A planning method for splicing multiple strips on the same track with quantifiable control for agile satellites

Abstract

The present invention relates to a method for quantifiable controllable co-orbital multi-strip splicing planning for an agile satellite, comprising the steps of constructing a search solution space; utilizing a genetic algorithm solution space search framework to invoke a parallel tracking diffusion search mode to search within the search solution space for feasible solutions for the co-orbital multi-strip splicing planning scheme for an agile satellite; evaluating the feasible solutions based on a quantitative analysis of the strip overlap rate; and obtaining an optimal solution for the co-orbital multi-strip splicing planning for an agile satellite through multiple iterations of the search and evaluation process for feasible solutions. The method implements quantitative analysis and quantitative control of the strip overlap rate based on satisfying the satellite's co-orbital multiple observation attitude maneuvering constraints, rapidly searching within the solution space to obtain a relatively optimal strip splicing solution that meets expectations.

Description

Technical Field

[0001] The present invention relates to the field of satellite imaging technology, and in particular to a method for planning the splicing of multiple strips on the same track that can be quantified and controlled by an agile satellite. Background Art

[0002] Imaging satellites are artificial satellites whose main purpose is to observe the earth. They fly around the earth in orbit and observe targets by changing their attitude. They are divided into optical, SAR, electronic, video and other star-like satellites based on payload use, traditional satellites and agile satellites based on satellite attitude maneuverability, and high orbit, medium orbit and low orbit based on orbit altitude.

[0003] The length of the satellite's observable time window for a target (about 10 minutes per time) and the number of passes (1 to 2 times per day) are limited. Traditional satellites conduct a push-scan observation of a target during each pass, with imaging lasting about 10 to 30 seconds. More than 95% of the observation window is not effectively used, and only the instantaneous state of the target is obtained, failing to obtain high-value information such as its motion characteristics. Agile satellites can observe targets within their visual range through rapid attitude maneuvers in roll, pitch, and yaw. Within a single pass window (about 10 minutes), multiple maneuvers and pointing can be performed to achieve multi-angle stereo imaging and multi-strip stitching imaging on the same track. More target features can be obtained in both spatial and temporal dimensions, supporting higher-level target pattern recognition and situation analysis, and the efficiency of satellite use is multiplied.

[0004] The goal of the multi-swath stitching observation mode is to utilize a single transit window to stitch together observations of the target area multiple times, thereby obtaining remote sensing data covering a larger area perpendicular to the orbital direction, which is not possible with a single swath imaging. Ground processing systems can stitch these multi-swath data into a single wide-band image. Key mission planning for this mode lies in determining the start and end observation times for each swath and the satellite attitude at the start of each observation. The observation plan must meet the following requirements: the observation interval between swaths must be no less than the maneuvering time required to travel between observation attitudes. Swath overlap should be neither too large nor too small. Excessive overlap reduces the effective coverage area, wasting resources. Excessive overlap hinders image stitching processing by the ground system. The imaging area of ​​a satellite observing Earth is not fixed in size. When the pitch angle is fixed and the roll angle is variable, the larger the roll angle, the wider the swath width. When the roll angle is fixed and the pitch angle is variable, the larger the pitch angle, the wider the swath width. On the other hand, as a satellite observes a target at different times, both its roll and pitch angles change. Roll angles change gently, while pitch angles change dramatically. Larger roll and pitch angles simultaneously increase coverage, but the image quality is poor.

[0005] The traditional strip splicing method mainly controls the strip spacing to meet the maneuvering travel time requirements. For the strip observation attitude, the roll angle offset method is usually used. The offset is the field of view angle minus the expected overlap angle. A feasible imaging solution is obtained by sliding search in the time dimension. The problem with the roll angle fixed offset method used in this mode is that it ignores the fact that different strips have different time dimensions and their pitch angles vary greatly. In the end, the overlapping areas between the imaging strips are of different widths, and quantitative control is not achieved. This relatively extensive co-orbit multi-strip splicing planning method is far from the current business goal of continuously improving the refinement and high efficiency of satellite control. Therefore, a method that can not only meet the satellite attitude maneuvering constraints but also effectively solve the quantitative control of the splicing overlap rate must be adopted to meet new business needs. Summary of the Invention

[0006] Aiming at the problem of multi-strip splicing mission planning for agile satellites on the same track, the present invention aims to provide a method for planning multi-strip splicing for agile satellites with quantifiable control. On the basis of satisfying the satellite's multiple observation attitude maneuvering constraints on the same track, the method realizes quantitative analysis and quantitative control of the strip overlap rate, and quickly searches in the solution space to obtain a relatively optimal strip splicing solution that meets expectations.

[0007] In order to achieve the above-mentioned object of the invention, the technical solution of the present invention is:

[0008] The present invention provides a method for planning the splicing of multiple bands on the same track that can be quantified and controlled by an agile satellite, comprising:

[0009] Construct search solution space;

[0010] A parallel tracking diffusion search mode is called by using a genetic algorithm solution space search framework to search for a feasible solution of a multi-band splicing planning scheme for agile satellites on the same track in the search solution space;

[0011] Based on the quantitative analysis of the stripe overlap rate, the feasible solutions are evaluated;

[0012] Through multiple iterative searches and evaluations of feasible solutions, the optimal solution for the co-orbit multi-strip splicing planning of agile satellites is obtained.

[0013] According to one aspect of the present invention, constructing a search solution space includes:

[0014] Converting the target imaging area of ​​the agile satellite into a grid set, taking the center point of each grid to obtain a target area dot matrix sequence;

[0015] The genetic gene sequence is constructed in the order of the target region dot array sequence.

[0016] According to one aspect of the present invention, the method of using a genetic algorithm solution space search framework to call a parallel tracking diffusion search mode to search for a feasible solution of a multi-band splicing planning scheme for agile satellites on the same track in the search solution space includes:

[0017] Using a genetic algorithm to perform crossover, mutation, and progeny screening on the genetic gene sequence;

[0018] Observe and arrange in the order of a certain genetic gene sequence of a genetic individual;

[0019] The time after the end point of the previous strip observation and the minimum maneuvering time of the satellite attitude are added together is taken as the starting point, and the end point of the entire track visible window is taken as the end point to construct the tracking diffusion sliding time window.

[0020] A two-dimensional diffusion search in both time and space dimensions is performed within a sliding time window to obtain a feasible solution for the splicing of multiple bands on the same track of an agile satellite.

[0021] A two-dimensional iterative diffusion search is performed on all points in the genetic gene sequence to form all feasible solutions for the current genetic individual same-track multi-band splicing planning scheme.

[0022] According to one aspect of the present invention, in the process of constructing the tracking diffusion sliding time window, the imaging duration of each strip and the number of strips are input, the observation starting point of each strip is determined, the satellite attitude maneuvering duration and the time difference between strips from the end point of each previous strip observation to the starting point of the current strip observation are calculated, and by continuously sliding the time of the subsequent strip observation starting point, the moment that satisfies the satellite attitude maneuvering duration is less than or equal to the time difference between each strip is searched, thereby determining the observation moments of all the strips.

[0023] According to one aspect of the present invention, performing a two-dimensional search in the time dimension and the space dimension within the sliding time window to obtain a feasible solution for the splicing planning of multiple strips on the same track of an agile satellite includes:

[0024] In the time dimension, the time period to be searched within the sliding time window is grouped and sliced, and multiple diffusion points are formed at the starting point of each time period. Each time the sliding point is taken, the target area dot matrix is ​​observed in sequence, and the sliding point is continuously diffused backward. The calculation results are tracked and recorded to complete the search calculation of the entire time period and obtain all feasible solutions;

[0025] In the spatial dimension, the target area dot sequence is sequentially sliced, skipping the covered points of the previous strip, and performing an iterative search in the uncovered point set. The overlap rate between the current strip and the previous strip is calculated. If the deviation between the strip overlap rate and the target overlap rate is less than the set tolerance, the search is terminated. Otherwise, the iterative search is continued in the uncovered point set. If all points in the uncovered point set do not meet the condition that the deviation between the strip overlap rate and the target overlap rate is less than the set tolerance, the strip closest to the target overlap rate is taken as the output.

[0026] According to one aspect of the present invention, during the search process, a stream computing map-reduce model is used to implement concurrent search calculation and merge processing, and quickly generate an agile satellite co-orbit multi-band splicing planning scheme.

[0027] According to one aspect of the present invention, the quantitative analysis based on the stripe overlap ratio to evaluate the quality of feasible solutions includes:

[0028] A satellite line-of-sight vector model is constructed, and the edge of the satellite sensor's contour is defined as the line-of-sight vector. Given any ephemeris and attitude conditions, the line-of-sight vector is calculated by finding the intersection of the line-of-sight vector and the ellipsoid model. The intersection point is the closer to the satellite. The line-of-sight vectors of all contours are solved to obtain the sensor's imaging area on the Earth's surface at the corresponding time and attitude.

[0029] The imaging area of ​​each strip is converted into a grid set by using the equal-size grid decomposition method. The overlapping part of the strips is obtained by taking the intersection and union of the grid sets, and the strip overlap rate of the feasible solution is calculated.

[0030] The evaluation function is used to perform weighted average of the planned multi-band splicing scheme according to the target coverage, the number of observation imaging, and the deviation between the band overlap rate and the target overlap rate to obtain the comprehensive fitness of the genetic individual;

[0031] The optimal agile satellite co-orbit multi-strip splicing planning scheme is selected based on the comprehensive fitness search.

[0032] According to one aspect of the present invention, the generation of the feasible solution satisfies the following conditions simultaneously: the stripe interval time is greater than the shortest time required for satellite attitude maneuvering between stripes; and the deviation between the stripe overlap rate and the target overlap rate is less than a set tolerance.

[0033] According to one aspect of the present invention, the conditions satisfied by the optimal multi-strip splicing planning scheme in the feasible solution include: maximum target coverage, minimum number of observation imaging times, shortest strip interval time, and strip overlap rate closest to the target overlap rate.

[0034] According to one aspect of the present invention, the iterative termination condition of the search and evaluation process of the feasible solution is that the number of iterations reaches a maximum iteration number and a no-improvement iteration number.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The solution space search algorithm framework of this invention utilizes a genetic algorithm, which offers good universality, supports parallel search, and achieves rapid convergence. By simulating crossover, mutation, and selection, it performs a two-dimensional iterative search in space and time within the solution space. The algorithm then calls upon feasible solutions, continuously evolving the search to find the optimal solution based on the feedback from the evaluation method regarding the fitness of the solutions.

[0037] According to one solution of the present invention, a parallel tracking diffusion search method is adopted to effectively search for feasible solutions for splicing multiple satellite strips on the same track. The streaming computing map-reduce model is used to perform concurrent search calculations and merge processing in the two dimensions of time and space, which can quickly generate strip splicing planning results.

[0038] According to one solution of the present invention, imaging coverage area calculation and grid overlap analysis based on a field-of-view vector model can accurately calculate the Earth's surface coverage area of ​​a satellite at any attitude angle. A global grid model is then used to perform regional grid decomposition and inter-regional intersection and union analysis. Incorporating this method into the technical solution for splicing multiple strips on the same track enables precise quantitative control of the inter-strip overlap ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0040] Figure 1 A flowchart schematically illustrates a method for planning simultaneous multi-band splicing of agile satellite quantized control systems according to an embodiment of the present invention;

[0041] Figure 2 Schematic diagram showing the splicing of multiple strips on the same track with quantitative control of the strip overlap rate provided by an embodiment of the present invention;

[0042] Figure 3 Schematically showing a splicing diagram of multiple strips on the same track without quantitative control of the strip overlap rate in a traditional method provided by an embodiment of the present invention;

[0043] Figure 4 The framework of an agile satellite quantitative control on-track multi-band splicing planning algorithm process provided by an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0044] The description of the embodiments in this specification should be combined with the corresponding drawings, which should be considered a complete part of this specification. In the drawings, the shapes and thicknesses of the embodiments may be exaggerated and indicated for simplicity or convenience. Furthermore, the various structural components in the drawings will be described separately. It is worth noting that components not shown in the drawings or not described in words are known to those of ordinary skill in the art.

[0045] The description of the embodiments herein and any references to directions and orientations are for ease of description only and are not to be construed as limiting the scope of the present invention. The following description of the preferred embodiments may involve combinations of features, which may exist independently or in combination. The present invention is not specifically limited to the preferred embodiments. The scope of the present invention is defined by the claims.

[0046] According to the concept of the present invention, the existing methods for planning the multi-strip splicing mission of agile imaging observation satellites on the same track have defects such as low computational efficiency and the inability to accurately quantify the overlap rate between control strips. This embodiment proposes a method for planning multi-strip splicing on the same track for agile satellites that can be quantified and controlled. The method uses a parallel tracking diffusion method to search for feasible solutions for the strip splicing planning scheme. At the same time, the field of view vector model and the global grid model are introduced to quantitatively analyze and calculate the strip area covered by the target imaging, and a quality evaluation is performed based on this. When planning the splicing scheme, the solution space search framework of the genetic algorithm is used. By continuously transforming the input variables, the parallel tracking diffusion search and quality evaluation of feasible solutions are continuously and iteratively called. Finally, the result with the highest score is selected as the final output. This method can quickly obtain a relatively optimal strip splicing planning scheme, which can significantly improve the efficiency of satellite use and the level of refined management and control.

[0047] like Figure 1 and Figure 4 As shown, the specific implementation process of the above-mentioned agile satellite quantifiable control multi-band splicing planning method includes the following steps:

[0048] S100, constructing a search solution space;

[0049] According to one embodiment of the present invention, the specific implementation process of constructing the search solution space in step S100 includes: S110, target area rasterization: converting the target imaging area of ​​the agile satellite into a grid set, taking the center point of each grid to obtain a target area raster sequence; S120, constructing a genetic sequence in the order of the target area raster sequence.

[0050] S200, using a genetic algorithm solution space search framework to call a parallel tracking diffusion search mode to search for a feasible solution for a co-orbit multi-band splicing planning scheme for an agile satellite in the search solution space;

[0051] According to one embodiment of the present invention, the specific implementation process of using the genetic algorithm solution space search framework to call the parallel tracking diffusion search mode in step S200 to search for a feasible solution of the agile satellite co-orbit multi-band splicing planning scheme in the search solution space includes:

[0052] S210, using a genetic algorithm to perform crossover, mutation, and offspring screening on a genetic gene sequence;

[0053] S220, observing and arranging in the order of a certain genetic gene sequence of a genetic individual;

[0054] S230, constructing a tracking diffusion sliding time window with the time obtained by adding the end point of the previous strip observation and the minimum maneuvering time of the satellite attitude as the starting point and the end point of the entire track visibility window as the end point;

[0055] S240: Perform a two-dimensional diffusion search in the time dimension and the space dimension within the sliding time window to obtain a feasible solution for the splicing planning of multiple strips on the same track of the agile satellite;

[0056] S250, performing a two-dimensional iterative diffusion search on all points in the genetic gene sequence to form all feasible solutions for the current genetic individual same-track multi-band splicing planning scheme.

[0057] Within the limited access time window of the agile satellite's visibility to the target, multiple attitude-locked push-broom imaging missions must be completed at different times, while simultaneously meeting the satellite's attitude maneuvering duration from one strip to the next, ultimately forming multiple stitched-together wide-band images exceeding the width of a single imaging pass. Specifically, during the construction of the tracking diffusion sliding time window in step S230, the imaging duration and number of strips are input, the observation starting point of each strip is determined, and the satellite attitude maneuvering duration and inter-strip time difference from the end point of each previous strip observation to the starting point of the current strip observation are calculated. By continuously sliding the time of the subsequent strip observation starting point, a search is performed to find a time when the satellite attitude maneuvering duration is less than or equal to the inter-strip time difference. This time is the feasible strip observation time, and the same process is repeated to determine the observation times for all the strips.

[0058] As a satellite orbits, the width of the swath formed by observing the starting point of a designated swath varies at different time points. The farther the satellite is from the observation point and the greater the pitch angle, the wider the swath width. Therefore, controlling the overlap between swaths requires a two-dimensional search in both time and space. The time dimension search space is from the end time of the previous swath (+ minimum maneuvering time) to the end point of the arc time window; the space dimension search space is the set of array points in the uncovered target area.

[0059] According to one embodiment of the present invention, in the above-mentioned step S240, a two-dimensional search in the time dimension and the space dimension is performed within the sliding time window to obtain a specific implementation process of a feasible solution for the agile satellite co-orbit multi-band splicing planning, including: in the time dimension, grouping and slicing the time period to be searched within the sliding time window, forming multiple diffusion points at the starting point of each time period, sequentially observing the target area point array at the sliding point time each time, continuously diffusing backward, tracking and recording the calculation results, completing the search calculation for the entire time period, and obtaining all feasible solutions; in the spatial dimension, sequentially slicing the target area point array sequence, skipping the covered points of the previous strip, performing an iterative search in the uncovered point set, calculating the overlap rate between the current strip and the previous strip, terminating the search if the deviation between the strip overlap rate and the target overlap rate is less than a set tolerance, otherwise continuing the iterative search in the uncovered point set; if all points in the uncovered point set do not meet the condition that the deviation between the strip overlap rate and the target overlap rate is less than the set tolerance, taking the strip closest to the target overlap rate as the output.

[0060] In the above iterative diffusion search process, the use of a streaming Map-Reduce model enables concurrent search and merge processing with a high degree of parallelism, rapidly generating agile satellite co-orbit multi-band splicing plans. Existing common methods, on the other hand, often use serial processing, searching in a single time dimension, resulting in slower algorithms.

[0061] S300: Evaluate feasible solutions based on quantitative analysis of strip overlap. Quantifying the overlap ratio of feasible solutions for strip splicing requires the ability to quickly and efficiently calculate the surface coverage of the strips, describe them as polygonal topology, and quantitatively calculate the strip overlap ratio.

[0062] According to an embodiment of the present invention, the specific implementation process of evaluating the quality of feasible solutions based on the quantitative analysis of the stripe overlap ratio in step S300 includes:

[0063] S310: Construct a satellite line-of-sight vector model. Define the line-of-sight vector as the edge of the satellite sensor's contour. Given any ephemeris and attitude, calculate the intersection of the line-of-sight vector and the ellipsoid model. The intersection point is the closer of the two intersection points to the satellite. Solve for all contours of the line-of-sight vector to obtain the sensor's imaging area on the Earth's surface at the corresponding time and attitude. Accurate calculation of the imaging area is crucial for achieving quantitative control stripe splicing planning.

[0064] S320: Use the equal-size grid decomposition method to convert the imaging area of ​​each strip into a grid set. Obtain the overlapping portion of the strips by calculating the intersection and union of the grid sets, and calculate the strip overlap ratio of the feasible solution. Perform a globally unique grid decomposition on the imaging area of ​​each strip. By performing intersection and union operations on the decomposed grid sets, a quantitative index of the overlap ratio can be quickly calculated.

[0065] S330: Using an evaluation function, a weighted average is performed on the planned multi-band splicing solutions based on the target coverage, the number of observations, and the deviation between the band overlap rate and the target overlap rate to obtain the comprehensive fitness of the genetic individuals. The closer the band overlap rate of the current feasible solution is to the target overlap rate, the higher its score.

[0066] S340. Search and select the better agile satellite co-orbit multi-strip splicing planning scheme based on comprehensive fitness.

[0067] That is, the generation of the above feasible solution (a feasible solution for multi-strip splicing planning) must simultaneously meet the following conditions: (1) the strip interval time is greater than the shortest time required for satellite attitude maneuvers between strips; and (2) the deviation between the strip overlap rate and the target overlap rate is less than the set tolerance. Specifically, the set tolerance defaults to 0.05.

[0068] S400. Through multiple iterations of searching and evaluating feasible solutions, the optimal solution for the multi-strip splicing planning of agile satellites on the same track is obtained.

[0069] Factors considered in evaluating the performance of a plan include target coverage, number of observations and imaging, stripe interval, and the deviation between the stripe overlap ratio and the target overlap ratio. The optimal multi-strip stitching plan meets the following conditions: maximum target coverage, minimum number of observations and imaging, shortest stripe interval, and a stripe overlap ratio closest to the target overlap ratio.

[0070] The criterion for terminating the iterations of the genetic algorithm's search and evaluation process for feasible solutions is whether the number of iterations reaches the maximum number of generations and the number of generations without improvement. The iterations terminate when both the maximum number of generations and the number of generations without improvement are reached. Specifically, the maximum number of generations and the number of generations without improvement default to 100 and 10, respectively.

[0071] This embodiment uses a genetic algorithm driven by a solution space search framework to continuously transform input variables, search for feasible solutions in the variable space, and use an evaluation function to screen the pros and cons of the solutions. After offspring population generation, crossover, mutation, and offspring screening, it eventually evolves an optimal same-track multi-band splicing solution that meets the expected overlap rate and maximizes coverage of the target area.

[0072] The common methods of existing strip splicing planning usually do not introduce the calculation and grid analysis methods of the imaging coverage area, and cannot achieve quantitative control. Figure 3 As shown in the figure, the traditional method uses a fixed roll angle offset, and the overlap rate between strips is variable. When the distance between the target and the subsatellite point changes, the fixed roll angle offset will also cause the size of the strip overlap area to change, making it impossible to quantitatively control. In contrast, the strip splicing planning scheme based on the genetic algorithm in this embodiment, which can quantitatively control the overlap rate of multiple strips on the same track, uses two-dimensional temporal and spatial variables for search and designs reasonable constraints and evaluation functions. This not only satisfies satellite constraints but also achieves quantitative control of the strip overlap rate. At the same time, it also considers factors such as the number of imaging times and time span to generate a relatively optimal strip splicing planning scheme with fine quantitative control, as shown in the figure. Figure 2 shown.

[0073] The serial numbers of the above-mentioned steps involved in the method of the present invention do not mean the order of execution of the method. The execution order of each step should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for planning the splicing of multiple bands on the same track with quantifiable control for an agile satellite, comprising: Construct search solution space; A genetic algorithm solution space search framework is used to search for feasible solutions for the multi-band splicing planning scheme for agile satellites on the same track within the search solution space by adopting a parallel tracking diffusion search mode. Based on the quantitative analysis of the stripe overlap rate, the feasible solutions are evaluated; Through multiple iterative searches and evaluations of feasible solutions, the optimal solution for the co-orbit multi-strip splicing planning of agile satellites is obtained.

2. The method according to claim 1, characterized in that The constructing a search solution space includes: Converting the target imaging area of ​​the agile satellite into a grid set, taking the center point of each grid to obtain a target area dot matrix sequence; The genetic gene sequence is constructed in the order of the target region dot array sequence.

3. The method according to claim 2, characterized in that The genetic algorithm solution space search framework is used to adopt a parallel tracking diffusion search mode to search for a feasible solution of the agile satellite co-orbit multi-band splicing planning scheme in the search solution space, including: Using a genetic algorithm to perform crossover, mutation, and progeny screening on the genetic gene sequence; Observe and arrange in the order of a certain genetic gene sequence of a genetic individual; The time after the end point of the previous strip observation and the minimum maneuvering time of the satellite attitude are added together is taken as the starting point, and the end point of the entire track visible window is taken as the end point to construct the tracking diffusion sliding time window. A two-dimensional diffusion search in both time and space dimensions is performed within a sliding time window to obtain a feasible solution for the splicing of multiple bands on the same track of an agile satellite. A two-dimensional iterative diffusion search is performed on all points in the genetic gene sequence to form all feasible solutions for the current genetic individual same-track multi-band splicing planning scheme.

4. The method according to claim 3, characterized in that In the process of constructing the tracking diffusion sliding time window, the imaging duration of each strip and the number of strips are input, the observation starting point of each strip is determined, the satellite attitude maneuvering duration and the time difference between strips from the end point of each previous strip observation to the starting point of the current strip observation are calculated, and by continuously sliding the time of the subsequent strip observation starting point, the moment that satisfies the satellite attitude maneuvering duration is less than or equal to the time difference between each strip is searched, thereby determining the observation moments of all the strips.

5. The method according to claim 4, characterized in that The two-dimensional search in the time dimension and the space dimension is performed within the sliding time window to obtain a feasible solution for the splicing planning of multiple strips on the same track of an agile satellite, including: In the time dimension, the time period to be searched within the sliding time window is grouped and sliced, and multiple diffusion points are formed at the starting point of each time period. Each time the sliding point is taken, the target area dot matrix is ​​observed in sequence, and the sliding point is continuously diffused backward. The calculation results are tracked and recorded to complete the search calculation of the entire time period and obtain all feasible solutions; In the spatial dimension, the target area dot sequence is sequentially sliced, skipping the covered points of the previous strip, and performing an iterative search in the uncovered point set. The overlap rate between the current strip and the previous strip is calculated. If the deviation between the strip overlap rate and the target overlap rate is less than the set tolerance, the search is terminated. Otherwise, the iterative search is continued in the uncovered point set. If all points in the uncovered point set do not meet the condition that the deviation between the strip overlap rate and the target overlap rate is less than the set tolerance, the strip closest to the target overlap rate is taken as the output.

6. The method according to claim 5, characterized in that During the search process, the map-reduce model is used to implement concurrent search calculations and merge processing, and quickly generate agile satellite co-orbit multi-band splicing planning solutions.

7. The method according to claim 6, characterized in that The quantitative analysis based on the stripe overlap ratio is used to evaluate the quality of feasible solutions, including: A satellite line-of-sight vector model is constructed, and the edge of the satellite sensor's contour is defined as the line-of-sight vector. Given any ephemeris and attitude conditions, the line-of-sight vector is calculated by finding the intersection of the line-of-sight vector and the ellipsoid model. The intersection point is the closer to the satellite. The line-of-sight vectors of all contours are solved to obtain the sensor's imaging area on the Earth's surface at the corresponding time and attitude. The imaging area of ​​each strip is converted into a grid set by using the equal-size grid decomposition method. The overlapping part of the strips is obtained by taking the intersection and union of the grid sets, and the strip overlap rate of the feasible solution is calculated. The evaluation function is used to perform weighted average of the planned multi-band splicing scheme according to the target coverage, the number of observation imaging, and the deviation between the band overlap rate and the target overlap rate to obtain the comprehensive fitness of the genetic individual; The optimal agile satellite co-orbit multi-strip splicing planning scheme is selected based on the comprehensive fitness search.

8. The method according to claim 7, characterized in that The generation of the feasible solution satisfies the following conditions simultaneously: the strip interval time is greater than the shortest time required for satellite attitude maneuvering between strips; and the deviation between the strip overlap rate and the target overlap rate is less than a set tolerance.

9. The method according to claim 8, characterized in that The conditions satisfied by the optimal multi-strip splicing planning scheme in the feasible solution include: maximum target coverage, minimum number of observation imaging times, shortest strip interval time, and strip overlap rate closest to the target overlap rate.

10. The method according to claim 1 or 9, characterized in that The iterative termination condition of the search and evaluation process of the feasible solution is that the number of iterations reaches the maximum iteration number and the number of iterations without improvement.

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