Remote sensing satellite point target task cluster preprocessing system and method with optimal observation window time

Abstract

The invention provides a remote sensing satellite point target task cluster preprocessing system and method with optimal observation window time, and the system comprises a point target grid deduplication module which is used for carrying out the modeling of a task cluster observation window based on the coarse distribution of remote sensing satellite point target matching grids, and obtaining the unique matching grid of all point targets; the target grid merging module is used for merging adjacent grids meeting preset maximum and minimum imaging duration constraints in all point target unique matching grids into a time window, and obtaining the time window and unprocessed grids; and the single imaging time window correction module is used for receiving the time window and the unprocessed grid, correcting the unprocessed grid to meet the maximum and minimum imaging duration constraint, obtaining an observation strip, and completing the preprocessing of the remote sensing satellite point target task cluster with the optimal observation window time. According to the technical scheme, the execution efficiency and success rate of observation tasks can be improved.

Description

Technical Field

[0001] This invention belongs to the field of aerospace remote sensing technology, specifically relating to a preprocessing system and method for remote sensing satellite point target mission clusters with optimal observation window time. Background Technology

[0002] With continuous technological advancements and improved satellite imaging capabilities, imaging satellites can acquire various types of data, including high-resolution, multispectral, and multi-angle data, enabling multi-mode imaging of ground targets. In fields such as military reconnaissance, environmental monitoring, and disaster early warning, accurate identification and repeated monitoring of specific targets are crucial. Preprocessing methods for remote sensing satellite point target clusters with optimal observation windows can significantly improve satellite observation efficiency, optimize resource utilization, enhance mission flexibility, and promote the continuous development of satellite observation technology. This provides efficient and accurate data support for scientific research, commercial applications, and emergency needs, thereby maximizing economic and social benefits.

[0003] In traditional research schemes, time windows conflict and overlap in dense observation scenarios. Existing studies have separated point target task clustering from task planning, but the high coupling between tasks makes it difficult to achieve globally optimal observation. This invention proposes a preprocessing method for remote sensing satellite point target task clusters with optimal observation window time. For densely observed targets, constraints on simultaneous and co-positional observations and inter-orbit overlap are checked before raster deduplication. Then, raster cells are preferentially merged based on relevant constraints. This effectively handles the task clustering problem in dense observation scenarios, reduces the observation time window period, and improves observation time efficiency. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a remote sensing satellite point target mission cluster preprocessing system and method with optimal observation window time, ensuring that each point target appears within its first appearance time window, minimizing the overall total time length of the point target mission cycle, and saving the total mission time cycle for subsequent planning.

[0005] A preprocessing system for remote sensing satellite point target mission clusters with optimal observation window time includes:

[0006] The point target raster deduplication module is used to model the observation window of the mission cluster based on the coarsely allocated remote sensing satellite point target matching raster to obtain a unique matching raster for all point targets.

[0007] The target grid merging module is used to merge adjacent grids that meet the preset maximum and minimum imaging duration constraints in all point target unique matching grids into a time window, and obtain the time window and the unprocessed grid.

[0008] The single imaging time window correction module is used to receive the time window and the unprocessed grid, and correct the unprocessed grid to meet the maximum and minimum imaging duration constraints, obtain the observation strip, and complete the preprocessing of the remote sensing satellite point target task cluster with the optimal observation window time.

[0009] Preferably, the point target grid deduplication module includes:

[0010] The grid selection unit is used to select the grid from which the observed point target first appears in the coarsely allocated point target matching grid, using the least period principle.

[0011] The task set acquisition unit is used to obtain a task set of point targets and unique point target matching grids based on the point target matching grid that first appears in the observation point target.

[0012] Preferably, the task set acquisition unit includes:

[0013] The first judgment subunit is used to determine whether the point target matching grid that first appears the selected target observation point is unique. If it is, it is observed first, and other grids that match the point target in the point target matching grid are deleted to obtain the task set of point targets and point target unique matching grids. If not, it finds all point targets in the point target matching grid according to the unique mapping relationship between the point target matching grid and the point target, and calculates the number of times each point target appears in all point target matching grids.

[0014] The second judgment subunit is used to determine whether there is a point target that appears only once in all point target matching grids. If so, the priority observation of the first judgment subunit is executed.

[0015] The third judgment subunit is used to determine whether there is a point target matching grid in the point target matching grid where the point target is located that has the same wave position as the point target matching grid observed in the first judgment subunit and the time interval is less than the time interval of the same wave position when the judgment result of the second judgment subunit is negative. If yes, the priority observation of the first judgment subunit is executed; if no, the point target matching grid with the smallest wave position number among all the first point target matching grids is found and the priority observation of the first judgment subunit is executed.

[0016] Preferably, the target grid merging module includes:

[0017] The first point target grid merging unit is used to determine whether the start time interval of adjacent grids in all point target unique matching grids meets the preset minimum duration constraint, and merges adjacent grids that meet the preset minimum duration constraint into a time window to obtain the time window and the unprocessed grid; the preset minimum duration constraint is any one of the following: less than the minimum duration of a single power-on or the minimum duration of a single imaging.

[0018] The second target grid merging unit is used to determine whether the time window and the unprocessed grid simultaneously meet the preset maximum duration constraint and the same-wavelength imaging time interval constraint, and merge the time window or the unprocessed grid that simultaneously meets the preset maximum duration constraint and the same-wavelength imaging time interval constraint to obtain the final time window and the unprocessed grid; wherein, the preset maximum duration constraint is less than the maximum imaging duration.

[0019] Preferably, in the second point target grid merging unit, the co-wavelength imaging time interval constraint includes a first constraint or a second constraint; wherein,

[0020] The first constraint is that the interval between the end time of the previous time window and the start time of the next time window is less than the time interval between the same wave positions.

[0021] The second constraint is that the difference between the start time of the previous time window and the start time of the next time window is less than the sum of the minimum single imaging duration and the time interval between co-positions.

[0022] This invention also provides a preprocessing method for remote sensing satellite point target mission clusters with optimal observation window time, and the system includes:

[0023] Based on the coarsely allocated remote sensing satellite point target matching grid, the observation window of the mission cluster is modeled to obtain a unique matching grid for all point targets;

[0024] Merge adjacent grids that satisfy the preset maximum and minimum imaging duration constraints in all uniquely matched grids of all point targets into a time window to obtain the time window and the unprocessed grids.

[0025] The system receives the time window and the unprocessed grid, corrects the unprocessed grid to meet the maximum and minimum imaging duration constraints, obtains the observation strips, and completes the preprocessing of the remote sensing satellite point target task cluster with the optimal observation window time.

[0026] Preferred methods for obtaining a unique matching raster for a point target include:

[0027] In the coarsely allocated point target matching grid, the minimum cycle principle is used to obtain the point target matching grid that first appears the observed point target;

[0028] Based on the point target matching grid that first appears in the observation point target, obtain the task set of point targets and point target unique matching grids.

[0029] Preferably, the method for determining whether all point targets uniquely match the raster and satisfies the preset maximum and minimum imaging time constraints includes:

[0030] Determine whether the first target observation point in the selected grid is unique. If so, observe it first, delete other grids that match the target in the grid, and obtain the task set of the target and the grid that uniquely matches the target. If not, find all the target points in the grid according to the unique mapping relationship between the target and the grid, and calculate the number of times each target appears in all the target matching grids.

[0031] Determine if a point target appears only once in all point target matching grids. If so, perform priority observation. If no, determine if there is a point target matching grid in the point target matching grid that has the same wave position as the observed point target matching grid in the first determination sub-unit and the time interval is less than the time interval of the same wave position. If so, perform priority observation. If no, find the point target matching grid with the smallest wave position number among all the first-appearing point target matching grids and perform priority observation.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] This invention, based on coarse-allocation point target task grid matching, employs a heuristic algorithm to handle inter-track overlap and wave position overlap issues, thereby determining a unique time window corresponding to each point target. This method ensures that point target tasks are included within the first occurrence time window as much as possible, efficiently completing the mapping relationship between point targets and task grids.

[0034] This invention introduces a constraint-optimized raster merging strategy, which first establishes a constraint-satisfaction model. This model minimizes the number of observation time windows and the total mission cycle. Furthermore, this raster merging strategy has significant advantages, directly meeting the requirements of a single imaging strip, thus making fuller use of satellite resources and minimizing the total mission cycle. This raster merging strategy not only significantly improves the utilization efficiency of satellite resources but also possesses high flexibility and adaptability, directly meeting various complex observation needs.

[0035] Taking into full account constraints such as wavefront strip continuity, maximum and minimum imaging duration per session, time interval between images at the same wavefront, and minimum startup time, a flexible grid merging strategy is adopted. A heuristic clique partitioning algorithm is used to solve the constraint-satisfying optimization model. This invention provides an effective solution for both multi-task scheduling in dense observation scenarios and high-precision observation of specific targets. Simultaneously, it ensures the shortest overall task cycle, thereby improving the execution efficiency and success rate of observation tasks. Attached Figure Description

[0036] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a block diagram of the task observation window preprocessing system according to an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of a unique grid mapping for point targets according to an embodiment of the present invention;

[0039] Figure 3 This is a diagram showing the selection of the observed grid in the case of multiple target grids according to an embodiment of the present invention; wherein, (a) is a schematic diagram of three grids to be observed appearing simultaneously at the same wave position; and (b) is a schematic diagram of the grid where the point target first appears existing at multiple wave positions.

[0040] Figure 4 The grid merging constraint satisfaction diagram is shown in the embodiment of the present invention;

[0041] Figure 5 The time window merging constraint satisfaction diagram is shown in the embodiment of the present invention;

[0042] Figure 6 This is a correction diagram for the single imaging time window in an embodiment of the present invention;

[0043] Figure 7 This is a flowchart of the remote sensing satellite point target task cluster preprocessing method with optimal observation window time according to an embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] Example 1

[0047] like Figure 1 As shown, a remote sensing satellite point target mission cluster preprocessing system with optimal observation window time includes: a point target raster deduplication module, a target raster merging module, and a single imaging time window correction module.

[0048] The point target raster deduplication module is used to model the observation window of a mission cluster based on the coarsely allocated remote sensing satellite point target matching raster, obtaining unique matching rasteres for all point targets. Specifically, based on the already obtained coarsely allocated point target matching raster, the observation window design model for multiple mission clusters is created. Starting with the principle of temporal optimality, the raster of the mission whose point target appears first satisfies the constraint continuity feature with the wavefront, ensuring that this raster contains all its point targets. Then, point targets appearing in this raster are deleted from the remaining rasteres. The next mission target raster is then searched, considering wavefront overlap and inter-orbit overlap factors. Rasteres with different times from the previous raster are prioritized as the optimal temporal sequence. This process is repeated until a unique matching raster is found for all point targets.

[0049] A further implementation method is that the point target raster deduplication module includes:

[0050] The grid selection unit is used to select the grid from which the observed point target first appears in the coarsely allocated point target matching grid, using the least period principle.

[0051] The task set acquisition unit is used to obtain a task set of point targets and unique point target matching grids based on the point target matching grid that first appears in the observation point target.

[0052] A further implementation method includes a task set acquisition unit comprising:

[0053] The first judgment subunit is used to determine whether the point target matching grid that first appears the selected target observation point is unique. If it is, it is observed first, and other grids that match the point target in the point target matching grid are deleted to obtain the task set of point targets and point target unique matching grids. If not, it finds all point targets in the point target matching grid according to the unique mapping relationship between the point target matching grid and the point target, and calculates the number of times each point target appears in all point target matching grids.

[0054] The second judgment subunit is used to determine whether there is a point target that appears only once in all point target matching grids. If so, the priority observation of the first judgment subunit is executed.

[0055] The third judgment subunit is used to determine whether there is a point target matching grid in the point target matching grid where the point target is located that has the same wave position as the point target matching grid observed in the first judgment subunit and the time interval is less than the time interval of the same wave position when the judgment result of the second judgment subunit is negative. If yes, the priority observation of the first judgment subunit is executed; if no, the point target matching grid with the smallest wave position number among all the first point target matching grids is found and the priority observation of the first judgment subunit is executed.

[0056] The target grid merging module is used to merge adjacent grids that satisfy preset maximum and minimum imaging duration constraints from all uniquely matched point target grids into a time window, obtaining the time window and unprocessed grids. The main module focuses on merging grids that satisfy the maximum and minimum imaging duration constraints. When merging grids that satisfy the minimum imaging duration constraint, based on the already obtained uniquely matched point target grids, it determines whether the start time of the previous grid and the start time of the next grid satisfy the single minimum imaging duration and single startup minimum duration constraints. If satisfied, the grids are merged into a time window; otherwise, they are not merged. When merging windows that satisfy the maximum imaging duration and co-wavelength minimum duration constraints, for the time window and grids obtained in the previous step, based on determining whether they satisfy the single maximum imaging duration constraint for consecutive windows or grids, it further determines whether the windows or grids within the continuous time period satisfy the relevant constraints of the co-wavelength imaging time interval. If satisfied, they are merged; otherwise, they are not merged.

[0057] A further implementation wherein the target grid merging module includes:

[0058] The first point target grid merging unit (the grid merging module that satisfies the minimum imaging duration and single-start constraint) is used to determine whether the start time interval of adjacent grids in the unique matching grids of all point targets meets the preset minimum duration constraint. It merges adjacent grids that meet the preset minimum duration constraint into a time window to obtain the time window and the unprocessed grids. The preset minimum duration constraint is any one of the following: less than the minimum single-start duration or the minimum single-imaging duration.

[0059] The second target raster merging unit (the window merging module that satisfies the maximum imaging duration and co-wavelength imaging interval constraints) is used to determine whether the time window and the unprocessed raster simultaneously satisfy the preset maximum duration constraint and the co-wavelength imaging interval constraint. It merges the time windows or unprocessed rasteres that simultaneously satisfy both constraints to obtain the final time window and unprocessed raster. The preset maximum duration constraint is less than the maximum imaging duration. Specifically, the time window is the merged raster; some rasteres have not yet been processed because they do not meet the aforementioned requirements, but these rasteres may meet the subsequent requirements. As long as the merging requirements are met, the time window can be merged with a raster or another time window, and the raster can also be merged with another raster.

[0060] A further implementation involves the following: in the second point target grid merging unit, the co-wavelength imaging time interval constraint includes either a first constraint or a second constraint; wherein,

[0061] The first constraint is that the interval between the end time of the previous time window and the start time of the next time window is less than the time interval between the same wave positions.

[0062] The second constraint is that the difference between the start time of the previous time window and the start time of the next time window is less than the sum of the minimum single imaging duration and the time interval between co-positions.

[0063] Specifically, in this embodiment, the detailed calculation process for each constraint condition is provided:

[0064] Let N be the set of tasks, i be a single task, T be the duration of satellite imaging, and S be the earliest start time for task i. i And a latest completion time E i .

[0065] Single maximum and minimum imaging duration constraints: The satellite must continuously image for at least a certain period of time, but cannot exceed the maximum imaging duration constraint.

[0066]

[0067] Co-wavelength imaging time interval constraint: For any two tasks i1, i2∈N (where i1≠i2) at the same wavelength, the execution time difference between them should satisfy:

[0068]

[0069] T same-beam A minimum time interval must be satisfied between two imaging operations at the same wavelength.

[0070] Maximum duration of a single power-on: The maximum duration during which the device is allowed to be powered on at one time.

[0071] T on ≤T max-on (3)

[0072] Among them, T on It is the actual duration of a single device startup, T max-on This is the maximum allowed duration for a single power-on.

[0073] The single-shot imaging time window correction module receives the time window and the unprocessed raster, corrects the unprocessed raster to meet the maximum and minimum imaging duration constraints, obtains the observation strip, and completes the preprocessing of the remote sensing satellite point target mission cluster with the optimal observation window time. The obtained time window and raster are input into the single-shot imaging window correction module, which mainly corrects the unprocessed mission raster to meet the minimum single-shot imaging duration constraint. Thus, the preprocessing of the mission cluster observation window with the minimum period for point targets is completed.

[0074] Example 2

[0075] This invention also provides a preprocessing method for remote sensing satellite point target mission clusters with optimal observation window time, and an application system comprising:

[0076] Based on the coarsely allocated remote sensing satellite point target matching grid, the observation window of the mission cluster is modeled to obtain a unique matching grid for all point targets;

[0077] Merge adjacent grids that satisfy the preset maximum and minimum imaging duration constraints in all uniquely matched grids of all point targets into a time window to obtain the time window and the unprocessed grids.

[0078] The system receives the time window and unprocessed graticule, corrects the unprocessed graticule to meet the maximum and minimum imaging duration constraints, obtains the observation strips, and completes the preprocessing of remote sensing satellite point target task clusters with the optimal observation window time.

[0079] A further implementation method for obtaining a uniquely matching raster for a point target includes:

[0080] In the coarsely allocated point target matching grid, the minimum cycle principle is used to obtain the point target matching grid that first appears the observed point target;

[0081] Based on the point target matching grid that first appears in the observation point target, obtain the task set of point targets and point target unique matching grids.

[0082] A further implementation method for determining whether all point targets uniquely match the raster and satisfy the preset maximum and minimum imaging duration constraints includes:

[0083] Determine whether the first target observation point in the selected grid is unique. If so, observe it first, delete other grids that match the target in the grid, and obtain the task set of the target and the grid that uniquely matches the target. If not, find all the target points in the grid according to the unique mapping relationship between the target and the grid, and calculate the number of times each target appears in all the target matching grids.

[0084] Determine if there is a point target that appears only once in all the point target matching grids; if so, perform priority observation.

[0085] Determine if there is a point target that appears only once in all point target matching grids. If so, perform priority observation. If not, determine if there is a point target matching grid in the point target matching grid that has the same wave position as the observed point target matching grid in the first judgment sub-unit and the time interval is less than the time interval of the same wave position. If so, perform priority observation. If not, find the point target matching grid with the smallest wave position number among all the first point target matching grids and perform priority observation.

[0086] Example 3

[0087] like Figure 7As shown, this embodiment provides a detailed flowchart of the preprocessing method for remote sensing satellite point target mission clusters with optimal observation window time:

[0088] Step 1: Based on the coarsely allocated point target task grid matching, find the grid where the target observation point first appears, based on the principle of minimum cycle.

[0089] Step 2: If there is more than one grid cell appearing at the beginning, proceed to Step 4. If there is only one grid cell appearing at the beginning, then this grid cell is observed first.

[0090] Step 3: Locate all point targets within the grid cell where the point target first appears. Then delete all other grid cells that match these point targets. This ensures a unique mapping between point targets and grid cells within this grid.

[0091] like Figure 2 As shown, when the target grid is a selected grid, point targets 1, 2, and 3 are all placed in this grid, while the overlapping grids matched by target point 3 are deleted, so that the point targets in the target grid can only exist in a unique grid.

[0092] Step 4: Consider inter-track overlap. If there is more than one grid that appears at the beginning, find all the point targets covered by these grids according to the preset grid-target mapping relationship, and calculate the number of times each point target appears in all grid sets.

[0093] Step 5: If a point appears only once, proceed to Step 7. If no point appears only once, consider inter-wavelength overlap, as well as the same-wavelength constraint and continuity characteristics. Determine if any of the grid cells containing these target points share the same wavelength as the previously observed grid cells, but the time interval between them is less than the time interval between the same wavelengths. If so, prioritize observing the grid cell containing this target point and repeat Step 3. For example... Figure 3 As shown in (a), three grids to be observed appear simultaneously at the same wavelength. The grids observed in the previous step are from the second wavelength, and the interval between the start time of the observed grids and the start time of the grids to be observed is less than the interval between the same wavelengths. Therefore, wavelength 2 is selected as the grid for the next observation step.

[0094] Step 6: If no point appears only once, consider inter-wavelength overlap, as well as the same-wavelength constraint and continuity characteristics. In the grid containing these target points, there is no grid with the same wavelength as the previously observed grid and the time interval is less than the same-wavelength time interval. Then, find the grid with the smallest wavelength number among all the earliest observed grids and repeat step 3. For example... Figure 3As shown in (b), the first grid to show a point target exists across multiple wavelengths. The previously observed grids are either, as in case 1: on the same wavelength as the grid to be observed but with a time interval greater than the same wavelength interval constraint; or, as in case 2: with a time interval less than the same wavelength interval constraint, but on different wavelengths. Therefore, the grid with the smallest wavelength number among all the first appearing grids, i.e., wavelength 1, is selected for priority observation.

[0095] Step 7: If there is a point target that appears only once, then prioritize observing the grid cell containing the first point target that appears only once in all the first grid cells, and repeat step 3.

[0096] Step 8: Repeat steps 1 through 7 until the task set is empty, thus obtaining a task set where all point targets and grids correspond one-to-one. This completes the point target grid deduplication module.

[0097] Step 9: For point targets that have already been deduplicated, begin time window correction processing. First, determine the start time interval between the start times of the previous and next grid cells. Check if this interval is less than either the minimum single-start duration or the minimum single-imaging duration, which is a specific value determined by project requirements. If the condition is met, merge the grid cells; otherwise, do not merge them. Figure 4 As shown.

[0098] Step 10: Constrain the time windows obtained from the merged graticets in Step 9 with the unprocessed graticets to meet the following criteria: Is the time interval between the start time of the previous time window and the end time of the next time window less than the maximum imaging duration, and does it satisfy any of the following conditions: 1. Is the interval between the end time of the previous time window and the start time of the next time window less than the time interval between co-positions? If it is less, merge the time windows; if it is not less, do not merge. Figure 5 As shown in Case 1. 2. If the difference between the start time of the previous time window and the start time of the next time window is less than the sum of the minimum single imaging duration and the time interval between co-positions, then merging is required; otherwise, merging is not required. Figure 5 As shown in scenario two, the merged window becomes the new starting window, and the process continues sequentially until the requirements are no longer met, at which point the loop exits. This completes the point target grid merging module.

[0099] Step 11: Correct the processed time windows shorter than the single imaging duration and the unprocessed graticules, and pad them to meet the minimum single imaging duration constraint. Update the task set, delete the merged graticules in the task set, and obtain the observation strips based on the merged and padded graticules. Figure 6 As shown. This completes the preprocessing of all point target task clusters with optimal observation window times.

[0100] In summary, this invention proposes an innovative preprocessing system and method for satellite point target observation windows with minimal cycles. Based on coarse-allocated point target task grid matching, the system employs a heuristic algorithm to handle inter-orbit and wavefront overlap issues, thereby determining a unique time window for each point target. This method ensures that point target tasks are included within their first occurrence time window as much as possible, efficiently completing the mapping relationship between point targets and task grids.

[0101] This invention introduces a constraint-optimized raster merging strategy, which first establishes a constraint-satisfaction model. This model minimizes the number of observation time windows and the total mission cycle. Furthermore, this raster merging strategy has significant advantages, directly meeting the requirements of a single imaging strip, thus making fuller use of satellite resources and minimizing the total mission cycle. This raster merging strategy not only significantly improves the utilization efficiency of satellite resources but also possesses high flexibility and adaptability, directly meeting various complex observation needs.

[0102] Taking into full account constraints such as wavefront strip continuity, maximum and minimum imaging duration per session, time interval between images at the same wavefront, and minimum startup time, a flexible grid merging strategy is adopted. A heuristic clique partitioning algorithm is used to solve the constraint-satisfying optimization model. This invention provides an effective solution for both multi-task scheduling in dense observation scenarios and high-precision observation of specific targets. Simultaneously, it ensures the shortest overall task cycle, thereby improving the execution efficiency and success rate of observation tasks.

[0103] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An optimal time window remote sensing satellite point target mission cluster preprocessing system, characterized in that, include: The point target raster deduplication module is used to model the observation window of the mission cluster based on the coarsely allocated remote sensing satellite point target matching raster to obtain a unique matching raster for all point targets. The target grid merging module is used to merge adjacent grids that meet the preset maximum and minimum imaging duration constraints in all point target unique matching grids into a time window, and obtain the time window and the unprocessed grid. The single imaging time window correction module is used to receive the time window and the unprocessed grid, and correct the unprocessed grid to meet the maximum and minimum imaging duration constraints, obtain the observation strip, and complete the preprocessing of the remote sensing satellite point target task cluster with the optimal observation window time.

2. The system of claim 1, wherein, The point target raster deduplication module includes: The grid selection unit is used to select the grid from which the observed point target first appears in the coarsely allocated point target matching grid, using the least period principle. The task set acquisition unit is used to obtain a task set of point targets and unique point target matching grids based on the point target matching grid that first appears in the observation point target.

3. The system of claim 2, wherein, The task set acquisition unit includes: The first judgment subunit is used to determine whether the point target matching grid that first appears the selected target observation point is unique. If it is, it is observed first, and other grids that match the point target in the point target matching grid are deleted to obtain the task set of point targets and point target unique matching grids. If not, it finds all point targets in the point target matching grid according to the unique mapping relationship between the point target matching grid and the point target, and calculates the number of times each point target appears in all point target matching grids. The second judgment subunit is used to determine whether there is a point target that appears only once in all point target matching grids. If so, the priority observation of the first judgment subunit is executed. The third judgment subunit is used to determine whether there is a point target matching grid in the point target matching grid where the point target is located that has the same wave position as the point target matching grid observed in the first judgment subunit and the time interval is less than the time interval of the same wave position when the judgment result of the second judgment subunit is negative. If yes, the priority observation of the first judgment subunit is executed; if no, the point target matching grid with the smallest wave position number among all the first point target matching grids is found and the priority observation of the first judgment subunit is executed.

4. The system of claim 1, wherein, The target grid merging module includes: The first point target grid merging unit is used to determine whether the start time interval of adjacent grids in all point target unique matching grids meets the preset minimum duration constraint, and merges adjacent grids that meet the preset minimum duration constraint into a time window to obtain the time window and the unprocessed grid; the preset minimum duration constraint is any one of the following: less than the minimum duration of a single power-on or the minimum duration of a single imaging. The second target grid merging unit is used to determine whether the time window and the unprocessed grid simultaneously meet the preset maximum duration constraint and the same-wavelength imaging time interval constraint, and merge the time window or the unprocessed grid that simultaneously meets the preset maximum duration constraint and the same-wavelength imaging time interval constraint to obtain the final time window and the unprocessed grid; wherein, the preset maximum duration constraint is less than the maximum imaging duration.

5. The system of claim 4, wherein, The second point target grid merging unit, the same wave position imaging time interval constraint includes a first constraint or a second constraint; wherein, The first constraint is that the interval between the end time of the previous time window and the start time of the next time window is less than the same wave position time interval; The second constraint is that the difference between the start time of the previous time window and the start time of the next time window is less than the sum of the single minimum imaging duration and the same wave position time interval.

6. A method for optimal time windowed point target mission cluster preprocessing of a remote sensing satellite, applying the system of any of claims 1-5, characterized in that, It includes: Based on the coarse allocation of remote sensing satellite point target matching grid, the task cluster observation window is modeled, and all point target unique matching grids are obtained; The adjacent grids in all point target unique matching grids that meet the preset maximum minimum imaging duration constraint are merged into a time window, and the time window and the unprocessed grid are obtained; Receive the time window and the unprocessed grid, and correct the unprocessed grid to meet the maximum minimum imaging duration constraint to obtain an observation strip, and complete the observation window time optimal remote sensing satellite point target task cluster preprocessing.

7. The method of claim 6, wherein, The method for obtaining the point target unique matching grid includes: In the coarse allocation point target matching grid, the least period principle is adopted to obtain the point target matching grid where the observation point target first appears; Based on the point target matching grid where the observation point target first appears, a task set of point targets and point target unique matching grids is obtained.

8. The method of claim 6, wherein, The method for judging whether all point target unique matching grids meet the preset maximum minimum imaging duration constraint includes: Judging whether the point target matching grid where the target observation point first appears is unique, if so, preferentially observing, deleting other grids matched by the point target in the point target matching grid, and obtaining a task set of point targets and point target unique matching grids; if not, according to the point target matching grid and the point target unique mapping relationship, finding all point targets in the point target matching grid, and calculating the number of times each point target appears in all point target matching grids; Judging whether there is a point target that appears only once in all point target matching grids, if so, preferentially observing; if not, judging whether there is a point target matching grid in the point target matching grid where the point target is located that has the same wave position as the point target matching grid that has been observed in the first judgment subunit and the time interval is less than the same wave position time interval, if so, preferentially observing; if not, finding the point matching grid with the smallest wave position number in all point target matching grids where the point target first appears, and preferentially observing.

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