A method, medium and device for scheduling of LEO satellites

Abstract

The application discloses a scheduling method for LEO satellites, a medium and equipment, and relates to the technical field of satellite scheduling.The method comprises the following steps: a LEO satellite cooperative observation region of interest coverage system is constructed; the region of interest is divided according to the orbits and shooting fields of view of the LEO satellites in the system; and under the premise of minimum full coverage of image acquisition to the RoI, the satellite is distributed with a shooting task according to the division, with the minimum satellite camera rotation energy consumption as the target.The application not only reduces the rotation energy consumption of the camera, but also guarantees the size of the image, so that the overall energy consumption of the satellite camera is minimized, and the minimum RoI full coverage is realized.The application has excellent performance and good robustness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite scheduling, in particular to a scheduling method for LEO satellites, a medium and an equipment. BACKGROUND

[0002] In recent years, Low Earth Orbit (LEO) satellites have become a focus of academic and industrial attention due to their high sensing accuracy, rapid communication capabilities, and low cost, leading to the successful launch of numerous LEO satellites. Equipped with high-resolution cameras, communication modules, and advanced processors, LEO satellites provide technical support for Earth observation missions, widely used in carbon emission monitoring, disaster response, and environmental analysis. In these missions, multiple satellites are often required to cooperate in observation to fully cover the Region of Interest (RoI).

[0003] However, as the complexity of tasks increases, the energy problem of LEO satellites becomes increasingly severe. Due to the small size of satellites and limited solar panel area, battery capacity is usually low, leading to energy shortages that affect satellite performance and mission execution. Secondly, multiple satellite cooperative observation may increase non-RoI image shooting, further increasing energy consumption. Therefore, it is necessary to reasonably allocate satellite image shooting tasks.

[0004] Currently, academia and industry mainly focus on reducing image size to optimize camera energy consumption for satellite remote sensing shooting tasks, and current research ignores the impact of satellite task allocation and camera rotation angle on camera energy consumption for remote sensing shooting tasks. SUMMARY

[0005] The present application aims to solve the problem that existing satellite camera scheduling focuses on reducing image size, which does not achieve effective energy optimization. A scheduling method for LEO satellites is proposed, including the following steps:

[0006] S1, a multi-LEO satellite cooperative observation Region of Interest coverage system is constructed;

[0007] S2, the Region of Interest is divided according to the orbits and shooting fields of view of the LEO satellites in the system;

[0008] S3, under the premise of minimizing the full coverage of the RoI by image acquisition, the satellite is scheduled for shooting tasks according to the division, aiming to minimize satellite camera rotation energy consumption.

[0009] Further, when the number of satellites in the coverage system is 2, the orbit intersection angle of the 2 satellites is 80°-100°; when the number of satellites in the coverage system is greater than 2, all satellites have two cases: parallel to each other and an intersection angle of 80°-100°.

[0010] Further, LEO satellite forms a strip with the width of the field of view when it orbits around the orbit, each LEO satellite camera captures the area on both sides of the orbit by rotating, forming two observation strips, all satellite observation strips form a series of lattices, which divide the RoI into a series of patches, and all patches form a complete RoI area.

[0011] Further, each satellite can only capture one of the patches on both sides of the orbit at the same time; satellites with two intersection angles of 80°-100° in the same space on the rising and falling orbit directions are analyzed as a group.

[0012] The satellite on the rising orbit satisfies:

[0013]

[0014] wherein, represents whether the nth satellite captures the patch with coordinates (i, j), yes, then No, then I represents the set of all row numbers of the patch, and J represents the set of all column numbers of the patch, represents whether the nth satellite is on the rising orbit, yes, then No, then

[0015] The satellite on the falling orbit satisfies:

[0016]

[0017] and satisfies:

[0018]

[0019] wherein, represents whether the nth satellite is on the falling orbit, yes, then No, then represents whether the nth satellite can capture the patch with coordinates (i, j).

[0020] Further, in order to realize complete coverage of the region of interest, it satisfies:

[0021]

[0022] wherein, N represents the set of LEO satellites.

[0023] Further, the energy consumption of the camera rotation of satellite n is represented as:

[0024]

[0025] wherein, θn represents the energy consumption of the n-th satellite camera rotation, M represents a set of fragments, suc((i,j)) represents the coordinates of the next photographed fragment after the n-th satellite photographs the fragment (i,j), (i',j') represents a coordinate in suc((i,j)), represents whether the n-th satellite photographs the fragment with coordinates (i',j'), E ro represents the energy required for one camera rotation;

[0026] and the photographing energy satisfies the following constraint:

[0027]

[0028] wherein, represents the photographing energy required for the n-th satellite to photograph the fragment (i,j), E thres represents an upper limit value of the photographing energy;

[0029] The objective of the scheduling is min∑ n∈N θ n .

[0030] The application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the LEO satellite-oriented scheduling method.

[0031] The application further provides an electronic device, which comprises a processor and a memory, the processor and the memory are connected with each other, the memory is used for storing a computer program, the computer program comprises computer readable instructions, the processor is configured to call the computer readable instructions to execute the LEO satellite-oriented scheduling method.

[0032] The application provides the following beneficial effects:

[0033] The application firstly divides a RoI into fragments according to a satellite observation strip, and then allocates a satellite photography task by jointly considering the image size obtained by photographing the RoI fragments by a satellite camera and the camera rotation energy consumption, and based on the minimum full coverage of the image acquisition to the RoI. The application not only reduces the camera rotation energy consumption, but also guarantees the image size, so that the minimum RoI full coverage is realized under the condition of the minimum overall energy consumption. The application has excellent performance and good robustness. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a flowchart of the LEO satellite-oriented scheduling method of the application;

[0035] Figure 2is a set of uplink and downlink satellite camera shooting fragment diagram of an embodiment of the present application;

[0036] Figure 3 is a three-fragment scene of an embodiment of the present application;

[0037] Figure 4 is a schematic diagram of the result of a fragment using different orbit direction satellites in a RoI area of an embodiment of the present application;

[0038] Figure 5 is a satellite camera rotation shooting schematic diagram of an embodiment of the present application;

[0039] Figure 6 is a block diagram of an electronic device in an exemplary embodiment of an embodiment of the present application;

[0040] Figure 7 is the sum of the rotation energy consumption and the content proportion of the picture of the satellite camera using three algorithms (CPO, REO, EC) under different maps, Figure 7 (a) is the rotation energy consumption of the satellite camera using three algorithms under different maps, Figure 7 (b) is the sum of the content proportion of the picture using three algorithms under different maps.

[0041] Figure 8 is the sum of the rotation energy consumption and the content proportion of the picture of the satellite camera using three algorithms (CPO, REO, EC) under different satellite quantities for shooting the same region of interest, Figure 8 (a) is the rotation energy consumption of the satellite camera using three algorithms under different satellite quantities for shooting the same region of interest, Figure 8 (b) is the sum of the content proportion of the picture using three algorithms under different satellite quantities for shooting the same region of interest.

[0042] Figure 9 is the sum of the rotation energy consumption and the content proportion of the picture of the satellite camera using three algorithms (CPO, REO, EC) under different shooting energies for shooting the same region of interest, Figure 9 (a) is the rotation energy consumption of the satellite camera using three algorithms under different shooting energies for shooting the same region of interest, Figure 9 (b) is the sum of the content proportion of the picture using three algorithms under different shooting energies for shooting the same region of interest. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present application clearer, the embodiments of the present application will be further described below with reference to the drawings.

[0044] The flowchart of the scheduling method for LEO satellites of an embodiment of the present application is as followsFigure 1 , specifically comprising the following steps:

[0045] S1, constructing a multi-satellite LEO satellite cooperative observation region coverage system.

[0046] The LEO satellite camera remains stationary and takes pictures while flying around its orbit, forming an observation strip. During the satellite flight, each satellite camera can take pictures of one of the strips on both sides of the orbit by rotating, so each satellite corresponds to two observation strips. The strips formed by multiple satellites in the ascending and descending orbits intersect vertically and horizontally, completing the coverage of the region of interest. The number of satellites is required to complete the full coverage of the RoI. Two satellites with an intersection angle of 80°-100° in the same space on the ascending and descending orbit directions are analyzed as a group (at the same time, the satellites on the ascending and descending orbits do not necessarily appear on the ascending and descending orbits at the same time.), and the intersection angle of the satellite orbits of each group is required to be 80°-100°. According to the range of the region of interest, the number of satellites in the system is required to be greater than or equal to 2, and the intersection angle of the satellite orbits is 80°-100°. When the number of satellites in the coverage system is 2, the intersection angle of the orbits of the 2 satellites is 80°-100°; when the number of satellites in the coverage system is greater than 2, there are two cases for all satellite orbits: parallel to each other and an intersection angle of 80°-100°. The observation strips of all satellites form a series of lattices, completely covering the region of interest.

[0047] S2, according to the orbits and shooting fields of view of the LEO satellites in the system, the region of interest is divided into pieces. The observation strips of all satellites divide the RoI into a series of lattices, called pieces, and all pieces form a complete RoI region.

[0048] S3, under the premise of minimizing the full coverage of the RoI by image acquisition, the goal is to minimize the energy consumption of satellite camera rotation, and the satellite is scheduled for shooting tasks according to the pieces.

[0049] Reference Figure 2 , Figure 2 is a camera shooting piece diagram of a group of ascending and descending orbit satellites of an embodiment of the present application, wherein Figure 2 The multiple vertically and horizontally intersecting observation strips are formed by multiple satellite cameras, and only the satellite camera distribution for the pieces in the figure is illustrated, Figure 2The observation strips of the two satellites in the figure are two observation strips on both sides of the respective running direction. The major axes of the orbits of the two LEO satellites in the figure are marked by arrows, the fields of view of the cameras of the two LEO satellites in the figure are marked in the state before the respective photographing sub-patch, there is a quadrilateral region of interest (RoI) in the figure, and there is a sub-patch in the region of interest. Since the photographing width is determined but the photographing length is variable, the satellite running on the ascending orbit can photograph the sub-patch with a smaller size, and the satellite running on the descending orbit needs to photograph an additional area on the photographing strip of the satellite on the descending orbit where the sub-patch is located, and the size of the photographed image is larger. Assigning the sub-patch to the satellite on the ascending orbit can reduce the size of the image, but the camera of the satellite on the ascending orbit needs to rotate the camera to turn the observation strip on the right side of the advancing direction of the satellite orbit to the observation strip on the left side in order to realize photographing, and the additional camera rotation causes more energy consumption.

[0050] In order to reduce the energy consumption of the camera of the satellite photographing the RoI as much as possible, and to effectively reduce the energy consumption caused by the rotation of the satellite remote sensing camera on the premise that the satellite photographed image can completely cover the RoI region, first, the task allocation of the target RoI sub-patch needs to be considered. Since the photographing width of the high-precision camera is fixed and the length is variable, different satellite orbits bring different photographing angles, which will cause differences in the size of the photographed RoI image. Therefore, the system divides the sub-patch into three types, one sub-patch scene where the RoI area ratio of the photographing angle of the satellite on the ascending orbit is larger, one sub-patch scene where the RoI area ratio of the photographing angle of the satellite on the descending orbit is larger, and finally, a scene where both the satellites on the two orbits are suitable for photographing. As shown in Figure 3 Figure 3 are three sub-patch scenes of the embodiment of the application. One side of sub-patch 1 is the same as the width of the photographing of the satellite on the ascending orbit, so the size of the image photographed by the satellite on the ascending orbit is small; one side of sub-patch 2 is the same as the width of the photographing of the satellite on the descending orbit, so the size of the image photographed by the satellite on the descending orbit is small; one side of sub-patch 3 is the same as the width of the photographing of the satellite on the ascending orbit, and the other side is the same as the width of the photographing of the satellite on the descending orbit, so both the satellites on the two orbits are suitable for photographing sub-patch 3.

[0051] As shown in Figure 4 Figure 4 ​​is a schematic diagram of the result of using different orbit direction satellites to shoot a tile of the RoI region of an embodiment of the present application. It can be found that shooting the tile of the RoI region by a descending orbit satellite will result in shooting extra non-RoI region (such as the blank area in the figure), increasing the shooting task amount and energy consumption; shooting by an ascending orbit satellite will only cover the RoI region, and the area of the non-target region is smaller, and the energy consumption is less, so the shooting task of the tile should be assigned to the ascending orbit satellite. In summary, first, the tiles need to be assigned to the most suitable satellite to reduce the camera shooting energy consumption and picture size.

[0052] Secondly, the satellite camera rotation strategy needs to be considered. In the process of satellite flight, the LEO satellite camera can rotate within a limited range, i.e. at time t1, it points to any one of the two strips on the left and right sides of the orbit where the satellite is located, at time t2, t2>t1, if the task target tile of the satellite is in the other strip, the satellite must rotate the camera to point to the other strip to complete the shooting of the tile. Therefore, the shooting task needs to be assigned to the satellite with the most matched orbit direction and camera rotation according to the tile position, to further reduce the energy consumption. When necessary, some tiles can be assigned to the satellite with unmatched orbit direction to reduce the camera rotation energy consumption, although the size of the non-RoI image is increased, but the energy is allocated between the image size and the camera rotation energy consumption, so as to minimize the total energy consumption of the shooting task. As shown in Figure 5 Figure 5 is a schematic diagram of satellite camera rotation shooting of an embodiment of the present application, Figure 5 In the figure, the satellite flight direction is from left to right, at time t1, the satellite camera shoots the tile on the right side of the flight orbit (i.e. the tile below in the figure), at time t2, the satellite camera needs to rotate by an angle of a to point to the strip on the left side of the orbit to shoot the tile on the left side of the flight orbit (i.e. the tile below in the figure).

[0053] The above strategy is expressed by formula, the region of interest coverage system is composed of a group of LEO satellites, denoted as N={1, 2, …, N}, N represents the set of LEO satellites. Use and to represent whether the nth satellite is in the ascending orbit or the descending orbit, 1 represents yes, otherwise 0. represents that the nth satellite is in the ascending orbit, represents that the nth satellite is not in the ascending orbit, represents that the nth satellite is in the descending orbit, represents that the nth satellite is not in the descending orbit. Wherein, Therefore, and satisfy the following constraint conditions: ​​​

[0054] The two-dimensional coordinate information of the RoI patches can be represented as a set M = {(i, j)}, each satellite can only observe the patches on the left and right sides of its orbit at any time, and the patches outside the orbit range cannot be observed. Using to represent whether the nth satellite can observe patch (i, j), where

[0055] Since the shooting width of the satellite camera is fixed and the length is adjustable, shooting the same patch from different directions will result in different image sizes. Therefore, the shooting energy of the camera will also be different. Using the shooting energy consumption to represent the energy required by the nth satellite to shoot patch (i, j).

[0056] For the shooting of each patch, there can be multiple satellites to choose from. In order to clearly indicate which satellite actually shot the patch, a new variable is introduced, where M represents the patch set, and represents whether the nth satellite shot patch (i, j), with the precondition that that is, and satisfies: I represents the set of all row numbers of the patch, and J represents the set of all column numbers of the patch. In order to ensure complete coverage of the RoI, it must satisfy In order to reflect that the total energy consumption of the camera (camera energy composition: shooting energy + rotation energy) can be reduced by sacrificing a little photo size, the shooting energy satisfies the following constraint:

[0057]

[0058] where, represents the shooting energy required by the nth satellite to shoot patch (i, j), and E thres represents the upper limit value of the shooting energy.

[0059] The goal of the present invention is to minimize the energy consumption caused by the rotation of the satellite camera. For the nth satellite, its rotation energy consumption is represented as:

[0060]

[0061] where, θ n represents the camera rotation energy consumption of the nth satellite, suc((i, j)) represents the coordinates of the next patch after the nth satellite shoots patch (i, j), (i', j') represents a coordinate in suc((i, j)), represents whether the nth satellite shoots patch with coordinates (i', j'), and E roEnergy needed for one rotation of the camera.

[0062] The final scheduling target of the problem can be expressed as min∑ n∈N θ n .

[0063] Using the fragmentation algorithm proposed in the present application, the fragments are allocated to the corresponding satellites, and then the fragmentation allocation result is uploaded to the corresponding satellites along with the remote sensing task. In the process of RoI fragmentation shooting task allocation, considering the energy consumption of satellite camera rotation and the content proportion of the image, the satellite with the lowest energy consumption is selected for each RoI fragmentation shooting task. When the allocated satellite is a satellite that has flown over, the algorithm traces back to the time when the satellite just arrived and clears the allocation results of the RoI fragmentation shooting tasks after that time, and allocates the fragmentation shooting task to the optimal satellite, and continues to allocate all the remaining RoI fragmentation shooting tasks, to ensure the minimization of the energy consumption of the entire shooting task. Subsequently, the satellite performs the shooting task and performs corresponding on-board processing of the image, and then the subsequent data is downloaded to the ground, and finally the data processing and result summarization are performed on the ground.

[0064] In an exemplary embodiment, a computer readable storage medium is included, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the above-mentioned scheduling method for LEO satellites.

[0065] Please refer to Figure 6 In an exemplary embodiment, an electronic device is also included, which includes at least one processor, at least one memory, and at least one communication bus.

[0066] The memory stores a computer program, and the computer program includes computer readable instructions. The processor calls the computer readable instructions stored in the memory through the communication bus, and executes the above-mentioned scheduling method for LEO satellites.

[0067] To evaluate the performance of the algorithm proposed in the present application, the performance improvement of the method of the present application in practical application scenarios is verified by using real-world maps and simulated environments. Specifically, the present application first simulates a constellation composed of 200 satellites, which are uniformly distributed on 20 orbits. Each satellite is uniformly arranged on its orbit, and a comprehensive coverage scan of the region of interest and the artificially generated map based on the region of interest is performed by multiple satellites from different directions. The present application sets the satellite camera to be able to rotate within a range of 15° on both sides of the orbit, wherein the satellite camera consumes about 100 joules of energy each time it rotates. Detailed parameters are set for the satellites in the present system: the satellite orbit height is set to 550 kilometers, and the satellite orbit inclination is set to about 53 degrees, which ensures that the satellite can cover the earth's surface from the polar region to most of the longitudinal region.

[0068] An algorithm considering only the content ratio (the content ratio refers to the ratio of the area of the RoI in the image taken of each segment to the area of the image taken of the segment, and the larger the value, the smaller the photo size) (also named as CPO): This scheme only considers maximizing the content ratio without considering the energy consumption generated by the rotation of the satellite camera, while ensuring full coverage of the RoI.

[0069] Considering only the rotation energy (also named as REO): This scheme only considers minimizing the energy consumption of the rotation of the satellite camera without considering the content ratio of the satellite image, while ensuring full coverage of the RoI.

[0070] The algorithm (EC) proposed in the present application, CPO, and REO are evaluated from three different angles, i.e., different maps, different numbers of satellites, and different shooting energy limits.

[0071] The algorithm EC, CPO, and REO are evaluated from the perspective of different maps Figure 7 , Figure 7 is the sum of the rotation energy consumption of the satellite camera using the three algorithms (CPO, REO, and EC) and the content ratio of the pictures under different maps, Figure 7 in (a) is the rotation energy consumption of the satellite camera using the three algorithms to shoot different maps, Figure 7 in (b) is the sum of the content ratio of the pictures using the three algorithms to shoot different maps.

[0072] From Figure 7 It can be seen that the EC algorithm reduces the rotation energy consumption of the camera while ensuring full coverage of the RoI, and also ensures the size of the image, so that the energy consumption of the RoI shooting task is minimized. Through the test of multiple maps, the EC algorithm not only has excellent performance, but also shows strong robustness.

[0073] Reference for evaluating the algorithms EC, CPO, REO from the perspective of different satellite numbers Figure 8 , Figure 8 is the sum of the rotation energy consumption of the satellite cameras and the content proportion of the pictures when the same region of interest is photographed using the three algorithms (CPO, REO, EC) under different satellite numbers, Figure 8 In (a), the rotation energy consumption of the satellite cameras when the same region of interest is photographed using the three algorithms under different satellite numbers, Figure 8 In (b), the sum of the content proportions of the pictures when the same region of interest is photographed using the three algorithms under different satellite numbers.

[0074] By comparing (a) in Figure 8 and (b) in Figure 9 , it can be found that when the number of satellites is 3, due to the limitation of the field of view of the satellite cameras, it is impossible to achieve full coverage of the RoI, resulting in a very low content proportion. When the number of satellites increases to 4, it can ensure full coverage of the RoI. In this case, algorithm EC not only significantly improves the content proportion of the image, but also reduces the rotation energy consumption of the camera to the minimum. However, due to the fact that some fragments are not photographed by the satellite in the correct direction, the content proportion does not reach the maximum value.

[0075] With further increase in the number of satellites, when the number of satellites increases to 6, the camera rotation energy consumption of algorithm REO and algorithm EC is reduced to 0, and the content proportion of algorithm EC and algorithm CPO reaches the maximum value, at which time the comprehensive performance of algorithm EC is best. In summary, regardless of the number of satellites, algorithm EC always maintains excellent performance.

[0076] Reference for evaluating the algorithms EC, CPO, REO from the perspective of different shooting energies Figure 9 , Figure 9 is the sum of the rotation energy consumption of the satellite cameras and the content proportion of the pictures when the same region of interest is photographed using the three algorithms (CPO, REO, EC) under different shooting energies, Figure 9 In (a), the rotation energy consumption of the satellite cameras when the same region of interest is photographed using the three algorithms under different shooting energies, Figure 9 In (b), the sum of the content proportions of the pictures when the same region of interest is photographed using the three algorithms under different shooting energies.

[0077] By comparing (a) in Figure 9 and (b) in ​As can be seen from the middle (b), when the shooting energy is 180J, the shooting energy is very small, the content ratio of the three algorithms is not high, and the camera rotation energy consumption is low, and the three algorithms cannot complete the overall coverage of the RoI. When the shooting energy is 195J, the algorithm CPO can complete the overall coverage of the RoI, although the content ratio of the algorithm EC is not the largest at this time, but the total energy consumption of the camera is reduced to the minimum. With the upper limit of the shooting energy increasing, the EC algorithm maintains the picture size at an optimal value, and reduces the camera rotation energy consumption to the minimum, so that the RoI shooting task energy consumption is the lowest, and the required upper limit of the shooting energy is not high. In summary, no matter how much the satellite shooting energy is, the algorithm EC always maintains excellent performance.

[0078] In summary, the algorithm proposed in the application not only has excellent performance, but also has good robustness and is not affected by the map, the number of satellites and the shooting energy.

[0079] The above description of disclosed embodiments enables a person skilled in the art to implement or use the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of scheduling for LEO satellites, characterized in that, The method comprises the following steps: S1, constructing a region of interest coverage system for multiple LEO satellites to cooperatively observe; S2, dividing the region of interest according to the orbits and fields of view of the LEO satellites in the system; S3, under the premise of minimizing full coverage of the region of interest by image acquisition, achieving the goal of minimizing satellite camera rotation energy consumption, and scheduling the satellites to take pictures according to the division; Each satellite can only take pictures of one of the two divisions on both sides of the orbit at the same time; two satellites in the same space with an intersection angle of 80°-100° in the ascending and descending orbit directions are analyzed as a group; The satellite in the ascending orbit satisfies: ， wherein, represents whether the n-th satellite photographed the tile with coordinates , yes, then = 1, no, then = 0; I represents a set of all the numbers of rows of the tile, represents a set of all the numbers of columns of the tile, represents whether the n-th satellite is in the ascending orbit, yes, then = 1, no, then = 0; The satellite in the descending orbit satisfies: ， And satisfies: ， wherein, represents whether the nth satellite is in a descending orbit, yes, then ; no, then = 0; represents whether the nth satellite can shoot a tile with coordinates N represents a set of LEO satellites, and M represents a set of tiles.

2. The LEO satellite oriented scheduling method of claim 1, wherein, When the number of satellites in the coverage system is 2, the orbit intersection angle of the two satellites is 80°-100°; when the number of satellites in the coverage system is greater than 2, there are two cases for the orbits of all satellites: parallel to each other and an intersection angle of 80°-100°.

3. The LEO satellite oriented scheduling method of claim 2, wherein, The LEO satellite completes one orbit around the orbit to form a strip with a width equal to the width of the field of view. Each LEO satellite camera takes pictures of the areas on both sides of the orbit by rotating to form two observation strips. The observation strips of all satellites form a series of grids, which divide the RoI into a series of divisions, and all the divisions form a complete RoI region.

4. The LEO satellite oriented scheduling method of claim 1, wherein, In order to realize complete coverage of the region of interest, it is necessary to satisfy: ， 。 5. The LEO satellite oriented dispatching method of claim 4, wherein, Satellite The camera rotation energy consumption is expressed as: wherein, represents the energy consumption of the rotation of the nth satellite camera, M represents the set of fragments, represents whether the nth satellite has taken a fragment with the coordinate represents a coordinate in the set of fragments, represents whether the nth satellite has taken a fragment with the coordinate represents the energy required for one rotation of the camera;​​​ And the shooting energy satisfies the following constraints: wherein, represents the nth satellite shot fragment the shooting energy required to be spent, represents an upper limit value of the shooting energy; The target of the scheduling is expressed as .

6. A computer readable storage medium storing a computer program, characterized in that: The computer program is executed by a processor to realize the method of any one of claims 1-5.

7. An electronic device, comprising: The device comprises a processor and a memory, wherein the processor and the memory are connected to each other, the memory is used to store a computer program, the computer program comprises computer readable instructions, and the processor is configured to call the computer readable instructions to execute the method of any one of claims 1-5.

Images