space conditions can be effectively identified, ensuring higher data coverage and time resolution in multi-satellite collaboration. By calculating the line-of-sight vector and altitude angle, the satellite's visibility of each key point can be quickly determined, significantly improving the accuracy, flexibility and efficiency of mission planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of the process flow of the mission planning method and system for multi-satellite multi-objective optimization provided by the present invention;

[0040] Figure 2 A technical route implementation diagram for the task planning method provided by the present invention;

[0041] Figure 3 It is a schematic diagram of the discretized space grid provided by the present invention;

[0042] Figure 4It is a schematic diagram of the three-dimensional grid matching task provided by the present invention;

[0043] Figure 5 A schematic diagram of the structure of the task planning system provided by the present invention;

[0044] Figure 6 This is a schematic structural diagram of an electronic device provided by the present invention.

[0045] Explanation of the accompanying drawings: 1. Task planning system; 11. Grid construction module; 12. Population optimization module; 13. Task planning module; 2. Electronic device; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. DETAILED DESCRIPTION

[0046] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Existing satellite mission planning methods often select known static ground points as research units, which are difficult to meet the needs of solving dynamic mission trajectories with unknown tasks. At the same time, multi-objective optimization itself is a complex mathematical problem, involving multiple mutually constrained objective functions, such as minimizing energy consumption, maximizing mission success rate, optimizing data transmission efficiency, etc. These goals often conflict and it is difficult to achieve the optimal solution at the same time. The present invention selects the unknown mission scenario as the research entry point, converts the planning target area into a discrete three-dimensional grid, performs task planning on the discrete three-dimensional grid, uses an improved genetic algorithm to solve the task, and matches the three-dimensional grid planning results and the mission trajectory in the subsequent known mission trajectory. The time position is matched to obtain the mission trajectory planning result. In the future, when satellite resources are tight and cannot provide real-time service support, the research results can provide a scientific basis for improving the solution efficiency of mission planning and optimizing decision-making plans.

[0048] The present invention discloses a mission planning method for multi-satellite multi-objective optimization, referring to Figure 1 and Figure 2 The steps of the method include steps S1 to S4.

[0049] Step S1, dividing the target space area into three-dimensional grids according to satellite orbit data and target space area data, and obtaining a list of visible satellites corresponding to the three-dimensional grids.

[0050] This step also includes steps S11 to S13.

[0051] Step S11, selecting the longitude and latitude position coordinates of the key point in the three-dimensional grid, and converting the longitude and latitude position coordinates from the longitude and latitude form into the geocentric coordinate form to obtain the geocentric coordinates of the key point.

[0052] In this step, the standard format of satellite orbit data is two-line orbit number TLE (Two-Line Element) data, which is used to describe the orbital parameters of the satellite. It consists of two lines of text containing satellite attribute information, and contains all the key information required for satellite orbit, allowing users to calculate the position and speed of the satellite at any given time.

[0053] Set the target space area to an area size of S target The area where the area is located is known, and the three-dimensional grid shape is a cube. The coordinates of the eight vertices are expressed as:

[0054] (x 1 ,y 1 ,z 1 ),(x 2 ,y 2 ,z 2 ),(x 3 ,y 3 ,z 3 ),(x 4 ,y 4 ,z 4 ),(x 5 ,y 5 ,z 5 ),(x 6 ,y 6 ,z 6 ),(x 7 ,y 7 ,z 7 ),(x 8 ,y 8 ,z 8 ).

[0055] Determine the position of the key points inside the grid (log, lat, alt), and convert the key point position information from longitude and latitude to geocentric coordinates (X g ,Y g ,Z g ), the specific expression is:

[0056] X g =(R+h)·cos(lat)·cos(lon)

[0057] Y g =(R+h)·cos(lat)·sin(lon)

[0058] Z g=(R+h)·sin(lat)

[0059] Among them, R represents the radius of the earth, h represents the altitude, log, lat and alt represent longitude, latitude and altitude respectively.

[0060] Step S12, calculating the sight vector from the satellite to the key point in the three-dimensional grid according to the satellite geocentric coordinates and the key point geocentric coordinates.

[0061] In this step, the satellite geocentric coordinates (X s ,Y s ,Z s ) can be obtained by parsing TLE data and calculating based on the Skyfield library. Skyfield has a built-in SGP4 (Simplified General Perturbations Model 4) orbit propagation model, which is an algorithm designed specifically for processing TLE data. SGP4 takes into account the influence of the Earth's non-spherical gravitational field, atmospheric drag and other factors on the satellite orbit, and can more accurately predict the position and speed of the satellite at a certain moment in the future.

[0062] Step S13, based on the distance between the satellite and the grid key point and the sight vector, calculate the satellite altitude angle corresponding to each key point in the three-dimensional grid to obtain a visibility satellite information list corresponding to the three-dimensional grid.

[0063] In this step, the vector from the satellite to the grid key point is calculated, that is, the line of sight vector between the two is calculated. The specific expression is:

[0064]

[0065] Calculate the distance d between the satellite and the grid key point. The specific expression is:

[0066]

[0067] Calculate the satellite elevation angle e. The specific expression is:

[0068] e=90°-θ

[0069] θ represents the angle between the satellite line of sight and the grid key point. The specific expression can be calculated by the dot product formula to calculate the cosine value of the angle θ:

[0070] The vector dot product formula is:

[0071] Mesh keypoint to geocentric vector:

[0072] in, Represents the distance from the grid key point to the center of the earth (i.e. the radius of the earth plus the altitude of the grid key point), and its expression is:

[0073]

[0074] is the distance between the satellite and the grid key point, that is

[0075] The satellite elevation angle is the angle of the satellite above the grid key point, specifically the angle between the line of sight between the grid key point and the satellite and the ground tangent. If the angle is greater than 0°, the satellite is considered visible to the grid key point; otherwise, it is considered invisible. Through this step, the visibility information between the three-dimensional grid and the satellite is obtained through loop calculation.

[0076] In this embodiment, by converting the latitude and longitude coordinates of key points in the three-dimensional grid into geocentric coordinates, the consistency and accuracy of position calculation are ensured, and the line of sight vector and altitude angle between the satellite and the key points of the grid are calculated, which can effectively identify the satellite resources available under specific time and space conditions, ensuring higher data coverage and time resolution in multi-satellite collaboration. Through the calculation of line of sight vector and altitude angle, the visibility of the satellite to each key point can be quickly determined, which significantly improves the accuracy, flexibility and efficiency of mission planning.

[0077] Step S2, converting the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid, forming a solution space composed of the four-dimensional grid and the visible satellite list, and performing mixed coding on each solution individual in the solution space, so that the solution individual is converted into a mixed coding population individual.

[0078] In this step, the 3D grid is combined with the time series corresponding to each key point in the 3D grid to obtain the 4D grid and the list of visible satellites corresponding to the 4D grid. The 3D grid is successfully combined with the time series data to generate a 4D grid with a time dimension, and the list of visible satellite information corresponding to each 4D grid point is obtained. This lays the foundation for subsequent hybrid coding and population generation, which is helpful for satellite visibility analysis and optimization in complex space-time environments.

[0079] Furthermore, the visible satellite list corresponding to the four-dimensional grid is specifically represented by a list of visible satellites of the four-dimensional grid at a certain moment, and its corresponding expression is:

[0080] S i (t i )={A 1 ,A 2 ···A n}

[0081] Among them, Si (t i ) indicates t i List of available satellites at the moment, A n Indicates the available satellite number.

[0082] Combining the four-dimensional grid and the visible satellite list in time series forms the solution space Y. The expression of the solution space Y is as follows:

[0083] Y={{S 1 (t 1 ),S 1 (t 2 )···S 1 (t i )},{S 2 (t 1 ),S 2 (t 2 )···S 2 (t i )},···,{S i (t 1 ),S i (t 2 )···S i (t i )}}

[0084] A hybrid coding method is used to encode each solution individual Y in the solution space i Encoding, the specific expression is:

[0085] Y i ={{S 1 (t 1 )A n ,S 1 (t 2 )A n ···S 1 (t i )A n},{S 2 (t 1 )A n ,S 2 (t 2 )A n ···S 2 (t i )A n},···,{S i (t 1 )A n ,S i (t 2 )A n ···S i (t i )An}}

[0086] Among them, S i (t i )A n Represented as a grid S i In t i The sequence number is A n satellite.

[0087] In one example, the population size, genetic crossover mutation parameters, number of iterations, etc. are set according to the algorithm parameters, and the various parameters and solution set population individuals are initialized. Choose an appropriate population size. A larger population can provide better diversity but increase the computational cost; a smaller population may converge quickly but easily fall into a local optimum. Based on the variable range in the problem definition, randomly generate individuals that meet the constraints. Ensure that the initial population has sufficient diversity. According to the selected algorithm, encode the solution into a mixed integer encoding form for the population individuals; set the maximum number of iterations for the algorithm to run or stop when a specific performance indicator is reached.

[0088] Choosing an appropriate population size can provide sufficient solution set diversity while ensuring that the computational cost is controllable. By reasonably setting the crossover probability and mutation probability, the convergence speed can be accelerated while maintaining the diversity of the population. Appropriate crossover and mutation operations can effectively explore the solution space and promote the generation of high-quality solutions. Based on the variable range in the problem definition, individuals that meet the constraints are randomly generated to ensure the diversity of the initial population. Encoding the solution in mixed integer coding form enables the algorithm to flexibly handle different types of variables (such as continuous and discrete variables), enhancing the applicability of the algorithm. At the same time, by setting the maximum number of iterations or the stopping condition of a specific performance indicator, the algorithm can be terminated in time when the expected effect is achieved, avoiding unnecessary waste of computing resources. Through reasonable parameter setting and individual generation, multiple optimization objectives can be comprehensively considered in the mission planning of multi-satellite multi-objective optimization to improve the overall optimization effect.

[0089] Step S3, calculate the population individual fitness of each hybrid coding population individual based on the target optimization function, if the current iteration number is less than the total iteration number, sort the solution set individuals corresponding to the hybrid coding population individuals, and output the elite solution set individuals in time series.

[0090] The expression of the objective optimization function is:

[0091] F=MaxΣαT+βL+λR

[0092]

[0093] L=Max∑a 1 f 1 +a2 f 2 +···+a p f p

[0094] R = b 1 y 1 +b 2 y 2 +···+b q y q

[0095] Among them, F represents the target optimization function, T represents the star selection function, α represents the weight coefficient corresponding to the optimization reward function, L represents the optimization reward function, β represents the weight coefficient corresponding to the optimization reward function, R represents the constraint reward function, λ represents the weight coefficient corresponding to the constraint reward function, t i represents the star selection task at the i-th time node, n represents the total number of time nodes, S represents the star selection parameter, if the star selection is successful, S = 1, if the star selection fails, S = 0, a p represents the weight coefficient corresponding to the pth optimized reward sub-function, f p represents the pth optimized reward sub-function in the constraint reward function, y q represents the qth constraint reward sub-function in the constraint reward function, b q Represents the weight coefficient corresponding to the qth constraint reward sub-function.

[0096] The optimization reward function includes one or more of a communication delay reward function, an accuracy reward function, a continuous communication duration reward function, a satellite configuration reward function, and a satellite usage quantity reward function.

[0097] The expression of the communication delay reward function is as follows:

[0098] f 1 =Max∑A(T s t i )

[0099] Among them, f 1 represents the communication delay reward function, T s represents the communication delay of the sth satellite, A represents the conditional function, if the conditional function is satisfied, A=1, if the conditional function is not satisfied, A=0.

[0100] The expression of the accuracy reward function is as follows:

[0101] f 2 =Max∑A(D s t i )

[0102] f 3 =Max∑A(V s ti )

[0103] Among them, f 2 represents the navigation positioning accuracy reward function, f 3 represents the real-time speed measurement accuracy reward function, D s represents the navigation positioning accuracy of the sth satellite, V s represents the speed measurement accuracy of the sth satellite, A represents the conditional function, if the conditional function is satisfied, A=1, if the conditional function is not satisfied, A=0.

[0104] The expression of the continuous communication duration reward function is as follows:

[0105] f 4 =Max∑A 1 (TX s )

[0106] Among them, f 4 represents the reward function for the duration of continuous communication, TX s represents the communication duration of the sth satellite, A 1 Represents a weight function, which is assigned according to the duration of continuous communication.

[0107] The expression of the satellite configuration reward function is as follows:

[0108] f 5 =∑A(if d ij ≥300km)

[0109] f 6 =∑A(if d i,j =d i+1,j =d i,i+1 )

[0110] Among them, f 5 represents the satellite distance reward function, f 6 represents the equilateral triangle configuration reward function, A represents the conditional function, if the conditional function is met, A = 1, if the conditional function is not met, A = 0.

[0111] The expression of the satellite usage quantity reward function is as follows:

[0112] f 7 =Min∑(number of satellites used)

[0113] Among them, f 7 Represents the navigation star selection reward function.

[0114] The specific expression for satellite distance calculation is:

[0115]

[0116] Among them, x i ,y i ,z i Represents satellite accuracy, latitude and altitude respectively.

[0117] This step also includes steps S31 to S32.

[0118] Step S31, if the current number of iterations is greater than or equal to the total number of iterations, genetic crossover mutation is performed according to the dynamic mutation rate function and the dynamic crossover rate function to obtain genetic offspring individuals.

[0119] In this step, the expressions of the dynamic mutation rate function and the dynamic crossover rate function are:

[0120]

[0121] Among them, MUTPB() represents the dynamic mutation rate function, gen represents the current iteration number, NGEN represents the total iteration number, inital mutpb represents the initial mutation rate, CXPB() represents the dynamic crossover rate function, inital cxpb represents the initial crossover rate, fina mutpb Represents the final mutation rate, final cxpb represents the final crossover rate.

[0122] Step S32, judging whether the genetic offspring individuals satisfy the task planning constraints, if the genetic offspring individuals do not satisfy the task planning constraints, repairing and optimizing the genetic offspring individuals that do not satisfy the task planning constraints.

[0123] Step S4, matching the task trajectory data with the four-dimensional grid planning data of the target area corresponding to the elite solution set individuals to obtain an optimized task planning solution.

[0124] Mission trajectory data consists of the latitude and longitude information of a series of trajectory points, which usually come from mission requirement definition, ground command input or pre-set mission plan.

[0125] In this step, the specific task planning four-dimensional grid result can be expressed as:

[0126] Y best ={{S 1 (t 1 )A 1 ,S 1 (t 2 )A 2 ···S 1 (t i )A 4},{S 2 (t 1)A 5 ,S 2 (t 2 )A 6 ···S 2 (t i )A 7},···,{S i (t 1 )A 8 ,S i (t 2 )A 9 ···S i (t i )A n}}

[0127] Mission track Y tasks The data format is:

[0128]

[0129] Among them, T n L n (t n ) represents the task planning T n In t n The position L at the moment n , L n It is composed of longitude and latitude information and can be expressed as L n ={L log (n),L lat (n),L alt (n)}.

[0130] The matching principle is to match the time position of the task trajectory with the time position of the four-dimensional grid, and divide the task trajectory into several discrete trajectory points. When the trajectory point is located in the divided grid, the grid task planning result is assigned to the trajectory point, so as to traverse and match to obtain the task trajectory planning result.

[0131] like Figure 3 and Figure 4 As shown, Figure 3 Represents the conversion process from two-dimensional geographic data to three-dimensional spatial grid, Figure 4 Represents the schematic diagram of the three-dimensional mesh matching task, Figure 4 The middle arrow points from P1 to P2, indicating the path movement plan from key point P1 to key point P2.

[0132] In this embodiment, by dividing the target space area into three-dimensional grids and converting them into four-dimensional grids, it is possible to perform pre-planning when the mission trajectory is unknown, and obtain a list of visible satellites corresponding to the three-dimensional grid and the four-dimensional grid, which can effectively identify the satellite resources available under specific time and space conditions. At the same time, a hybrid coding method is used to encode the solution set individuals, which can better represent complex mission planning problems and improve the accuracy of fitness calculation. The mission planning solution is based on the improved genetic algorithm, and the crossover and mutation probabilities are dynamically adjusted, which avoids the problems of premature convergence and insufficient population diversity that may occur in traditional algorithms, reduces observation redundancy, and thereby improves the efficiency of multi-satellite regional mission planning.

[0133] The agile satellite pre-task planning method for multi-objective optimization uses a pre-planning method to deal with the satellite mission planning and star selection problem. It converts the flight mission trajectory star selection task into a four-dimensional grid task planning in advance, which can cope with the scenario where satellite resources are tight and real-time planning cannot be provided in the future. It has good foresight. The task planning algorithm solves the problem based on the improved genetic algorithm. Considering that the fixed parameters of the genetic operator in the early, middle and late stages are prone to premature convergence and insufficient population diversity in the algorithm solution, the dynamic crossover mutation parameters are used to better adapt to the early, middle and late stages of the algorithm and better find the global optimal solution.

[0134] Based on the above method, the present application embodiment discloses a mission planning system for multi-satellite multi-objective optimization, referring to Figure 5 The task planning system 1 includes a grid construction module 11, a population optimization module 12 and a task planning module 13, wherein:

[0135] The grid construction module 11 is used to divide the target space area into three-dimensional grids according to the satellite orbit data and the target space area data, and obtain a visible satellite list corresponding to the three-dimensional grid;

[0136] The population optimization module 12 is used to convert the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid, a solution set space composed of the four-dimensional grid and the visible satellite list, and hybrid coding each solution set individual in the solution set space to convert the solution set individual into a hybrid coding population individual, and calculate the population individual fitness of each hybrid coding population individual based on the target optimization function. If the current number of iterations is less than the total number of iterations, the solution set individuals corresponding to the hybrid coding population individuals are sorted, and the elite solution set individuals are output according to the time series;

[0137] The task planning module 13 is used to match the task trajectory data matched by the elite solution set individuals with the four-dimensional grid planning data corresponding to the target space area to obtain an optimized task planning solution.

[0138] In one example, the grid construction module 11 is used to select the longitude and latitude position coordinates of key points in the three-dimensional grid, and convert the longitude and latitude position coordinates from longitude and latitude form to geocentric coordinate form to obtain the geocentric coordinates of the key points; calculate the line of sight vector from the satellite to the key points in the three-dimensional grid based on the satellite geocentric coordinates and the geocentric coordinates of the key points; calculate the satellite altitude angle corresponding to each key point in the three-dimensional grid based on the distance between the satellite and the grid key points and the line of sight vector to obtain a visibility satellite information list corresponding to the three-dimensional grid.

[0139] In one example, the population optimization module 12 is used to combine the three-dimensional grid with the time series corresponding to each key point in the three-dimensional grid to obtain a four-dimensional grid and a visible satellite list corresponding to the four-dimensional grid.

[0140] In one example, the population optimization module 12 is used to perform genetic crossover mutation according to the dynamic mutation rate function and the dynamic crossover rate function to obtain genetic offspring individuals if the current number of iterations is greater than or equal to the total number of iterations; determine whether the genetic offspring individuals meet the task planning constraints, and if the genetic offspring individuals do not meet the task planning constraints, repair and optimize the genetic offspring individuals that do not meet the task planning constraints.

[0141] In one example, the expression of the objective optimization function is:

[0142] F=Max∑αT+βL+λR

[0143]

[0144] L=Max∑a 1 f 1 +a 2 f 2 +···+a p f p

[0145] R = b 1 y 1 +b 2 y 2 +···+b q y q

[0146] Among them, F represents the target optimization function, T represents the star selection function, α represents the weight coefficient corresponding to the optimization reward function, L represents the optimization reward function, β represents the weight coefficient corresponding to the optimization reward function, R represents the constraint reward function, λ represents the weight coefficient corresponding to the constraint reward function, t i represents the star selection task at the i-th time node, n represents the total number of time nodes, S represents the star selection parameter, and a p represents the weight coefficient corresponding to the pth optimized reward sub-function, f prepresents the pth optimized reward sub-function in the constraint reward function, y q represents the qth constraint reward sub-function in the constraint reward function, b q Represents the weight coefficient corresponding to the qth constraint reward sub-function.

[0147] In one example, the optimization reward function includes one or more of a communication delay reward function, an accuracy reward function, a continuous communication duration reward function, a satellite configuration reward function, and a satellite usage quantity reward function.

[0148] In an example, the expressions of the dynamic mutation rate function and the dynamic crossover rate function are:

[0149]

[0150] Among them, MUTPB() represents the dynamic mutation rate function, gen represents the current iteration number, NGEN represents the total iteration number, inital mutpb represents the initial mutation rate, CXPB() represents the dynamic crossover rate function, inital cxpb represents the initial crossover rate, fina mutpb Represents the final mutation rate, final cxpb represents the final crossover rate.

[0151] See also Figure 6 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 2 may include: at least one processor 21 , at least one network interface 24 , a user interface 23 , a memory 25 , and at least one communication bus 22 .

[0152] The communication bus 22 is used to realize the connection and communication between these components.

[0153] The user interface 23 may include a display screen (Display) and a camera (Camera), and the optional user interface 23 may also include a standard wired interface and a wireless interface.

[0154] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0155] Among them, the processor 21 may include one or more processing cores. The processor 21 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 25, and calling data stored in the memory 25. Optionally, the processor 21 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 21 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 21, and it can be implemented separately through a chip.

[0156] Among them, the memory 25 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 25 includes a non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 25 may also be optionally at least one storage device located away from the aforementioned processor 21. As Figure 6 As shown, the memory 25 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a mission planning method for multi-satellite multi-objective optimization.

[0157] exist Figure 6In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 21 can be used to call a mission planning method for multi-satellite multi-objective optimization stored in the memory 25. When executed by one or more processors, the electronic device executes one or more methods in the above embodiments.

[0158] A computer-readable storage medium stores instructions, which, when executed by one or more processors, enable the computer to execute one or more methods in the above-mentioned embodiments.

[0159] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0160] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0162] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0163] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0164] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0165] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the technical field that are not recorded in the present disclosure.

[0166] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A mission planning method for multi-satellite multi-objective optimization, characterized in that: The method comprises: Divide the target space region into three-dimensional grids according to the satellite orbit data and the target space region data, and obtain a list of visible satellites corresponding to the three-dimensional grids; Convert the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid, form a solution set space composed of the four-dimensional grid and the visible satellite list, and perform mixed coding on each solution set individual in the solution set space, so that the solution set individual is converted into a mixed coding population individual; The fitness of each individual in the mixed coding population is calculated based on the target optimization function. If the current number of iterations is less than the total number of iterations, the solution set individuals corresponding to the mixed coding population individuals are sorted, and the elite solution set individuals are output in time series. The task trajectory data is matched with the four-dimensional grid planning data of the target area corresponding to the elite solution set individuals to obtain an optimized task planning solution.

2. The mission planning method for multi-satellite multi-objective optimization according to claim 1, characterized in that: The obtaining of a visible satellite list corresponding to the three-dimensional grid specifically includes: Selecting the longitude and latitude position coordinates of the key points in the three-dimensional grid, and converting the longitude and latitude position coordinates from the longitude and latitude form into the geocentric coordinate form to obtain the geocentric coordinates of the key points; Calculating a sight line vector from the satellite to the key point in the three-dimensional grid according to the satellite geocentric coordinates and the key point geocentric coordinates; Based on the distance between the satellite and the grid key point and the sight vector, the satellite altitude angle corresponding to each key point in the three-dimensional grid is calculated to obtain a visibility satellite information list corresponding to the three-dimensional grid.

3. The mission planning method for multi-satellite multi-objective optimization according to claim 1, characterized in that: The converting the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid specifically includes: The three-dimensional grid is combined with the time series corresponding to each key point in the three-dimensional grid to obtain a four-dimensional grid and a visible satellite list corresponding to the four-dimensional grid.

4. The mission planning method for multi-satellite multi-objective optimization according to claim 1, characterized in that: The method further comprises: If the current number of iterations is greater than or equal to the total number of iterations, genetic crossover mutation is performed according to the dynamic mutation rate function and the dynamic crossover rate function to obtain genetic offspring individuals; Determine whether the genetic offspring individual meets the task planning constraints. If the genetic offspring individual does not meet the task planning constraints, perform repair optimization on the genetic offspring individual that does not meet the task planning constraints.

5. The mission planning method for multi-satellite multi-objective optimization according to claim 1, characterized in that: The expression of the objective optimization function is: F=MaxΣαT+βL+λR L=MaxΣa1f1+a2f2+···+a p f p R=b1y1+b2y2+···+b q y q Among them, F represents the target optimization function, T represents the star selection function, α represents the weight coefficient corresponding to the optimization reward function, L represents the optimization reward function, β represents the weight coefficient corresponding to the optimization reward function, R represents the constraint reward function, λ represents the weight coefficient corresponding to the constraint reward function, t i represents the star selection task at the i-th time node, n represents the total number of time nodes, S represents the star selection parameter, and a p represents the weight coefficient corresponding to the pth optimized reward sub-function, f p represents the pth optimized reward sub-function in the constraint reward function, y q represents the qth constraint reward sub-function in the constraint reward function, b q Represents the weight coefficient corresponding to the qth constraint reward sub-function.

6. The mission planning method for multi-satellite multi-objective optimization according to claim 5, characterized in that: The optimization reward function includes one or more of a communication delay reward function, an accuracy reward function, a continuous communication duration reward function, a satellite configuration reward function, and a satellite usage quantity reward function.

7. The mission planning method for multi-satellite multi-objective optimization according to claim 4, characterized in that: The expressions of the dynamic mutation rate function and the dynamic crossover rate function are respectively: Among them, MUTPB() represents the dynamic mutation rate function, gen represents the current iteration number, NGEN represents the total iteration number, inital mutpb represents the initial mutation rate, CXPB() represents the dynamic crossover rate function, inital cxpb represents the initial crossover rate, fina mutpb Represents the final mutation rate, final cxpb represents the final crossover rate.

8. A mission planning system for multi-satellite multi-objective optimization, characterized in that: The task planning system (1) comprises a grid construction module (11), a population optimization module (12) and a task planning module (13), wherein: The grid construction module (11) is used to divide the target space area into three-dimensional grids according to the satellite orbit data and the target space area data, and obtain a visible satellite list corresponding to the three-dimensional grid; The population optimization module (12) is used to convert the three-dimensional grid into a four-dimensional grid to obtain a visible satellite list corresponding to the four-dimensional grid, form a solution space composed of the four-dimensional grid and the visible satellite list, and perform hybrid coding on each solution individual in the solution space so that the solution individual is converted into a hybrid coding population individual, calculate the population individual fitness of each hybrid coding population individual based on the target optimization function, and if the current number of iterations is less than the total number of iterations, sort the solution individuals corresponding to the hybrid coding population individuals, and output elite solution individuals in a time series; The task planning module (13) is used to match the task trajectory data with the target area four-dimensional grid planning data corresponding to the elite solution set individuals to obtain an optimized task planning solution.

9. An electronic device, characterized in that: The electronic device (2) comprises a processor (21), a memory (25), a user interface (23) and a network interface (24), wherein the memory (25) is used to store instructions, the user interface (23) and the network interface (24) are used to communicate with other devices, and the processor (21) is used to execute the instructions stored in the memory (25) so that the electronic device (2) executes the method according to any one of claims 1 to 7.

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 method according to any one of claims 1 to 7 are implemented.

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