An on-orbit mission planning method for space-based space target patrol observation based on time window conflict degree

Abstract

The present invention relates to a method for planning on-orbit missions of patrol observation of space-based space targets based on time window conflict degree, comprising: constructing a mathematical model of the planning problem of patrol observation of space-based space targets, determining the optimization target of the patrol observation of space-based space targets; slicing the observation time window; performing a first round of solving the visible window set after slicing according to the time window conflict degree according to the priority rule, so as to meet the first-level planning target as much as possible: the maximum number of patrol targets within the patrol cycle; making fine adjustments based on the results of the first round of solutions, so as to achieve the goal of observing as much as possible; making adjustments based on the results of the previous step, so as to achieve the balance of workload of each satellite, so as to meet the second-level planning target as much as possible: the variance of the number of patrol targets of each satellite is minimized. The present invention realizes the efficient and rapid solution of the on-orbit mission planning problem of patrol observation of space-based space targets under the condition of relatively low computing power, and solves the problems of large computational load, slow convergence speed and large computing power requirement.

Description

Technical Field

[0001] The present invention belongs to the field of satellite observation mission planning, and in particular relates to an on-orbit mission planning method for patrol observation of space-based space targets based on time window conflict degree. Background Art

[0002] Satellite mission planning involves rationally allocating satellite observation resources and prioritizing satellites in executing observation missions, while satisfying various constraints between the satellites and the targets being observed. It is essentially an NP-hard constraint satisfaction problem. The upper bound on the size of the solution space increases dramatically with the number of satellites and the demand for observations.

[0003] Space-based patrol observation of space targets refers to the use of space-based observation satellites to conduct patrol observations of space targets, such as spacecraft and space debris, based on their orbital element information. Currently, mission planning algorithms applicable to space-based patrol observation of space targets include local search algorithms, mixed integer programming, genetic algorithms, and ant colony algorithms. Optimization algorithms such as local search algorithms and mixed integer programming algorithms produce globally optimal solutions, but the computational complexity increases exponentially with the number of satellites and targets. These algorithms are only suitable for small-scale mission planning problems and are not suitable for large numbers of satellites or targets. Multi-satellite mission planning is a complex problem involving multiple task requests, multiple available resources, multiple constraints, and multiple optimization objectives. Traversing the entire solution space is impractical. For large-scale missions and high scheduling complexity, intelligent optimization algorithms such as genetic algorithms and ant colony algorithms take a long time to solve and cannot adapt to dynamic mission changes. When autonomously planning missions, satellites often have limited onboard computing power and cannot meet the computational resource requirements of intelligent optimization algorithms such as genetic algorithms and ant colony algorithms. The satellite's single observation time window for the target is relatively long. When solving the on-orbit mission planning problem of space-based target patrol, it is necessary to optimize the selection of the observation window and the selection of the observation time period within the observation window at the same time, and the optimization coupling is relatively high. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention aims at the problem of on-orbit mission planning for patrol observation of space-based space targets, takes into account the limitation of on-board computing resources, and proposes a method for planning on-orbit mission of patrol observation of space-based space targets based on time window conflict in order to meet the efficient utilization of satellite observation resources and the patrol observation mission requirements of space targets based on the greedy algorithm idea. On the basis of constructing a mathematical model of the problem of planning on-orbit mission of patrol observation of space-based space targets, by slicing the time window and adopting a task priority determination method based on the time window conflict degree, a three-round planning method is adopted to achieve the optimization goals of observing all space targets and balancing the workload of each satellite, thereby realizing the efficient solution of the problem of planning on-orbit mission of patrol observation of space-based space targets. At the same time, this method is also applicable to the on-orbit observation mission planning of ground targets. The method specifically includes the following steps:

[0005] Step 1: Construct a mathematical model for the space-based space target patrol observation mission planning problem and determine the optimization goal of the space-based space target patrol observation mission;

[0006] Step 2: Slice the observation time window;

[0007] The visible time window can be expressed as Win=<winST mr ,winET mr ,winDT mr ,sat m ,tar r >, its set is represented by Wins={Win1,Win2,…,Win i ,…,Win n}, slice the observation time window to form a sliced ​​visible window set

[0008] Step 3: According to the priority rule, perform the first round of solving the sliced ​​visible window set according to the time window conflict degree, and try to meet the first-level planning goal as much as possible: the maximum number of target patrols within the patrol cycle;

[0009] Step 4: Make fine adjustments based on the results of the first round of solutions to achieve the goal of full observation;

[0010] Step 5: Based on the results of step 4, make adjustments to achieve a balanced workload for each satellite and meet the secondary planning goal as much as possible: minimize the variance in the number of patrol targets for each satellite.

[0011] The calculation method of the above time window conflict degree is:

[0012] Count the conflicting window sets of each window in list_Win. If the load capacity constraint or the observation transition time constraint is not met, it is considered a conflict. The judgment rules are as follows:

[0013] If Win i |sat=Win j |sat, then perform conflict judgment:

[0014] if winST i ≤winST j

[0015] if(winST j -winET i )<B i,j

[0016] then conflict i,j =1

[0017] if winST i >winST j

[0018] if(winST i -winET j )<B i,j

[0019] then conflict i,j =1

[0020] Among them, conflict i,j =1 indicates window Win i and Win j A conflict occurs, B i,j Represents the window Win i ,Win j The duration of the turntable maneuvering between

[0021] Win i The conflict set can be expressed as

[0022] The conflict degree of a time window is defined as the number of time windows in all time windows that have conflicting observation time and switching duration with this window, that is:

[0023]

[0024] Where, Indicates window win i and win j There is a conflict, Indicates that there is no conflict;

[0025] If the time window Win i The conflict is combined into The time window Win i The conflict degree is k.

[0026] The above priority rules are:

[0027] For the same target, the target with fewer windows has higher priority; for windows with the same conflict degree, the window with higher average observed brightness during the window duration has higher priority;

[0028] For windows between different targets, the target with fewer windows has a higher priority; for targets with the same number of windows, the target with a smaller window conflict has a higher priority; for targets with the same number of windows and the same conflict degree, the target with a higher average observed brightness has a higher priority.

[0029] Step 1 specifically includes:

[0030] Step 1.1: Description of the space-based space target patrol observation mission planning problem;

[0031] The space-based space target patrol mission planning problem is described as N satellites observing r targets in a planning cycle, so that the optimization goal of the space-based space target patrol mission is optimal. The final output of the mission planning is mainly the allocation plan of the observation tasks.

[0032] The single-star patrol mission planning problem can be expressed as:

[0033] SPMP={Sat,Tar,Wins,RES}

[0034] in:

[0035] Sat——satellite set, there are N satellites in total, Sat={sat1,sat2,…,sat N};

[0036] Tar——the set of observation targets, there are r targets to be observed, Tar={tar1,tar2,...,tar i ,...,tar r};

[0037] Wins——The visible window set of the satellite Sat observation target Tar, n represents the total number of time windows to be planned, Wins={Win1,Win2,...,Win i ,...,Win n}; Win represents satellite sat m Observation target tar r Visible window, Win=<winST mr ,winET mr ,winDT mr ,sat m ,tar r>winST mr 、winET mr Indicates satellite sat m Observation target tar r The start time and end time of the visible window; winDT mr , satellitesat m Observation target tar r The duration of the visible window, winDT mr =winET mr -winST mr ;

[0038] RES - constraint set;

[0039] Step 1.2: Mathematical description of the space-based space target patrol mission constraints;

[0040] The specific constraints are: load capacity constraint, observation time window constraint, observation transition time constraint and patrol time interval constraint;

[0041] Payload capacity constraints: Due to the limitations of satellite payload characteristics, a satellite can only observe one target at a time. That is, each onboard remote sensor can only perform one observation mission at any time.

[0042] Observation time window constraint: If the target tar r Satellite m To make an observation, the duration interval of the satellite's observation must be a subset of the time window during which the satellite is visible to it;

[0043] For the visible window win i If the target tar r Satellite m To conduct observations, the following conditions must be met:

[0044] ST mr ≥winST mr ,ET mr ≤winET mr

[0045] Observation transition time constraint: for satellite sat m For a task sequence, the time interval between the end of the previous task and the start of the next task should be greater than or equal to a transition time;

[0046] If the target tar i and tar j All of them need to occupy satellite sat m , and for the target tar i The observation mission is followed by the target tar jThe observation task is executed after the following conditions are met:

[0047] ST mj -ET mi ≥B i,j

[0048] Among them B i,j Indicates satellite sat m Observation target tar i and tar j Required observation transition time;

[0049] Patrol time interval constraint: When performing a single satellite patrol, it is necessary to ensure that the observation arcs are as dispersed as possible, and the time interval between two observations of the same target must be greater than one time period:

[0050]

[0051] tar i =tar j ,tar k ∈Win k ,k=i,j

[0052]

[0053] Where, MinObserveInterval is the interval between two adjacent time windows of observing the same target;

[0054] Step 1.3: Mathematical description of the optimization target of the space-based space target patrol mission:

[0055] The optimization objectives of the space-based space target patrol mission are divided into primary planning objectives and secondary planning objectives:

[0056] Level 1 planning goal: maximize the number of target patrols within the patrol cycle, i.e. ensure that all targets are observed once;

[0057] For the target set to be patrolled, Tar={tar1,tar2,...,tar i ,...,tar r}, the number of targets is r, and the observed state is V = {v1, v2, ..., v i ,...,v r}, where v i = 0 or 1, 0 means not observed, 1 means observed, then the first-level planning target can be expressed as:

[0058]

[0059] Secondary planning goal: Minimize the variance of the number of patrol targets for each star;

[0060] Assume that the target number set of each star patrol is Amount={a1,a2,...,a i ,…,a r},a i ≥0, a i represents the target number of the i-th satellite patrol, then the secondary planning target can be expressed as:

[0061]

[0062] Step 2: The method for segmenting the visible window is as follows:

[0063] Set the window split time split_time to the required observation time of the satellite observation target, that is, for the split time window Win i ,split_time=|winET i -winST i |; The start time difference interval_time of the split window is the satellite sat after splitting m tar the target r The difference between the start times of two adjacent time windows of observation, that is: |winST i -winST j |=interval_time×n(n=1,2,…,m);

[0064] Determine the duration of the time window winDT mr Is it greater than the split duration split_time? If so, continue splitting; otherwise, split the next time window.

[0065] Calculate the number of time window divisions m:

[0066]

[0067] The time window is divided and the parameter calculation method for each time window is as follows:

[0068] winST i =winST mr +interval_time×(i-1),(i=1,2,...,m..)

[0069] winET i =winST i +split_time

[0070] winDT i =split_time

[0071] Win i = <winST i ,winET i ,winDT i ,sat m ,tar r >

[0072]

[0073] For the split time window set Sort by the order of the time window start time winST.

[0074] Step 3 specifically includes:

[0075] Step 3.1: Calculate the conflict degree of each time window;

[0076] Step 3.2: Determine the priority of the time window according to the priority rule and select the time window with the highest priority win select ;

[0077] Step 3.3: Determine whether the planning is completed and update the time window sequence list_Win;

[0078] Step 3.3 According to the selected window win select , determine whether the planning is completed and update the time window sequence list_Win. The specific steps are as follows:

[0079] Step 1: Count the number of observations of each target. If all targets have been observed, step 3 ends. Otherwise, execute step 2.

[0080] Step 2: Calculate win in the time window sequence list_Win select The conflict set And delete it in the time window sequence list_Win;

[0081] Step 3: Delete win in the time window sequence list_Win select Window observation target tar select All windows of

[0082] Step 4: If there is no window in the time window sequence list_Win, then step 3 ends, otherwise execute step 3.1.

[0083] The steps to select the time window with the highest priority are as follows:

[0084] Calculate the number of windows for each target, and obtain the minimum number of target windows Min by comparing the number of windows for each target tar , set the number of windows to Mintar The target window is stored in the window collection list_Win1;

[0085] Compare the conflict degree Con of each window in the window set list_Win1 i , get the minimum conflict degree Min conflict , set the conflict degree to Min conflict The window is stored in the window collection list_Win2;

[0086] Compare the brightness of each window in the window set list_Win2 and select the window with the highest brightness win select , add it to the task planning result sequence list_win_chose.

[0087] Step 4 is as follows:

[0088] Step 4.1: Count the targets that were not observed in the first round of solution;

[0089] Step 4.2: Count the time window Win of each unobserved target i Con conflict with list_win_chose i , and Win i Corresponding conflict window set Conflict i ;

[0090] Step 4.3: Prioritize the unobserved targets according to the observed target priority rules, and first fine-tune the target with the highest priority, Tar prio2 , for win select2 ∈Tar prio2 , adjust the win in list_win_chose select2 Conflicting target windows, make target Tar prio2 can be observed;

[0091] Step 4.4: Update the priorities of all remaining targets and obtain the new highest priority target Tar prio2 , perform step 4.3 again.

[0092] Step 4.3 The specific steps are as follows:

[0093] Traversing Tar prio2 The available time window is counted in the planning result sequence list_win_chose with win select2 The time window with conflicts is added to the conflict sequence list_win_conflict;

[0094] Traverse the conflict sequence list_win_conflict, for the window win i , if there is a time window win that observes the same target j , win j It is not in the result sequence list_win_chose and does not conflict with the time window in the result sequence, and win j with win select2 If there is no time conflict, win j with win select2 Add to the planning result sequence list_win_chose and remove win i .

[0095] Step 5: Based on the results of step 4, make adjustments to achieve a balanced workload for each satellite. The specific steps are as follows:

[0096] Step 5.1: Determine whether the satellite workload is balanced: Let the number of targets observed by the satellite be x i , calculate the average and standard deviation std respectively, definition: if x i >average+3×std, then the workload of satellite i is considered to be too much; if x i <average-3×std, then the workload of satellite i is considered too small;

[0097] Step 5.2: Count the satellites with too much workload and too little workload respectively, and record the satellite sequence with too much workload as list_sat over , the satellite sequence with too little workload is recorded as list_sat below ;

[0098] Step 5.3: Prioritize list_sat over The most workload satellite over Make adjustments, for win∈sat over , first adjust the observation satellite corresponding to the target of win to list_sat below The satellite with the least workload below , try to make the number of satellite observation targets within the interval [average-3×std,average+3×std];

[0099] Step 5.4: Update the satellite workload and count the satellites with too little workload. The satellite sequence with too little workload is recorded as list_sat below , the rest is list_sat other ;

[0100] Step 5.5: List_sat otherSatellite with normal workload other Make adjustments, for win∈sat other , adjust the observation satellite corresponding to the target of win to list_sat below The satellite with the least workload below , increase the number of observation windows of satellites with less workload, and try to make the number of satellite observation targets within the range of [average-3×std,average+3×std].

[0101] Beneficial effects:

[0102] 1. By slicing the time window, the coupled optimization problem of selecting the observation window and the observation time period within the observation window is decoupled and converted into a planning method for the sliced ​​time window;

[0103] 2. A method for determining task priorities based on time window conflict is proposed, which clarifies the optimization order between different objectives and the selection method between different windows for the same objective.

[0104] 3. Based on the greedy idea, the present invention achieves efficient and rapid solution to the on-orbit planning problem of space-based space target patrol missions by setting rules under the condition of using relatively low computing power, solving the problems of large computational complexity of existing optimization algorithms, slow convergence speed of intelligent optimization algorithms and large computing power requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 This is a schematic diagram of the time window constraints for the observation task;

[0106] Figure 2 Schematic diagram of the observation transition time constraint;

[0107] Figure 3 This is a schematic diagram of time window segmentation;

[0108] Figure 4 Segment the flow chart for time windows;

[0109] Figure 5 Flowchart for the first round of solution for step 3;

[0110] Figure 6 Fine-tune the flow chart for the window;

[0111] Figure 7 Assign a Gantt chart to the first round of planning tasks for 60 targets in a 1-hour simulation patrol scenario;

[0112] Figure 8 Assign a Gantt chart for the second round of planning tasks for 60 targets in a simulated patrol scenario for 1 hour;

[0113] Figure 9 This is a comparison chart of the cumulative observation time of each satellite in the second round of planning within 1 hour of simulation;

[0114] Figure 10 Assign a Gantt chart to the third round of planning tasks for 60 targets in a simulated patrol scenario for one hour;

[0115] Figure 11 This is a comparison chart of the cumulative observation time of each satellite in the third round of planning within 1 hour of simulation. DETAILED DESCRIPTION

[0116] The technical solution of the present invention is further described below with reference to the embodiments, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention that does not depart from the spirit and scope of the technical solution of the present invention should be included in the scope of protection of the present invention.

[0117] A method for planning an on-orbit mission for patrol observation of space-based space targets based on time window conflict degree specifically comprises the following steps:

[0118] Step 1: Construct a mathematical model for the space-based space target patrol observation mission planning problem and determine the optimization goal of the space-based space target patrol observation mission;

[0119] Step 1 specifically includes:

[0120] Step 1.1: Description of the space-based space target patrol observation mission planning problem;

[0121] The space-based space target patrol observation mission planning problem is described as N satellites observing r targets in a planning cycle, so that the optimization goal of the space-based space target patrol mission observation is optimal. The final output of the mission planning is mainly the allocation plan of the observation tasks.

[0122] The single-star patrol observation mission planning problem is expressed as:

[0123] SPMP={Sat,Tar,Wins,RES}

[0124] in:

[0125] Sat——satellite set, there are N satellites in total, Sat={sat1,sat2,…,sat N};

[0126] Tar——Observation target set, there are r satellites in total, Tar={tar1,tar2,...,tar i ,…,tar r};

[0127] Wins is the set of visible windows of the satellite Sat observation target Tar, n represents the total number of time windows to be planned, Wins = {Win1, Win2, ..., Win i ,…,Win n}; Win represents satellite sat m Observation target tar r Visible window, Win=<winST mr ,winET mr ,winDT mr ,sat m ,tar r >winST mr 、winET mr Indicates satellite sat m Observation target tar r The start time and end time of the visible window; winDT mr , satellitesat m Observation target tar r The duration of the visible window, winDT mr =winET mr -winST mr ;

[0128] RES - constraint set;

[0129] At the same time, the following assumptions are made:

[0130] ② Each observation task is independent;

[0131] ② Satellite equipment failure is not considered;

[0132] ③ Extreme and special working conditions are not considered.

[0133] Based on the above definitions and assumptions, the purpose of space-based space target patrol observation mission planning is to select a subset from the visible window set Wins so that the generated planning scheme can obtain the maximum target observation benefit while satisfying the constraint set RES.

[0134] Step 1.2: Mathematical description of the space-based space target patrol observation mission constraints;

[0135] The specific constraints are: load capacity constraint, observation time window constraint, observation transition time constraint and patrol time interval constraint;

[0136] Payload capacity constraints: Due to the limitations of satellite payload characteristics, a satellite can only observe one target at a time. That is, each onboard remote sensor can only perform one observation mission at any time.

[0137] Observation time window constraints: such as Figure 1 As shown, if the target tar r Satellite m To make an observation, the duration interval of the satellite's observation must be a subset of the time window during which the satellite is visible to it;

[0138] For the visible window win i If the target tar r Satellite m To conduct observations, the following conditions must be met:

[0139] ST mr ≥winST mr ,ET mr ≤winET mr

[0140] Observation transition time constraints: such as Figure 2 As shown in the figure, when a satellite performs two adjacent tasks, a certain transition time must be taken into account to ensure that the satellite attitude and imaging instrument working status are adjusted during this period. Therefore, for satellite sat m For a task sequence, the time interval between the end of the previous task and the start of the next task should be greater than or equal to a transition time;

[0141] If the target tar i and tar j All of them need to occupy satellite sat m , and for the target tar i The observation mission is followed by the target tar j The observation task is executed after the following conditions are met:

[0142] ST mj -ET mi ≥B i,j

[0143] Among them B i,j Indicates satellite sat m Observation target tar i and tar j Required observation transition time;

[0144] Patrol time interval constraint: When performing a single satellite patrol, it is necessary to ensure that the observation arcs are as dispersed as possible, and the time interval between two observations of the same target must be greater than one time period:

[0145]

[0146] tar i =tar j ,tar k ∈Win k,k=i,j

[0147]

[0148] Where, MinObserveInterval is the interval between two adjacent time windows of observing the same target;

[0149] Step 1.3: Mathematical description of the optimization target of the space-based space target patrol observation mission:

[0150] The optimization objectives of the space-based space target patrol observation mission are divided into primary planning objectives and secondary planning objectives:

[0151] Level 1 planning goal: maximize the number of target patrols within the patrol cycle, i.e. ensure that all targets are observed once;

[0152] For the target set to be patrolled, Tar={tar1,tar2,...,tar i ,…,tar r}, the number of targets is r, and the observed state is V = {v1, v2, ..., v i ,…,v r}, where v i = 0 or 1, 0 means not observed, 1 means observed, then the first-level planning target can be expressed as:

[0153]

[0154] Secondary planning goal: Minimize the variance of the number of patrol targets for each satellite, that is, the workload of each satellite is as balanced as possible;

[0155] Assume that the target number set of each star patrol is Amount={a1,a2,...,a i ,...,a r},a i ≥0, a i represents the target number of the i-th satellite patrol, then the secondary planning target can be expressed as:

[0156]

[0157] Step 2: Slice the observation time window;

[0158] The visible time window can be expressed as Win=<winST mr ,winET mr ,winDT mr ,sat m ,tar r >, its set is represented by Wins={Win1,Win2,...,Win i ,...,Winn}, slice the observation time window to form a sliced ​​visible window set

[0159] An observation window slicing method that takes into account the overlap of time windows is adopted. After slicing, the time windows are not connected at the end, but the start times of adjacent time windows are overlapped and divided according to the set difference. In this way, more windows can be generated, which is conducive to subsequent planning.

[0160] The schematic diagram and flow chart of segmenting the visible window are shown in Figure 3 and Figure 4 , the segmentation method is as follows:

[0161] Set the window split time split_time to the required observation time of the satellite observation target, that is, for the split time window Win i ,split_time=|winET i -winST i |; The start time difference interval_time of the split window is the satellite sat after splitting m tar the target r The difference between the start times of two adjacent time windows of observation, that is: |winST i -winST j |=interval_time×n(n=1,2,…,m);

[0162] Determine the duration of the time window winDT mr Is it greater than the split duration split_time? If so, continue splitting; otherwise, split the next time window.

[0163] Calculate the number of time window divisions m:

[0164]

[0165] The time window is divided and the parameter calculation method for each time window is as follows:

[0166] winST i =winST mr +interval_time×(i-1),(i=1,2,…,m)

[0167] winET i =winST i +split_time

[0168] winDT i =split_time

[0169] Win i = <winST i ,winET i ,winDT i ,sat m ,tar r >

[0170]

[0171] For the split time window set Sort by the order of the time window start time winST.

[0172] Step 3: According to the priority rule, perform the first round of solving the sliced ​​visible window set according to the time window conflict degree, and try to meet the first-level planning goal as much as possible: the maximum number of target patrols within the patrol cycle;

[0173] The calculation method of the above time window conflict degree is:

[0174] Count the conflicting window sets of each window in list_Win. If the load capacity constraint or the observation transition time constraint is not met, it is considered a conflict. The judgment rules are as follows:

[0175] If Win i |sat=Win j |sat, then perform conflict judgment:

[0176] if winST i ≤winST j

[0177] if(winST j -winET i )<B i,j

[0178] then conflict i,j =1

[0179] if winST i >winST j

[0180] if(winST i -winET j )<B i,j

[0181] then conflict i,j =1

[0182] Among them, conflict i,j=1 indicates window Win i and Win j A conflict occurs, B i,j Represents the window Win i ,Win j The duration of the turntable maneuvering between

[0183] Win i The conflict set can be expressed as

[0184] The conflict degree of a time window is defined as the number of time windows in all time windows that have conflicting observation time and switching duration with this window, that is:

[0185]

[0186] Where, Indicates window win i and win j There is a conflict, Indicates that there is no conflict;

[0187] If the time window Win i The conflict is combined into The time window Win i The conflict degree is k.

[0188] The above priority rules are:

[0189] For the same target, the target with fewer windows has higher priority; for windows with the same conflict degree, the window with higher average observed brightness during the window duration has higher priority;

[0190] For windows between different targets, the target with fewer windows has a higher priority; for targets with the same number of windows, the target with a smaller window conflict has a higher priority; for targets with the same number of windows and the same conflict degree, the target with a higher average observed brightness has a higher priority.

[0191] Step 3 is as follows, see the specific process Figure 5 :

[0192] Step 3.1: Calculate the conflict degree of each time window;

[0193] Step 3.2: Determine the priority of the time window according to the priority rule and select the time window with the highest priority win select ;

[0194] Step 3.3: Determine whether the planning is completed and update the time window sequence list_Win;

[0195] Step 3.3 According to the selected window winselect , determine whether the planning is completed and update the time window sequence list_Win. The specific steps are as follows:

[0196] Step 1: Count the number of observations of each target. If all targets have been observed, step 3 ends. Otherwise, execute step 2.

[0197] Step 2: Calculate win in the time window sequence list_Win select The conflict set And delete it in the time window sequence list_Win;

[0198] Step 3: Delete win in the time window sequence list_Win select Window observation target tar select All windows of

[0199] Step 4: If there is no window in the time window sequence list_Win, then step 3 ends, otherwise execute step 3.1.

[0200] The steps to select the time window with the highest priority are as follows:

[0201] Calculate the number of windows for each target, and obtain the minimum number of target windows Min by comparing the number of windows for each target tar , set the number of windows to Min tar The target window is stored in the window collection list_Win1;

[0202] Compare the conflict degree Con of each window in the window set list_Win1 i , get the minimum conflict degree Min conflict , set the conflict degree to Min conflict The window is stored in the window collection list_Win2;

[0203] Compare the brightness of each window in the window set list_Win2 and select the window with the highest brightness win select , add it to the task planning result sequence list_win_chose.

[0204] Step 4: Fine-tune based on the first round of solution results. See the flowchart of fine-tuning. Figure 6 , we should observe as much as possible to achieve the goal, as follows:

[0205] Step 4.1: Count the targets that were not observed in the first round of solution;

[0206] Step 4.2: Count the time window Win of each unobserved target iCon conflict with list_win_chose i , and Win i Corresponding conflict window set Conflict i ;

[0207] Step 4.3: Prioritize the unobserved targets according to the observed target priority rules, and first fine-tune the target with the highest priority, Tar prio2 , for win select2 ∈Tar prio2 , adjust the win in list_win_chose select2 Conflicting target windows, make target Tar prio2 can be observed;

[0208] Step 4.4: Update the priorities of all remaining targets and obtain the new highest priority target Tar prio2 , perform step 4.3 again.

[0209] Step 4.3 The specific steps are as follows:

[0210] Traversing Tar prio2 The available time window is counted in the planning result sequence list_win_chose with win select2 The time window with conflicts is added to the conflict sequence list_win_conflict;

[0211] Traverse the conflict sequence list_win_conflict, for the window win i , if there is a time window win that observes the same target j , win j It is not in the result sequence list_win_chose and does not conflict with the time window in the result sequence, and win j with win select2 If there is no time conflict, win j with win select2 Add to the planning result sequence list_win_chose and remove win i .

[0212] Step 5: Based on the results of step 4, make adjustments to achieve a balanced workload for each satellite and meet the secondary planning goal as much as possible: minimize the variance of the number of patrol targets for each satellite. The specific steps are as follows:

[0213] Step 5.1: Determine whether the satellite workload is balanced: Let the number of targets observed by the satellite be x i , calculate the average and standard deviation std respectively, definition: if xi >average+3×std, then the workload of satellite i is considered to be too much; if x i <average-3×std, then the workload of satellite i is considered too small;

[0214] Step 5.2: Count the satellites with too much workload and too little workload respectively, and record the satellite sequence with too much workload as list_sat over , the satellite sequence with too little workload is recorded as list_sat below ;

[0215] Step 5.3: Prioritize list_sat over The most workload satellite over Make adjustments, for win∈sat over , first adjust the observation satellite corresponding to the target of win to list_sat below The satellite with the least workload below , try to make the number of satellite observation targets within the interval [average-3×std,average+3×std];

[0216] Step 5.4: Update the satellite workload and count the satellites with too little workload. The satellite sequence with too little workload is recorded as list_sat below , the rest is list_sat other ;

[0217] Step 5.5: List_sat other Satellite with normal workload other Make adjustments, for win∈sat other , adjust the observation satellite corresponding to the target of win to list_sat below The satellite with the least workload below , increase the number of observation windows of satellites with less workload, and try to make the number of satellite observation targets within the range of [average-3×std,average+3×std].

[0218] simulation:

[0219] In order to verify the above method, the present invention uses 6 satellites to simulate patrol of 60 space targets, and the simulation scenario duration is 1 hour. The observation duration of each target, i.e., the window split duration split_time, is set to 200s. After splitting, the satellite sat m tar the target rThe interval_time difference between the start times of two adjacent observation time windows is 30 seconds. A total of 250 time windows of the constellation to the target are calculated. The observation time windows are sliced ​​according to the method in step 2 to obtain 900 sliced ​​time windows.

[0220] After solving the first round of planning for 60 targets in a patrol scenario for 1 hour, the Gantt chart of the constellation patrol task planning results is as follows: Figure 7 As shown in the figure, the horizontal axis is the time axis, the vertical axis is the satellite number, and each rectangular square in the figure represents the time window of the observation mission. The first round of planning completed the observation of 57 targets within 1 hour.

[0221] After solving the second round of planning for 60 targets in a patrol scenario for 1 hour, the Gantt chart of the constellation patrol mission planning results is as follows: Figure 8 As shown in the figure, the second round of planning completed the observation of 58 targets within 1 hour. In the second round of solution, the observation window of satellite 4 for target 47 was advanced, thus inserting the time window for target 33 and increasing the number of target observations in the constellation.

[0222] The second round of planning results within 1 hour show the cumulative satellite working time as follows: Figure 9 As shown in the figure, after the second round of planning, the average cumulative observation time for each satellite was 1933.34 seconds. Satellites 1 and 5 had the longest cumulative operating time, with cumulative observations of 2200 seconds each, and satellite 6 had the shortest operating time, with a cumulative observation of 1400 seconds. The number of observation windows for the six satellites was 11, 10, 10, 11, 9, and 7, respectively.

[0223] After fine-tuning the third round of planning for 60 targets in a 1-hour patrol scenario, the Gantt chart of the constellation patrol mission planning results and the cumulative star observation time are compared as shown below: Figure 10 As shown in the figure, the comparison of the cumulative observation time of each satellite in the third round of planning within 1 hour is shown in Figure 11 As can be seen from the figure, after the third round of planning, the number of observation windows of the six satellites are 10, 10, 10, 10, 9, and 9 respectively, and the loads of each satellite are significantly balanced.

Claims

1. A method for planning an on-orbit mission for a space-based space target patrol observation based on time window conflict degree, comprising the following steps: Step 1: Construct a mathematical model for the space-based space target patrol observation mission planning problem and determine the optimization goal of the space-based space target patrol observation mission; The steps include step 1.1: description of the space-based space target patrol mission planning problem; The space-based space target patrol mission planning problem is described as N satellites observing r targets in a planning cycle, so that the optimization goal of the space-based space target patrol mission is optimal. The final output of the mission planning is mainly the allocation plan of the observation tasks. The single-star patrol mission planning problem can be expressed as: SPMP={Sat,Tar,Wins,RES} in: Sat——satellite set, there are N satellites in total, Sat={sat1,sat2,…,sat N }; Tar——the set of observation targets, there are r targets to be observed, Tar={tar1,tar2,...,tar i ,...,tar r }; Wins is the set of visible windows of the satellite Sat observation target Tar, n represents the total number of time windows to be planned, Wins = {Win1, Win2, ..., Win i ,…,Win n }; Win represents satellite sat m Observation target tar r Visible window, Win= <winST mr ,winET mr ,winDT mr ,sat m ,tar r >winST mr 、winET mr Indicates satellite sat m Observation target tar r The start time and end time of the visible window; winDT mr , satellitesat m Observation target tar r The duration of the visible window, winDT mr =winET mr -winST mr ; RES - constraint set; Step 2: Slice the observation time window; The visible window is represented by Win= <winST mr ,winET mr ,winDT mr ,sat m ,tar r >, whose set is represented by Wins={Win1,Win2,…,Win i ,…,Win n }, slice the observation time window to form a sliced ​​visible window set Step 3: According to the priority rule, perform the first round of solving the sliced ​​visible window set according to the time window conflict degree, and try to meet the first-level planning goal as much as possible: the maximum number of target patrols within the patrol cycle; The precedence rules are: For the same target, the target with fewer windows has higher priority; for windows with the same conflict degree, the window with higher average observed brightness during the window duration has higher priority; For windows between different targets, the target with fewer windows has a higher priority; for targets with the same number of windows, the target with a smaller window conflict has a higher priority; for targets with the same number of windows and the same conflict degree, the target with a higher average observed brightness has a higher priority; Step 4: Make fine adjustments based on the results of the first round of solutions to achieve the goal of full observation; Step 5: Based on the results of step 4, make adjustments to achieve a balanced workload for each satellite and meet the secondary planning goal as much as possible: minimize the variance in the number of patrol targets for each satellite.

2. The method for planning on-orbit missions for space-based target patrol observation based on time window conflict degree according to claim 1, characterized in that: The time window conflict degree calculation method is: Count the conflicting window sets of each window in list_Win. If the load capacity constraint or the observation transition time constraint is not met, it is considered a conflict. The judgment rules are as follows: If Win i |sat=Win j |sat, then perform conflict judgment: if winST i ≤winST j if(winST j -winET i )<B i,j then conflict i,j =1 if winST i >winST j if(winST i -winET j )<B i,j then conflict i,j =1 Among them, conflict i,j =1 indicates window Win i and Win j A conflict occurs, B i,j Represents the window Win i ,Win j The duration of the turntable maneuvering between Win i The conflict set can be expressed as The conflict degree of a time window is defined as the number of time windows in all time windows that have conflicting observation time and switching duration with this window, that is: Where, Indicates window win i and win j There is a conflict, Indicates that there is no conflict; If the time window Win i The conflict is combined into The time window Win i The conflict degree is k.

3. The method for planning on-orbit missions for space-based target patrol observation based on time window conflict degree according to claim 1, characterized in that: Step 1 also includes: Step 1.2: Mathematical description of the space-based space target patrol observation mission constraints; The specific constraints are: load capacity constraint, observation time window constraint, observation transition time constraint and patrol time interval constraint; Payload capacity constraints: Due to the limitations of satellite payload characteristics, a satellite can only observe one target at a time. That is, each onboard remote sensor can only perform one observation mission at any time. Observation time window constraint: If the target tar r Satellite m To make an observation, the duration interval of the satellite's observation must be a subset of the time window during which the satellite is visible to it; For the visible window win i If the target tar r Satellite m To conduct observations, the following conditions must be met: ST mr ≥winST mr ,AND mr ≤winET mr Observation transition time constraint: for satellite sat m For a task sequence, the time interval between the end of the previous task and the start of the next task should be greater than or equal to a transition time; If the target tar i and tar j All of them need to occupy satellite sat m , and for the target tar i The observation mission is followed by the target tar j The observation task is executed after that, and must meet the following requirements: ST mj -AND mi ≥B i,j Among them B i,j Indicates satellite sat m Observation target tar i and tar j Required observation transition time; Patrol time interval constraint: When performing a single satellite patrol, it is necessary to ensure that the observation arcs are as dispersed as possible, and the time interval between two observations of the same target must be greater than one time period: takes i =takes j ,takes k ∈Win k ,k=i,j Where, MinObserveInterval is the interval between two adjacent time windows of observing the same target; Step 1.3: Optimize the mathematical description of the space-based space target patrol observation mission: The optimization objectives of the space-based space target patrol observation mission are divided into primary planning objectives and secondary planning objectives: Level 1 planning goal: maximize the number of target patrols within the patrol cycle, i.e. ensure that all targets are observed once; For the target set to be patrolled, Tar={tar1,tar2,...,tar i ,...,tar r }, the number of targets is r, and the observed state is V = {v1, v2, ..., v i ,...,v r }, where v i = 0 or 1, 0 means not observed, 1 means observed, then the first-level planning target can be expressed as: Secondary planning goal: Minimize the variance of the number of patrol targets for each star; Assume that the target number set of each star patrol is Amount={a1,a2,...,a i ,...,a r },a i ≥0, a i represents the target number of the i-th satellite patrol, then the secondary planning target can be expressed as:

4. The method for planning on-orbit missions for space-based target patrol observation based on time window conflict degree according to claim 1, characterized in that: Step 2: The method for segmenting the visible window is as follows: Set the window split time split_time to the required observation time of the satellite observation target, that is, for the split time window Win i ,split_time=|winET i -winST i |; The start time difference interval_time of the split window is the satellite sat after splitting m tar the target r The difference between the start times of two adjacent time windows of observation, that is: Win j ∈Win,|winST i -winST j |=interval_time×n(n=1,2,…,m); Determine the duration of the time window winDT mr Is it greater than the split duration split_time? If so, continue splitting; otherwise, split the next time window. Calculate the number of time window divisions m: The time window is divided and the parameter calculation method for each time window is as follows: gain i =winST mr +interval_time×(i-1),(i=1,2,…,m) winET i =winST i +split_time windDT i split_time Win i = <winST i ,winET i ,windDT i ,sat m tar r > For the split time window set Sort by the order of the time window start time winST.

5. The method for planning on-orbit missions for space-based target patrol observation based on time window conflict degree according to claim 1, characterized in that: Step 3 specifically includes: Step 3.1: Calculate the conflict degree of each time window; Step 3.2: Determine the priority of the time window according to the priority rule and select the time window with the highest priority win select ; Step 3.3: Determine whether the planning is completed and update the time window sequence list_Win.

6. The method for planning on-orbit missions for space-based target patrol observation based on time window conflict degree according to claim 5, characterized in that: The steps to select the time window with the highest priority are as follows: Calculate the number of windows for each target, and obtain the minimum number of target windows Min by comparing the number of windows for each target tar , set the number of windows to Min tar The target window is stored in the window collection list_Win1; Compare the conflict degree Con of each window in the window set list_Win1 i , get the minimum conflict degree Min conflict , set the conflict degree to Min conflict The window is stored in the window collection list_Win2; Compare the brightness of each window in the window set list_Win2 and select the window with the highest brightness win select , add it to the task planning result sequence list_win_chose.

7. The method for planning on-orbit missions for space-based target patrol observation based on time window conflict degree according to claim 6, characterized in that: Step 4 is as follows: Step 4.1: Count the targets that were not observed in the first round of solution; Step 4.2: Count the time window Win of each unobserved target i Con conflict with list_win_chose i , and Win i Corresponding conflict window set Conflict i ; Step 4.3: Prioritize the unobserved targets according to the observed target priority rules, and first fine-tune the target with the highest priority, Tar prio2 , for win select2 ∈Tar prio2 , adjust the win in list_win_chose select2 Conflicting target windows, make target Tar prio2 can be observed; Step 4.4: Update the priorities of all remaining targets and obtain the new highest priority target Tar prio2 , perform step 4.3 again.

8. The method for planning on-orbit missions for space-based target patrol observation based on time window conflict degree according to claim 1, characterized in that: Step 5: Based on the results of step 4, make adjustments to achieve a balanced workload for each satellite. The specific steps are as follows: Step 5.1: Determine whether the satellite workload is balanced: Let the number of targets observed by the satellite be x i , calculate the average and standard deviation std respectively, definition: if x i >average+3×std, then the workload of satellite i is considered to be too much; if x i <average-3×std, then the workload of satellite i is considered too small; Step 5.2: Count the satellites with too much workload and too little workload respectively, and record the satellite sequence with too much workload as list_sat over , the satellite sequence with too little workload is recorded as list_sat below ; Step 5.3: Prioritize list_sat over The most workload satellite over Make adjustments, for win∈sat over , first adjust the observation satellite corresponding to the target of win to list_sat below The satellite with the least workload below , try to make the number of satellite observation targets within the interval [average-3×std,average+3×std]; Step 5.4: Update the satellite workload and count the satellites with too little workload. The satellite sequence with too little workload is recorded as list_sat below , the rest is list_sat other ; Step 5.5: List_sat other Satellite with normal workload other Make adjustments, for win∈sat other , adjust the observation satellite corresponding to the target of win to list_sat below The satellite with the least workload below , increase the number of observation windows of satellites with less workload, and try to make the number of satellite observation targets within the interval of [average-3×std,average+3×std].

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